LBL.33717

UC-350

BASELINE DATA FOR THE RESIDENTIAL SECTOR

AND DEVELOPMENT OF A RESIDENTIAL FORECASTING DATABASE

James W. Hanford, Jonathan G. Koomey, Lisa E. Stewart, Matthew E. Lecar, Richard E. Brown, Francis X Johnson, Roland J. Hwang, and Lynn K. Price

Energy Analysis Program Energy and Environment Division

Lawrence Berkeley Laboratory University of California

Berkeley, CA 94720

May 1994

This 

Planning 

work was supported by the Assistant Secretary for Energy Eficiency and Renewable Energy, Office of and Analysis and Office of Building Technologies of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.

EE-RM-94-230 COMMENT 61C

LBr.;33717 

. UC-3S0 

BASELINE DATA FOR THE.-RESIDENTIAL SECTOR 

AND DEVELOPMENT OF A 

RESIDENTIAL FORECASTING DATABASE 

lames W. Hanford. Ionathan G. Koomey, Lisa E. Stewart, Matthew B. Lear, 

Richard E. Brown, Francis X. Johnson. Roland 1. Hwang. and Lynn K. Price 

Energy Analysis Program 

Energy and Environment Division 

Lawrence Berkeley J..aboratory 

University of Califomia 

Berkeley, CA 94720 

May 1994 

This work Was supported by Ibe Assistant Sei:reW"y for Energy Emcieuc;y and Renewable Energy. Office of, 

Planning and Analysis and Office of Building Tf:Cbnologies of abe U.s. DepartmeDt of Energy under 

Contract No. DE-AC03-76SF00098. . 
ABSTRACT

This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting

database. It provides a description of the methodology used to develop the database and

describes the data used for heating and cooling end-uses as well as for typical household

appliances. This report provides information on end-use unit energy consumption (UEC)

values of appliances and equipment, historical and current appliance and equipment market

shares, appliance and equipment efficiency and sales trends, cost vs. efficiency data for

appliances and equipment, product lifetime estimates, thermal shell characteristics of

buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings,

baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and

other economic drivers. Model inputs and outputs, as well as all other information in the

database, are fully documented with the source and an explanation of how they were

derived.

i

EE-RM-94-230 COMMENT 61C

ABSTRACT 

This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting 

database. It provides a description of the methodology used to develop the database and 

describes the data used for heating and cooling end-uses as well as for typical household 

appliances. This repon provides information on end-use unit energy consumption (UEC) , 

values of appliances and equipment, historical and cUITent appliance and equipment market 

shares, appliance and equipment efficiency and sales trends, cost vs. efficiency data for 

appliances and equipment, product lifetime estimates, thermal shell characteristics of 

buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings, 

baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and 

other economic drivers. Model inputs and outputs, as well as all other information in the ' 

database, are fully documented with the source and an explanation of how they were 

derived. 

i 
A CKNOWLEDGMENTS

This project was first conceived in discussions between Jonathan Koomey, James E.

McMahon, and Mark D. Levine at Lawrence Berkeley Laboratory (LBL). In those

discussions, Koomey and McMahon expressed their frustration at how difficult it was for

LBL staff to consolidate and systemize their knowledge on residential data and forecasting

in the face of constant policy-related "fire-drills". This database grew out of that

frustration, and it represents the first attempt to compile our forecasting information in a

computerized form. It will undoubtedly grow and change as our forecasting capabilities

develop.

We would like to thank the members of the Energy Conservation Policy Group in the

Energy Analysis program at LBL for their time spent gathering data and explaining the

technical details of appliance efficiency improvements. In particular, we thank Jim

McMahon, Peter Chan, Greg Rosenquist, Ike Turiel, and Jim Lutz. Joe Huang also

provided guidance concerning modeling of residential buildings:

We would also like to thank the analysts who reviewed this report, including Jim

McMahon, Alan Meier, Steve Meyers, Dan Nore (RER), John Cymbalsky (EIA), and

Andrew Nicholls (PNL).

The work was funded by Eric Petersen and David Patton of the Office of Planning and

Assessment and Dick Jones of the Office of Building Technologies in the U.S. Department

of Energy. We are grateful for their support and insights.

i

EE-RM-94-230 COMMENT 61C

ACKNOWLEDGMENTS . 

This project was flI'St conceived in discussions between Jonathan Koorney, James E. 

McMahon. and Mark D. Levine at Lawrence Berkeley Laboratory (LBL). In those 

discussions, Koorney and McMahon expressed their frustration at how difficult it was for 

LBL staff to consolidate and systemize their knowledge on residential data and forecasting 

in the face of constant policy-related "fire-drills". This database grew out of that 

frustration. and it represents the first attempt to compile our forecasting information in a 

computerized fonn. It will undoubtedly grow and change as our forecasting capabilities 

develop. 

We would like to thank the members of the Energy Conservation Policy. Group in the 

Energy Analysis program at LBL for their time spent gathering data and explaining the 

technical details pf appliance efficiency improvements. In particular. we thank Jim 

McMahon. Peter Chan. Greg Rosenquist. Ike Turiel, and Jim Lutz. Joe Huang also 

provided guidance concerning modeling of residential buildings: 

We would also like to thank the analysts who reviewed this report. including Jim 

McMahon. Alan Meier. Steve Meyers, Dan Nore (RER). John Cymbal sky (EIA). and 

Andrew Nicholls (pNL) . 

. The work was funded by Eric Petersen and David Patton of the Office of Planning and 

Assessment and Dick Jones of the Office of Building Technologies in the U.S. Department 

of Energy. We are grateful for their support and insights. 

n 
TABLE OF CONTENTS

ABSTRACT ...... ....... ...... ........................................... i

ACKNOWLEDGMENTS ...................................................... ii

1. INTRODUCTION ....................................................... 1

2. METHIODOLOGY ......................................................... 2

2.1. ECs ................................................................................ 2

2.2. lMarketShares ...................................................... 3

2.3. Technology Characteristics .................................... 4

2.4. Building Characteristics, Building Prototypes, and Building Loads.........4

2.5. Building Component Costs ....................................................... 4

2.6. Bibliographic References ......................................................... 5

2.7. The Database Program ........................................................ 5

3. HEATING AND -COOLING END-USE DATA .......................... 8

3.1. ECs .... ....................................................................... 8

3.2. Technology Data for HVAC Equipment and Distribution Systems .......... 17

3.3. Technology Datafor Shell Measures ............................................ 25

3.4. Fuel and Equipment Shares ......... ........... ......... ...... 25

3.5. Forecasting Prototypes ....................... ................30

3.6. Standards ........................................................................ 62

4. WATER HEATING END-USE DATA .................................. 64

4.1. Water Heating UECs ............................................................ 64

4.2. HotWater Usage ................................................................. 65

4.3. Water Heating Technology Data...... ................................... 65

4.4. Shares ....... 7..........................

4.5. Standards ....................................................................... 70

5. REFRIGERATOR END-USE DATA ................................... 72

5.1. Refrigerator UECs ................................................................. 72

5.2. Refrigerator Usage....................................................... 73

5.3. Refrigerator Technology Data .................................................... 73

5.4. Shares .............................................................................. 76

5.5. Standards ....................................................... 76

6. FREEZER END-USE DATA ............................................ 7 9

6.1. Freezer UECs. ...................................................................... 79

6.2. Freezer Usage ...................................................................... 80

6.3. Freezer Technology Data .................... ............................ 80

6.4. Shares...............................................................................83

6.5. Standards .................. ............................................... 83

7. DISHWASHER END-USE DATA ...................................... 86

7.1. Dishwasher UECs ........................................ 86

7.2. Dishwasher Usage ................................................................ 87

7.3. Dishwasher Technology Data ............................................ 87

7.4. Shares ............................................ ............ .90

iii ...

EE-RM-94-230 COMMENT 61C

TABLE OF CONTENTS 

ABSTRA(:T ••••••••••••••••••••••••••••••••••••••••••.•••••••• ~ ••••••••••••••••• 'j 

ACKNOWLEDGMENTS •••. _ •••••••••••••••••••••••••••••••••••••••••••••••••• I_ 

I. INTRODUCTION ••••••••• ,_ •••••••••••••• ~ ••.••••••• '~ ....................... 1 

.2 • ~TE[ODOLOGY ••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 2, 

2.1. 'Uiscs •...•..•....••.•••.•..•.•••••.•••.•.••••••.•••.•.••.••••..•••••.....•.••.•.••.•. 2 

2.2. Market Shares •..•.•••.••.•.•.•••••••. ~ •••••••••••••••••••••.•••••••..•....•.... · .•••• 3 

2.3. Technology Chara.cteristics •.••.•••••••••••••••••••••••.•••••••• ~ •••••••••••.••••• 4 

2.4. Building Characteristics. Building Prototypes, and Building Loads ......••. 4 

2.5. Building Component C~ts .........•....• ~ •.•..••...•.•.•.•.••...................•. 4 

2.6. Bibliographic References .........•.•.....••........•..••.....•....... ' ...........• .5 

2.7. The Database Program ......•.•••..•.•.•.. ~ ...•...........•..••••............•.••. .5 

3. HEAT.ING AND -COOLING END·USE DATA ........................... 8 

3.1. rn~cs .... ~ ........................................................................... 8 

3.2. Technology Data for HV AC Equipment and Distribution Systems .....•.••. 17 

3.3. Technology Pata for Shell Measures ............................................ 25 

3.(. Fuel an.d E;q,lIipment Shares ..•...•.•...••..•..•.• ~ .••.•.• ~ •..•..................•. 25 

.3.5 •. Forecasting Prototy'peS •••.•.•••••••••••••••••••••••••••••••••••••••••••••••.•••••• .30 

3.6. Standards .•.••...•...•.•..•.••••• ~ •••.•..•••••••••.•••••.••..•.•.••.•••••.•.•••.•••... 62 

4. WATE:R HEATING END-USE DATA ••••••••••••••••••••••••••••••••••• 64 

4.1. Water Heating 'UECs •••••••••••••••••••••• ' •••••••••••••••••••••••••••••• ~ ••••••••• 64. 

4.2. Hot Wa1J:,t Usa.ge ••••••••••••••••••••• ~ .•••••••••••••••••••••••••••••••• ~ ••••••••••••• 65 

4.3. Water Heating Technology Data. ........................................... ~ ....•• 6S 

4.4. Sh..a.res ..•.....••......••.................••.• ~ .....••.••... ~ .......................... 70 

4.5. Standards .•.•....•.•..•.........•.•......•..••.... ~ •.....•..•..•..................••• 70 

5. REFRJrGERATOR END·USE DATA ••••••••••••••••••••••••••••••••••••• 72 

5.1. Refrigerator UECs ••••••••.••••.•••••••••••••.••.•••••.••••••••••••.••••••••••••.•• 72 

S.2. Refrigemtor Usa.ge ••••••••.•••••.••••••.•••••••.••••••.••••••••••••.•••.••••••••••. 73 

5.3. Refrigerator Technology Data ...................•................................ 73 

5.4. Shares •.....•....••..............•....•.....•....................................•... 76 

5.5. Standards .•.•.•••.•••.•.••..•..•.•.••••.•••••••••••••••.•••••..•••.•••••••.•••••••••• 76 

6 • FREEZER END-USE' DATA , ••••••••••••••••••••••••••••••••••••••••••••• 79 

6.1. 

.6.2. 

6.3.-

6.4. 

6.5. 

Freezer UBes .....••..•.•••.••..•.••••••••••...•••.•..•.....••••.••...•.•••.•••••••. 79 

Fn:ez.er Usage ......••.•....•..•..•............••..•....•••...•.•....•..•..•......•. 80 

F~ Tecimology Dam ..............•.•..•.•..•...•.•...•.•.•................... 80 

Shares . ~ ...........•.•..••.......•.•....••..•......••.••.•..•.•..•.•............•..•. 83 

Standards .......•........•........••.•.••••••...•.•..•..•••..............•.•.•...•.•. 83 

7. DISmVASHER END-USE DATA •••••••.••••••••••••••••••••••••••••.••• 86 

7.1. Dishwasher 'UOCs ..•................ ~ .•...•... ~.' ...............•....... ~ ........... 86 

7.2. Dishwasher Usage ...................... ' ..•.•........•.....•.....•................. 87 

7.3. Dishwasher Technology Data ...........•......................................... 87 

7.4. -Shares ................•...............•...........•................................•. 90 

iii 
7.5. Standards............. ... ....................................... ................... 92

8. CLOTHES WASHER END-USE DATA ........... ... ...... 93

8.1. Clothes Washer UECs ....................................... ............. 93

8.2. Clothes Washer Usage ............. ................ 94

8.3. Clothes Washer Technology Data ........................... .............. 94

8.4. Shares ............................................................................... 95

8.5. Standards.....................................................................99

9. CLOTHES DRYER END-USE DATA ..................... ........... 100

9.1. Clothes Dryer UEC s ............................... 100

9.2. Clothes Dryer Usage..............................................................101

9.3. Clothes Dryer Technology Data......... ................................... 101

9.4. Shares .............. ................................................................. 104

9.5. Standards..... ....................................... ..................... 104

10. LIGHTING END-USE DATA ......................... ................ 06

10.1. Baseline Lighting Usage .................................................... 1.06

10.2. Distribution of Installed Wattage .............................. ......... .106

10.3. Energy Consumption per Socket...................... .......... 1...... 06

10.4. Energy Consumption per Household ......................................... 10.5. Total Energy Consumption by Housetype........................... 107

10.6. Costs of Efficiency Improvements in Lighting............................. 110

11. COOKING END-USE DATA ................. ................... 111

11.1. Cooking UECs...........................................................

11.2. Cooking Technology Data .................. ................ ............ 11.3. Shares .... ............ .................... ... 113

11.4. Efficiency Standards ................................................ 113

12. TELEVISION END-USE DATA ...... ..... ................ 119

12.1. Television UECs ............... .... 119

12.2. Television Usage........ ............... .................. . 1. 19

12.3. Television Technology Data................................. .............. 120

12.4. Shares .. ..................................................... ... ............ . 120

12.5. Standards . ........ ............. 2o 0........120

13. MISCELLANEOUS END-USE DATA ........ ............ 124

14. DEMOGRAPHIC AND MACROECONOMIC DATA ............. 125

15. FUTURE WORK...... K..K.......... ........................... .... 126

REFERENCES ....... ........ ..... 127.................................. 127

APPENDIX A. RESIDENTIAL FORECASTING

DATABASE DESCRIPTION .................. ........ 133

APPENDIX B. UEC DATABASE DESCRIPTION ........ . ....... 139

iv

EE-RM-94-230 COMMENT 61C

7.S. Standards ...•.......... ~ ........•..........•.•...•...•..•.... ~ .••.•.....•....•....•..•. 92 

8. CLOTHES WASHER ~ND-USE DATA •••••••••••• ~ ••••••••.••••••••••• 93 

8.1. Clothes Washer UECs ....•..•. __ .•.......•.•.......••.••.••.•.............••.••.... 93 

8.2. Clothes Washer U~ge ••.....••.••..• ~ ••••..••...•.•.•••••••••••.•..••.•..••••••••. 94 

8.3. Oodles Washer Technology Data.~ .....••.....•...•.•...••••.... ~ .......•.••.•.•.• 94 

8.4. Shires ........•.............................................•.•.............•••.•..•. 95 

8.5. Standards ••.•..•...•.........••.•.•.........•••.....•.•.•.•.•..•...•...•......•.•.... 99 

9. CLOTHES DRYER END-USE DATA ••••••••••••••••••••••••••••••••••• 100 

9.1 .. Clodles Dryer UECS ...•....•...•..••.•..•..••.•...•.••..••...•...... \ ...•.•.•...... 100 

9.2. 

9.3. 

9.4. 

9.S. 

Clothes Dryer ()'sage .................................... ~ .•••.•....• ~ ....••••••.•... 101 

Clothes Dryer TechnolC?gy Data .........•..•.......•.....................•...•... 101 

Shares •.•••••.•.•.•. · ..••••......•••••••.••••..•..••...•••.•••.••...........•..•.•••.•. 1 ()4. 

Standards ...............................•.•••••..•...........•..•...............•..... 104 

10. LIGHTING END-USE DATA •• ~ •••••••••••••••••••••••••••••••••••••••• 106 

10.1. 

10.2. 

Ba.seliIle Lighting Usage .......••...•......•.......•..•••...•...•.........••..... l 06 

DistJibution of Installed Watta.ge ...••..•.•••••••••••••••..••.•.••..••• ~ ••••••.• 1 06 

10.3. Energy Consumption per 

10.4. Energy Consumption per 

Socket .....••..•.....•..•..•.....•..•.......••....... l06 

Household .......•..•••••...•..••.........•••..•.... 101 

10.5. Total Energy Consumption by Housetype ...•..•..•••••.•......•..•.......... 107 

10.6. Costs of Efticiency Improvements in Ughting .••..•••..•...•...•..••. ~ ...... 110 

11. COOKING END-USE DA T A •••••• ~ ••••••••••••••••••••••••••••••••••••• 111 

11.1. 

11.2. 

11.3. 

C:ooking 'UECS .••.•.••.••••.••••••••••• ~ ••••••••.•.••.•••••..•..•.....• ~ •••.••••• 111 

Cooking Technology Data· ...•...........•..•..............................•.... 111 

Shares ••••••••••••• ~ ••••••••••••••••••••••••••••••••••••••••• ~ ••••••••••••••••••••• 113 

11.4. Efficiency Slan dards .....•..••.•..........•.....•..••.••..•............•..••..•.. 113 

12. TELEVISION END-USE DATA •••••••••••••••••••••••••••••••••••••••• 119 

12.1. Television 'UECS ....•.•.•..••.•..•..••...•.•.••..•..••••.•••••.••..•.••.••.•.•.•. 119 

12.2~ 1"elevision Usage ... ~ ......••....••..............••••• : .•..............•.•.•...... 119 

12.3. 1~ele'Vision Technology Data •••• ~ ••••••.•••.•••••.••••••••••••••••••..•• .; ••••••.. 120 

12.4. Shares .•.•....................•.•.....•......•.....•..•...•...............•... ~ .... 120 

12.5. Sta.ndards •......•..•.••..........••••.•........•...•....•...............••...••••• 120 

13. MISCELLANEOUS END-USE DATA •••••••••••••••••••••••••••••••••• 124 

14. DEMOGRAPmC AND MACROECONOMIC DATA ••••.••••••••••••• 125 

15. FUTURE WORK •• · ••••••••••••••••••••••••••••••••••••••••••••••••••••••• 126 

REFERENCES ••• ~ ••••••.••••• · ••••••••••••••••••••••••••••••••••••••••••••••••• 127 

APPENDIX A. RESIDENTIAL FORECASTING 

DATABAS~ DESCRIPTION ••••••••••••••••• · ••••••••••••• 133 

APPENDIX B. UECDA T ABASE DESCRIPTION •••••••••••••••••••••••• 139 , 

iv 
1. INTRODUCTION

The residential forecasting database is designed to support improved energy demand

forecasting at Lawrence Berkeley Laboratory (LBL) and within the U.S. Department of

Energy (US DOE). It is a compilation of the major data elements necessary for end-use

energy demand forecasting in the residential sector. The work represents an attempt to

systematically assess and document these data, and to incorporate them into a computerized database system. This report describes the methodology used in collecting and assessing

these data, the sources used, and presents the major pieces of data in graphical or tabular

form. The residential forecasting database includes the following model input data:

* Unit energy consumption (UECs) of appliances and equipment;

* Historical and current appliance and equipment market shares;

* Appliance and equipment efficiency and sales trends;

* Cost vs. efficiency data for appliances and equipment;

* Product lifetime estimates;

* Thermal shell characteristics of buildings and heating and cooling loads;

* Shell measure cost data for new and retrofit buildings;

* Baseline housing stocks;

* Forecasts of housing starts; and

* 'Forecasts of energy prices and other economic drivers.

In the future, the-database will be designed to allow results from various forecasting

scenarios to be stored in a readily accessible form. Forecast data types will include:

* Total energy use by fuel;

* Energy use by end-use; and

* Market shares, UECs, and energy factors.

Model inputs and outputs, as well as all other information in the database, are fully

documented with the source and an explanation of how they were derived. The database

will serve as the source of input data for the residential forecasting models used in the

Energy Analysis Program at LBL.

In Chapter 2, we describe the major elements of the residential database and the

methodology and sources used in developing the estimates. In Chapter 3, we describe the

data for the heating and cooling end-uses. In Chapters 4 through 13, we discuss the data

for typical household appliances. In Chapter 14, we describe the general sector data such

as fuel prices, housing starts, etc. that are included in the database. In Chapter 15, we

provide suggestions for areas where we feel the database could still be improved. The

database structure, as well as samples of the reports, are included in Appendix A.

1

EE-RM-94-230 COMMENT 61C

1. INTRODUCTION 

The residentiul forecasting database is designed to support improved energy demand 

forecasting at Lawrence Berkeley Laboratory .(LBL) and wi~ the U.S. Department of 

Energy (US DOE). It is a compilation of the major data elements necessary for end-use 

energy demand forecasting in the residential sector. The work represents an attempt to . 

systematiec'l1ly assess and' document these data, and tDincorporate them into a computerized 

database system. This report describes the methodology used in collecting and assessing 

these da~ the sources used, and presents the major pieces of data in graphical or tabular 

form. The :residential forecasting database includes the following model input data: 

• Unit energy consumption (UECs) of appliances and equipment; 

• . His1tOrical and CWTent appliance and equipment market shares; 

• Appliance and equipment efficiency and sales trends; 

• Cost vs. efficiency data for appliances and equipment; 

• Product· lifetime estimates; 

. • Thermal shell characteristics of buildings and heating and cooling loads; 

• SheU measure cost data for new and retrofit buildings; 

• Baseline housing stocks; 

• Fon:casts of housing staIts; and 

• 'Fon~ts of energy prices and other economic drivers . 

. In the fntwre, the· database will be designed to allow resultS from various forecasting 

scenarios to be stored in a readily accessibl~ fonn. Forecast data types will include: 

• Total energy use by fuel; 

• Energy use by end-use; and 

• Market shares. UE~ and energy factors. 

Model inputs and outputs, as well as all other information in the database, are fully 

documented with the source and an explanation of how they were derived. The database 

will serve 2lS tbe source of input data for the residential forecasting .models used in the 

Energy Analysis Program at LBL. . . . . 

In Chapter 2,. 'we describe the major elements of the residential database and the 

methodology and sources used in developing the estimates. In'Chapter 3, we d~ribe.the 

data for the heating and cooling end-uses. In Chapters 4 through 13, we discuss the data 

for typical household appliances. In Chapter 14, we describe the general sector data such 

as fuel priCI~S, housing starts, etc. that are included in the database. In Chapter 15, we 

provide suggestions for areas where we feel the database could still be improved. The 

database stnlctme, as well as samples of the reports. ~ included in Appendix A. 

1 
2. METHODOLOGY

The residential forecasting database allows for detailed characterizations of the residential

sector. The database is based on several major data sources as well as a number of smaller

studies. Primary data sources include:

* Residential sector characteristics surveys, referred to as RECS (US DOE 1982a,

1986, 1989a, 1992);

Appliance efficiency standards analyses (US DOE 1988, 1989b, 1989c, 1990b,

1993);

* Appliance and equipment manufacturer data (AHAM 1991; ARI 1991; GAMA 1991);

* Surveys of current housing and construction (US Bureau of the Census 1988, 1990a,

1990b, 1992; NAHB 1989);

Surveys of sector energy use (US DOE 1990a; AGA 1991; EEI 1983; LBLREM

1991);

UEC estimating studies (various utility studies; US DOE 1988; US DOE 1989b; US

DOE 1989c; US DOE 1990b; US DOE 1993; AGA 1991; Cohen et al. 1991);

* Building characterization projects (Ritschard et al. 1992a; NAHB 1986; NAHB 1989;

MHI 1991); and

* Building heating and cooling simulation databases (LBL 1987; Huang et al. 1987b).

The types of data in the database are listed in Table 2.1, while the definitions for the

variables used in subdividing the data are listed in Table 2.2.

2.1. UECs

Data on end-use unit energy consumptions (UECs) were collected to verify the accuracy of

UECs used in engineering models that estimate energy savings from conservation

improvements. We collected data from metered studies and other estimates that measure

actual field usage of a particular appliance or house. From this data, we developed a

database of over 1300 records for all major residential end-uses. Because of the large variability in estimates for any particular value, we selectively aggregated the data based on

the quality of the study and the methodology used to derive the estimate. 1 This UEC

database, which is included in this report as Appendix B, was used as guidance in

developing the final values for the overall residential forecasting database.

1 The method we used was: 1) collect information on the estimate concerning its representation, including

region of the country, specific house type studied, specific appliance type studied, etc, to ensure we were

comparing like values, 2) assign a subjective quality rating (1-5) to each estimate based on the sample size

or other measure of the quality of the estimate, and 3) record the type of methodology ("study type") used to

calculate the estimate (e.g. measurement, statistical - "conditional demand", an aggregate of other

estimates, etc.), and 4) calculate averages of the UEC estimates based on quality and study type to determine

the best estimate from the available data.

2

EE-RM-94-230 COMMENT 61C

2. METHODOLOGY 

The residential forecasting database allows for detailed characterizations of the residential 

sector. The database is based on several major data sources as well as a number of smaller 

studies. Primary data sources include: . 

• 

• 

• 

• 

• 

• 

• 

• 

Residential sector characteristics surveys, refeITed to as RECS (US DOE 1982a, 

1986, 1989a, 1992); 

Appliance efficiency standards analyses (US DOE 1988, 1989b, 1989c, 1990b, 

1993); 

Appliance~dequipmentmanufacturerdata(AHAM 1991;ARI 1991; GAMA 1991); 

Surveys of current housing and construction (US Bureau of the Census 1988, 1990a, 

1990b, 1992; NAHB 1989); 

Surveys of sector energy use (US DOE 1990a; AGA 1991; EEl 1983; LBLREM 

1991); 

UEe estimating studies (various utility studies; US DOE 1988; US DOE 1989b; US" 

DOE 1989c; US DOE 199Ob; US DOE 1993; AGA 1991; Cohen et ale 1991); 

Building characterization projects (Ritschard et ale 1992a; NAHB 1986; NAHB 1989; 

MHI 1991); and 

Building beating and cooling simulation databases (LBL 1987; Huang et ale 1987b). 

The types of data in the database are listed in Table 2.1, while the definitions for the 

variables Used in subdividing the data are listed in Table 2.2. 

2.1. UECs 

Data on end-use unit energy consumptions (UECs) were collected to verify the accuracy of 

UECs used in engineering models that estimate energy savings from conservation 

improvements. We collected data from metered studies and other estimates that measure 

actual field usage of a particular appliance or house. From this data, we developed a 

database of over 1300 records for all major residential end-uses. Because of the large 

variability in estimates for any particular value, we selectively aggregated the data based on 

the quality of the study and the methodology used to derive the estimate.l This VEC 

database, which is included in this report as Appendix B, was used as guidance in 

developing the final values for the overall residential forecasting database. 

1 The method we used was: 1) collect information on the estimate concerning its representation, including 

region of the country, Specific house type studied, specific appliance type studied, etc.. to ensure we were 

comparing like values, 2) assign a subjective quality rating (1-5) to each" estimate based on the sample size 

or other measure of the quality of the estimate, and 3) record the type of methodology ("study type") used to 

calculate the estimate (e.g. measurement, statistical - "conditional demand", an aggregate of other" 

estimates, etc.), and 4) calculate averages of the UEC estimates based on quality and study type to determine 

the best estimate from the available data. 

2 
Database Titles and Contents

Database File

Number lame Description

-

Base Year (1990) UECs

Appliance and equipment shaes

Historical shipments, efficiency, and capacity data

Cost vs. efficiency data for appliances

Base Year (1990) HVAC system shares

Cost vs. efficiency and cost vs. capacity data for heating and cooling equipment

Efficiency, capacity, usage, and UEC units for each end-use

Basic building prototype descriptions

U-values and shading coefficients of building shell components

Building prototype shell component dimensions and thermal integrity

SP53 regression coefficients for building components

Solar load regression coefficients

Housing stock data, 1990 (will be 1980-90)

Fuel prices and income - historical and forecasts

Housing starts forecast

Shell measure costs for new buildings

Shell measure costs for building retrofits (SF only)

Completions of new construction annually, 1980-90

Conditioned floor area of new construction, 1980-90

Housing starts forecast

Annliance lifetime estimates

.3

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

BYUECO1

B3YApSh02

HstShlO3

TchEff)4

IBYHShr05

empty

HVACEq07

Units08

BldPrt09

UVWkSIO

BldCmpll

LdTbll2

ShWbI13

HsStck14

Fuel15

empty

empty

empty

empty

ShlCst20

RtrCst21

HstCmp22

HsArea23

HsFcst24

AnlLft25

Table 2.1.

EE-RM-94-230 COMMENT 61C

Table 2.1. Database Titles and Conterits 

Database File 

Nmnber Name Description 

1 lBYUECOl Base Year (1990) UECs .. 

2 J3YApSb02 Appliance and equipment sImeS 

3 HstShlO3 Historical shipments. efficiency. and capacity data . 

4 TchEtt04 Cost w. efflciency data for appliances 

S laYHSIuOS Base Year (1990) HVAC system shares 

6 empty 

7 IIVACIJ.rI)7 . Cost vs. efficiency and rost vs. capacity data for beating and cooling equipment. 

.. 

8 Units08 Efficiency. capacity, usage, and UEC units for each end-use 

9 BldPrt09 . Basic building prototype desaiptions 

10 . tNWkSlO U-values aDd shading coeffiCients of building shell oomponents .. 

11 BIdCmpll Building prototype shell component dimensions and thermaI integrity 

12 l4Ib112 SPS3 regression coefficients for building components 

13 S1flb113· Solar load regreSsion coefficients 

14 HsSrck:14 Housing stoclc data, 1990 (will be 1980-90) 

IS FuellS Fuel prices and income - bistorical and forecasts 

16 empty Housing starts forecast . 

17 emply 

18 empty 

19 empty 

20 ShICst20 Shell measure costs for new buildings 

21 RtrC..st21 Shell measure costs for building retrofits (SF only) 

22 HSlCmp22 Completions of new constructiOIi annually, 1980-90 

23 HsArea23 ~ondilioned floor area of new coDStruCtion, 1980-90 

24 HsFest24 Housing starts forecast 

2S ARILft25 Appliance lifetime estimates 

.3 
Table 2.2. Definitions for the Residential Database

Datbase

Field_ jICode Description Nil m_

Vintage

S Stock buildings or equipment, i.e. those in existence during the year specified

N New buildings or equipment, i.e. those currently being built, manufactured,

or purcased

House Type

SF Single family house types (detached and attached)

MF Multifamily house types (2 or more units)

MH Manufactured home house types

AL Averages across all building types

Fuel

E Elecricity

G Natual Gas

O Fuel Oil (includes kerosene)

L Liquid Petroleum Gas (LPG)

T Other

N None

Region

0 National

1 North Region (Federal regions 1, 2,3, 5, 7, 8, and 10)

2 South Region (Federal regions 4, 6, and 9)

Year

Enduse

AC Air Conditioning

CK Cooking

CW Clothes Washer

DR Dryer

DW Dishwasher

FZ Feezer

HT Space Heating

LT Lighting

MS Miscellaneous

MW Microwave

RF Refrigerator

TV Television

WH Water Heating

Technology

these entries are specific to each end-use

I I

4

EE-RM-94-230 COMMENT 61C

Table 2.2. Definitions ror the Residential Database 

Dalabase 

Flcld Code Desaiption 

Vmtage 

S SlOCk buildings or equipment. i.e. Ihose in existence during the year specified 

N New buildings or equipmenl, i.e. Ihose aurentJy being built. manufactured. 

or purchased 

House Type 

SF Single family bouse types (detacbed and auached) 

MF Multifamily bouse types (2 or more units) 

MH Manufactured home bouse typeS 

AI.. Averages across all building types 

Fuel 

E E1edricity 

G Natural Gas 

0 Fuel Oil (includes kerosene) 

L Liquid Petroleum Gas (LPG) 

T Other 

N None 

Region 

0 National 

1 NortbRegion (Federal regions 1,2, 3, 5, 7, 8, and 10) 

2 South Region (Federnl regions 4, 6, and 9) 

Year . 

Enduse 

AC Air Conditioning 

CK Cooking 

CW Ootbes Washer 

DR Dryer 

OW DishWasher 

FZ Freezer 

lIT Space Heating 

LT Ughting 

MS Miscellaneous 

MW Miaowave 

RF Refrigerata 

TV Television 

WH Wau:r Heating 

- Technology 

Ihese entries are specific to each end-use 

4 
Appliance End-Uses

UECs for appliance end-uses in the existing housing stock were derived from analyses

performed on the UEC database. For new appliances entering the market, we relied upon

engineering estimates developed for the U.S. DOE appliance standards analysis (US DOE

1988, 1989b, 1989c, 1990b, 1993). These engineering estimates represent test data rather

than field data, however, and should be used with care.

Heating and Cooling End-Uses

For heating and cooling end-uses, we used a North/South region division of the U.S. to

better describe the variation in energy use across climates. Federal regions 1, 2, 3, 5, 7,

and 10 make up the North region, and federal regions 4, 6, and 9 make up the South

region. The UEC database did not provide readily usable values for heating and cooling

UECs, since the estimates were typically averages for the entire nation or regionallyspecific estimates for small climatic regions.

Therefore, we relied on a combination of data, including RECS conditional demand

estimates (US DOE 1982a, 1986, 1989a, 1992), estimates in the LBL-REM forecasting

model (LBLREM 1991), American Gas Association (AGA) gas space heating survey data

(AGA 1991), some regional data from the UEC database, and the BECA-B database

compiled at LBL (Cohen et al. 1991) for heating and cooling UECs in existing buildings in

the North and South regions of the U.S. In some cases, we also used the heating and

cooling loads from prototype buildings defined for the database to estimate UECs.

Determining UECs for typical new buildings is even more difficult than for existing

buildings since there are few data on the energy usage of new buildings, particularly across

large parts of the country. Therefore, for new building heating and cooling UECs, we

adjusted the UECs for existing buildings based on: 1) different heating and cooling loads

between the existing buildings and new buildings entering the stock, and 2) different

heating and cooling equipment efficiencies of new vs. existing equipment.

2.2. Market Shares

Appliance Shares

Appliance market shares from the RECS surveys (US DOE 1982a, 1986, 1989a, 1992),

LBL-REM forecast estimates (LBLREM 1991), data from the American Housing Survey

(US Bureau of the Census 1988, 1990b, 1992), and industry estimates reported in

Appliance magazine were compared for the residential forecasting database. The sources

are in agreement for appliance shares in the existing housing stock for the major end-uses.

Appliance shares for existing buildings by housing type have been entered into the database

from the RECS surveys. We also include estimates from the RECS surveys for new

construction by segmenting the RECS data to include only buildings built in the last 5 to 7

years. Since this is a relatively small sample, these estimates have a larger error.

Improvements to the data may be possible in the future by extracting data from utility

Residential Appliance Saturation Surveys (RASS).

Heating and Cooling Equipment Shares

The residential forecasting database includes RECS data on heating and cooling equipment

shares from 1981-1990 for existing buildings (US DOE 1982a, 1986, 1989a, 1992).

Heating and cooling equipment shares for new construction are taken from U.S.

5

EE-RM-94-230 COMMENT 61C

Appliance End-Uses. 

UECs for appliance end-uses in the existing housing stock were derived from analyses 

perfonned on the UEC database. For new appliances entering'the market. we relied upon 

engineering estimates developed for the U.S. DOE appliance standards analysis (US DOE 

1988. 1989b. 1989c. 1990b. 1993). These engineering estimates represent test data rather 

than field data, however. and should be used with care. 

Heating and Cooling End-Uses 

For heating and cooling end-uses. we used a North/South region division of the U.S. to 

better describe the variation in energy use across climates~ Federal regions 1. 2. 3, 5. 7. 

and 10 make. up the North region, and federal regions 4, 6, and ? make up the South 

region. The DEC database did not provide readily usable values for heating and cooling 

UECs, since the estimates were typically averages for the entire nation or regionallyspecific estimatJes for small climatic regions. . 

Therefore, we relied on a combination of data. including RECS conditional demand 

estimates (US DOE 1982a, 1986. 1989a. 1m). estimates in the LBL-REM forecaSting 

model (LBLREM 1991). American Gas Association (AGA) gas space heating survey data 

(AGA 1991). some regional data from the UEC database, and the BECA-B database 

compiled at LBL (Cohen et ale 1991) for heating and cooling UECs in existing buildings in 

the North and South regions of the U.S. In some cases. we also used the heating arid 

cooling loads from prototype builcW:tgs defined for the database to estimate UECs. 

Detennining UECs for· typical new buildings is even more difficult than for existing 

buildings since there are few data on the energy usage of new buildings, particularly acroS$ 

large parts of the country. Therefore. for new building heating and cooling UECs. we 

adjusted·the UECs for existing buildings based on: 1) different heating and cooling loads 

between the existing buildings and new buildings entering the stock, and 2) different 

heating and cooling equipment efficiencies of ne",: vs. existing equipment. . 

2.2. Market Shmres 

Appliance Sh.ares 

Appliance market shares from the RECS surveys (US DOE 1982a. 1986. 1989a, 1992). 

LBL-REM forecast estimates (LBLREM 1991), data from the American Housing Survey 

(US Bureau of the Census 1988, 1990b. 1992), and industry estimates reported in 

Appliance magllzine were compared for the residential forecasting database. The sources 

are in agreement for appliance shares in the existing housing stock for the major end-uses. 

Appliance shm:s for existing buildings by housing type have been entered into the database 

from the RECS· surveys. We also include estimates from the RECS surveys for new 

construction by segmenting the RECS data to include only buildings built in the last 5 to 7 

years. Since r.his is a relatively small sample, these estimates have a larger error. 

Improvements to the data may be possible in the future by extracting data from utility 

Residential Appliance Saturation Surveys (&ASS). 

Heating and Cooling Equipment Shares 

The residential forecasting database includes RECS data on heating and cooling equipment 

shares from 1981-1990 for existing ·buildings (US. DOE 1982a. 1986, 1989a, 1992). 

Heating and cooling equipment shares for new construction are taken from U.S. 

5 
ntment of Census Reports Series C25 on new construction characteristics, and are

ided for 1980-90 (US Bureau of the Census 1990a). Data on the shares of heating and

ing equipment combinations (HVAC shares) are included in a separate sub-section of

latabase. These were developed for the year 1990 from the RECS data for existing lings and by combining estimates from the Census C25 data and RECS data for new

Jings.

Technology Characteristics

torical Sales, Efficiencies, and Sizes of Appliances and Equipment

a on shipments of appliances and equipment from 1950 to the present were compiled for

major end-uses. These data also show the evolution of appliance efficiencies over time

ting from the early 1970s. Furthermore, the shipments (or sales) data allow the user to

mate product lifetimes and the average efficiency of the current appliance stock. These

i are from industry reports produced by the major trade associations (ARI 1991; AHAM

1; and GAMA 1991) as well as data derived for the U.S. DOE appliance standards

lysis and incorporated in the LBL Residential Energy Model (LBLREM 1991). These

i are not adjusted for any imports, exports, or use in buildings other than residences

;. a residential-type water heater in a commercial establishment), and thus may introduce

ae error into the analysis.

uipment Cost vs. Efficiency Data

uipment cost vs. efficiency data were gathered primarily from the U.S. DOE appliance

ndard analyses (US DOE 1988, 1989b, 1989c, 1990b, 1993) as well as other

:uments for appliances not yet analyzed under this process. The data can be used to

rive forecasting model inputs. Data for all of the major residential end-uses have been

mpiled in the database.

I. Building Characteristics, Building Prototypes, and Building Loads

lilding characteristics data for both the existing stock and for typical new construction

:re compiled from previous LBL work on prototype development for GRI, U.S. DOE,

d the U.S. EPA as well as more recent data from RECS (US DOE 1982a, 1986, 1989a,

>92) and the C25 surveys (US Bureau of the Census 1990a). There are two regional

ototypes for existing single-family and multi-family buildings (representing average linsulated buildings and insulated buildings) and single regional prototypes for

anufactured homes and new single-family and multi-family buildings. The prototypes are

so segmented by heating fuel to recognize that the thermal efficiency of a building is

>mewhat different between fuel-heated and electrically-heated buildings. Populations of

ich type are included, and each prototype building is linked to an HVAC system type.

.eating and cooling loads for the prototype buildings are calculated in the residential

)recasting database based on the building component characteristics (wall area and Ralue, etc.) using a database developed at LBL in support of the ASHRAE Special Project

3 (Huang et al. 1987b). This database provides heating and cooling loads for each

uilding component based on the component area and the thermal characteristics. These

omponent loads can also be used to estimate changes in the loads with improved

omponents.

6

EE-RM-94-230 COMMENT 61C

, lrtIIlent of Census Reports Series C25 on new construction characteristics. and are 

Ided for 1980-90 (US Bureau of the Census 1990a). Data on the shares of heating and 

ing equipment combinations (HV AC shares) are included in a separate sub-section of 

jatabase. These were developed for the year 1990 from the RECS data for existing 

jings and by combining estimates from the Census C25 data and RECS data for new 

jings. 

Technology Characteristics 

tori cal Sales, Efficiencies, and Sizes of Appliances and JEquipment 

l. on shipments of appliances and equipment from 1950·to the present were compiled for 

major end-uses. These.data also show the evolution of appliance efficiencies over time 

ting from the early 1970s. Furthermore. the shipments (or sales) data allow the user to 

mate product lifetimes and the average efficiency of the current appliance stock. These 

1 are from industry reports produced by the major trade associations (ARl 1991; AHAM 

:1; and GAMA 1991) as well as data derived for the U.S. DOE appliance standards 

lysis and incorporated in the LBL Residential Energy Model (LBLREM 1991). These 

l. are not adjusted for any imports. exports. or use in buildings other than residences 

~. a residential-type water heater in a commercial establishment). and thus may introduce 

le error into the analysis. . 

uipment Cost vs. Efficiency Data 

uipment cost vs. efficiency data were gathered primarily from the U.S. DOE appliance 

.ndard analyses (US DOE 1988, 1989b, 1989c. 1990b. 1993) as well as other 

:uments for appliances not yet analyzed under this process. The data can be used to 

:lve forecasting model inputs. Data for all of the major residential end-uses have been 

rnpiled in the database. 

I. Building Characteristics, Building Prototypes, and Building Loads 

illding characteristics data for both the existing stock and for typical new construction 

!fe compiled from previous LBL work on prototype development for GRI, U.S. DOE, 

.d the U.S. EPA as well as more recent data from RECS (US DOE 1982a, 1986. 1989a, 

192) and the C25 surveys (US Bureau of the Census 1990a). There are two regional 

ototypes for existing single-family and multi-family buildings (representing average 

linsulated buildings and insulated buildings) and single regional prototypes for 

anufactured homes and new single-family and multi-family buildings. The prototypes are 

so segmented by heating fuel to recognize that the thennal efficiency of a building is 

>mewhat different between fuel-heated and electrically-heated buildings. Populations of 

lch type are included. and each prototype building is linked to an HV AC system type. 

:eating and cooling loads for the prototype buildings are calculated in the residential 

)recasting database based on the building component characteristics (wall area and Ralue. etc.) using a database developed at LBL in support of the ASHRAE Special Project 

3 (Huang et al. 1987b). This database provides heating and cooling loads for each 

uilding component based on the component area and the thennal characteristics. These 

omponent loads can also be used to estimate changes in the loads with improved 

omponents. 

6 
2.5. Building Component Costs

Costs for increasing levels of thermal integrity in new buildings have been derived from an

NAHB cost database (NAHB 1986). Costs for retrofitting single-family buildings with

improved levels of thermal integrity were also been derived from previous LBL work

(Boghosian 1991). The database does not currently contain cost estimates for retrofitting

existing multi-family or manufactured home buildings.

2.6. Bibliographic References

Each piece of the above-mentioned data is accompanied in the residential forecasting

database by one field that references the source of the datum and another field which

describes any manipulations made on the datum. There is a listing of the bibliographic

references that supply the source for each piece of data, and it is linked to each record in the

database.

2.7. The Database Program

All of the data are stored in a programmed database that allows the user to choose outputs

of specific pieces of data to be written to printed reports, to the computer screen, or to data

files which will allow the data to be graphically displayed, further manipulated, or input

into forecasting models. In addition, the database is programmed to provide basic

manipulations of the data.

Existing: Capabilities

Currently the database allows the user to make pre-defined printed reports, text files, or

spreadsheet files that can be used for the user's own analysis. The data can also be written

to the screen This capability is described fully in Appendix A. The database calculates the

base case heating and cooling loads for the prototype buildings using a procedure which is

described in Chapter 3 and Appendix A. The database includes an algorithm to calculate

product lifetimes from historical shipment and stock data and produces average appliance

efficiency and capacity data for specified product vintage bins. This program is described

in Appendix A.

Future Capabilities

There are several immediate and long term developments envisioned for the database.

These are discussed in Chapter 15 of this report.

7

EE-RM-94-230 COMMENT 61C

2.5. Building Component Costs 

Costs for increasing levels of thennal integrity in new buildings have been derived from an 

NAHB cost database (NAHB 1986). Costs for retrofitting single-family buildings with 

improved levels of thermal integrity were also been derived from previous LBL work 

(Boghosian 1991). The database does not currently contain cost estimates for retrofitting 

existing multi-family or manufactured home buildings. 

2.6. Bibliographic References 

Each pie:ce of the above-mentioned data is .accompanied in the residential forecasting 

database by one field that references the source of the datum and another field which 

describes any manipulations made on the datum. There is a listing of the bibliographic 

references that supply the source for each piece of data. and it is linked to each record in the 

database..' . 

2.7. The Database Program 

All of the data are stored in a programmed database. that allows the user to. choose outputs 

of specific pieces of data to be written to printed reports. to the computer screen. or to data 

fues whil::hwill allow the data to be graphically displayed. further manipulated. or input . 

into fore.casting models. In addition, the database i~ programmed to provide basic 

manipulations of the data. 

Existing: Capabilities 

Currently the database allows the user to make pre-defmed printed reports. text files. oX' 

spreadshr.etfiles that can be used for the user's own analysis. The data can also be written 

to the screen, This capability is described fully in Appendix A. The database calculates the 

base case heating and cooling loads for the prototype buildings using a procedure which is 

described! in Chapter 3 and Appendix A. The database includes an algorithm to calculate 

product lifetimes from historical shipment and stock data and produces average appliance 

efficiency and capacity data for specified product vintage bins. This program is described 

in Appendix A. 

.Future Capabilities 

There am several immediate and long term developments envisioned for the database. 

These are discussed in Chapter 15 of this report. 

7 
3. HEATING AND COOUNG END.USE DATA

Heating and cooling together account for about 30% of electricity consumption, 70% of gas

consumption, and 90% of oil consumption in the U.S. residential sector. These end-uses

are a major source of conservation potential as well as energy demand growth (see

Koomey et al. 1991a). In this section, we discuss UECs, heating and cooling equipment

characteristics, and building thermal characteristics. Energy consumption for heating and

cooling is a function of many variables, including HVAC equipment characteristics,

building shell characteristics, occupant behavior, climate (both across regions and year to

year within the same region), microclimates, and regional energy prices. For heating and

cooling, we use a regional disaggregation to segment the housing population to capture the

major variations in climate and building characteristics across the country. As shown in

Figure 3.1, we use a North and South regional breakdown similar to that used in earlier

LBL work (e.g. Koomey et aL 1991a). We provide UECs and building prototype

characteristics for these two regions.

3.1. UECs

The UECs for heating and cooling are important since the current level of energy

consumption determines potential energy savings from improvements in building thermal

shell characteristics as well as equipment We show these estimates in Tables 3.1 and 3.2.

The sources used in developing UECs for the forecasting database include national data

sources as well as regional data from utilities and weatherization studies. These include the

U.S. Residential Energy Consumption Survey (RECS) data sets (US DOE 1982a, 1986,

1989a, 1990), LBL-REM estimates (LBLREM 1991), the American Gas Association Gas

Househeating Survey (AGA 1991), the BECA-B data set (Cohen et aL 1991) and many

different regional utility estimates compiled as part of the UEC database (Appendix B).

Generalized UEC equations

The generalized equations for calculating heating and cooling UECs are given below. In

the generalized equation, the efficiency is the combined heating or cooling system

efficiency, where the system efficiency includes effects of both the equipment and the

thermal distribution system. These are discussed in a following section.

Load Fuel heating: UEC (MMBt/r)= (ficiency/o10)

where: Load is building heating load (MMBtu/yr)

Efficiency is heating AFUE (%) plus a factor to account for distribution efficiency

Load Electric heating: UEC (k )(Efficiency/10) * 0.003413

where: Load is building heating load (MMBtl/yr)

Efficiency is heating AFUE (%) plus a factor to account for distribution efficiency

0.003413 converts units (MMBtu/kWh)

Air Conditioning,

Load Ht Pump Heating: UEC (kWh/yr) Efficincy 0.001

where: Load is building heating or cooling load (MMBtu/yr)

Efficiency is EER, SEER, or HSPF (kBtu/kWh) plus a factor to account for distribution

efficiency

0.001 converts units (MMBtu/lOO0kBtu)

8

EE-RM-94-230 COMMENT 61C

3. HEATING AND COOUNG END-USE DATA 

Heating and cooling together account for about 30% of electricity consmnption. 70% of gas , 

consumption. arnd 90% of oil consumption in the U.S. residential sector. These end-uses 

are a major solirce of conservation potential as well as energy demand groWth (see 

Koomey et ale 1991a). In this section. we discuss UECs. beating and cooling equipment 

characteristics, and building thennal characteristics. Energy consumption for heating and ' 

cooling is a function of many variables. including HV AC equipment characteristics. 

building shell characteristics. occupant behavior. climate (both across regions and year to 

year within the same region). microclimates. and regional energy prices. For heating and 

cooling. we use a regional disaggregation to segment the housing population to capture the 

major variations in climate and building characteristics across the country. As shown in 

Figure 3.1, we use a North and South regional breakdown similar to that used in earlier 

LBL work (e.g. Koomey et al. 199Ia). We provide UECs and building prototype 

characteristics for these two regions. 

3.1. UEC, 

The UECs for heating and cooling are important since the current level of energy 

consumption determines potential energy savings from improvements in building thennal 

shell characteristics as well as equipment We show these estimates in Tables 3.1 and 3.2. 

The sources used in developing UECs for the forecasting database include national data 

sources as well as regional data from utilities and weatherization studies. These include the u.S. Residential Energy Consumption Survey (RECS) data sets (US DOE 1982a, 1986, 

1989a, 1990), LJBL-REM estimates (LBLREM 1991), the American Gas Association Gas 

Housebeating Survey (AGA 1991), the BECA-B data set (Cohen et al. 1991) and many 

different regional utility estimates compiled as, part of the UEC database (Appendix B)~ 

Generalized VEe equations 

The generalized equations for calculating heating and cooling UECs are given below. In 

the generalized equation, the efficiency is the combined heating or cooling system, 

efficiency. where the system efficiency includes effects of both Ute equipment and the 

thermal distribution system. These are discussed in a following section. 

fuel beating: 

where: 

Electric beating: 

wbCle: 

Air Conditioning. 

Ht Pump Heating: 

where: 

Load 

uec, (MMBtuIyr) = (EfflCiency/lOO) 

Load is bailding bcadog load (MMBtuIyr) 

Eff"lciency is beating AFUE (9&) plus a factor to accouut for distribution efficiency 

, Load 

UEC (kWblyr) - (Efficiency/I 00) • 0.003413 

Load is building beating load (MMBtuIyr) 

Efficiency is bc:ating AFUE (%) plus a'factor to account for distribution efficiency 

0.003413 converts units (MMBtulkWb) , 

Load 

UEC (kWh/yr) = Efficiency • 0.001 

Load is building beating or cooling load (MMBtulyr) 

Efficiency is EER. SEER. or HSPF (kBtulkWb) plus a factor to account for distribution 

efficiency " 

0.001 converts units (MMBtull000kBtu) 

8 
0.

0

:.w I !W England

)nnecuut (CT)

line (ME)

aschuseut (MA)

.w Hampshire (NH)

:ode Iland (R)

:nnont (VT)

·* York/

w Jersey

w Jersey (NJ)

. York (NY)

gtk 3

;d Atlatic

:lwue (DE)

strict of Columbia (DC)

iryland (MD)

nsylvania (PA)

rgnia (VA)

st Virginia (WV)

Region 4

South Atlantic

Albama (AL)

Florida (FL)

Georgia (GA)

Kentucky (KY)

Missippi (MS)

North Carolina (NC)

Soauth Carolina (SC)

TennMsee (TN)

Region 5

Illioib (IL)

Indiana (IN)

Michigan (MI)

Mineso ta(MN)

Ohio (OH)

Wionsin (WI)

Redon 4

Southweat

Arkansa (AR)

Louisiana (LA)

New Mexico (NM)

Oklahoma (OK)

Texa (TX)

Re7gion

Ce-nl

lowa (IA)

Kana (KS)

Mimoun (MO)

Nebraska (NE)

Resdi I

North Cenral

Colorado (CO)

Montana (MT)

Nonh Dakota (ND)

South Dakota (SD)

Utah (UT

Wyoming (WY)

Regia 9

West

Aiana (AZ)

California (CA)

Hawaii(HM

Nevada (NV)

Reglon 10

Northwest

Alaska (AK)

Idaho (ID)

Oregon (OR)

Wahington (WA)

South Region is defined as Federal Regions 4,6, and 9.

North Region is defined as Federal Regions 1, 2, 3, 5, 7, 8, and 10

Figure 3.1. Federal Regions and North/South regional breakdown in the Residential

Database.

o

EE-RM-94-230 COMMENT 61C

·1doa , !. u!dud 

lnacccicul (CT) 

aine (ME) 

wachusau (MA) 

!W Hunpshire (NH) 

:ode Island (Rn 

:nnoat (VT) 

.• York! 

:·"uwr 

:w Jeney (NJl 

:w Yort (NY) 

.. ~ 

:. Adaadc 

:!aware (DE) 

slricl of Columbia (I>C) 

Iryiand (MO) 

nasylvania (PA) 

rsinia(VA) 

:st Virginia (WY) 

Redoa • 

Sol dII Allaalic 

AlabUlla (AL) .. 

Florida (FL) 

Oeorgia (OA) 

lCealucky (ICY) 

Misf.issi"pi (MSl 

North Carolina (NC) 

~lb Carolina (SC) 

TCftIICStCC (TN') 

ReIIoe 5 

MIll ... 

IlliIloil (I L) 

ladiaDa (IN) 

Micbipa (MI) 

Miaaaoca (MN) 

Oh1o(OH) 

W"1ICODSia(WI) 

a .. , SoatIa ... 

Attusu (All) 

Louisiuaa (LA) 

New Mellica (NM) 

Oklahoma (OIC) 

Teus(l'X) 

IeIIoe 7 

Catnl 

IoWa (IA) 

1Caus(1CS) 

Miaouri (MO) 

. NcbnsU (HE) 

South Region is defined as Fcdcra1 Regions 4, 6, and 9. 

Nonh Region is defmed as Federal Regions 1,2, 3, S, ',8, and 10 

RIII_' 

NortII CcauU 

CoIondo (CO) 

MOIlWIa (M'O 

Nonh' Oakaca (ND) 

Scalb Dakoca (SD) 

Ucab (t1l1 

Wyomias (WY) 

..... ' 

Wed 

A~(AZ) . 

Calil'omia (CA) 

fIawaii'(HQ 

Nevada (NY) 

R .... 'O 

NonIaWBI 

Alaska (Ale) 

Idaho (ID) 

Oresoa (Oil) 

Wabiastoa (WA) 

Figure ·3.1~ . Fedt:m1 Regions and North/South regional breakdown in the Residential 

Database. 

a 
3le 3.1. Calibrated Database UEC Estimates for Heating

UEC by Housing Type

Existing Existing Existing New New New

Single-Family Multi-Family Manufactured Single-Family Multi-Family Manufactured

(kWh) (kWh) (kWh) (kWh) (kWh) (kWh)

ation Fuel Technology (MMBtu) (MMBtu) (MMBtu) (MMBtu) (MMBtu) (MMBtu)

rth

Electric Furnace 14000 8700 8000 11301 4320 6488

Room 14000 8700 8000 11301 4320 6488

HP 9000 4000 6300 9648 2614

Gas Furnace 93 69 65 64 27 56

H20 111 65 - 74 24 -

Room 83 63 63 - - 61

Oil Furnace 83 66 59 62 - 56

H20 112 66 - 79 26 -

Room 79 60 -- - -

jth

Electric Furnace 6000 3700 4500 4903 1940 3391

Room 6000 3700 4500 4903 1940

HP 5000 2100 1500 3935 948 1947

Gas Furnace 52 31 36 26 11 29

H20 79 35 - 39 12 22

Room 38 19 28 8 -

Oil Furnace 55 - 61 30 - 24

H20 86 68 - - 25 -

Room 46 11 18 - 10

urce: Table 3.20.

:ble 3.. Calibrated Database UEC Estimates for Cooling

UEC by Housing Type

Existing Existing Existing New New New

Single-Family Multi-Family Manufactured Single-Family Multi-Family Manufactred

cation Fuel Technology (kWh) (kWh) (kWh) (kWh) (kWh) (kWh)

)rth

Electric Central 1160 515 1443 1132 307 1630

Room 375 160 447 352 89 499

HP 1176 517 1544 1425 342 -

)uth

Electric Central 3821 1366 2988 2297 928 2702

Room 1358 424 1007 756 273 886

HP 4077 1371 3175 3316 808 3463

)urce: Table 3.21.

10

EE-RM-94-230 COMMENT 61C

'I ) e 3 1 C .. al rb ra ted D a tab ase VEC Esd ma tes Ii or H ea ti I!g 

UEe by Housing Type 

Existing Existing Existing New New New 

Single-Family Multi-Family Manufactured Single-Family Multi-Family Manufactured 

(kWh) (kWh) (kWh) (kWh) (kWb) (kWb) 

;atiOn Fuel Technology (MMBtu) (MMBtu) (MMBtu) (MMBtu) (MMBtu) (MMBtu) 

rth 

Electric FUrnace 14000 8700 8000 11301 4320 6488 

Room 14000 8700 8000 11301 4320 6488 

HP 9000 4000 6300 9648 2614 - Gas Furnace 93 69 6S 64 27 S6 

H2O 111 6S - 74 '24 - Room 83 63 63 - - 61 

Oil Furnace 83 66 S9 62 - S6 

H2O· 112 66 - 79 26 - Room 79 60 - - - - Jth 

Electric Furnace 6000 3700 4500 4903 1940 3391 

Room 6000 3700 4500 4903 1940 - HP 5000 2100 1500 3935 948 1947 

Gas Furnace 52 31 36 26 11 29 

H2O 79 3S - 39 12 22 

Room 38 19 28 - 8 - Oil Furnace S5 - 61 30 - 24 

H2O 86 68 - - 2S - Room 46 11 18 - - 10 \ 

urce: Table 3.20. 

: e bl 3.2. CaUb ra ted D a tab ase VEC Esti mates Ii or C U '00 ng 

trEe by Housing Type 

Existing, Existing Existing' New New New, 

Single-Family Multi-Family Manufactured Single-Family ,Multi-Family Manufactured 

<:ation Fuel Technology (kWh) (kWh) (kWh) (kWh) (kWh) (kWh) 

lIth 

Electric Centtal 1160 515 1443 1132 307 1630 

Room 375 160 447 352 89 499 

lIP 1176 517 1544 1425 342 - !uth 

Electric Centtal 3821 1366 2988 2297 928 2702 

Room 1358 424 1007 756 213 886 

lIP 4077 1371 3175 3316 808 3463 

JUICe: Table 3.21. 

10 
Existing Building UECs

For natural gas space heating, the American Gas Association's (AGA's) Gas Househeating

Survey provides estimates of average space heating and "other" consumption for singlefamily and multi-family buildings. The survey also provides an average across the two

building types on a national level and across the four census regions (AGA 1991). These

data are derived from surveys of gas utilities, and are shown over the period 198C0through

1990 in Figures 3.2 and 3.3. Also shown are end-use estimates of gas space heating

consumption from RECS (US DOE 1982a, 1986, 1989a, 1992) which are estimated from

utility bill data using a statistical regression analysis model. The figures also show national

gas heat UECs from the LBL Residential Energy Model (LBLREM 1991).

Since all sources are in fairly close agreement for national average natural gas space heating

UECs, we developed the UECs for natural gas using the RECS data. At the same time, we

used the RECS data for estimating all fuel space heating UECs. The RECS format allows

easy stratification of the data by house type, region, and heating technology, and is thus

more flexible.

Electric space heating consumption for all house types and single-family houses are shown

in Figures 3.4 and 3.5. For electric space heat, there are no utility surveys that provide

national average electricity space heating consumption analogous to the AGA data for

natural gas. The two primary sources, the RECS end-use estimates and the LBLREM

forecasts, are in wide.disagreement on electric heat UECs. The UEC database contains

almost 250 estimates of electric heating UECs for different regions, technologies, house

types vintages, etc. (see Appendix B). In general, electric heat UECs show wide. variations

across regions and even within regions.

Regional utility.estimates for electric heating from the UEC database are shown in Figure

3.6 for resistance heat and Figure 3.7 for heat pump heating, with the estimate plotted

against heating degree days for the federal region incorporating the utility service area. The

BECA-B database of single-family retrofit programs and savings contains several entries

with end-use estimates of electric space heating UECs (primarily electric resistance) (Cohen

et aL 1991). These data are plotted in Figure 3.8. All of these data are from the Pacific

Northwest region (except for three data points from the Tennessee Valley Authority), and

thus may not be representative of the rest of the U.S.

For the residential database, we use the BECA-B data to develop electric resistance space

heat UECs for the North region and the regional utility data to estimate UECs for resistance

heat in the South and heat pump UECs in both regions since these sources provide data

best for single-family dwellings.' The single-family estimates are used to estimate UECs

'for the other building types. Furnace fan energy consumption is not included in either the

natural gas or electric space heating data. Table 13.1 in the Miscellaneous End-Use Data

section of this report provides an estimate of furnace fan UECs.

For cooling, UEC estimates show wide variation across sources, as shown in Figures 3.9

and 3.10. In addition, the values from year to year derived from the RECS data are more

variable than are the heating data. Records in the UEC database also show wide variation,

even within the same North/South regions we have defined (e.g. California locations are in

the same region as Florida locations). For the residential database, we use values derived

during an earlier LBL study (Koomey et al. 1991a), which are in reasonable agreement

with the data in the UEC database (Appendix B).. Central air conditioning fan energy use is

included in the Seasonal Energy Efficiency Ratio (SEER) and thus in the UECs.

__

EE-RM-94-230 COMMENT 61C

Existing Building UECs 

For natural gas space heating, the American Gas Association's (AGA's) .Gas Househeating 

Survey provide.s estimates of average space heating and "other" consumption for singlefamily and multi-family buildings. The survey also provides an average across the two 

building types on a national level arid across the four census regions (AGA 1991). These 

data are derived from surveys of gas utilities, and are shown over the period 198(Jthrough 

1990,in Figures 3.2 and 3.3 •.. Also shown are end-use estiniates of gas space heating 

consumption from RECS (US DOE 19823, 1986, 1989a, 1992) which are estimated from 

utility bill data using a statistical regression analysis model. The figures also show national 

gas heat UECs 110m the LBL Residential Energy Model (LBLREM 1991). 

Since all sources are in fairly close agreement for national average natural gas space heating 

UECs, we developed the UECs for natural gas using the RECS data. At the same time. we 

used the RECS data for estimating an fuel.space heating UECs. The RECS fonnat allows 

easy stra,tification of .the data by house type. region, and heating technology. and is thus 

more flexible. . 

Electric space heating consumption for all house types and single-family houses are shown 

in Figures 3.4 and 3.5. For electric space heat, there are no utility surveys that provide 

national average electricity space heating consumption analogous to the AGA data for 

,natural gas. The two primary sources, the RECS end-use estimates and the LBLREM 

forecasts. are in wide. disagreement on electric heat UECs. The UEC database contains 

almost 250 estiniates of electric heating UECs for different regions, technologies. house 

types Vintages, etc. (see Appendix B). In general, electric heat UECs show wide. variations 

across regions and even within regions. , . 

Regional utility.estimates for electric heating from the UEC database are shown in Figure 

3.6 for resistance heat and Figure 3.7 for heat pump heating. with the estimate plotted 

against heating degree days for the federal.region ~corporating the utility service area. The 

BECA-B database of single-family retrofit p~grams and savings contains several entries 

with end-use estimates of elecaic space heating UEC'i (primarily electric resistance) (Cohen 

et aL 1991). These data are· plotted in Figure 3.8. All of these data are from the Pacific 

Northwest region (except for three data points from the Tennessee Valley Authority). and 

thus may not be representative of the rest of the U.S. . . 

For the residential database. we use the BECA-B data to develop electric resistance space 

heat UECs for the North region and the regional utility data to estimate UECs for resistance 

heat in the South and heat pump UECs in both regions since these sources provide data 

best for single:-family dwellings; The single-family estimates are used to estimate UEes 

Jor the other building types. Furnace fan energy consumption is not included in either the 

natural gas or electric space heating data. Table 13.1 in the Miscellaneous End .. Use Data 

section of this report provides an estimate of furnace fan UECs .. 

For cooling, UEC estimates show wide variation across sources. as shown in Figures 3.9 

and 3.10. In addition. the values from year to year derived from the RECS data are more 

variable than are the heating data. RecQrds in the UEC database also show wide variation, 

even within the same North/South regions we have defmed (e.g. CalifonU.a locations are in 

the same region as Aorida locations).. For the residential database. we use values derived 

during an earlier LBL study (Koomey et al. 1991a). which are in reasonable agreement 

with the data in the UEC database (Appendix B) .. Cential air conditioning fan energy use is 

included in the Seasonal Energy Efficiency Ratio (SEER) and thus in the UECs. . 
Figure 3.. National Average Gas Space Heating Consumption - All House Types

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

Source: US DOE 1982a, 1986, 1989a, 1992; AGA 1991; LBLREM 1991.

Figure 3.3. National Average Gas Space Heating Consumption - Single-Family.Houses

1981 1982 1983 1984 1985 1986 1987 1988 .1989 1990

Source: US DOE 1982a, 1986, 1989a, 1992; AGA 1991; LBLREM 1991.

12

I

a

.s

cn

I

I

100.0 - -

90.0 --

80.0

70.0

'60.0 - -_

50.0 -

40.0 ---

30.0

20.0 -

10.0 -

0.0 -

1980

..

a

80

0

U

C^

100.0

90.0

80.0 --

70.0 -

60.0 - -

50.0 - -

40.0 - -

30.0 -

20.0 -

10.0 --

0.0 -

1980

EE-RM-94-230 COMMENT 61C

Figure 3.2. National Average ~as Space Heating Consumption - All House Types 

l00n~---~--------~--~----~--~--~r---~--~--~ 

~ 

90.0 -,·---+---'"ii----+·- i i - [1-_ .... I i ~ 80.0 _. ""':1 ~---l-_'_~i ---+-----ii.---:---~---.. ---i 

.a 11"'--- i. I . i 

=70.0 . ~ 1 

~. ~ 60.0;-- I .... =+==~+--\ -4!"---.~ .l=--=-~ li ~i .... -.' .. - ~;~::l:::: _ ... .t1. 

.s 

-~ 

cu 

.c: 

50.0 

~ 40.0 

a :-._ ... -. ---_1-- RECS 

i 30.0 ~.-.- -q-- AGA 

= c: 

c: 

< 

20.0 --- --....A. ----REM 

10.0 ~.--

0.0 +----+--+----+----+--.,......j---+---f----+---+--~ 

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 .1990 

Source: US DOE 1982&. 1986. 1989a, 1992; AGA 1991; LBLREM 1991. 

F"lgure 3.3. NatiQlnal A vera~e Gas Space Heating Consumption - Single-Family,Houses 

100.0 

90.0 

50.0 

~ .40.0 

a. 

- 30.0 ~ 

c: = 

c: 

< 

20.0 

10.0 

0.0 

-----RECS 

-O--AGA 

---.t--REM 

, . 

i . . ...----....... _- I I ! --+---4.-.------+----:c------..-- : :" : : :: : 

! 

t -----::':-- ~ 

--r I 

1980 1981 1982 1983 1984 1985 1986 1987 1988. 1989 1990 

Source: US DOE 19823.1986. 1989a, 1992; AGA 1991; LBLREM 1991. 

12 
.4. National Average Electric Space Heating Consumption - All House Types

12000

0000 -

81000 ...........- .-----.-- - .... .....- _....-.........-........................ ..

4000 --- ------ -

4000 ................ .......... ..

*--RECS '

2000 ----- REM

0*.., ... , .. .... i

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

US DOE 1982a, 1986, 1989a, 1992; LBLREM 1991.

3.5. National Average Electric Space Heating Consumption - Single-Family Houses

14000

12000 - ----- --------

·10000

8000 - . .. . .

6000 - _.. : ... ..-'... 

4000 -- --- --- RECS "..' '

2000 ---, -- -- REM ---- . ...................... ..............................

0 I I

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

:: US DOE 1982a, 1986, 1989a, 1992; LBLREM 1991.

13

EE-RM-94-230 COMMENT 61C

.4. National Average Electric Space Heating Consumption - All House Types 

t~~----~--~----------~----~----~--~----------~----~ 

. ~ 

10000 [ .... J.-,"'._:=... -'T-'---1-r"-]~-'r-T-f--:'-'-r8000 -... -.-.... ...;. .. _. ':.1::.' --.... - .. ~::::: .. ..;.. . .. -.-~:~.... ...- -1'''' ..... --"!'--

6tW _ 1.--4--..--;.-----.. -.-.;.---.1----1----;----1..-.-. i . __ _ I~ -- ; , i i ! i I .. L 

4000 • RECS -. -1-- . ...;. ---- --]'"", -0, 

2000 

~ REM -'l--T---r-"-r--'T'--r-'-- O+-----~--~-----r----~----+_----+---~----_+----_+----~ 

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

: US DOE 1982a.1986. 1989a. 1992; LBLREM 1991. 

3.5. National Average Electric Space Heating Consumption - Single-Family Houses 

14000 

12000 

.10000 

8000 

6000 1 

l. 

, 

--U--!----r·--··-···-t·--··-··-t·---·-·-·I'·---·-··l· .. _ ..... _ ... -.! .... -.-.-~.! ... _--

: : 

---7 '1- 1---- I .. _- -_ .. _.. . . 

--j-'-r~--J-... --l-=r~I=:=.I==r== i 

l 4000 

:; 

--II.f-- RECS ...... _.. i -·----.. -t·-··-···T·--.. -·t .. -.... -· i .. --... _. I ._. . 2000 : ~ ~ . ~ ~ ·_.·_. ___ .• ·_ .. ~ __ · .•. · ___ .. ·_40·· ___ •.. _ ..... _ •• · __ · __ ._._ ........... _ ..... _ .................... _ ....... -:---._ .. _--

l I I j 1 I 

--G-REM 

O+-----~---4----_r----~---+_----~---;----+----r----~ 

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

:e: US DOE 1982a.1986. 1989a. 1992; LBLREM 1991. 

13 
Figure 3.6. Electric Resistance Space Heat UECs from Utiity Studies

1000

* Single Family

o All Housing Types

2000 3000 4000 5000 6000 7000

Regional HDD base 65F

Source: Data in Residential UEC Database (Appendix B).

Figure 3.7. Electric Heat Pump Space Heat UECs from Utility Studies

* Single Family

0 AU Housing Types

0 1000 2000 3000 4000 5000 6000 7000

Regional HDD base 65F

Source: Data in Residential UEC Database (Appendix B).

14

14000

12000

1 8000

. 6000 i 4000

2000

C0

3 * 1

* I i

j.. I- i-F- 0

ib I I

0

16000

14000

12000

,10000

0 8000

W 6000

4000

2000

0

:i i

I i:

. i ' I _

1 ,- -

i . ' I f

{~~~~~~~~U. :1:31:3 · · i

EE-RM-94-230 COMMENT 61C

. . FIgure 3.6. Electric Resistance Space Reat VECs from UtUlty Studies 

14000 

_12000 

,UXXlO 

c!! 

.. 

i 8000 

1,6000 

fIl 

(JI 

I I I I I I I 

I 

i • I I I 

I 

! I , I I I f:. I I . 

, i 1 i- f . i I. I I ~ I- I i ~ I • 

i i I 0 i IJ ! (J. ! 

i r 

i ; I I I ! 

f 

i. i i ! I I 

! i I i i pc I ! I I 

! I 

i I I i 

o 1000 2000 3000·4000 5000 6000 7000 

Regional BDD base 65F 

Source: Dara in Residential UEC Database (Appendix B). 

FIgure 3.7 •. Eledrlc Heat Pump Space Reat UECs from UtlUty Studies 

16000 

i ! i i .j • I i ! ! ! I I : . I I I .i I I 1---1 I 

! ! 

i I i i i 

i ! i. ! I i 

+- , 

I I I. I I * ! I I jD i I 

~ : 

~ ! ! 

b~ i I i 

~ : ! • ! I i I jo : I I I i ! I 

~ I I ! } 

I I i • I 

I I I I I 

!Cb I I I 

l f 

o 

o 1000 2000 3000 4000 5000 . 6000 7000 

Regional HDD base 65F 

Source: Data iIl Residential UEC Database (Appendix B). 

14 

• Single Family 

IJ All Housing Types 

• . Single Family 

o All Housing Types 
Figure 3.8. Average Electric Space Heat Use from Retrofit Programs in BECA-B Database

25000 -

22500 -

17500 -°- _

g 15000 _ - ii -a' .U

12500 i-r--q*- j *-

o.10000

17500 .--

5000---- -------

2500

2O t i ~ i =

oooo _-i........ ---

* 0i O.

3000 3500 4000 4500 5000 5500 6000 6500 7000

Heating Degree Days Base 65F

* Pre-retrofit

o Post-retrofit

Source: Cohen et aL 1991.

15

EE-RM-94-230 COMMENT 61C

Figure 3.8. Average EJec:trlc Space Heat Use from Retrofit Programs In BECA·B Database 

~~~--~--~---~I·--~i---'--·--~r---~~ 

22500 +---+---0---'11-1 ~ 

:g 20000 I' o--i-;, --.:i..----+-- i ~ 17500 +-~~----+_--~--~-~~--~---.I----+_~·~i ~ 1 I I 0 I.-

i 15~ ~- i j i IFT- :-r- 0 1 

~ 1~00 ---r---:i--ra.·~·.~·-.-"-· 1 --I ~ 10000 ;' 0 r 8-.. t"~1 B-- ~-a--;:I·---·-t---1 li 7500 l '? 0 I 0 r:r-- t---t-~ ~ i I i ! &l 5000 ---~!_::: -0-..... iii I : ~ 

~oo i I i 1 O+-----+----~---4----~--~~--_+----+_--~ 

3000 3500 4000 4500 5000 5500 6000 6500 7000 

Beating Degree Days Base 6SF 

Source: Cohen et al. 1991. 

15 

• Pre-retrofit 

o Post-retrofit 
Figure 3.9. National Average Central Air Conditioner (CAC) UEC

r~~~~~II

iiI

_------ ! i------ +S i

-~~ ~~ ~~~~~~~ I--,---

980 1982 1984 1986 1988

-*- RECS SF

-°-- REM SF

*- RECS ALL

---- REM ALL

1990

Source: US DOE 1982a, 1986 1989a, 1992; LBLREM 1991.

Figure 3.10. National Average Room Air Conditioner (RAC) UEC

1800

1600

1400

1200

1000

800

600

400

200

0

I!980 1982

...... -- - - I ---

I i I

-- 1 .

i I

1984 1986 1988

-- RECS SF

-- -- REM SF

-- RECS ALL

- -- REM ALL

1990

Source: US DOE 1982a, 1986, 1989a, 1992; LBLREM 1991.

16

4000

3500

_ 3000

2500

= 2000

I 1500

< 1000

500

0

1

es

3

<!

EE-RM-94-230 COMMENT 61C

Figure 3.9. NatiollJal Average Central Air Conditioner (CAC) VEe 

4000 

I~ 3500 1-' 

r~ 

~ 2500 

12000 

~ . 

.. 1500 

i 

< 1000 

soo 

?--

. 

. 

._. 

o -

1980 

-0 

----.......... 

1982 

, 

J 

I 

~ - I -0- . I..;.- --- 1-

---- 1 

I --t I i 

! ! i I i . 

I . ! 

! I ! ! I 

I I I 

1984 1986 1988 

Source: us DOE 19828, 1986. 198930 1992; LBLREM 1991. 

..1""1 

Figure 3.10. NatiolDai Average Room AIr ConditioDel' (RAC) VEe 

.1800 

1600 

.- 1400 ~ 

~~ 

I 

I 

-....-.-. I -

I I 

! I 

I 

t '1.J' 1 ....... ..... 

II . 

.'r' 

. 

.. -

1990 

~ 1200 

e 

T ~ .:.T .... - J 1000 

~ 800 r::----- -a § 600 -

-< 400 

200 

o 

19HO 1982 

.. ~ 

.~.Ah 

_T i 

i 

• . I 

I 

I I 

! ! 

.1 i 

I ! 

i 

t i 

I 

1984 1986 

Source: US DOE 19828, 1986, 19891. 1992; LBLREM 1991. 

16 

:--:--- -. 

• I 

! 

I '1 

i 

I 

I 

1988 1990 

• RECSSF 

--G-REM.SF 

• . RECSAU. 

~REMAlL 

• RECSSF 

~REMSF 

~ RECSAlL 

---<>- REM AIL 
New Building UECs

For the residential database, we estimate UECs for space heating and cooling in new

buildings by first calibrating the UECs for existing buildings with UECs estimates from

building descriptions, a building loads model, and equipment efficiencies for existing

buildings, and then applying the calibration multiplier to the model for new buildings and

equipment. This ensures that the UECs for new buildings, which are not well represented

in available measured data, are calculated in a consistent manner to UECs for existing

buildings. This process is discussed further in Section 3.5 (below).

3.2. Technology Data for HVAC Equipment and Distribution Systems

Historical Efficiency of Equipment

Efficiencies of heating and cooling equipment have been generally rising since the early

1970s, when data are first available. The sources of data on HVAC equipment efficiency

trends include appliance manufacturers trade associations (AHAM 1991; ARI 1991; GAMA

1991). Fuel-fired furnace and boiler efficiencies are determined from standardized testing

procedures which simulate seasonal performance. The measure of efficiency for this

equipment is the Annual Fuel Utilization Efficiency (AFUE), which is expressed as a

percent Electric resistance heating equipment, both furnaces and room heating, is assumed

to have an AFUE of 100%. Electric equipment that uses a compressor, including heat

pumps for heating and cooling and electric air-conditioners, have unique measures of

efficiency which are also derived from standardized testing procedures.

The measure of efficiency for central air conditioning (CAC) and the cooling mode for

electric heat pumps (HP) is the Seasonal Energy Efficiency Ratio (SEER), while the

efficiency for heat pumps in heating mode is the Heating Seasonal Performance Factor

(HSPF). Each of these measures is a ratio of the useful cooling or heating provided, in

kBtu, to the electrical energy required, in kWh. For room air conditioners, the efficiency

measure is the Energy Efficiency Ratio (EER), which is based on full load operation of the

equipment (The SEER accounts for seasonally induced part-load operation).

The average efficiency of new residential heating and cooling equipment sold each year,

sometimes called the SWEF (shipment-weighted energy factor), is shown with the

shipments data in Figures 3.11 through 3.16. Shipments of equipment include both new

construction markets and replacement markets.

Gas furnaces represent the major portion of the residential heating equipment market, with

current sales around 2 million units per year. Heat pumps are the major central heating

competition for gas furnaces, with current sales of about 0.75 million units per year. Since

1972, furnace efficiency (AFUE) has increased from 63% to 78% in 1990, which is the

legal minimum under the NAECA appliance standards. Oil furnaces are slightly higher in

efficiency. Changes in residential boiler efficiencies are not well known. Air conditioning

equipment efficiency has also risen dramatically over the last 20 years, as have shipments

of residential cooling equipment

17

EE-RM-94-230 COMMENT 61C

New Building UECs 

For the residential database, we estimate UECs for space heating and cooling in new 

buildings by f1l'st calibrating the UECs for existing buildings with UECs estimates from 

building descriptions, a building loads model. and equipment efficiencies for existing 

buildings, and then applying the calibration multiplier to the model for new buildings and . 

equipment This ensures that the UECs for new buildings, which are not well represented 

in available measured data. are calculated in a consistent manner to UECs for existing 

buildings. This process is discussed further in Section 3.5 (below). 

3.2. Technology Data for HVAC Equipment and Distribution Systems 

Historical Efficiency of Equipment 

Efficiencies of heating and cooling equipment have been generally rising since the early 

1970s, when data are first available. The sources of data on BV AC equipment efficiency 

trends include appliance manufacturers trade associations (AHAM 1991; ARI 1991; GAMA 

1991). Fuel-fIred furnace and boiler efficiencies are detennined from standardized testing 

procedures which simulate seasonal performance. The measure of efficiency for this 

equipment is the Annual Fuel Utilization Efficiency (AFUE). which is expressed as a 

percent. Electric resistance heating equipment, both furnaces and room heating, is assumed 

to have an AFUE of 100%. Electric equipment that uses a compressor, including heat 

pumps for heating and cooling and electric air-conditioners. have unique measures of 

efficiency which are also derived from standardized testing procedures. 

The measure of efficiency for central air conditioning (CAC) and the cooling mode for 

electric heat pumps (lIP) is the Seasonal Energy Efficiency Ratio (SEER), while the 

efficiency for heat pumps in heating mode is the Heating Seasonal Performance Factor 

(HSPF). Each of these measures is a ratio of the useful cooling or heating provided, in 

kB tu, to the electrical energy required, in kWh. For room air conditioners, the effIciency 

measure is the Energy Efficiency Ratio (EER), which is based on full load operation of the 

equipment. (The SEER accounts for seasonally induced part-load operation). 

The average efficiency of new residential heating and cooling equipment sold each year, 

sometimes called the SWEF (shipment-weighted energy factor), is shown with the 

shipments data in Figures 3.11 through 3.16. Shipments of equipment include both new 

construction markets and replacement markets. 

Gas furnaces represent the major portion of the residential heating equipment market, with 

current sales around 2 million units per year. Heat pumps are the major central heating 

competition for gas furnaces, with CUlTent sales of about 0.75 million units per year. Since 

.1972, furnace efficiency (AFUE) has increased from 63% to 78% in 1990, which is the 

legal minimum under the NAECA appliance standards. Oil furnaces are slightly higher in 

efficiency. Changes in residential boiler efficiencies are not well known .. Air conditioning 

equipment efficiency has also risen dramatically over the last 20 years, as have shipments 

of residential cooling equipment 

17 
Figure 3.11. Annual Residential Furnace and Heat Pump Shipments, 1951-1990

2.5 .I

mec

2.0

O-----ias

15. . . ..

..... Heat Pum

, 1.0 ..

/

0.5

0.0 --- i _

1950 1955 1960 1965 1970 1975 1980 1985 1990

Source: GAMA 1991; Fechtel et aL 1980; LBLREM 1991 for Furnaces; ARI 1991 for Heat Pumps.

Shipments not adjusted for imports, exports, or non-rsidential uses.

Flgure 3.12. Shipment-Weighted Efficiencles for Residential Furnaces and Heat Pumps, 1975.1990

80

75

70 "

55-= ' _ _ _ _ ' . i' 

160' ~ m

- Heat Pump

50~~~ ------ i I ------ t ~ i Ii ---- i ---- I ---- I -- 1975 1977 1979 1981 1983 1985

I

i

I

4

1987 - 1989 1991

Source: US DOE 1982b; GAMA 1991 for Furnaces; ARI 1991 for Heat Pumps.

Electric Furnaces assumed to be 100% efficient

18

EE-RM-94-230 COMMENT 61C

Figare 3.11. Amlul Residential Furnace and Heat Pump Shlpm~ts, 1951-1990 

2S ~----~------~----~------~-----r------~,------~----~ I ! 

I :! 

20 

----- Elce 

-----Qas I I \ II' i -V 

I " \ /\! / I:. __ ~1 ____ ~,.~~ ____ ~~._~,/ ___ \~1 __ ~1 __ ~ ____ .~ 

i I \1/ r\1 

.• -------- Oil 

! 1. 1 \il . 1 \ I 

;..!..I -J Iii l 

" . ! ! : i / ,7.(" i I: 

.- - - - - - HeatPump 

l,,'_1 I: 1 1 

. "":~. ..... -, -- ~ :.': ' 

0.5 I i .~~/~'. l 

·-.~,-~--~~.~.L·-·-·-_-__ --·--_-__ -_------~t------------_~~--.-----

i I _--r ----- -- .. -- . 0.0 i------;---=~~--~---_+---+_--_+_---__I--___I 

1950 1955 1960 1965 1970 1975 1980 1985 

Source: GAMA 1991; Feebtel et al. 1980; LBLREM 1991 for Furnaces; ARI 1991 for Heat Pumps •. 

Shipments not adjlJsted for imports. exports, or nOD-~dentiaI uses. 

1990 

FIgure 3.U. Shlp,ment-Welghted EfOciencles for Residential Furnaces and Heat Pumps,.197S-1990 

80 

i' E 70 ~---'~~-----+-------+------Tr-----+------+-----~~--~ 

~ ; 

~ ~+-____ ~ ___ ~~~_-+ _______ ~ _____ ~~ __ ~ _____ +i _____ ~ 

~ 

i 

_ 60 +---.----+------r------+-------~----~---

~ • Oil 

55 +-----~----~-------+-----~----~----

i ------ Heat Pump 

SOi-~,--~---~------+_----~----~-----+_----~----~ 

1975 19TI 1979 1981 1983 1985 1987' 

Source: US DOE 1982b; GAMA 1991 for Fumaces;ARI 1991 for Heat Pumps. 

Elecuic Furnaces assumed to be 100% efficienL 

18 

1989 1991 
gure 3.13. Annual Boiler Shipments for Residential Size Boilers, 1951-1990

I

g

1950 1955 1960 1965 1970 1975 1980 195

urce: GAMA 1991; Fechtel et al. 1980.

sidential size defined as up to 300 kBtu per hour.

ipments not adjusted for imports, exports, or non-residential uses.

yure 3.14. Shipments of Direct Heating Equipment for Gas and LPG

a

.

I

I

100%

80%

70%

60%

50%

40%

30%

20%

10%

0%

1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 193 1984 1985 1986 1917 1988 1989 1990 1991

11Vntd S-Wal 1 1 E Direc VntW ll I TR Floor Furme I Vntd Room

=I I Unvntd Room -- TOTAL

Shipments

arce: GAMA 1991.

19

1990

1200

1000

800 Soo k9

I

600 C

a

.3

400 2

200

0

EE-RM-94-230 COMMENT 61C

gore 3.13. Annual Boller Shipments for Residential Size Boilers, 1951-1990 

I 

C 

D 

~ ~ 

0.3 

0.2S 

0.2 

0.15 

0.1 

0.05 

+-____ --;..-_-_-_-: T-'-- 0"'" ~i -~·--~---·.·----..;i----_+_----+__+_:~~~------..... r---· 

! 

i 

! i 

+---------------J~ I.: 

~ ! - .. - .. ----; -. -... --.'O----..;...-.---~----.li' 

! i 

: : 

--I ~ ~ T 

i -..... ---_.-

~ .,-' ..... :" , '- , /; , ---.,L-+.--.---.. ~ 

~ I 

o+--------+--------~------~------~--------+_------_+--------+_~----~ 

1950 19l5 1960 15165 1970 1975 1980 1985 1990 

un:e: GAMA 1991; Feeblel et aI. 1980. 

sidential size defmed as up to 300 kBtu perbour. 

ipmenu not adjusted for impOMS, exports, or non·residential uses. 

~re 3.14. Shipments of DIrect HeaUng Equipment for Gas and LPG 

100., 

~ 

1041. 

! 7O'l(, :I 

I 

.So ~ ~ 

. 

'i 

! so.. .,. 

J 

i 

4I0'Io 

i ~ 

0 

~ 

100. 

00. 

1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1913 1984 1915 1986 1917 

_._ VIIId Wall 

"--__ --..' Uavnld Room 

1111 II 1111 Direct Vat will 1111111111 Aoor FunIace 

--1.1-- TOTAL 

SbipaleDts 

ilfce: GAMA 1991. 

19 

1200 

1000 

800 i 

! 

1 

600 ... 

:i: • :I 

i 

11 

400 ~ 

200 

0 

1911 1989 1990 1991 

] VDIdRoom 
Figure 3.15. Atnual Residential Cooling Equipment Shipments, 1951-1990

6

5

.t

o

m

4

3

2

1

1950 1955 1960 1965 1970 1975 1980 1985

Source: ARI 1991 (CAC and HP); AHAM 1991; Fechtl et aL 1980 (RAC). Shipments not adjustd

for imports, expots, and non-residential uses Data are for CACs of <65 kBtuh and IHPs of <65 kBtuh.

Figure 3.16. Shipment-Weighted Efficiencies for Cooling Equipment, 1972-1990

1990

P

10

9

U

=

.E

2--%

C

E

8

7

6

5 -- Iiiii 2 i i

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990

Source: ARI 1991 (CAC and HP); AHAM 1991 (RAC). Prior to 1981, CAC and HP ratings are EER. not SEER.

20

.:

S

;5

i

c

..i

i

? 1I

1.

j I

I

I

___· · ·__ · ·I _··

i

EE-RM-94-230 COMMENT 61C

:. -"-' ..• - . 

FIgUre 3.15. AlltnDalI Residential Cooling Equipment ShiplDeats, 1951·1990 

6 

S 

... 4 ell 

., 

.... 

:!J 

c 

= 3 c 

E 

~ 2 

1 .... __ .. 

# ~ 

0 

1950 

---CAe 

-----IIP 

# • - • - •• - • RAe 

1955 1960 

: . : . 

!' 

.: 

. : . : 

1 

.: 

1965 

. 

. 

• 

. • :- . : ,. • f • . 

.: 

; 

. ~ 

· : · : • ! · : 

. , 

- - : "., ~ --, .;, , 

! ' -: ~ , -

. -.. 

: /-r---- ~ "..,.-......:-- ...... ...... ----. 

1970 1975 1980 1985 . 1990 

Source: ARJ 1991 (Cite and HP); AHAM 1991; Fechtc1 et al. 1980 (RAC). Shipmems Dot adjusted 

for impons. expons. and non-residential uses. Data are for CACs of <6S kBtuh and lIPs of <.65 1cBtuh. 

.Flgure 3.16. Shipment. Weighted Efficiencies lor Cooling Equipment, ~7l-1990 

10 I 

i 

~. 

I j 

9 T--....... --~----:-+---.--- i 

I I : I 

8 r·---·-i--·--,··-.;.----.;-

ii' ..:..!~~~ 

7 PT ' ..... ""' ... =-.~ ... :::. ==-"----

6 ~.-- I .:---+-.-... ~. : .. -, __ -L __ _ 

--1.1-- CAe (~EER) 

--:-0- HP (SEER) 

---+- RAe (EER) ! " II 

5 ~,--_+------r_----+_----~------~----~----_+----~----~ 

19n 1974 1976 1978 1980 1982 1984 1986 1988 1990 

Source: ARl 1991 (CAe and HP); AHA..\I{ 1991 (RAC). Prior to 1981, CAC and HP ratings are EER. not SEEL 

20 

.:.1 

.. 

,1; 

,; 

" :.. 

. 

~ .~ 

; 

OJ 

.1 

, 

" 

'. i. 

~i 

. , 

;. 

" r. 

1 
Distribution System Efficiency

Recently, residential heating and cooling distribution systems have been shown to be major

sources of inefficiency in overall heating and cooling performance in residential buildings

(Modera 1993). The inefficiency was found in both air distribution through ducting

systems and hydronic distribution through piping. Inefficiencies in ducts occurithrough

two paths: air leakage so that conditioned air is lost from the duct and unconditioned air

enters the duct, and conduction of heat through the duct wall. Thus, duct system

performance is based on the quality of the construction in addition to the duct location.

Andrews and Modera (1991) estimate that ducts in unconditioned spaces (attics and crawl

spaces) are 70% efficient, and ducts in partially conditioned spaces are 80% efficient since

not all of the energy lost by ducts is wasted when the ducts are in conditioned spaces. About one-half of the heat losses in ducts are attributable to air leakage, and half are due to

conduction. 'They also estimate that hydronic systems are typically 90% efficient in singlefamily buildings and approximately 70% efficient in multi-family buildings.

Modera (1993) estimates that distribution system performance in new construction is of the

same level as that in existing buildings. Proctor (1992a) suggests that in California, at

least, air distribution system performance may actually be worse in new buildings than in

existing buildings due to poor construction quality. We assume that existing and new

distribution systems have the same performance characteristics.

For the residential forecasting database, we set distribution system efficiency for forced air

systems at 80% in the North region, where basements are the predominant foundation type and thus the most likely location for duct systems, and 70% in the South region where

crawl spaces and attics are the most likely location for duct systems. For hydronic

systems, we use a baseline efficiency of 90% for all locations (hydronic systems are

typically in partly-conditioned spaces). These data are represented in the cost vs. efficiency

database for distribution systems, and are assumed to be applicable for both existing

buildings and new construction. These data are specified in the cost vs. efficiency database

for distribution systems described below.

Cost vs. Efficiency and Cost vs. Capacity for Equipment and Distribution

Systems in Single-Family Homes

The residential forecasting database contains coefficients that can be used to estimate the

installed cost of heating and cooling equipment These data are based on typical unit costs

and the cost vs. heating or cooling capacity found in the MEANS construction estimator

(1992), and cost vs. efficiency data from an analysis of energy conservation potential for

new equipment (ADM 1987). The database gives coefficients that are used in the equation with the associated data found in Table 3.3.

Table 3.4 provides estimates of distribution system costs. These are based on typical

systems from the MEANS construction estimator. In addition, we also include variations

in the system cost based on the thermal efficiency of the system. The costiefficiency data is

based on Andrews and Modera (1991) estimates of efficiency for different types of

construction, costs for insulation from MEANS (1992), and costs for'duct leak sealing from Proctor (1992b).

___ =

EE-RM-94-230 COMMENT 61C

,Distribution System Efficiency 

Recently, residential heating and cooling distribution systems have been shown to be major 

sources of in,efficiency in overall heating and cooling perlonnance in residential buildings 

(Modera 1993). The inefficiency ,was found in both air disaibution through ducling 

systems and hydronic distribution through pipmg. Inefficiencies in ducts occur...through 

two paths: air leakage so that conditioned air is lost from the duct and unconditioned air 

enters the duct" and conduction of heat through the duct wall. Thus, duct system 

perfonnance is based on the quality of the construction in addition to the duct location. 

Andrews and! Modera (1991) estimate that ducts in unconditioned spaces (attics and crawl 

spaces) are 70% efficient, and ducts in partially conditioned spaces are 80% efficient since 

not all of the energy lost by, ducts is,wasted when the ducts are in conditioned spaces. 

About one-ruM of the heat losses in ducts are attributable to air leakage, and half are due to 

conduction. 'They also estimate that hydronic systems are typically 90% efficient in single- , family buildiltlgs and appI'Qximately 70% efficient in multi-family buildings. , ' 

Modera (199:3) estimates that distribution system performance in new construction is of the 

same level as that in existing buildings. p'roctor (1992a) suggests that in California, at 

least, air distribution system performance may actually be worse in new buildings than in 

existing bUildings due to poor construction qUality. We assume that ensting and new 

distribution systems have the same performance characteristics. 

For the residential forecasting database, we set disaibution system efficiency for forced air 

systems at 80% in the North regio~ where basements are the predominant foundation type 

and thus the most likely location for duct systems, and 70% in the South region where . crawl spaces and attics are the most likely location for duct systems. For hydronic 

systems. we use a baseline efficiency of 90% for all locations (hydronic systems are 

typically in p,anly-conditioned spaces). These data are represented in the cost vs~ efficiency 

database for distribution systems., and are assumed to be applicable for both existing 

buildings and new construction. These data are specified in the cost vs. , efficiency database 

for distribution systems ,described below. ' 

Cost vs. Efficiency and Cost vs. Capacity for Equipment and Distribution. 

Systems in Single-Family Homes 

The residential forecasting database contains coefficients that can be used to. estimate the ., installed cost of heating and cooling equipment. These data are based on typical unit costs 

and the cost vs. heating or cooling capacity found in the MEA.. ~S consuuction estimator 

(1992). and cost vs .. efficiency data from an analysis of energy conservation potential for 

new equipme.nt (ADM 1987). The database gives coefficients that are used in the equatio~ 

with the associated data found in Table 3.3. 

Table 3.4 provides estimates of distribution system costs. These are based on typical 

systems from the MEANS construction estimator. In addition, we also include variations 

in the system cost based on the thennal efficiency of the system. The. cost,lefficiency data is 

based on Andrews and Mod~ra (1991) estimates of efficiency for different types of 

construction. costs for insulation from MEANS (1992), and costs for "duct leak: sealing 

from Proctor (1992b). 
Table 3.3. Parameters for New Single-Family HVAC Equipment Cost Functions

Base

Base Capacity Cost

Cost (Ouut) Base . Efici Slope Efficiecy

End-se T Fuel (S1990) (kBtnr) Efficiency Units (S/Bth) lastiity

Heating Furnace FRN Electric E 1165 65 100 AFUE 7.6 - n/a

Heating Furnace FRN Gas G 1280 80 77.2 AFUE 7.9 1.44

Heaing Funac FRN Oil 0 1837 100 803 AFUE 7.4 3.91

Heating Hydronic H20 Gas G 2102 120 79.6 AFUE 8.1 2.73

Heating Hydronic H20 Oil 0 2735 120 84.6 AFUE 9.1 3.14

Heating Room RM Electric E 1085 20 100 AFUE 35.8 n/a

Heating Room . RM Gas G 822 30 70.0 AFUE 14.8 0.15

Heating Room RM Oil 0 1837 100 75.0 AFUE 7.4 195

Cooling Cenral Air CAC Elecric E 2097 36 9.24 SEER 31.8 0.76

Cooling Heat Pump HP Electic E 3449 36 9.41 SEER 60.0 0.46

Cooling Room AC RAC Electric E 522 12 8.73 EER 279 1.50

=~~~~~~~~~~~~~ .i

The Purchase Cost of Equipment is a function of Capacity and Efficiency according to the following equation:

Cost= (b + m*[(C-Cl)*(E/El) A

eff

wh¢cre

b = Cast at Base Capacity and Efficiency (S)

m = Cost Slope (S/kBu/hr)

C = Eidpment Capacity (Output. kBto/hr)

Cl = Base Capacity (Output. kBtuhr)

E = Equipment Efficiency

El = Base Efficiency

cff= Elasticity of cost with respect to efficiency

(1) Heat pump (HP) cost are based on data for split systems. Hydroic (H20) costs are based on data for

hot water boilers. Electric oom (E RM) costs are based on data for elecric baseboards, with increasing

capacity frmn adding additional baseboards.

(2) Base cost capacity, and cost vs. capacity rlationship from MEANS 1992 residential cost data (MEANS 1992).

Converted to 1990 using the producer price index. Costs include installation but not thermal distribution system.

(3) Cost vs efficiency relationship from ADM 1987. Converted to 1990S using the producer price index.

(4) Base efficiency and capacity ar not necessarily the typical efficiency and capacity of current units.

and are only used as a reference point for cost purposes.

(5) HP base unit HSPF is 7, and HP base unit heating capacity is 36 kBtuh. To first approximation, HSPF and

heating capacity scale more or less linearly with their cooling counterparts.

Valid Rangs for Equipment Cost Functions . ·

Heating Output Capacity (kBtnh) Efficicncy

End-use System Technolo gy Fuel Lower Upper Lower Upper Units

Heating Forced Air Furnace Electric 30 131 n/a n/a n/a

Heating Forced Air Fiunace Gas 42 160 62 92 AFUE

Heating Forcd Air Furnace Oil 55 200 80 91 AFUE

Heating Hlydronic HW Boiler Gas 80 203 68 90 AFUE

Heating Hydronic HW Boiler Oil 109 236 82 89 AFUE

Heating Room Baseboard Electric 8 38 n/a n/a n/a

Heating Room Furnace Gas 18 50 73 80 AFUE

Heating Room Heater Oil 24 94 64 87 AFUE

Cooling Forced Air Ccntal Air Electric 24 60 7.0 14.1 SEER

Cooling Forced Air Heat Pump Electric 18 60 6.8 14.7 SEER

Coong Room Room Air Electric 6 21 93 13.5 EER

'' I

EE-RM-94-230 COMMENT 61C

Tahle 3.3. Parameters for New SiagJe-FamiJy HVAC Equipmeat Cost FUIlcUoDS 

Base 

Base Capacity Cost 

Cost (Output) Base Slope 

E 1165 65 100 AFUE 7.6 Dla· 

G 1280 80 77.2 AFUE 7;9 1.44 

0 1837 100 803 AFUE 7.4 3.91 

G 2102 120 79.6 AFUE 8.1 2.73 

q 2735 120 84.6 AFUE 9.1 3.14 

E lOBS 20 100 AFUE 3S.8 Dla 

G 822 30 70.0 AFUE 14.8 0.15 

0 1837 100 75.0 AFUE 7.4 1.95 

CcotrnI Air CAC E 2097 36 9.24 SEER 31.8 0.76 

HeatPump lIP 3449 36 9.41 SEER 60.0 0.46 

RoomAC RAC 522 12 8.73 EER 27.9 1.50 

Purcba:;c Cost of Equipmcut is a fuDCtion of Capacity and Efficieoc:y accarding to the following equation: 

Cost: (b +m·[C~IJ)·(ElE1)Aeff 

b = C3St at Base Capacity and Efficicacy (S) E = Equipmcut EfficieDcy 

m = O:JSt Slape (SJkBtuIbr) E1 = Base Efficic:ucy 

C = DlUiPu=t Capacity (Outpal. tBtuIbr) ef( = Elasticity of cOst with respect to efficiency 

Cl : B;~ Capacity (Output,kB*) 

(1) Hc:at p1Dllp (lIP) costs arc based em data for split systems. (H2O) costs an: based OD data for 

bot wata boilers. ElCClric room (E RM) costs an: based CD data far cJccP: baseboards, with iDaeasiDg 

c:apacity fran addiag additicmaJ. baseboards. 

(2) Base cost, Q)pacity. BDd. cost vs. capacity n:latioDship from MEANS 1992 resideatial cost data (MEANS 1992). 

Converted to 1990$ DSing the producer.price index. Costs iDcludc iasta1latioo but Dot thermal dislributiOD system. 

(3) Cost vs c:fficiency relaQoaship from ADM 1987. Converted to 1990S using the producer price iPde1. 

(4) Base efficiency and capacity an: Dot Decessarily Ibc typical efficiency aDd capacity of cmrent uuits. 

and are only used as I n:feRuce point for cost purposes. 

(5) HPbase unit HSPF is 7. and HP base UDit heating capacity is 36 kBtuh. To fU'Sl approximatioD. HSPF lUld 

heaziDgcapacity scale more or leSs 1ineariy with their cooling co~tcrparts. 

Valid RaDg'~ -for Equipmaat Cost Faac:tioDS .... .; ... . Heating Output Capacity (kBtuh) Efficien~ 

End-ase S~ Tcchuol~v Fuel Lower Upper Lower Upper Units 

Heating FcntCclAir Fumace Electric 30 131 . nla Dla oJa 

Heating Forcccl Air Furnace Gas 42 160 62 92 AFUE 

Heating Fon:ccll Air Fumace Oil 55 200 80 91 AFUE 

Heating Hydronic HWBoiler Gas 80 2m 68 90 AFUE 

Heating HydroDic HWBoilcr Oil 109 236 82 89 AFUE 

Heating Room Baseboard Electric 8 38 oJa Dla oJa 

Heating Room Furnace Gas 18 SO 73 . 80 AFUE 

Heatin! Room HeaLer Oil 24 94 64 81 AFUE 

Cooling Forced Air Central Air Electric 24 60 7.0 . 14.1 SEER 

Cooling Forced Air Heat Pump Electric 18 60 6.8 i4.7 SEER 

Cooling Room RoomAir' Flectric 6 21 9.3 13.5 EER 
Table 3.. Distribution System Cost, Size, and Efficiency Relationships for Single-Family Housing

(Costs for 1750 Square foot House)

Notes: Costs are installed costs to consumer including all contractor markups.

Base costs calculated for 1750 square foot house.

Unconditioned spaces include attics and crawl spaces.

Partly conditioned spaces are basements.

Forced air (duct) data primarily derived from single family construction data.

Source: Base case efficiencies for forced air systems from Moder 1993, Treidler and Modem 1993, and Jansky

and Modem 1994.

Base case efficiencies for hydronic systems from Andrews and Modera 1991.

Savings estimates from Andrews and Modera 1991. We calculate efficiency from their energy savings

data as efficiency = base efficiency/(l-savings (%)).

Duct leakrepair costs from Proctor 1992b, $300 ($200 labor, $100 materials) for 65% tighter duct

systemL We assume twice this cost will achieve ducts that are 80% tighter than the base case.

Duct insulation costs estimated at $798 for R5-8 from MEANS 1992 for 1750 sqft house.

Piping insulation estimated at $627 from MEANS 1992 for 1750 sqft house.

Base Increm. Efficiency by

Single-Family Base Incremental Cost/ Cost/ System Location

Distribution System Insulation Leakage Sealing Total Floor Area Floor Uncon- Partly

System Cost Level Cost Level Cost Cost (1990$/ Slope ditioned Cond.

Description (1990S) (R-val) (19905) (% sealed) (1990S) (1990) sqft) ($sqft) Space Space

FORCED AIR IUCTING

Base Case 2361 RO 0 0% 0 2361 135 0.97 0.70 0.80

65% Tighter 2361 RO 0 65% 300 2661 1.52 0.97 0.78 0.84

R5-8, 65% Tighter 2361 R6 798 65% 300 3459 1.98 1.47 0.84 0.87

R12, 80% Tighter 2361 R12 1596 80% 600 4557 2.60 1.47 096 0.98

HYDRONIC PIPING SYSTEM

Base Case 3591 RO 0 n/a n/a 3591 2.05 1.52 n/a 0.90

Insulated PipingJ 3591 insulated 627 n/a n/a 4218 2.41 , 1.63 n/a 0.95

EE-RM-94-230 COMMENT 61C

~able lA. DistIribatioD System Cost, Size, and Emdency Relationships Cor SiDg1e-Family Housing 

(Costs for 1750 Square foot House) 

Base InCIeID. 

Single-Family Base Incremental Cost/ CostJ 

Distribution System Insulation Leakage Sealing Total Floor Area Floor 

System Cost Level Cost Level Cost Cost (1990$1 Slope 

Desc:ril)tion (1990$) (R-val) (1990$) (~sealed) (1990$) (1990$) ~ft) ($I~ 

FORCED AIR J)UC1lNG 

Base Case 2361 RO 0 :09& 0 2361 1.35 0.97 

65%TIgbter 2361 RO 0 659& 300 2661 .1.52 0.97 

RS-8, 65% TIghter 2361 R6 798 65% 300 3459 1.98 1.47 

RI2, 80% TIgb~er 2361 R12 1596 80% 600 4557 2.60 1.47 

HYDRONIC PIPING SYSTEM 

Base Case J 3591 RO 0 nJa nJa 3591 2.05 1.52 

lnsula~ Piping; 3591 insulated 627 . nJa nJa 4218 2.41 I 1.63 

Notes: Costs lire iIIlStaUed costs to consumer including all contractor marlwps. 

Base costs calcalated for 1750 square foot bouse. 

Unconditioned spaces include attics and crawl spaces. . 

Partly c:ODditioDed spaces are basements. 

Fon:ecll air (duct) data primarily dcri.ved from single family cODStruction data. 

Efficiency by 

System Location 

Uocon- Partly 

ditioaed Cond. 

~ . Space 

0.70 0.80 

0.78 0.84 

0.84 0.87 

0.96 0.98 

o/a 0.90 

o/a 0.95 

Source: Base case efficiencies for forced air systems from Madera 1993. Treidler and Modem 1993, and Jansky 

and Madera 1994. 

Base case efficiencies for hydronic systems from Andrews and Madera 1991. 

Savings estimates from Andrews and Madera 1991. We calculate efficiency from their energy savings 

data as efficiency = base efficiency/(1-savings (%». 

Du~ leak repair costs from Proctor 1992b, $300 ($200 labor, $100 materials) for 65% tigh~ duct 

~L We assume twice this cost will achieve ducts that are 80% tighter than the base case. 

Duct iJasulaDon co~ts estimated at $798 for RS-8 from MEANS 1992 for 1750 sqft bouse. 

Piping insulation estimated at $627 from MEANS 1992 for 1750 sqft house. 

o· 
Product Lifetimes

The database contains estimates from several sources of the lifetimes of heating and cooling

equipment These are presented in Tables 35 and 3.6.

Table 3.5. Estimates of Residential Heating Equipment Lifetimes

Lifetime in Years

Gas

Heat Gas Oil Electric Gas Oil Room

Source Pump Furnace Furnace Furnace Boiler Boiler Heater

Low 9 13 12 15 13 12 ' 13

Appliance .Avg 11 16 15 18 17 15 16

High 15 20 19 22 22 19 20

ASHRAE Median n/a 18 18 n/a 30 30 n/a

Low 10 15 15 20 20 20 15

Lewis/Clark Point 12 18 17 20 20 20 18

High 15 20 20 25 25 25 20

Low 8 18 18 18 n/a n/a 18

LBLJREM Avg 14 23 23 23 n/a n/a 23

High 16 29 28 29 n/a n/a 29

Sources: Appliance 1992 (first owner lifetime only); ASHRAE 1987; Lewis and Clarke 1990;

LBLREM 1991.

Table 3.6. Estimates of Residential Cooling

Equipment Lifetimes

Lifetime in Years

Room Central

Air Air Heat

Source Cond. Cond. Pump

Low 8 9 9

Appliance Avg 11 12 11

High 14 15 15

ASHRAE Median 10 15 n/a

Low 10 11 10

Lewis/Clark Point 11 14 12

High 15 16 15

Low 12 ·8 8

LBL/REM Avg 15 12 14

High - 18 16 16

Sources: Appliance 1992 (first owner lifetime only);

ASHRAE 1987; Lewis and Clarke 1990; LBLREM 1991.

24

EE-RM-94-230 COMMENT 61C

Product Lifetimes 

The database contains estimates from several sources of the lifetimes of heating and cooling 

equipment These are presented in Tables 3.5 and 3.6. 

Table 3.5. Estimates of Residential Heating Equipment Lifetimes 

Lifetime in Years 

Heat Gas Oil Electric Gas Oil 

Source Pump FlD'D3Ce Furnace Furnace Boiler Boiler 

Low 9 13 12 15 13 12· 

Appliance .Avg 11 16 15 18 17 15 

High 15 20 19 22 22 19 

ASHRAE Median nJa 18 18 nJa 30 30 

Low 10 15 15 20 20 20 

LewislClark Point 12 18 17 20 20 20 

High 15 20 20 2S 2S 25 

Low 8 18 18 18 nJa nla 

LBUREM Avg 14 23 23 23 Dla nla 

High 16 29 28 29 nla nJa 

Sources: Appliance 1992 (lust owner lifetime only); ASHRAE 1987; LeWIS and Clarke 199(r, 

LBLREM 1991. 

Table 3.6. Estimates of Residential Cooling 

E ~quipment i Llf et i mes 

Lifetime in Years 

Room Central 

Air Air . Heat 

Source Cenci. Cond. Pump 

Low 8 9 9 

Appliance Avg 11 12 11 

Higb 14 15 15 

ASHRAE Median 10 15 oJa 

Low 10 11 10 

Lewis/Clark Point 11 14 12 

Higb 15 16 15 

Low 12 .8 8 

LBLIREM Avg 15 12 14 

Higb - 18 16 16 

Sources: Appliance 1992 (fust owner lifeume only); 

ASHRAE 1987; Lewis and Clarke 1990; LBLREM 1991. 

24 

Gz 

Room 

HeareL 

13 

16 

20 

nla 

15 

18 

20 

18 

23 

29 
3.3. Technology Data for Shell Measures

The database includes the costs for various levels of efficiency of the major building heat

loss and heat gain components. The costs for new buildings are the incremental costs from

a certain base case level, and represent the incremental costs at the time of original

construction. For existing buildings (or retrofit cases) we only have costs for single-family

buildings.

The database includes shell measure costs for new single-family, multi-family, and

manufactured home building types on a cost per square foot of component basis for roofs,

walls, underfloor insulation, and windows; cost per linear foot of foundation for slab and

heated basement foundations, and a cost per house basis for infiltration measures. Using

the forecasting prototypes, these costs can be converted into cost per floor area data. These

data are provided by region and as national averages, and are derived from NAHB data

(NAHB 1986). The costs for single-family buildings are shown in Table 3.7.

The database also includes retrofit measure costs for single-family building types only.

The cost units are the same as for new buildings. These data are provided by region and as

national averages, and are derived from previous LBL work which relied on a variety of

regional studies of building retrofit costs (Boghosian 1991). The costs for single-family

buildings are shown in Table 3.8.

3.4. Fuel and Equipment Shares

Market shares of heating and cooling equipment are included in the database in two places.

First, shares of heating and cooling equipment by region and for the national average are

included in the appliance shares database. Second, we have constructed a data set which

estimates HVAC system type shares (combinations of heating and cooling equipment) for

both existing buildings and new construction in 1990. The primary sources used for these

data are RECS (US DOE 1982a, 1986, 1989a, 1992) and the U.S. Census Bureau Current

Construction Reports, Series C25 (US Bureau of the Census 1990a).

Stock Shares

Stock shares of main heating fuels and cooling equipment are included in the database from

the RECS data sets by building type and region (US DOE 1982a, 1986, 1989a, 1992).

The database also includes HVAC system shares for existing buildings from the 1990

RECS data (US DOE 1992). We present some of these data in a series of figures that

follow.

Figure 3.17 shows the heating fuel shares for 1981 through 1990. The data highlight the

slowness of changes in housing stock for a major element such as fuel shares. Figure 3.18

shows the breakdown of fuel and technology shares for the year 1990 on a national level.

It shows that gas furnaces are the heating technology of choice for almost 40% of the

residential sector. Heat pumps comprise only about 7% of the heating systems.

Air conditioning shares have experienced large changes during the last decade. As shown

in Figure 3.19, the share of central air conditioning (not including heat pumps) rose from

about 22% in 1981 to about 32% of the stock in 1990. Heat pump shares grew from 3% to

7% over this same period. The percentage of buildings with room air conditioners or no air

conditioning has dropped during this period. Figure 3.20 shows that the' 1990 shares for

air conditioning are relatively consistent across housing types, except that manufactured

homes have a much larger percentage of evaporative coolers.

25

EE-RM-94-230 COMMENT 61C

3.3. Techllology Data for Shell Measures 

The database includes the costs for various levels of efficiency of the major building heat 

loss and hent gain components. The costs for new buildings are the increm~ntal costs from 

a certain base case level. and represent the incremental costs at the time of original 

COnStruCtiOIl. For existing buildings (or rettofit cases) we only have costs for single-family 

buildings.. ' ' , 

The database includes shell measure costs for new single-family, multi~fami1y. and 

manufactured bome building types on a cost per square foot of component basis for roofs. 

walls. underfloor insulati,on. and windows; cost per linear foot of foundation for slab and 

heated basementt foundations. and a cost per house basis for infiltration measures., Using 

the forecasting prototypes. these costs can be converted into cost per floor area data. These 

data are provided by region and as national averages. and are derived from NAHB data 

(NAHB 1986). The costs for single-family buildings are shown in Table 3.7. 

The database also includes retrofit measure costs for single-family building 'types only., 

The cost units are the same as for new buildings. These data, are provided by region and as 

national averages. and are derived from previous LBL work which relied on a variety of 

regional stlll(Jiesof building retrofit costs (Boghosian 1991). The costs for single-family 

buildings aJ'e shown in Table 3.8. 

3.4. Fuel and Equipment Shares ' ' . . . Market shaJ-es of heating and cooling equipment are included in the database in two places. rll'St, share:; of heating and cooling equipment by region and for the national average are 

included in the appliance shares database. Second. we have constructed a data. set which 

estimates HV AC system type shares (combinations of heating and cooling equipment) for 

both existing buildings and new construction in 1990. The primary sources used for these 

data are RE.cS (US DOE 1982a. 1986. 1989a. 1992) ,and the U.S. Census Bureau Current 

Construction Reports, Series C2S (US Bureau of the Census 1990a). 

Stock Sbllires 

Stock shares of main heating fuels and cooling equipment are included in the database from 

the RECS data sets by building type and region (US DOE 19823, 1986. 1989a. 1992). 

The database also 'includes HV AC system shares for existing buildings from the 1990 

RECS data (US DOE 1992). We present some of these data in a series of figures that 

follow. . 

Figure 3.J7 shows the heating fuel shares for 1981 through 1990. The data highlighqhe 

slowness of ch:mges in housing stock for a major element such as fuel shares. Figure 3.18 

shows the breakdown of fuel and technology,shares for the year 1990 on a national level. 

It shows that gas furnaces are the heating technology of choice for almost 40% of the 

residential sector. Heat pumps comprise only about 7% of the heating systems. 

Air conditioning shares have experienced large changes during the last decade. As shown 

in Figure 3 .. 19, the share of central air conditioning (not including heat pumps) rose from 

about 22% :in 1981 to about 32% of the stock in 1990. Heat pump shares grew from 3% to 

7% over this same period. The percentage of buildings with room air conditioners or no 'air 

conditioning has dropped during this period. Figure 3.20 shows that the' 1990 shares for 

air conditioning are relatively consistent across housing types, except that manufactured 

homes have: a much larger percentage of evaporative coolers. 

2S 

... 
e 3.7. Shell Measure Costs for New Single-Family Buildings

Component Unit Cost Cost/sqft of Conditioned Floor Area ($1990/sqft)

(1990$) for different protty)es

North South US North Region South Region US Region

:1 Region Region Regiony Story 2 SoSryt Story 2 StoryStory 2

Insulation (per sqfi of Roof)

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.35 031 0.33 0.35 0.17 0.31 0.15 033 0.16

0.49 0.46 0.47 0.49 0.24 0.46 0.23 0.47 0.24

0.67 0.64 0.65 0.67 033 0.64 0.32 0.65 033

0.83 0.84 0.83 0.83 0.41 0.84 0.42 0.83 0.42

1.04 1.02 1.03 1.04 0.52 1.02 0.51 1.03 0.51

1.22 1.21 1.21 1.22 0.61 1.21 0.61 1.21 0.61

I Insulation (per sqft of Net Wall)

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.38 037 038 0.29 033 0.27 0.31 0.28 0.32

0.64 0.62 0.63 0.48 0.55 0.46 0.53 0.47 0.54

1.39 1.39 1.39 1.03 1.18 1.03 1.18 1.03 1.18

7r Insulation (per sqft of Foundation)

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.42 039 0.41 0.42 021 0.39 0.19 0.41 0.20

0.65 0.60 0.63 0.65 032 0.60 030 0.63 031

0.80 0.73 0.77 0.80 0.40 0.73 036 0.77 0.39

7 Insulation (per lin.ft of Foundation)

n/a 0.00 0.00 n/a n/a 0.00 0.00 0.00 0.00

2ft n/a 2.66 2.66 n/a n/a 0.29 0.15 0.29 0.15

) 4ft n/a 6.85 6.85 n/a n/a 0.74 038 0.74 0.38

Itration Reduction (per House)

ach 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

ach 1 592 560 575 0.38 0.26 0.36 0.25 037 0.26

idows (per sqft of Window)

ane 0.00 0.00 0.00 0.00 0.00 0.00

ane 4.07 3.55 3.84 0.49 0.43 0.46

ane w/LowE 6.13 5.34 5.78 0.74 0.64 0.69

ane w/LowE and Argon fill 6.77 5.90 6.38 0.81 0.71 0.77

ane w/LowE, Spect Select 6.72 5.85 632 0.81 0.70 0.76

.erwindow 9.42 8.21 8.87 1.13 0.99 1.06

at Mirror 9.70 8.45 9.14 1.16 1.01 1.10

-rces: 1) Insulation and infiltration measures from Koomey et al. 1991b. Data originally from NAHB 1986.

Adjusted to Regional costs using MEANS 1989 data. Adjusted from $1988 to $1990 using CPI inflator

of 1.102.

2) Window measure costs from Koomey et al. 1994a. Costs for base windows taken from NAHB 1986.

Costs premia forother technologies from Eley Associates 1991. Adjusted to Regional costs using

MEANS 1989 data. Adjusted from $1989 to $1990 using CPI inflator of 1.054.

3) Two Story Prototype: 2240 sqft, dimensions 28x40 ft. window area = 12% of floor-area. One Story

Prototype: 1540 sqft, dimensions 28x55 ft. window area = 12% of floor area.

26

EE-RM-94-230 COMMENT 61C

:e 3.7. SheD Measure Costs for New Single-Family Buildings 

Component Unit Cost Costlsqft of Conditioned floor Area (S1990/sqft)· 

(1990$) for different prototypes 

North South US NonhRegion South Region US Region 

;1 Region Region Region 1 Story 2 Story 1 Story 2 Story 1 Story 2 Story 

~/nsulalion (per sqft o/Roof) 

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

0.35 0.31 0.33 0.35 0.17 0.31 0.15 0.33 0.16 

0.49 0.46 0.47 0.49 0.24 0.46 0.23 0.47 0.24 

0.67 0.64 0.65 0.67 0.33 0.64 0.32 0.65 0.33 

0.83 0.84 0.83 0.83 0.41 0.84 0.42 0.83 0.42 

1.04 1.02 1.03 1.04 0.52 1.02 0.51 1.03 0.51 

1.22 1.21 1.21 1.22 0.61 1.2l! 0.61 1.21 0.61 

(Insulation (per sqft o/Net Wall) 

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

0.38 0.37 038 0.29 0.33 0.27 0.31 0.28 0.32 . 

0.64 0.62 0.63 0.48 0.55 0.46 0.53 0.47 0.54 

1.39 1.39 1.39 1.03 1.18 1.03 1.18 1.03 1.18 

JT Insulalion (per sqft o/Fowuialum) 

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

0.42 0.39 '0.41 0.42 0.21 0.39 0.19 0.41 0.20 I 0.65 0.60 0.63 0.65 0.32 0.60 0.30 0~63 0.31 

) 0.80 0.73 0.77 0.80 0.40 0.73 036 0.77 0.39 

} Insulation (per lin.ft of Fowu.itJ.tion) 

nla 0.00 0.00 nJa nla 0.00 0.00 0.00 0.00 

2ft nla 2.66 2.66 nla nla 0.29 0.15 0.29 0.15 

) 4ft nla 6.85 6.85 nla nla 0.74 0.38 0.74 0.38 

[valion Reduclion (per House) 

a~ I 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

ach S92 560 575 0.38 0.26 0.36 0.25 0.37 0.26 

zdows (per sqft o/Window) 

ane 0.00 0.00 0.00 0.00 0.00 0.00 

ane 4.07 3.sS 3.84 0.49 0.43 0.46 

anew/LowE 6.13 5.34 5.78 0.74 0.64 0.69 

ane w/ LowE and Argon fill . 6.n 5.90 6.38 0.81 0.71 0.77 

ane wI LowE. Spect. SelecL 6.72 5.85 632 0.81 0.70 0.76 

JCIWindow 9.42 8.21 8.87 1.13 0.99 1.06 

at Mirror 9.70 8.45 9.14 1.16 1.01 1.10 

.uces: 1) Insulation and inflluation measures from Koomey et aI. 1991b. Data originally from NAHB 1986. 

Adjusted to Regional costs using MEANS 1989 data. Adjusted from $1988 to $1990 using CPI inf1a1or 

of 1.102. ' 

2) Window measure costs from Koomey et aI. 1994a. Costs for base windows taken from N AHB 1986. 

Costs premia for other technologies from Eley Associates 1991. Adjusted to Regional costs using 

MEANS 1989 data. Adjusled from $198910$1990 using CPI inflator of 1.054. 

3) Two SlOry Prototype: 2240 sqft. dimensions 28x40 ft, window area = 12% of floor-area. One SlOry 

Prototype: 1540 sqft. dimensions 28x55 flo window area = 12% of floor area. 

26 
'able 3.8. Shell Measure Costs for Existing Single-Family Buildings (Retrofit Costs)

Component Unit Cost Cost/sqft of Conditioned Floor Area ($1990/sqft)

(1990$) for different prototypes

North South US North Region South Region US Region

evel Reion Region Regionon 1 2 Story Story 2 Story Sto ry 2 Story

?ooflnsulation (per sqft of Roo)

idd R8 0.49 0.42 0.46 0.49 0.25 0.42 0.21 0.46 0.23

iddRll 0.47 0.41 0.44 0.47 0.24 0.41 0.20 0.44 0.22

idd R19 0.45 039 0.42 0.45 023 039 0.19 0.42 0.21

dd R27 0.57 0.49 0.53 0.57 0.29 0.49 0.25 0.53 0.27

idd R30 0.65 0.56 0.61 0.65 0.33 0.56 0.28 0.61 0.30

add R38 0.93 0.80 0.87 0.93 0.47 0.80 0.40 0.87 0.44

addR49 1.26 1.09 1.18 1.26 0.63 1.09 0.54 1.18 0.59

add R60 1.47 1.27 138 1.47 0.74 1.27 0.63 1.38 0.69

Wall Insulation (per sqfi of Net Wall)

upgrade to R-11l(blown-in) 0.79 0.68 0.74 0.59 0.67 0.59 0.67 0.55 0.63

add R-5 (exterior sheathing) 1.89 1.63 1.77 1.40 1.61 1.40 1.61 1.32 1.51

Slab Insulation (per lin. ft of Foundation)

add R5 2ft 13.68 11.79 12.81 1.47 0.83 1.27 0.72 1.38 0.78

add R10 2ft 14.74 12.71 13.81 1.59 0.89 1.37 0.77 1.49 0.84

add R5 4ft 19.19 16.55 17.98 2.07 1.17 1.78 1.00 1.94 1.09

addR104ft 21.87 18.85 20.49 236 133 2.03 1.14 2.21 1.24

Floor Insulation (per sqft of Foundation)

add Rl 1 0.65 0.56 0.61 0.65 033 0.56 0.28 0.61 030

add R19 0.85 0.73 0.80 0.85 0.43 0.73 037 0.80 0.40

addR30 1.11 0.96 1.04 1.11 0.56 0.96 0.48 1.04 052

Infiltration Reduction (per House)

reduce ACH by 25% | 258 223 242 0.17 0.12 0.14 0.10 0.16 0.11

Windows (per sqft of Window)

1 Pane 13.10 11.41 1233 1.57 1.57 1.37 1.37 1.48 1.48

2 Pane 17.17 14.96 16.17 2.06 2.06 1.79 1.79 1.94 1.94

2Panew/LowE 19.23 16.75 18.11 2.31 231 2.01 2.01 2.17 2.17

2 Pane w/LowE and Argon fill 19.87 1731 18.71 238 238 2.08 2.08 2.25 2.25

2 Pane w/LowE,Spect Select 19.81 17.26 18.66 238 238 2.07 2.07 2.24 2.24

Superwindow 22.52 19.62 21.21 2.70 2.70 2.35 2.35 2.54 2.54

Heat Miruor 22.80 19.86 21.47 2.74 2.74 2.38 238 2.58 258

Sources: 1) Insulation and infiltration measures from Boghosian 1991. Adjusted to Regional costs using

MEANS 1989 data Adjusted from $1989 to $1990 using CPI inflator of 1.054.

2) Window measure costs from Koomey et al 1994a. Costs for base windows taken from NAHB 1986.

Costs premia for other technologies from Eley Associates 1991. Adjusted to Regional costs using

MEANS 1989 data. Adjusted from $1989 to $1990 using CPI inflator of 1.054. Costs shown are total

window costs.

3) Two Story Prototype: 2240 sqft. dimensions 28x40 ft, window area = 12% of floor area. One Story

Prototype: 1540 sqft, dimensions 28x55 ft. window area = 12% of floor area.

27

EE-RM-94-230 COMMENT 61C

~able 3.8. Shell Meas:ure Costs (or Existing Single-Family Buildings (Retrofit Costs) 

Component Unit Cost Cost/sqft of Conditioned Floor Area ($ 1990/sqfl) 

(1990$) for different protolyJiJCS 

Nonh South US NonhRegion Soudl Region US Region 

..evel Region Region Region 1 SlO!Y_ 2 ,Story 1 Story 2 Story 1 Story 2 Story 

?oo/lnsulalion (per Sf/ft o/Rooj) 

ufdRS ' 0.49 0.42 0.46 0.49 0.25 0.42 0.21 0.46 0.23 

lddRll 0.47 0.41 0.44 '0.47 0.24 0.41 0.20 0.44 0.22 

lddR19 0.45 0.39 0.42 0.45 0.23' 0.39 0.19 0.42 0.21 

lddRZl 0.57 0.49 ' 0.53 0.57 0.29 0.49 0.25 0.53 0.27 

lddR30 0.65 0.56 0.61 0.65 0.33 0.56 '0.28 0.61 0.30 

lddR38 0.93 ' 0.80 0.87 0.93 0.47 0.80 0.40 0.87 0.44 " 

addR49 1.26 1.09 1.18 1.26 0.63 1.09 0.54 1.18 0.59 

lddR60 1.47 1.27 1.38 1.47 0.74 1.27 0.63 1.38 0.69 

Wczliinsulation (per sqft - o/Net Wall) 

upgrade 10 R-ll (blown-ira) , I 0.79 0.68 0.74 0.59 0.67 0.59 0.67 0.55 0.63 

addR-S (exterior sheathin2) 1.89 1.63 1.77 ,1.40 1.61 1.40 1.61 1.32 1.51 

Slab Insulation (per lin.ft of Foundtltion) 

addRS 2ft 13.68 11.79 12.81 1.47 0.83 1.27 0.72 1.38 0.78 

add RIO 2ft 14.74 12.71 13.81 1.59 0.89 1.37 0.77 1.49 0.84 

addRS4Ct 19.19 16.55 17.98 2.07 1.17 1.78 1.00 1.94 1.09 

add RIO 4ft 21.87 18.85 20.49 2.36 1.33 2.03 1.14 2.21 1.24 

Floor Insrdalion (per sqft o/FolllU'ialion) : 

addR11 0.65 0.56 0.61 0.65 0.33 0.56 0.28 0.61 0.30 

'addRl9 0.85 0.73 0.80 0.85 0.43 0.73 0.37 0.80 ' 0.40 

laddR30 1.11 0.96 1.04 1.11 0.56 0.96 0.48 1.04 

Infiltration Reduction (per House) 

reduce ACH by 25% ' f 258 223 242 0.17 0.12 0.14 0.10 0.16 

Windows (per sqft qfWindow) , 

1 Pane 13.10 11.41 12.33 1.57 1.57 1.37 1.37 1.48 

2 Pane 17.17 14.96 16.17 2.06 ' 2.06 1.79 1.79 1.94 

2 Pane wi LowE 19.23 16.75 18.11 2.31 2.31 2.01 2.01 2.17 

2 Pane wI LowE and Argon fill 19.87 17.31 18.71 2.38 2.38 208 2.08 2.2S 

2 Pane wi LowE. Sp<:ct. SeiecL 19.81 ' 17.26 18.66 2.38 2.38 2.07 2.07 2.24 

Superwindow 22.52 19.62 21.21 2.70 2.70 2.35 ' 235 2.S4 

Heat MInor 22.80 19.86 21.47 274 274 ,2.38 2.38 2.58 

Sources: 1) WUlatiOID and infiltration measures from BoghOsian 1991. Adjusted to Regional costs using 

MEANS '1989 dara. Adjusted from $1989 10 $1990 using CPI inflator ofl.054. 

0.52 

0.11 

1.48 

1.94 

2.17 

2.2S 

2.24 

2.54 

2.58 

2) WIndow measure costs from Koomey et a1. 1994a. Costs for base windows raken from NAHB 1986. 

Costs premia fl)r odler technologies from Eley Associates 1991. Adjusted to Regional costs usIng' 

MEANS 1989 data. Adjusted from $198910 $1990 using CPI inflator of 1.054. Costs shown are total 

window costs. 

3) Two Stelry Prototype: 2240 sqft. dImensions 28x40 flo window area = 12% of floor area. One Story 

Prototype: 1540 sqft, dimensions 28XSS ft. window area = 12% of floor area. , 

27 
Figure 3.17. Space Heating Fuel Shares in Total Housing Stock, National, 1981-1990

0.60 -

0.50 -

A 0.40 - 0

, 0.30--

" 0.20 -

0.10

0.00

1980 1982 1984 1986 1988

*-- Gas

-- LPG

-*--- Oil

---- Elec Res

Elec HP

-- --- Other

1990

Source: US DOE 1982a, 1986, 1989a, 1992.

Oil includes kerosene. Elec Res = electric resistance heating, Elec HP = electric heat pump heating.

Other is primarily wood. Values are "primary heating fuel" from US DOE 1982a, 1986,1989a and 1992.

Figure 3.18. Space Heating Fuel/Technology Shares by House Type, National, 1990

0.50

0.45

0.40

0.35

° 0.30

g 0.25

a 0.20 agl

0.15

0.10

0.05

0.00

GH20

I GFRN

[ GRM

D OH20

OFRN

O EFRN

ERM

HI EHP

MOTH

SF MF MH ALL

Source: US DOE 1992. G = natural gas, O = oil (includes kerosene), E = electricity. Other is primarily wood.

H20 = steam or hot water systems, FRN = furnace, RM = room heating. Values are "primary heating

fuel" from US DOE 1992.

28

EE-RM-94-230 COMMENT 61C

FIgure 3.17. Space Heating Fuel Shares in Total Housing Stock, NaUonal, 1981·1990 

0.60 I 1 

! ! - -- I J 1 .. I " 1-1_- ! I 

0.50 

.C' 0.40 

~ 

~ 0.30 

~ 

, ... _._---;._---

I 

I I ---~-----r--- CIII 

.t: 

III 0.20 . 

0.10 .. 

0.00 

1980 

.... 

~ 

-

i 

I 

I . 

! 

! 

, 

, 

I 

1982 1984 

Source: US DOE 1982a, 1986, 1989a, 1992. 

! i 

I 

i --to , i -- I i 

: I j I 

I ! 1 

• 

i - ! I J 

1986 1988 1990 

------Gas 

-G--LPG 

• Oil 

--<>-- E1ec Res 

• ElecHP 

----fr-- Other 

on includes kerosene. Elec Res = electric resistance heating, Bee HP = electric heat pump beating. 

Otberisprimarilywood.. Values are "primary heating fuel" from US.DOE 1982a, 1986,1989aand 1992. 

FIgure 3.18. Space Heating Fuelfl'ecbnology Shares by House Type, National, 1990 

0.50 ~GH20 

0.45 

0.40 ~GFRN 

0.35 I!BI GRM 

e-

~ 0.30 rn 01120 Col 

g 0.25 E30FRN u 

.. 

CIII 0.20 DEFRN .t: 

VI 

0.15 .ERM 

0.10 

0.05 [JEHP 

0.00 m·OlH 

SF MF MH All 

Source: US DOE 1992. G = nawraI gas, 0 = oil (includes kerosene), E = electricity. Other is primarily wood. 

H2O = steam or hot water systems, FRN = furnace, RM = room beating. Values are "primary heating 

fuel" (rom US DOE 1992. 
Figure 3.19. Air Conditioning Shares in Total Housing Stock, National, 1981-1990

1982 1984 1986 1988

-*- None

-- O- CAC

--- RAC

-A-- HP

-*-- EC

--- Fuel AC.

1990

Source: US DOE 1982a. 1986, 1989a, 1992.

CAC = electric central air conditioning, HP = heat pump, RAC = room air conditioning, EC =

evaporative cooler, Fuel AC = gas driven air conditioning. In 1990, RAC homes averaged 1.50 units.

Figure 3.20. Air Conditioning Shares in Housing Stock by House Type, National, 1990

0.4......

E None

CAC

EC

0.1

:A Fuel AC

U ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . . ..-.

0

SF MF ALL

Source: US DOE 1992.

CAC = electric central air conditioning, HP = heat pump, RAC = room air conditioning, EC =

evaporative cooler, Fuel AC = gas driven air conditioning.

29

0.4 --

0.3

o.2

r, 0.2

e

0.1

0.1--

1980

EE-RM-94-230 COMMENT 61C

Figure 3.19. Air Conditioning Shares in Total Housing Stock, National, 1981·1990 

-c: .2 

-

" ell 

0.4 ........ -~--:----------~--------...., 

• 03 .---~--- . I 

1 

':. i 

I 

6 0.2 

i:· j 

- [ - .. I,.: ,---.--t 2! 

ell 

.c 

(IJ ~ 

i ~ 0.1 .-----!----.-:.-.---~.-----: -'-'-'-

!.~ I I 

o 

,1980 , 1982 1984 1986 

: ' 

.: 

1988 1990 

Source: US DOE 19828.1986, 1989~ 1992-

• None 

• RAe 

• EC 

---tr-- Fuel AC, 

CAC = e1eclric cenaal air a»nditioning, lIP = heat pump, RAC = 'room air conditioning, BC = 

evaporative cooler, Fuel AC = gas driven ,air conditioning. In 1990, RAC homes averaged 1.50 units. 

F"tgUre 3.20. Aill" Conditioning Shares in Housing Stock by House Type, National, 1990 

0.4 

o None 

0.3 

- ~CAC Ii: .2 . 

- ~RAC " ell 6 0.2 mDHP u 

.. 

ell 

.c 

(IJ 

E3EC 

0.1 

BJ FuelAC 

o 

SP MF MH ALL 

Source: US DOEl992. 

CAC = electric cenual air conditioning, HP = heat pump. RAe = room air conditioning. BC = 

evaporative cooler, Fuel AC = gas driven air conditioning.' . 

29 
Figures 3.21, 3.22, and 3.23 show HVAC system shares for the three housing types by

region and for the national average. These figures highlight: 1) the dominance of the gas

furnace/central air conditioning HVAC system in single-family buildings in all regions

(30%), 2) the high portion of hydronic heating systems in multi-family buildings, 3) the

greater percentage of electrically-heated homes in the south region, and 4) the use of LPG

as a heating fuel in manufactured houses.

New Shares

Shares of heating and cooling equipment for new buildings are taken from combinations of

data from the Census C25 survey (US Bureau of the Census 1990a) and the 1987 RECS

data for buildings built between 1980 and 1987 (US DOE 1989a). We have also developed

HVAC system shares using these same data sets. Some of these data are shown in Figures

3.24 through 3.29.

Figures 3.24 and 3.25 show the heating fuel shares and central air conditioning shares in

new construction for single-family buildings. Figures 3.26 and 3.27 show the same for

multi-family, while Figures 3.28 and 3.29 show the same for manufactured homes. The

striking observation from these data is that the use of electricity as a heating fuel,

particularly for electric resistance heating, decreased between 1985 and 1990. At the same

time, the percentage of new buildings with central air conditioning has been rising

dramatically, so that 80% of new single-family homes and 60% of new multi-family units

have central air conditioning installed at the time of construction.

3.5. Forecasting Prototypes

For the analysis of conservation potential from building envelope measures, we define a set

of building prototypes that represent the major characteristics of the residential building

population. The important parameters include the component areas of the building (roof,

wall, floor, etc.) and the thermal characteristics of those components. The prototypes are

characterized from data taken from surveys of either the building stock or recently

constructed buildings. Once defined, the heating and cooling energy consumption of these

buildings can be assessed with improved building components to estimate potential energy

savings from improvements to the building envelopes.

We define building prototypes that represent the existing building stock and average new

construction patterns for three building types (single-family, multi-family, and

manufactured homes), two regions (North and South), and three different heating fuel

types (electric resistance, heat pump, and other fuels (mostly gas)). The specification of

different prototypes for different fuels is an attempt on our part to account for the fact that

buildings with electric heating, and heat pumps in particular, are generally newer and

therefore have greater thermal integrity.

Because the existing building stock includes a diverse building population in terms of age,

building size, and insulation levels, we also segment the existing building stock for singlefamily and multi-family into older uninsulated ("loose") buildings and newer insulated

("tight") buildings. We create prototypes for loose and tight existing single-family and

multi-family homes. Each prototype is associated with a particular fraction of the existing

stock in that heating fuel category. We call this fraction the "shell share." -The population

of any specific building prototype is thus the (total stock) X (heating fuel share) X (shell

share).

30

EE-RM-94-230 COMMENT 61C

Figures 3.21, 3.22, and 3.23 show HVAC system shares for the three housing types by 

region and for the national average. These figures highlight 1) the dominance of the gas 

furnace/central air conditioningHV AC system in single-family buildings in all regions 

(30%),2) the high portion of hydronic heating systems in multi-family buildings, 3) the 

greater percentage of electrically-heated homes in the south region. and 4) the use of LPG 

as a heating fuel in manufactured houses. .. 

New Shares 

Shares of heating and cboling equipment for new buildings are taken from combinations of 

data from the Census C25 survey (US Bureau of the Census 1990a) and the 1987 RECS 

data for buildings built between 1980 and 1987 (US DOE 1989a). We have also developed 

HV AC system shares using these same data sets. Some of these data are shown in Figures 

3.24 through 3.29. 

Figures 3.24 and 3.25 show the heating fuel shares and central air conditioning shares in 

new construction for single-family buildings. Figures 3.26 and 3.27 show the same for 

multi-family, while Figures 3.28 and 3.29 show the same for manufactured homes. The 

striking observation from these data is that the use of electricity as a heating fuel, 

particularly for electric resistance heating, decreased between 1985 and 1990. At the same 

time. the percentage of new buildings with central air conditioning has been rising 

dramatically, so that 80% of new single-family homes and 60% of new multi-family units 

have central air conditioning installed at the time of construction. 

3.5. Forecasting Prototypes 

For the analysis of conservation potential from building envelope measures. we define a set 

of building prototypes that represent the major characteristics of the residential building 

popUlation. The important parameters include the component areas of the building (roof, 

wall, floor, etc.) and the thermal characteristics of those components. The prototypes are 

characterized from data taken from surveys of either the building stock or recently 

constructed buildings. Once derIDed. the heating ·and cooling energy consumption of these 

buildings can be assessed with improved building components to estimate potential energy 

savings. from improvements to the building envelopes. 

We define building prototypes that represent the existing building stock and average new 

construction patterns for three building types (single-family, multi-family, and 

manufactured homes), two regions (North and South), and three different heating fuel 

types (electric resistance, heat pump, and other fuels (mostly gas». The specification of 

different prototypes for different fuels is an attempt on our part to account for the fact that 

buildings with electric heating, and heat pumps in particular, are generally newer and 

, therefore have greater thermal integrity . 

. Because the existing building stock includes a diverse building population in tenus of age, 

building size. and insulation levels. we also segment the existing building stock for singlefamily and multi-family into older uninsulated ("loose") buildings and newer insulated 

("tight") buildings. We create prototypes for loose and tight existing single-family and 

multi-family homes. Each prototype is associated with. a particular fraction of the existing 

stock in that heating fuel category. We call this fraction the "shell share." "The population 

of any specific building prototype is thus the (total stock) X (heating fuel share) X (shell 

share). 

30 
Figure 3.21. Existing Stock HVAC System Shares for Single-Family Homes: National and Regional

0.50

0.40

0.30

a

g 0.20

At i.

·- National -----

Eqmt .Hat -> FR HP RM H20 Heat Eqmt -> FRN HP RM H20

Heat Fuel > - Electric -

0.50

cooling type

0 CAC EBHP 5 RAC E NON

_____I__I1 ____R\\\~~~__ I_ ____%X% _ _~

A

FRN RM FRN RM H20 FRN RM OTH

-Gas- -LPG - Oil- Other

''~North ~~"'""" -'"~~" 

I- -. I -- _-----

cooling type

O CAC M HP [ RAC O NON

___·- -~ ----------- . ... 'A4

Heat Eqmt -> FRN HP RM H20 FRN RM FRN RM H20 FRN RM OTH

Heat Fuel -> -Electric- -Gas -- -LPG- I-Ol-- Other

0.50 ,--

OUUm

cooling type

E CAC [ HP RAC ] NON

n

H -NE0.00 0 1 _ H---20 -

Heat Eqmt -> FRN HP RM H20 FRN RM FRN RM H20 FRN RM OTH

Heat Fuel -> -Electric - -Gas- - LPG -Oil Other

Source: US DOE 1992. Oil heating fuel category includes kerosene. Other heating fuel is primarily-wood. H20 = steam or

hot water, FRN = furnace, HP = heat pump. RM = room heating, OTH is all other heating technologics. US DOE

1992 data converted to north and south using census divisions and HDD to approximate the federal region

breakdown.

31

0.40

u 030-

O 0.20-

0.1

0.10

0.40

0

= 0.30

c 0.20

g

~~, f........ .

r~~~~~~~ - --^ ~ ~ ~ ~ -- -- - - - - -

···-- ·--- ·- ·^-I--·-e··-··-·-,·,--··

IlIR! JII I .___ r f RA r .r-. -----s - ^ X r r ^ *

· I--------------------.----

d---1.11--^- * .^.-*--·_------ ---..-.. ____-____._...__..___.__.__.__..

nMn

--- - E

-F;;; M 6m0 _ W 6 Z

EE-RM-94-230 COMMENT 61C

Figure 3.21. Existing Stock HVAC SystemSbares (or Siagle-Family Homes: National aad Regioaal 

O~O~·------------------------------------------------~------, 

cooling type 

0.40 Natioaal ---.-.--------.-~ 

- ~ CAe ED HP ~ RAe 0 NON 

j 

~ o.~~-----------------------·---~~~-----------------·-----·---------·-----------~ 

~ 

S 0.20 oJ··-----·-------·· ...... ·---·-·----· 

iI 

0~10 +, ...... --.----.... ------.---.--.. 1:1 

o.oo~~~~~~~~~~~~~~~~~~~~~~zg~~~~--~~~ 

~~~~ ~ ~ ~ ~ ~.~ ~ ~ ~ ~ ~ 

Heal Fuel-> . - Electric - . - Gas - - LPG - - 011 - Other 

o~o~----------------------~---------------------~----------------~ 

0.40 cooling type 

~ CAe m HP ~ RAe ONON 

North 

030~ .. ··--·-·---···---.. -·---·-· .. -.. ·--·-~~ 

O.2Oi·--------------------·~--·--~~·-----------·---------·------------·----·-----~ 

0.10i------------------~~~~~··------------------·_===~---·--------------~ 

0.00 .f-C~:zZl....r ....... -L_,_I---,..&:I:z:a::a...,..~""'...,...I:I~~ ....... ~a;..,...I=---~~.a....,.~"""'~-_..,,-II:II-i...4 

Heat Eqmt -> FiN HP ~ H2O ~ ~ FRN RM H20 FRN RM om 

Heat Fuel -> - Elec:tric - - Gas -- - LPG - - on - Other . 

o~o~-----------------------------------------~-----------~--------~ 

·0.40 ,._ .. _ ... ,_ .. , ... _e_ .. _ ....... _._ ......... _ .... _._. __ ........ __ .. __ cooling type 

-c: ~ CAe III HP ~ RAe 0 NON 

.2 0.30 .] 

-u 

.. 0.20 til 

J: 

'" 

0.10 

0.00 

. Heat E'qmt -> FRN HP RM H2O FRN RM FRN ~ H2O FRN RM ant 

Heat Fuel -> - Electric: - - Gas - - LPG - - 011- Qtber Source: US DOE 1992. Oil heating ~ category includes kerosene. Other heating fuel is primarilywoOc!.. H2O = steam ~ 

h~ water'. J~RN = furnace. HP = heat pump. RM = room heating. om is all oth" heating ·technologies. US DOE 

. 1992 data CUlVcrted 10 north and sOU1h using census divisions and HDD 10 approximate the federal region ~~. . . 

31 
ire 3.2. Existing Stock HVAC System Shares for Multi-Family Homes: National and Regional

....... National cooling type

CAC HP S RAC O NON -

FRN HP RM H20 FRN RM FRN H20 FRN RM OTH

-Electri- - Gas- LPG -Oil- Other

0.30 --- North------

- 0.20

o000.20 . --- -

0.15 a

' 0.10

0.05

o.oo , -- -- at Eqmt-> FRN HP RM

t Fuel -> - ElectricH20 FRN

-GasRM FRN H20 FRNLPG - Oil -- RM OTH

Other

0.35

0.30 -- South -cooling type

_ 0.25 - ---- [--------- CAC HP RAC O NON --

Zi 0.20

! 0.15 . ... ___

0.05

' 0.10 . ._ .. ..._____- .

0.05 ... .....

0.00 .

!eatEqmt-> FRN HP RM H20 FRN RM FRN H20 FRN RM OTH

Icat Fuel -> -Electric- -Gas- LPG -Oil- Other

urce: US DOE 1992. Oil heating fuel includes kerosene. Other heating fuel is primarily wood. H20 = steam or hot water,

FRN = furnac HP = heat pump. RM = room heating, OTH = all other heating technologies. US DOE 1992 data

converted to north and south using census divisions and HDD to approximate the federal region breakdown.

32

0.35

0.30

^ 0.25

.2

, 0.20

e 0.15

W 0.10

0.05

0.00

at Eqmt -:

at Fuel ->

035

---- ---- -cooling type'

-___- CAC E HP O RAC [ NON ---

.o r______

- xxcm ---..........--- .____.....__..__.--

I r I-----g ^ s i LYYY

.............. _- _. _ ...---- I

EE-RM-94-230 COMMENT 61C

lire 3.22. Existing Stock HVAC System Shares for Multi-Family Homes: Nmtional and Regional 

Q3S.--------------------------------------------------------------

0.30 Nationat----···--···-····-·------····------' --.---- cooling type 

_ 0.25 +-.-.. -,-----------.--.-.-------- j ~ CAC W HP ~ RAe 0 NON 

~ 020t---------------~·r---r--i 

~ f 0.15 +.-----.. -------.-.. ---.-~ .. 

.c 

lit 0.10 

0.05 

0.00 ~.c..c..c.Ul..~u;&;l~~~"-r-..IIZIcz:zz'_T_~u.a...._J-a:a....._---~ 

:at Eqmt -> FRN HP RM 

:at Fuel -> - Electrlc -

H2O FRN RM 

-G85-

FRN 

LPG 

H2O FRN RM OTI{ 

-011- Other 

O.3s.---------------------------------------------------------~ 

0.30 North ... ---------'-1 

_ 0.25 +-------------·-i c 

.S! 

U 020+··-------------·---~ __ ~ g 

~ 0.15 +·---------·i .. .cI 

us 0.10 +·-----,-·----·t 

0.05 +---.------~~~-.-,~ 

type 

~ CAC EB HP ~ RAC 0 NON 

o.oo~~~~~~~~~~~~~~~~~~---~~~~~~----~----~ 

:at !=qtnt -> FRN HP RM 

!at FUel -> - E1ec:trIc - H2O FRN RM FRN H2O FRN IUd om 

-Gas- LPG - 011 -- Other 

O.3S~------------------------------------------------------------~ 

0.30 South '-"-'-'-"'-'--' -.. -----.---.-. ._--._._ .. -'-. cooling type ... -----.---.. -.... -.-l 

_ O~+··-----------~·-·------·-·-----··-·-·--- ~ CAC m HP t§l RAe 0 NON c 

.2 

-g 0.20 

e 

~ 0.15 

.a 

U) 0.10 

0.05 

O.OO~~~~~~~~~~~~~~~~~----~~aL~~~~--~~~ 

~~~FRN HP RM ~ FRN RM FRN H2O ~ RM om 

leat Fuel-> - Electric: - - Gas- LPG - 011- Other 

)urce: US ooE 1992. Oil heating fuel includes kc:roscne. Other heating fuel'is priMarily wood. H20 = steam or hot water. 

FRN = furnace. HP = heat pump. RM = room heating. om = all other heating technologies. US DOE 1992 data 

converted to north and south wing census divisions and HOD 10 approximate the federal region breakdown. 

32 
Figure 3.23. Existing Stock HVAC System Shares for Manufactured Homes: National and Regional

0.45 ---

0.40 Natonalcooling type 035 .. CAC -B HP l RAC ONON 0 030-

0.25

020

0.15

0.10 -

0.05

o.0 .- - =.:_--

OOp WW RSPCT kwxj WW l---L,_ ^g^ tsssgj ^ S· , · , . --

Heat Eqmt -> FRN HP RM FRN RM FRN RM FRN RM OTH

Heat Fuel -E Electric- -Gas- -LPG- -Oil- Other

0.45 -

0.40 cooling type 035s _North

' CAC a HP . [RAC ONON

g 0.30

0.25 _ ..... _

0. 2

0.15

Heat Eqmt -> FRN HP RM FRN RM FRN RM FRN RM OTH

Heat Fuel -> -Electric- Gas- -PG - -011- Other

0.45

0.40 South0.40 * cooling type _ South

0-_ 0°35 W] CAC iHP RAC ONON ' 0.30---- 

0.25 ._. __. _

0.20

A 0.15

0.10

0.05

0.00 . .

Heat Eqmt -> FRN HP RM FRN RM FRN RM FRN RM OTH

Heat Fuel - -- Electric- - Gas- -LPG- -Oil - Other

Source: US DOE 1992. Oil heating fuel category includes kerosene. Other heating fuel is primarily wood. FRN = furnace,

HP = heat pump, RM = room heating, OTH is all other heating technologies. RECS data converted to north and

south using census divisions and HDD to approximate the federal region breakdown.

33

EE-RM-94-230 COMMENT 61C

FIg1ll"e 3.23. lExlstlng Stock HV AC System Sbares for MaDuf~ctured Homes:.Nationai and Regional 

0.45 ,..--~-------------------------_ 

0.40 

0.35 National --------r-,--------- i 0.30 +--.---------1 

i 0.25 i------------i ~ - 020~·------·-----------------~~ 2: 

• ii 0.15 i--='""---------

0.10 i·-t.oo~~------_w. 

O.OS 

0.00 -l-lOIi"""'~.,.....r;I==__...._~~~ 

Heal Eqmt -> fRlIIl HP RM FRN RM 

Heat Fuel-> - Electric - -Gascooling type 

~CAC HlHP ~RAC ONON 

FRN RM 

-LrofRN RM 

-011-

OTH 

Other 

0.45 -r-.-.------------------------------:--.., 

0.40 i-·-------------r--,..----·-----cooliDg typc-------f 

North . 

0.35 ------I ~ CAC e UP ~ RAC 0 NON i 0.30 '1----------'1 

i 0.25 +---------_t'\ & 

- 0.20 +.-.---------~ e .a 0.15 i-r-..,.--------f'!~ 

0.10 +-i.._...J.------~~ 

o_os +·~Wh~-------~~'1----~ 

0.00 i-L'~ .... .4.~ ___ ~=~~c;.u:,,"-A....._-........... r;u:; "" ...... ==....,......""" ""'""-...---'-'-CSOI...! 

Heal Eqmt -> fRN HP RM 

Heat Fuel-> - Electric:-

fRN RM 

-GasFRN RM 

-LPGFRN RM 

-Oll01H 

Other 

0.45 -r-------------------~-----------, 

0.40 +·-·----~----------------CQOling typc------i 0.35 South ___________ _ 

i ~»of--.----------------~-~----- i 0.15 ... 

...... 0.20 +.-------------1 ~ ii 0.15 i··-----------

0.10 +-i:'~~.--------~ 

0.05 

0.00 +-"' ....... ~ ............................... ~ ....... ..._ 

Heal Eqmt -> l~ HP RM 

Heal Fuel-> - Electric - FRN RM 

-Gas-

~CAC S; lIP ~ RAC ONON 

FRN RM 

-LPGFRN RM ~Oilom 

Oaher 

Source: US DOE 1992. Oil beating fuel category includes kerosene. ()lhcr beating fuel is primarily wood. FRN = fumace. 

HP = bcaJt pump, RM = room hcaUng, om is aD other beating technologies. RECS data converted to north and 

IOUth using ccmus divisions and HOD to approximate the federal regioa breakdown. 

33. 
Figure 3.24. Selected Space Heat FuelTechnology Shares in New Construction, Single-Family, National

i I i

_ 030 <=-<

JO.20^^:.: 0o.5o0 ^- - : -: :---;--00 ,e - -... -0--' - G

1980 191 1982 1983 1984 1985 1986 1987 1988 1989 1990

i I ' { { t { {

Source: US Bureau of the Census 1990 data on heating fuel shares and eating equipment shares,

combined using estimates forheating/echnology combinations in US DOE 1989a.

Figure 3.25. Total Cent AC Shares (CAC+HP) In New Costucton, Single-Family, National

1.00

0.90

0.80

0.70

la

1 0.60

0.50

" 0.40

030

0.20

0.10

0.00

sFurn

Pump

c Furn

cRoom

] . ... i _ _ _ __iI _ ' _ i I

CAC

1980 1981 1982 1983 1984 1985 1986 1987 1988 ' 1989

Source: US Bureau of the Census 1990.

HP data is from heating equipment and subtracted from total central AC to get CAC.

1990

34

iI i

I - J - -

EE-RM-94-230 COMMENT 61C

Figure 3.24. Selected Space Heat FueVfechnology Shares In New Construction, Sfngle-FamUy, National 

0.60 ~-"""--~-""i----:"---"----:----'--~-""'--'" 

j 

~ ~ I 

O :!. 0.5 -+--~---+~--: -r--.;.-~---;--~r--i· 

'Vi' ! i ! GI : I I 

oj: E I ! ! . 

-: 0.40 ~ 

! 

-.. 

o 

I: 0.30 I 

.s i 

~ I 

! 0.20 -+--+--+--l.---!---!:---t---i 

f ! I I J : ! l WI 

: . 

! 0.00 +--.;.----.,t---+--T-......;---;---;----+--_+_~ 

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

----" - Gas Fum 

--ee-- Ht Pump 

--....-- E1ec Fum 

~ElecRoom 

Source: US Bureau of the Census 1990 data on beating fuel shares and beating equipment shares. 

combined using estimates for bealing/technology combinations in US DOE 1989a 

FIgure 3.25. ".fotal Central AC Shares (CAC+HP) In New Construction, Single-Family, National , 

l.00,-----,,...--~--~-'""""""!---r--_;__--~-____;_--__:_-__, 

O.OO-+--~~--~---+--~-----+----~-----~--~----~------j 

0.80 .;-----;.----+-----+---+-.---+----+---f---~--.:_+_--__1 

0.70 +----i----i:.-.-:=:;:...:------'---==:::.---

'C' i 0.60 

~ 0.50 

1/ 

:; 0.40 

.I: 

v.I 

0.30 

0.20 " 

0.10 

0.00 

CAC 

1980 1981 1982 1983 1984 1985 1986 1987 1988' 1989 1990 

Source: US Bureau of the Census .1990. 

lIP data is from beating equipment and subtracted from total central AC to get CAC. 

34 
Figure 3.26. Selected Space Heat Fuel/Technology Shares in New Construction, Multi-Family, National

0.40

035

0.30-- .

0 o.25

0.20

0.15 . . . . . . . . -- I--... 

0.10

0.05 --------

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

Source: US Bureau of the Census 1990; US DOE 1989a for new construction (1980-87).

~-- Gas Furn

-0-- Gas H20

--- HtPump

-- Elec Fum

~-- Elec Room

Figure 3.27. Total Central AC Shares (CAC+HP) in New Construction, Multi-Family, National

1.00

0.90 -

0.80 ---- - -

0.70 - '

o 0.60 -

'" 0 ^'~~ ~CAC

| 0.40

0.30

1980 1981 1982 1983 1984 1985 1986 1987

Source: US Bureau of the Census 1990.

HP data is from heating equipment and subtracted from total central AC to get CAC.

1988 1989 1990

35

i

i

.-----c---- i .. .

EE-RM-94-230 COMMENT 61C

F"lgUre 3.26. Selt!Cted Space Heat Fuelfl'echnology Sbares ita New Construction, Multi.Family, Nati01Ul1 

0.40 -r--~----:"-~---'!""--:---"'!'---:---:----, 

035 ~.=~""~~--+-+-! t--j ! 

030 '-~-!--~~~--~-1----i!----i---l---1 

. ~ ----- Gas Fwn 

-S 0.25 --O-GasH20 .~ 

III 6 0.20 ". HtPump t __ ·~L 

~ 0.15 ~~~t::..::::"=-~j!:=:~:"" 

0.10 ---_.-

• ElecFwn 

---<>- Elec Room 

. . -i 1---t-l-11 I 

0.00 +---T----Ir----r---i---;---;--"t--;-----ir----i 

0.05 

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

Source: US Bureau of the Census 1990; US DOE 1989a for new consttuction (1980-87). 

Figure 3.27. Total Central AC Sbares (CAC+HP) in New ConstructJon, Multi.Family, National 

1.00 

0.90 

0.80 

0.70 

-c: 

.2 0.60 -

Col 

III 6 0.50 

t: 

til 0.40 .z:. 

CAe . 

~ 

030 

0.20 

0.10 

0.00 

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

Source: US Bureau of the Census 1990. 

HP data is.from hcclting equipment and subtracted from total central AC to get CAe. 

35 
Figure 3.28. Space Heating Fuel Shares In New Construction, Manufactured Homes

0.70

0.60

0.50

.40 - ------ _____________ US

Gas LPG Oi Electricity

Source: US DOE 1989a for buildings built 1980-87. Oil includes kerosene.

Figure 3.29. Central Air Conditioning Shares (includes HP) In New Construction, Manufactured Homes

0.30 ....

* I- I NIorth

0.10 _

°° 'i

0.00

Gas LPi Oil Electricity

US 1980 DOE 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990includ kesene.

Source: US Bureau o29. C ent C eond ioning data converted N t orth/South by Housing Sarts by State.

0.70

.' i

0.00 j

36

EE-RM-94-230 COMMENT 61C

FIgure 3.28. Space Reatlng Fuel Shares In New Construction, ManuCactured Homes 

0.70 T-------------.,...-------------, 

0.60 +-------.--------.--------

050+------------------------------·--------------

co 

. ~ '0.40 +----------.--------------==--

.G 

-; 0.30 +----

at:: 

VI 

0.20 

0.10 

0.00 +'--

Gas LPG Oil Electricity 

Source: US DOE 1989a for buildings built 1980-87. Oil inclu~ kerosene.. 

Dus 

.Nonh 

1m South 

FIgure 3.29. Central AIr CondiUoning Shares (includes HP) In New Construction, ManuCactured Homes 

0.70 I 

I : 

. ~ I l ~ 0.60 I i I Ld-- ~ ~ 

I i . T . • I . ! ; ~ . 050 . '. i 

"l'" 1 I 1 ~ C- I I 

T : 0 I : 

= 0.40 . l' : 

Col 1 j .6- ~ ..l.. ,l, ~ 

·W - ~ 'f T 1 

0.30 .. .. - - i III y- I I .c: 

VI • US i 0.20 1 I 

I i ! I i ---cr- North 

I i I. 

i ! I I I 0.10 

1 

, I I • South - ! 

i I ! 

f i i 

0.00 I . 

1980 ·1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

Source: US Bureau of lhe Census 1990. Census region data converted 10 NorthISoulh by Housing S'rarts by State. 

-36 
Existing Single-Family

For existing single-family buildings, we developed a new set of prototypes using the 1987

RECS data (US DOE 1989a). Other existing single-family prototypes have been defined

previously by the Gas Research Institute (GRI) (Ritschard et aL 1992b) and by LBL

(Boghosian 1991), but these are not readily usable in the residential energy demand

forecasting models at LBL. The GRI prototypes are highly region-specific (9 census

divisions, 16 base cities) and are not related to specific heating or cooling system types. For example, we expect that buildings heated by electricity will, in general, be newer and

better insulated than those heated by natural gas or oil. Therefore, we use the RECS data to

define the prototypes, supplemented by data from the GRI and LBL prototypes where the

RECS data are either not complete or have missing data for individual houses. Ultimately, the prototypes defined in this work provide similar results in terms of component

specifications and baseline heating and cooling loads to those from the other studies.

The RECS data set is stratified by region, and for each sample building we characterized: 1)

thermal parameters based on the RECS data and other estimates (Koomey et al. 1991a,

Koomey et al. 1991b, Boghosian 1991, Huang et al. 1987b), 2) conditioned floor areas

and number of stories, 3) foundation types, and 4) heating fuel. We then stratified the

sample into partially insulated, or "tight", buildings and virtually uninsulated, or "loose"

buildings, based on combinations of roof and wall insulation and average number of

glazing layers across all windows in the house. Loose buildings are assumed to be easily and cost-effectively insulated, whereas tight buildings are already somewhat insulated.

Buildings representing new construction in the period 1987-1990 are added to the data set

as "tight" buildings (see the New Single Family prototypes) to fully characterize the

housing stock in 1990. Finally, for each heating fuel type and "tight" and "loose" thermal

shell package in each region, we calculate the number of buildings represented, average

building conditioned floor area, typical foundation type and number of stories, and average

component insulation level. The component R-values are converted to U-values, then

averaged, and are then converted back to R-values to more accurately characterize overall

building heal: loss. All buildings are assumed to be wood-frame walls and roof systems.

The final specifications are given in Table 3.9 and are included in the database.

Table 3.9 shows that across the different heating fuels within either the North or South

region, the average thermal characteristics of the "tight" prototypes are similar. Note,

however, that for electrically heated buildings, both with resistance heat and heat pumps, the "tight" buildings represent a greater portion of the stock than for the fuel heated

buildings. The fuel heated buildings tend to be older, and thus, less well insulated.

New Single-Family

The new single-family prototypes for the North and South regions are taken directly from

the LBL electricity conservation supply curve study (Koomey et al. 1991a). These

prototypes were originally derived from data in the 1987 National Association of Home

Builder Annual Builder Survey (NAHB 1989) as described elsewhere (Koomey et al.

1991b). These buildings are significantly better insulated than the existing buildings, with

ceilings up to R30, walls above Rll, and double-glazed windows with foundation

insulation, yet also have significantly larger conditioned floor areas. The specifications are

found in Table 3.9.

37

EE-RM-94-230 COMMENT 61C

" 

Existing Single-Famil.y 

For existing single-family buildings, we developed a new set of prototypes using the 1987 

RECS data (US DOE 1989a)~ Other existing single-family prototypes have been defmed 

previou~ily by the Gas Research Institute (GRI) (Ritschard et a1. 1992b) and by LBL 

(Boghosian 1991), but these are not readily usable in the residential energy demand 

forecastiing models at LBL. The ORI prototypes are highly region-specific (9 census 

division:~ 16 base cities) and are Dot related to specific heating or cooling system types. 

For example~ we expect that buildings heated by electricity will. in general, be newer and 

bettel' inmllatedthan those heated by natural gas or oil. Therefore, we use the RECS data to 

defmetll.e prototypes, sU'pplemented by data from the GR! and LBL prototypes where the . 

RECS data are either not complete or have missing data for individual houses. Ultimately. 

the prototypes defined in this work provide similar results in terms of component 

specifications and baseline heating arid cooling loads to those from the other studies. 

The RECS data set is stratiflCd by region, and for each sample building we characterized: I) 

thermal parameters based on the RECS data and other estimates (Koomey et ala 199180 

.' Koomey et al. 1991b, Boghosian 1991, Huang et ala 1987b). 2) conditioned floor areas 

and number of stories. 3) foundation types, and 4) heating fuel. We then stratified the 

sample into partially insulated, or "tight", buildings and virtually uninsulated, or "loose" 

buildings, based on combinations of roof and wall insulation and average number of 

glazing layers across all windows in the house. Loose buildings.are assumed to be easily 

and cost·effectively insulated, whereas tight buildings are already somewhat insUlated. 

Buildin~i representing new coilstnlction in the period 1987-1990 are added to the data set 

as "tight" buildings (see the New Single Family prototypes) to fully characterize the 

housing stock in 1990. Fmally, for each heating fuel type and "tight" and "loose" thermal 

shell package in .each region,. we calculate the number of buildings represented, average 

building c:onditioned floor area, typical foundation type and number of stori~ and average' 

compone:llt insulation level. The component R-values are converted to U-values. then . 

averaged, and are then converted back to R-values to more accurately characterize overall 

building heat: loss. All buildings are assllmed to be wood~frame walls and roof systems. 

The {mal ,specifications are given in Table 3.9 and are included in the database. 

Table 3.9 shows that across the different heating fuels .within either the Nonh or South 

region, the average thermal characteristics of the "tight" prototypes are·similar. Note, 

however, that for electrically heated buildings, both with resistance heat and heat pumps, 

the "tight" buUdings represent a greater portion of the stock than for the fuel heated 

buildings. The fuel heated buildings tend to be older, and thus, less well insulated. 

New Single-Family 

The new single-family prototypes for the North and South regions are taken directly from' 

the LBL electricity conservation supply curve study (Koorney et ala 1991a). These 

prototypes were originally derived from data in the 1987 National Association of Home 

Builder Annual Builder Survey (NAHB 1989) as described elsewhere (Koomey et ale 

1991b). lbese buildings are significantly better insulated than the existing buildings. with 

ceilings up to R30, walls above RU, and double-glazed windows with foundation 

insulation, yet also have significantly larger conditioned floor areas. 11)e specifications are 

found in Table 3.9. . 

37 
Table 39. Building and Thermal Characteristics of Single-Family Building Prototypes

Cond. Foundation

Regional Floor No. Insulation

Heat Shell Popln. Fndn Area of Roof Wall Glazing Infiltration Floor Perim.

Type Group (%of stock) Type (sqft) Stories (R) (R) Layers ELF ACH (R) Config.

EXISTING BUILDINGS (Po ilation is percent of existing stock in 1990)

North Region 993%

Electric Tight 7.2% Bsmt 1560 1 21 8 2.0 0.00036 0.47 R06 n/a

Electric Loose 2.1% Bsmt 1220 1 7 2 1.6 0.00046 0.59 R03 n/a

HPamp Tight 2.1% Bsmt 1830 2 25 11 2.0 0.00035 0.43 R08 n/a

HPump Loose 0.1% Slab 2470 1 11 7 1.0 0.00027 036 n/a R1.2

Fuel Tight 45.0% Bsmt 1700 2 22 5 1.9 0.00044 057 R06 n/a

Fuel Loose 42.8% Bsmt 1420 2 6 1 1.7 0.00059 0.76 R05 n/a

South Region 99.9%

Electric Tight 10.3% Slab 1640 1 19 7 1.4 0.00065 0.67 n/a R2_2

Electric Loose 4.2% Slab 1170 1 6 2 13 0.00065 0.67 n/a Rl_2

HPump Tight 11.0% Slab 1650 1 21 8 1.7 0.00069 0.70 n/a R2_2

HPump Loose 1.8% Slab 1480 1 6 1 1.2 0.00062 0.64 n/a Rl_2

Fuel Tight 32.2% Crawl 1650 1 20 5 1.5 0.00070 0.71 R03 n/a

Fuel Loose 40.4% Crawl 1370 1 5 1 1.2 0.00068 0.69 R02 n/a

NEW BUILDINGS (Population is percent of new construction)

North Region 99%

Electric All 8% Bsmt 1860 2 29 15 2.0 0.00031 0.40 R15 n/a

HPump All 13% Bsmt 2220 2 28 14 1.9 0.00031 0.40 R13 n/a

Fuel Al 78% Bsmt 2180 2 28 14 1.7 0.00044 0.56 R12 n/a

South Region 100%

Electric All 13% Slab 1890 1 28 10 1.5 0.00060 0.62 n/a R4_2

HPump All 31% Slab 1820 1 25 11 1.7 0.00061 0.63 n/a R2.2

Fuel AH 56% Slab 2070 1 25 12 1.7 0.00061 0.63 n/a R2_2

Existing Single Family:

1) Building areas, shell group populations, ceiling R-values and window glazing layers from 1987 RECS data,

updated to 1990 populations using new prototypes from Koomey et a 199 la. Populations by heating type

from US DOE 1992a.

2) Data from Boghosian 1991 and Ritschard et aL 1992a for roof, wall, foundation, and window measures are

used where data not available in US DOE 1992a.

3) Breakdown between "Tight" and "Loose" determined approximately as follows (see writeup):

North: "Loose" has roof R-value<10 or wall R-value<4 and average glazing layers<l.7. All others "Tight".

South: "Loose" has roof R-value<10 or wall R-value<4 or wall R-value=<7 and average window layers<l.4.

New Single Family:

4) Prototype descriptions from Koomey et al. 1991b, as presented in Koomey et al. 1991a. Original data source

is the 1987 NAHB Builders Survey data (NAHB 1989). Populations by heating type from US Bureau of the

Census 1990 series heating fuel shares in new construction.

Existing and New:

5) Component dimensions are not shown here but are included in the database.

6) Window area assumed as 12% of floor area.

7) Wall height assumed to be 8 feet per story in all locations.

8) Infiltration air changes per hour (ACH) from Boghosian 1991. Equivalent leakage fraction (ELF) calculated

from ACH using simulated ACH in Huang et al. 1987b assuming ACH is for heating season.

EE-RM-94-230 COMMENT 61C

T a bl e 3.9 • BuUding and Thennal C ha racteristlcs of Single-Family Bunding Prototypes 

Cond. Foundation 

Regional Floor" No. Insulation 

Heat Sbell Popln. Fodn Area of Roof Wall Glazing Infiltration Floor Perim. 

Type Group (% of stock) Type (sqft) Stories (R) (R) Layers ELF ACH (R) Config. 

EXISTING BUIlDINGS (PO] ulation is percent of existing stock in 1990) 

North Region 99.3% 

Electric Tight 72% Bsmt 1560 1 21 8 2.0 0.00036 0.47 R06 nla 

Electric LOose 2.1% Bsmt 1220 1 7 2 1.6 0.00046 0.59 R03 nla 

HPump Tight 2.1% Bsmt 1830 2 2S 11 2.0 0.00035 0.43 R08 nla 

HPump Loose 0.1% Slab 2470 I 11 7 1.0 0.00027 0.36 nla RIJ 

Fuel Tight 45.0% Bsmt 1700 2 22 5 1.9 0.00044 0.57 R06 nla 

Fuel Loose 42.8% Bsmt 1420 2 6 I 1.7 0.00059" 0.76 R05 nla 

South Region 99.9% 

Electric Tight 10.3% Slab 1640 I 19 7 1.4 0.00065 0.67 nla R2_2 

Electric Loose 4.2% Slab" " 1170 1 6 2 1.3 0.00065 0.67 nla RC2 

HPump Tight 11.0% Slab 1650 I 21 8 1.7 0.00069 0.70 nla R2_2 

HPump Loose 1.8% Slab 1480 I 6 1 1.2 0.00062 0.64 nla Rl_2 

Fuel Tight 32.2% Crawl 1650 1 20 5 1.5 0.00070 0.71 R03 oJa 

Fuel Loose 40.4% Crawl 1370 1 5 1 1.2 0.00068 0.69 R02 nla 

NEW BUILDINGS (population is perceDt of new construction) 

North Region 99% 

Electric All 8% Bsmt 1860 2 29 15 2.0 0.00031 0.40 R15 nla 

HPump All 13$ Bsmt 2220 2 28 14 1.9 0~00031 0.40 R13 nla 

Fuel AD 78$ Bsmt 2180 2 28 14 1.7 0.00044 0.56 R12 nla 

South Region 100% 

Electric All 13% Slab 1890 I 28 10 1.5 0.00060 0.62 nla R4_2 

HPump All 31% Slab 1820 1 2S 11 1.7 0.00061 0.63 nla R2J 

Fuel AD 56% Slab 2070 1 25 12 1.7 0.00061 0.63 nla R2_2 

Existing Single Family: 

1) Building areas. shell group populations. ceiling R-values and window glazing layers from 1987 RECS data. 

updated to 1990 populations using new prototypes from Koomey et. al. 1991a. Populations by beating type 

from US DOE 1992a. " 

2) Data from Boghosian 1991 and Ritsclwd et al. 1992a for roof. wall. foundation. and window measures are 

used where data Dot available in US DOE 1992a. 

3) Breakdown between "Tight" and "Loose" determined approximately as follows (see writeup): 

North: "Loose" bas roofR-value<IO or wall R-value<4 and average glazing layers<l.7. All others "1ight". 

South: "Loose" bas roof R-value<IO or wall R-value<4 or wall R-value:<7 and average windowlayers<l.4. 

New Single Family: 

4) Prototype descriptions from Koomey et al. 1991 b. as presented in Koomey et aI. 1991a. Original data source 

is the 1987 NAHB Builders Survey data (NAHB 1989). Populations by heating type from US Bureau of the 

Census 1990 series beating fuel shares in new construction. 

Emling and New: 

5) Component dimensions are not sbown here but are included in the database. 

6) Wmdow area assumed as 12% of floor area 

7) Wall beight assumed to be 8 feet per story in alllocatioDS. _ 

8) Infilttation air changes per bour (ACH) from Boghosian 1991. Equivalent leakage fraction (ELF) calculated 

from ACH using simulated ACH in Huang et al. 1987b assuming ACH is for heating season. 
Existing and New Multi-Family

Existing and new multi-family prototypes are taken from the GRI multi-family residential

database (Ritschard et al. 1992b). The GRI database includes 16 different prototypes

defined for four census regions, with three to five prototypes per census region, and

simulated in sixteen base cities, with two to five cities per census region. The combination

produces 60 different combinations of cities and building prototypes.

For the prototypes defined here, we updated the building populations to 1987 populations

based on the RECS data (US DOE 1992), extrapolated the prototypes to represent the entire

sector (as described in Hanford and Huang 1992), and applied heating types to the

prototypes. We then segmented the prototypes into North and South regions using the

same strategy as for single-family buildings, and averaged the building component areas

and thermal values as in the existing single-family analysis.

We also define two prototypes for existing buildings based on building vintage. The

thermal characteristics of the GRI prototypes showed that insulation levels for pre-1980

buildings were significantly different than post-1980 buildings, with pre-1980 buildings

being typically uninsulated or not well insulated Therefore, we create a pre-1980 and

post-1980 vintage in the existing stock for each region and heating fuel type. The pre-1980 and post-1980 buildings are similar across heating fuels, but electrically heated buildings

generally have a larger proportion of the better insulated buildings than the fuel heated

buildings. The post-1980 prototypes are also used as the new multi-family prototypes. This assumes that new multi-family buildings in 1990 are similar to 1980 vintage

buildings. The specifications are given in Table 3.10.

Existing and New Manufactured Homes

Existing and new manufactured home prototypes are taken directly from the previous LBL

electricity conservation supply curve study (Koomey et al. 1991a). As with single-family

buildings, the new prototypes are better insulated than existing buildings but are larger.

These are listed in Table 3.11.

Prototype Heating and Cooling Loads

Heating and cooling loads are calculated for the baseline prototypes, and improved buildings, using building component loads generated from DOE-2 simulations of prototype

buildings done under ASHRAE Special Project 53 (SP53) (Huang et al. 1987b). The

building prototypes considered in this project include a one-story single-family building, a

two-story townhouse, and an apartment module. Simulations are performed with a wide

variety of insulation packages and window configurations in 45 different climates.

Changes in building loads from improvements to single building components are reduced

to a set of component loads for each component on a component dimension basis (square

feet or lineal feet). In addition, these component loads are further reduced to a set of

coefficients by regressing the component loads versus component U-value or some other

measure of thermal integrity. Each heat gain or loss component-is considered to be

independent of another. The components considered include ceiling, walls, foundations

(slab, heated basement, unheated basement, and crawl space), infiltration, window

conduction, and window solar loads which are non-linearly dependent on window area,

window orientation, and glazing shading coefficient. In addition, there is a residual load,

which represents the effect of internal gains and other non-temperature related effects.

EE-RM-94-230 COMMENT 61C

Existing and New Multi-Family 

Existing 8l.1d new multi-family prototypes are taken from the GR! multi-family residential 

database (Ritschard et al. 1992b). The OR! database includes 16 different prototypes 

defmed fOlr four census regions, with three to five prototypes per census region, and 

simulated iIn sixteen base cities. with two to five cities per census'region. The combination 

produces 60 different combinations of cities and building prototypes.' 

For the prototypes defmed here, we updated the building populations to 1987 populations 

based on the RECS data (US DOE 1992), extrapolated the prototypes to represent the entire 

sector (as described in Hanford and Huang 1992), and applied heating types to the 

prototypes. We then segmented the prototypes into North and South regions using the 

same stratc~gy as for single-family buildings, and averaged the building component areas 

and thennm values as in the existing single-family analysis. " . 

We also defme two prototypes for eXisting buildings based on building vintage. The 

thennal characteristics of the ORI prototypes showed that insulation levels for pre-1980 

buildings were significantly diff~nt than post-1980 buildings, with pre-1980 buildings 

being typically uninsulated or not well insulated. Therefore. we'create a pre-1980 and 

post-1980 vintage in the exiSting stock for each region and beating fuel type. The pre-1980 

and post-l~~80 buildings are similar across heating fuels. but el~trically heated buildings . generally have a .arger proportion of the better insulated buildings than the fuel heated 

buildings. The poSt-1980 prototypes are also used as the new multi-family prototypes. 

This assumes that new multi-family buUdings in 1990 are similar to 1980 vintage 

buildings. The specifications are given in Table 3.10. 

Existing and New "Manufactured Homes 

Existing and new manufactured 'home prototypes are taken directly from the previous LBL 

electricity conservation supply curve study (Koomey et a1.1991a). As with single-family 

buildings, the new prototypes are better insulated than existing buildings but are larger. 

These are listed in Table 3.11. . - . 

Prototype Heating ·and Cooling Loads 

Heating and cooling loads are calculated for the baseline prototypes. and improved 

buildings. using building component loads generated from nOB-2 simulations of prototype 

buildings done under ASHRAE Special Project S3 ($PS3) (Huang et al. 1987b). The 

building prototypes considered in this project include a one-story single-family building. a 

two-story townhouse, and an apartment module. Simulations are performed with a wide 

variety of insulation packages and window configurations in 45 diff~t climates. 

Changes in building loads from improvements to single building components are reduced 

to a set of (:omponent loads for each component on a component dimension basis (square 

feet or lineal feet). In addition. these component loads are 'further reduced to a set of 

coefficient:; by regressing the component loads versus component U-value or some other 

measure of thenna! integrity. Each heat gain or loss component -is considered to be 

independent of another. The components considered include ceiling, walls. foundations 

(slab. heated basement. unheated basement. and crawl space), inflltration. window 

conduction, and window solar loads which are non-linearly dependent on window area, 

window ori.entation, and glazing shading coefficient In additiori, there is a residual load, 

which reprc:sents the effect of internal gains and other non-teinperature related effects. 

"''' 
Table 3.10. Buiding and Thermal Characteristics of Multi-Family Building Prototypes

Cond. Foundation

Regional Floor Insulation

Heat Shell Popln. Fndn Area Roof Wall Glazing Infiltration Floor Perim.

Type Group (% of stock) Type (sqft) (R) (R) Layers ELF ACH (R) Config.

EXISTING BUILDINGS (Population is percent of existing stock in 1990)

North Region 99.8%

Electric pre-80s 16.7% Bsmt 903 2 1 1.2 0.00047 0.62 n/a RO

Electric 1980s 3.0% Bsmt 1017 23 13 2.0 0.00035 0.47 n/a R5_4

HPump pre-80s 1.1% Bsmt 914 4 3 1.2 0.00043 0.57 n/a RO

HPump 1980s 0.8% Bsmt 1020 22 13 2.0 0.00035 0.47 n/a R5_4

Fuel pre-80s 74.9% Bsmt 1054 2 2 1.7 0.00047 0.62 n/a RO

Fuel 1980s 3.3% Bsmt 1115 27 13 2.0 0.00035 0.47 n/a R5_4

South Region 100.2%

Electric pre-80s 24.4% Slab 1038 4 1 1.0 0.00046 0.49 n/a RO

Electric 1980s 11.4% Slab 1084 22 13 2.0 0.00035 0.37 n/a RS_2

HPump pre-80s 4.8% Slab 1036' 4 1 1.0 0.00047 0.50 n/a RO

HPump 1980s 8.8% Slab 983 22 13 2.0 0.00035 0.37 n/a R5_4

Fuel pre-80s 45.7% Slab 925 2 1 1.0 0.00045 0.48 n/a RO

Fuel 1980s 5.1% Slab 1015 22 13 2.0 0.00035 0.37 n/a R5 4

NEW BUILDINGS (Population is percent of new construction)

North Region

Electric All 23% Bsmt 1017 23 13 2.0 0.00035 0.47 n/a R5_4

HPump All 13% Bsmt 1020 22 13 2.0 0.00035 0.47 n/a R5.4

Fuel All 64% Bsmt 1115 27 13 2.0 0.00035 0.47 n/a R5_4

Soth Region

Electric All 30% Slab 1084 22 13 2.0 0.00035 0.37 n/a R5.2

HPump All 35% Slab 983 22 13 2.0 0.00035 0.37 n/a R5_4

Fuel All 35% Slab 1015 22 13 2.0 0.00035 0.37 n/a R5_4

1) Prototype characteristics from Ritschard and Huang 1989. New Prototype is 1980s prototype from Ritschard

and Huang 1989.

2) Prototype populations and heating types updated using US DOE 1992 data for existing stock and US Bureau

of the Census 1990 data on heating fuel shares in new construction for new buildings.

3) Building dimensions are not shown here, but are included in the database. Building dimensions are averages

across all units in building types, including bottom/mid/top floor units and middle/end units (e.g, foundation

perimeter is exposed perimeter length).

4) Air changes per hour (ACH) calculated from Equivalent Leakage Fraction (ELF) given in Ritschard and

Huang 1990 using simulated ACH in Huang et a. 1987b assuming ACH is for heating season.

40

EE-RM-94-230 COMMENT 61C

T a e • bl 3 10 . B UI ild· lOR aD dTh erma leba racteristlCS 0 r MI· u b-F amll}' •• B UJ ·Id· Pr lOR ototypes 

Cond. Foundation 

Regional Roor Insulation 

Heat Shell Popln. Fndn Area Roof Wall Glazing Inflluation Floor Perim. 

-Type Group_ (% of stock) T~ (sqft) (R) (R) Layers ELF ACH (R) Config. 

EXISTING Bun.DINGS (population is percent of existing stoCk in 1990) 

North Rtgion 99.8% 

Electric pre-80s 16.7% Bsmt 903 2 1 1.2 0.00047 0.62 nla RO 

Electric 1980s 3.09& Bsmt 1017 23 13 2.0 0.00035 0.47 nla RS_4 

HPurnp pre-8Os 1.1% Bsmt 914 4 3 1.2 0.00043 0.57 nla RO 

HPurnp 19805 0.8% Bsmt 1020 22 13 2.0 0.00035 0.47 nla RS_4 

'Fuel pre-80s 74.9% Bsmt. 1054 2 2 1.7 0.00047 0.62 ilia RO 

Fuel 1980s 3.3% Bsmt 1115 27 13 2.0 0;00035 0.47 nla RS4 

South Rtgion 100.2% 

Electric pre-8Os 24.4% Slab 1038 4 1 1.0 0.00046 0.49 nla RO 

Electric 19805 11.490 Slab 1084 22 13 2.0 0.00035 0.37 nla RS_2 

HPurnp pre-80s 4.8% Slab 1036' 4 1 1.0 0.00047 0.50 nla RO 

HPurnp 19805 8.8%· Slab 983 22 13 2.0 0.00035 0.37 nla RS_4 

Fuel pre-80s 45.7% Slab 92S 2 1 1.0 0'()OO45 0.48 nla RO 

Fuel 1980s 5.1% Slab 1015 22 13 . 2.0 0.00035 0.37 nla RS4 

NEW B UD..DINGS (Population is percent of new construction) 

North Rtgion 

Electric All 23% Bsmt 1017 23 13 2.0 0.00035 0.47 nla RS_4 

HPurnp All 131J1 Bsmt 1020 22 13 2.0 0.00035 0.47 nla RS_4 

Fuel All 64% Bsmt 1115 27 13 2.0 0.00035 0.47 nla RS_4 

South Region 

Electric All 30% Slab 1084 22 13 2.0 0.00035 0.37 nla RS_2 

HPump All 35% Slab 983 22 13 2.0 0.00035 0.37 nla RS_4 

Fuel All 359& Slab 1015 22 13 2.0 0.00035 0.37 nla RS_4 

I} Prototype characteristics from Ritschard and Huang 1989. New Prototype is 1980s prototype from Ritschard 

and Huang 1989. 

2) Prototype populations and heating types updated using US DOE 1992 dara for existing stock and US Bureau 

of the Census 1990 data on heating fuel shares in new construction for new buildings. , 

3) Building dimensions are not shown here. but are included in the database. Building dimensions are averages 

aaoss all units in building lypCS, including bottom/mid/trip floor units and middle/end units (e.g .. foundation 

perimeter is exposed perimeter length). 

4) Air changes per hour (ACH) calculated &om Equivalent Leakage Fraction (ELF) given in Ritscbard and 

Huang 1990 using simulated ACH in Huang et al1987b assuming ACH is for heating season. 

40 
Table 3.11. Building and Thermal Characteristics of Manufactured Home Building Pr pes

Cond. Foundation

Regional Floor No. Insulation

Heat Shell Popln. Fndn , Area of Roof Wall Glazing Infiltration Floor Perim.

Type Group (%ofstock) Type (sqft) Storie (R) (R) Layers ELF ACH (R) Config.

EXISTING BUILDINGS (Population is percent of exising stock in 1990)

North Region

Electric All 19.1% Crawl 1025 1 14 11 2.0 0.00035 0.45 11 n/a

HPump All 0.8% Crawl 800 1 14 11 2.0 0.00035 0.45 11 nla

Fuel -All 802% Crawl 804. 1 14 11 2.0 0.00035 0.45 11 n/a

Sout Region

Electric All 19.8% Crawl 940 1 11 11 1.0 0.00053 0.56 7 n/a

HPump All 4.0% Crawl 1040 1 11 11 1.0 0.00053 0.56 7 n/a

Fuel AllU 76.0% Crawl 847 1 11 11 1.0 0.00053 0.56 7 n/a

NEW BUILDINGS (Population is pecent of new construcion).

North Region

All All 1009o Crawl 1195 1 26 18 2.0 0.00028 0.36 14 n/a

South Region

All All | 100% Crawl 1195 1 20 12 1.3 0.00042 0.45 10 n/a

1) Prototype caracteristcs from Koomey et al. 1991a

2) Prototype populations and heating types are updated using US DOE 1992 data for existing building stock.

Because of limited data, new buildings are not segmented by heating type, and we assume there is not a strong

correlation between heating fuel and thermal integrity for new buildings.

3) Building dimensions are not shown here, but are included in the database. Foundation dimensions are based

on average width of 20 feet (average between single and double-wide).

4) Equivalent Ieakage Fracion (ELF) calculated from air changes per hour (ACH) given in Koomey et aL

1991a using simulated ACH in Huang et aL 1987b assuming ACH is for heating season.

. . .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

41

EE-RM-94-230 COMMENT 61C

" 

Tb 8 Ie 3.11. Uultdln~ and Thermal Characteristics or Manuractured Home B undln2 Prototypes 

Cond. Foundaticm 

Regional Floor No. lDsDladon 

Heat SbeD Papin. Fneln ,Area of Roof Wall Glazing InfiJtratioo Floor Perim. 

Type Group (9& of stock) T~ (~ft) Stories (R) (R) J..avers ELF ACH (R) Config. 

EXISIlNG BUILDINGS (Population is percent of existing 'stock in 1990) 

North Region 

Electric· All 19.1% Crawl 1025 1 14 11 2.0 0.Q0035 0.45 11 wa 

HPump All 0.8% Crawl 800 1 14 11 2.0 0.00035 0.45 11 . ~a 

Fuel . All 80.2% Cra:wl 804. 1 14 11 2.0 0.00035 0.45 11 uIa 

South Region 

Electric All 19.8% Crawl 940 1 11 11 1.0 ·0.00053 0.56 7 wa 

HPump Au 4.0% Crawl 1040 1 11 11 1.0 . 0.00053 0.56 7 . 'nla . 

Fuel . All 76.0% Crawl 847 1 11 11 1.0 . 0.00053 0.56 7 nla 

NEW BUILDING!) (Population is peiCeDt of ~ew cOnstruCtion) , 

North Region· 

All All I 100% Crawl 1195. 1 26 18 2.0 0.00028 0.36 14 nla 

South Region 

All All' 100% Crawl 1195 . 1 20 12 1.3 0.00042 0.45 10 oJa 

1) Prototype cbaracteristics from Koomey et al. 1991a 

2) Prototype P'lpulatiODS and heating types arc updated using US DOE 1992 data for existing building stock. 

Because of limited data, new buildings are not segmented by heating type, and we assume there is Dot a strong 

c:ouelatioo between healing fuel and thennal integrity for new buikimgs. 

3) Building dilneusiODS Ire not sbown here. but are included in the database. Foundation dimensions are basecII 

on average widlh of 20 feet (average betweeD single and double-Wide). 

4) Equivalent l.cabge Fraction (ELF) calcula!ed from air cbanges per hour (ACH) given in Koomeyet aI. 

1991a using simulated ACH in Huang et al. 1987b AASllII1ing ACH is for heating season.. 

,. 

41 
There are two ways this database can be used. First, the database gives component loads

per unit of component for specific levels of thermal integrity. Second, there is a set of

regression coefficients that can be used to determine the component load for any level of

thermal integrity. The procedure is summarized in Table 3.12.

The SP53 project includes simulations for 45 different locations. We consider only three

of those locations in this project. We use Washington DC to represent the national average

climate, Chicago IL to represent the North region, and Charleston, SC to represent the

South. The component loads for these locations are given in Tables 3.13 and 3.14. These

component loads are additive. For example, ceiling area is multiplied by the appropriate

ceiling load, the appropriate foundation dimension (square feet or linear feet) is multiplied

by the appropriate foundation load, etc., and the results are summed.

For the regression coefficients the methodology is the same in that the components are

treated individually, and the results are summed to calculate the building load. The

regression coefficients are given in Tables 3.15 and 3.16. The coefficient methodology is

used within the database to calculate heating and cooling loads. The U-value assumptions

for the different component constructions are given in Table 3.17.

Windows have a conductive component and a solar component, and the SP53 methodology

treats each of these separately. We use the data to calculate total window loads

(conductance + solar) for a typical configuration for simplicity of use. These are shown in

Table 3.18.

In some ways, the SP53 database is not the best data to use for this project. The database

was originally constructed to analyze the impact of conservation measures in new

construction. Therefore, the building prototypes are chosen to represent average

characteristics of newer buildings. However, since the loads are reduced to component

loads, such that the important parameters are only the component U-value and thermal

integrity, the methodology is also applicable to older buildings. Secondly, the simulations

were originally performed to calculate design energy use for buildings, and were not meant

to represent actual conditions in real life. For example, the simulations assume a constant

heating and cooling thermostat set point. Occupants actually may set back heating

thermostats at night or when away from the house. Cooling usage may be even more

erratic.

On the whole, however, the SP53 database provides a simple method for calculating

heating and cooling loads as well as a method for calculating changes in loads from

improvements in the thermal integrity of the building. To account for differences between

the design energy use and actual field usage, the building loads are calibrated to the baseline

UEC derived from other data. This process will be described in the following section. The

building loads calculated from the SP53 database are given in Table 3.18, and are

calculated in the database program using the coefficient method described above.

42

EE-RM-94-230 COMMENT 61C

There are two ways this database can be used. FlI'St, the database gives component loads 

per unit of component for specific levels of thermal integrity. Second. there is a set of 

regression coefficients that can be used to determine the component load for any level of 

.thermal integrity. The procedure is summarized in Table 3.12. 

The SPS3 project includes simulations for 45 different locations. We consider only three 

of those locations in this project. We use Washington DC to represent the national average 

climate. Chicago n. to represent the North region. and Charleston. SC to represent the 

South. The component loads for these locations are given in Tables 3.13 and 3.14. These 

component loads are additive. For example. ceiling area is multiplied by the appropriate 

ceiling load. the appropriate foundation dimension (square feet or linear feet) is multiplied 

by the appropriate found~on load. etc .• and the results are summed. 

For the regression coefficients the methodology is the same in that the components are 

treated individually. and the results are summed to calculate the building load. The 

regression coefficients are given in Tables 3.1S and 3.16. The coefficient methodology is 

used within the database tp calculate heating and cooling loads. The U-value assumptions 

for the different component constructions are given in Table 3.17. 

Windows have a conductive component and a solar component. and the SPS3 methodology 

treats each of these separately. We use the data to calculate total window loads 

(conductance + solar) for a typical configuration for simplicity of use. These are shown in 

Table 3.18. 

In some ways. the SPS3 database is not the best data to use for this project. The database 

was originally constructed to analyze the impact of conservation measures in new 

construction. Therefore. the building prototypes are chosen to represent average 

characteristics of newer buildings. However. since the. loads are reduced to component 

loads. such that the important parameters are only the component U-value and thermal 

integrity. the methodology is also applicable to older buildings. Secondly. the simulations 

were· originally performed to calculate design energy use for buildings. and were not meant 

to represent actual conditions in real life. For example. the simulations assume a constant 

heating and cooling thermostat set point. Occupants actually may set back heating 

thermostats at night or when away from the house. Cooling usage may be even mor~ 

erratic. 

On the whole. however, the SPS3 database provides a simple method for calculating 

heating and cooling loads as well as a method for calculating changes in loads from 

improvements in the thenn3.l integrity of the building. To account for differences between 

the design energy use and actual field usage. the building loads are calibrated to the baseline 

UEC derived from other data. This process will be described in the following section. The 

building loads calculated from the SP53 database are given in Table 3.18. and are 

calculated in the database program using the coefficient method described above. 

42 
Table 3.12. Building Heating and Cooling Load Calculation Methodology

Building load (MMBtu) = roofload + wallload + fndnload + infilload + windload + solarload + resload, whee:

roofioad ,a heating or cooling load from roof

walload heating Is or cooling load from walls

fndnload = heating or cooling load from foundation

infilload heating or cooling load from infilration

windload = heating or cooling load from conduction through windows

solarload heating or cooling load from solar gain through windows

resload = residual heating or cooling load

Method 1: Component loads given as kBtu per square foot or kBtu per lineal foot are multiplied by the component dimension. These values are given in Table 313 and 314

Method 2: Component loads are derived from the component dimension, the thermal parameterparticular to the

component, and the comoonent.coeficients riven in Table 3.15. and 3.16 as follows:

Roofs, Walls, Windows, and Crawl Spaces and Unheated Basements

load (MMBtu) = area*(uvalue*slope*24 + uvalue2*curv*576 + intrcept 1000)/106

with: area in f 2

uvalue in Btuhr-F-ft2

slope in F-day/yr

curve in F-day/yr)., and

interept in kBtu/ft2 (only applicable to foundation loads).

Slab and Heated Basement Foundation

load (MMBtu) = perimeer*(uvalue*slope*24 + uvalue2tcuve*576 + intercept* 1000)/106

with: perimeter in ft,

uvalue in Btu/hr-F-ft

slope in F-day/yr

curve in (F-day/yr)2

intercept in kBtu/ft

Infiltration

load (MMBtu) = flooraa*((ELF*1000)*slope + (ELF 000)2*curve)/1000

with: floorarea in ft2 (total conditioned floor area of building)

ELF dimensionless (eakage area/total conditioned floor area)

slope in kBtuD.001 ELF.

curve in 3Btu/(0.001 ELF)2

Window Solar

a. unadjustedsolar load:

A (MBiBt) = _(windarea*shadco*alpha)/1000 over the four cardinal directions (N E, S. and W)

with: windarea is window area in ft2

shadco is the glazing shading coefficient

alphas in kBtu/ft2 are preliminary solar load estimates assuming a linear relationship with window solar

aperatue (rea * shading coefficient)

b. adjusted solar load

A * (1 +Beta A)

with: A is the sum of the preliminary solar load estimates from above (MMBtu)

(1 + Beta * A) is a dimensionless term for solar usability to account for its deacreasing effectiveness to

offset heating and increasing penalty to increase cooling loads. This usability is a linear function of the

total building solar heat gain (A).

Residual

load (MMBtu) =resid (MMBu)

Source: Huang ci al. 1987b. Values for Beta can be found in tables in this report

43

-

EE-RM-94-230 COMMENT 61C

Table 3.12. ' Bllilding Heating and Cooling Load Calculat,ion Methodology 

Building load (Mlvmbll) I: roofioad + waUload + fndnlo3d + infilload ... Windload + sOlarload + resload. where: 

rooOoad G beating or coolillg load from tool 

walUoad 1= heating or cooling load from walls , 

fndnload:: beating or cooling load from foundation 

infiUoad I: he.'Uing or cooling load from infaJlnUion 

wiDdJoad I: h~g or cooling load from conduction through windows 

soJarload a h~g or cooling load from solar gain through windows 

resload c: :residual heatinr or c:oolirut load ' 

Method 1: Compollelllloadl givDI tU kBtll peT sqlUiTe/oOiorkBtll per lineal/DOt ore 'mIdtiplied by the component 

dWrasion. TMse valuu me giwn in Table 3.13 and 3.14 

Method 2: Compoll'Dllloads ore derivedfrom lhe componenl dimension. 1M IhennDl paTtllneler paniclllQr 10 the 

. companen' and lhe cOmDonent,coefficienls fiven in Table 3.15. and 3.16 as follows: 

Roofs, Walls, Windows, and trawl Spaces and Unheated Basements' 

load (MMBtu) = area*(uvalue*slope*24 + uvalue2*curvc*S76 + intercepl*IOOO)/106 

with: area in ff? 

uvalue in :BWJhr-F-ftl 

slope in p.-day/yr 

curve in (F-day/yr)l, and 

intercept in kBbJ/Ct2 (only applicable lO foundation loads). 

Slab and Heated Basement Foundation 

load (MMBtu) = perimecer*(uvaluc*sJ~24 + uvalue2*curve*S76 + intercept* 1000)/106 

with: perimeter in ft. 

uvalue in l~tu/hr-F-ft ' 

slope in F·day/yr 

curve in (F-day/yr)l 

intercept illl kBbJ/ft 

InfiltratioD ' 

load (MMBtu) = Ooorarea*«(ELF*l000)*s1ope + (ELF*IOOO)2*curve)/lOOO 

with: tloOrarea illI ft2 (tara! conditioned floor area of buil~g) ELF dimensionless (leakage area/lOtal conditioned floor mea) 

slope in kB~.OOI ELF, 

curve in kBtuJ(O.OOl ELF)2 

Window Solar 

a unadjusted Solar load; . 

A (MMBtu) I: L<windarea"shad~a1pha)/lOOO over the four cardinal directions (N? E. S, and W) 

with: windarea i:; window area in ft2 . 

sbadco is lItle glazing shading coefficient ' 

alphas in kBtu/ft2 are preliminary solar load estimaaes assuming a linear relationship,with window solar 

aperature (lues. * shading coefficient) 

b. adjusted solar load: 

A" (I + Beca .. A) 

with: A is the mm of the preliminary solar lOad estimates from above (MMBtu) 

(I + Beta .' A) is a dimensionless tenn for solar usability to account for its deaaeasing effectiveness to 

offset healing &nd increasing penalty to increase cooling loads. This usability is a linear function of the 

total building solar heat gain (A). 

Residual 

Joad (MMBtu) = resiid (MMBtu) 

Source: Huang et a:1. 1987b. Values for Bera can be found 10 rabies 10 this repon. . , 

43 
Table 3.13. Bulding Component Loads for Sinele-Family Bldngs (also used for Manufactured Homes)

Component Component US (Washington DC) North (Chicago IL) South (Charleton SC)

Descptions Level Heating Cooling eating Cooling Heating Cooling

Ceiling R-0 25.63 7.04 34.40 5.42 14.45 8.49

(kBtu/sqft of ceiling) R-7 10.21 2.89 13.73 2.17 5.69 3.05

ceilig insulation R-11 7.75 2.23 10.43 1.65 4.29 2.18

R-value R-19 5.54 1.63 7.47 1.18 3.04 1.40

R-22 4.69 139 6.33 1.01 256 1.17

R-30 3.55 1.05 4.80 0.77 1.92 0.86

R-38 2.87 0.85 3.87 0.63 1.54 0.67

R-49 2.26 0.67 3.05 0.49 1.23 055

R-60 1.87 0.55 2.52 0.39 1.04 0.47

Wall R-0 23.61 353 32.85 2.61 1225 3.90

(kBtu/tqftofwall) R-7 1159 1.83 16.01 1.32 ' 5.71 1.49

wall insulation R-11 9.87 159 13.62 1.14 4.78 1.14

R-value R-13 7.78 1.26 10.72 0.88 3.64 0.78

R-19 6.74 1.09 9.28 0.75 3.08 0.60

R-27 4.86 0.79 6.68 0.56 2.26 0.46

R-34 3.70 0.62 5.08 0.43 1.75 0.37

Slab R-0 42.63 -751 65.02 -7.72 34.26 -4254

(kBtulin. ft of slab) R-5 2ft 18.89 -739 3158 -6.46 22.16 -42.18

pertieterR-value R-5 4ft 12.15 -6.90 22.01 -5.49 19.32 -4151

and depth R-10 2ft 1450 -733 25.38 -6.10 20.17 -42.06

R-10 4ft 5.10 -6.60 12.07 -4.89 16.61 -41.21

Heated Bsmt R-0 79.86 8.28 116.95 246 52.82 -21.69

(kBtlin. ftofbsmt) R-5 4ft 52.51 3.76 76.71 0.77 35.23 -22.84

perterR-value R-5 8ft 43.41 3.40 63.63 0.83 3035 -22.60

and depth R-10 4ft 45.52 2.55 66.10 0.23 31.01 -23.14

R-10 8ft 3136 1.89 45.92 0.29 24.81 -22.90

Unheated Bsmt R-O 8.61 0.89 1261 0.26 5.69 -234

(kBtu/sqftoffudn) R-ll fir 134 2.53 3.25 1.59 258 -1.09

underfloorR-value R-19 fir -0.65 2.97 0.60 1.94 1.80 -0.80

R-30 fir -1.93 3.25 -1.10 2.16 130 -0.61

CrawlSpace R-O 15.10 3.04 23.22 2.14 10.29 -059

(kBtusqft of fndn) R-11 fir 134 3.73 3.93 2.71 3.10 0.01

underflorR.value R-19 flr -099 3.83 0.63 2.75 2.00 0.01

R-30 flr -2.41 3.90 -1.46 2.80 1.43 0.03

R-38 flr -2.74 3.91 -1.93 2.82 130 0.03

R-49 flr -3.67 3.96 -331 2.85 0.93 0.04

Infiltration 0.0007 14.43 1.70 21.74 0.98 5.79 3.64

(kBtu/sqft of floor) 0.0005 10.21 1.22 1538 0.68 3.67 2.63

ELZF 0.0003 6.07 0.73 9.14 039 1.93 1.60

Window Conduction l-Pane(U=1.10) 112.34 2.09 158.16 2.43 45.91 -728

(kBtu/sqft of window) 2-Pane(U=0.49) 53.20 0.95 73.47 1.08 15.99 .6.47

number ofpane 3-Pane (U=031) 33.83 0.60 46.64 0.68 9.85 -4.28

R-10 (U=0.10) 11.05 0.19 15.08 0.22 2.62 -1.71

Window Solar 1.00 -53.09 40.88 -70.68 31.08 -3158 64.76

(kBtu/sqft of window) 0.80 -43.73 3231 -58.07 2437 -26.63 51.89

Shading coefficient 0.60 -33.74 23.95 -44.70 17.91 -21.00 38.97

Residual Load (MMBtu/unit) 1.98 -2.06 2.79 -196 -0.18 9.38

1) Component loads are from DOE-2 simulations done in Huang et al. 1987b, in support of ASHRAE Special Project 53.

Component loads are additive. Simulations assume thermostat sctpoints of 70F for heating with no setback and 78F for

cooling with no setup, typical internal gains, and window shading coefficients of 0.80 during winter to account for framing

effects and 0.60 during summer for shades above the glazing SC given in the table.

2) For infiltration, air changes per hour (ACH) during heating season are Washington (0.79,0.56,036), Chicago

(0.89,0.64,039). and Charleston (0.71,053,032) for ELF=0.0007, 0.0005,0.0003, respectively.

3) Window solar loads given are for windows @ 12% of floor area, equally distributed on four sides of the building.

44

EE-RM-94-230 COMMENT 61C

Table 3.13. BuDding Compoaeat Loads (or Slagle-Family BaJldlnes {also used (or Manu(actured Hoines) 

Component 

I .... 

Camp:m.ent US (WashingtoD OC) North (Chicago IL) South (Charleston SC) . . ons Level Heatinlt Coolinlt IJcating Coolinlt Heating Cooling 

CeiliDg R-o 25.63 7.04 34.40 S.42 14.45 8.49 

(lcBtulsqft of ceiling) R-7 10.21 2.89 13.73 2.17 S.69 3.OS 

ceiling insultztioll R-ll 7.75 2.23 10.43 1.65 4.29 2.18 

R-vable R-19 554 1.63 7.47 1.18 3.04 1.40 

R-22 4.69 1.39 6.33 1.01 256 1.17 

R-30 305S I.OS 4.80 0.77. 1.92 0.86 

R-38 2.87 O.IS 3.87 0.63 1.54 0.67 

R-49 2.26 0.67 3.05 0.49 1.23 0055 

R-6) 1.87 Oo5S 2.52 0.39 1.04 0.47 

Wall R-O 23.61 3053 32.85 2.61 12.25 3.90 

(kBtulsqft of wall) R-7 It59 1.83 16.01 1.32 • 5.71 1.49 

wall ill.flllatiorl R-ll 9.87 1059 13~62 1.14 4.78 l.l4 

R-vable R-13 7.78 1.26 10.72 0.88 3.64 0.78 

R-19 6.74 1.09 9.28 0.7S 3.08 0.60 

R-Z7 4.86 0.79 6.68 0.56 2.26 0.46 

.R-34 3.70 0.62 S.08 0.43 1.75 0.37 

Slab R-O 42.63 -7.51 65.02 -7.72 34.26 -42.54 

(kBtuIlin. ft of slab) R-5 2ft 18.89 -7.39 31.58 -6.46 22.16 -42.18 

jPerimeter R-vaUle R-5 4ft 12.15 -6.90 22.01 -S.49 19.32 -41051 

and depth R-I0 2ft 14.50 -7.33 25.38 -6..10 20.17 -42.06 

R-I0 4ft S.10 -6.60 12.07 -4.89 16.61 -41.21 

Heated Bsmt R-O 79.86 8.28 116.95 2.46 52.82 -21.69 

(kBtuJlin. ft of bsmt) R-54ft S2.S1 3.76 76.71 0.77 35.23 -22.84 

_rR-vaUle R-5 8ft 43.41 3.40 63.63 0.83 30.35 -22.60 

tIIJIl depth R-I04ft 45.52 2.SS 66.10 0.23 31.01 -23.14 

R-I0 8ft 31.36 1.89 45.92 0.29 24.81 -22.90 

UDbeatecl Bsmt R-O 8.61 0.89 12.61 0.26 5.69 -2.34 

(kBtulsqft of fudn) R-ll fir 1.34 2.53 3.15 1.59 258 -Ul9 

IlNierjfoor R-value R-19 fir -0.65 2.97 0.60 1.94 1.80 -0.80 

R-3D fir -193 3.25 -1.10 2.16 1.30 -0.61 

CrawlSpace R-O IS.10 3.04 23.22 2.14 10.29 -0059 

(kBtuIsqft of fndn) R-ll fir 1.34 3.73 3.93 2.71 3.10 0.01 

IlNierjfoor R-value R-19 fir -099 3.83 0.63 2.7S 2.00 0.01 

R-3D fir -2.41 3.90 -1.46 2.80 1.43 0.03 

R-38 fir -2.74 3.91 -1.93 2.82 1.30 0.03 

R-49 fir -3.67 3.96 -3.31 2.8S 0.93 0.04 

Iardtntiou 0.0007 14.43 1.70 21.74 0.98 5.79 3.64 

(kBtulsqft of ftoor) 0.0005 10.21 1.22 15.38 0.68 3.67 2.63 

ELF 0.0003 6.07 0.73 9.14 0.39 1.93 1.60 

Wladow CoadacdoD I-Pane (U=1.l0) 112.34 2.09 158.16 2.43 45.91 . -7.28 

(kBtulsqft of window) 2-Pane (U=O.49) S3.20 0.9S 73.47 1.08 . 15.99 -6.47 

I1II1ftber of panu 3-Pane (U=O.31) 33.83 0.60 46.64 0;68 9.8S -4.28 

R-lO (U=O.10) 11.05 0.19 15.08 0.22 2.62 -1.71 

Window Solar 1.00 . -53.09 40.88 -70.68 31.08 -31.s8 64.76 

(kBtulsqCt of window) 0.80 -43.73 32.31 -S8.07 24.37 -26.63 Sl.89 

ShtuJing coefflCiml 0.60 -33.74 23.9S -44.70 17.91 -21.00 38.97 

Residual Load (MMBtulunit) 1.98 -2.06 2.79 -1.96 -0.18 9.38 

1) Compoaeut loads arc from OOE-2 simulations done in Huang et ale 1987b. in support of ASHRAE Special Project 53. 

Component 10ads arc additive. Simulations assume thermostat sctpoints of 70F for heating with no setback and 78F for 

cooling with no setup. typical internal gains. and window shading coefficients of 0.80 during winter to account for framing 

effects aad 0.60 during summer for shades above the glazing SC given in the table. 

2) For infdtration, air changes per hour (ACH) dwing heating season arc Washington (0.79.0.s6.0.36). Chicago 

(0.89.0.64.0.39). and Charleston (0.71.0.53.0.32) for ELF=O.OOO7. 0.0005.0.0003. respectively. 

3) Window solar loads given are for windows @ 12% of floor area, equally distributed on four sides of the building. 

44 
Table 3.14. Building Component Loads for Mutl-Family Bulldings

Component Component US (Washington DC) North (Chicago IL) South (Charleston SC)

Descriptions Level Heating Cooling Heating Cooling Heating Cooling

Ceiling R-0 26.00 6.24 35.26 4.96 14.70 7.10

(kBtu/sqftof ceiling) R.7 9.92 2.26 13.62 1.88 527 2.45

ceiling iution R-11 7.35 1.62 10.16 139 3.76 1.71

R-valu R-19 5.04 1.05 7.06 0.94 2.41 1.04

R-22 4.25 0.87 5.96 0.79 2.01 0.87

R-30 3.19 0.64 4.48 0.59 1.49 0.65

R-38 255 0.49 3.59 0.47 1.17 0.51

R-49 2.03 0.41 2.85 038 094 0.40

R-60 1.69 0.36 2.38 .032 0.80 0.33

Wal R-0 23.11 2.46 32.45 2.24 11.26 248

(kBt/sqft of wall) R-7 10.63 0.99 15.10 1.12 4.55 0.56

wal insulation R-11 8.85 0.78 12.63 0.96 3.60 0.29

R-value R-13 6.86 0.56 9.83 0.78 2.65 0.14

R-19 5.87 0.45 8.45 0.69 2.18 0.07

R-27 4.22 0.34 6.06 0.49 157 0.03

R-34 3.21 0.26 4.60 0.36 1.20 0.00

Slab R-O 54.52 -16.00 85.83 -13.22 24.07 -80.04

(Btu/lin. ft of slab) R-5 2ft 29.52 -15.17 49.66 -1139 12.74 -79.04

perimeterR-value R-5 4ft 22.85 -14.00 39.33 -9.55 10.91 -78.88

and depth R-10 2ft 25.19 -14.67 43.00 -10.55 11.57 -79.71

R-104ft 16.02 -1333 29.16 -8.72 9.41 -78.04

Heated Bsmt R-0 109.69 8.17 161.66 0.78 45.74 -4654

(kBt/lin. ftofbsmt) R-5 4ft 64.02 3.50 9433 -039 21.41 -46.04

peri R-value R-5 8ft 51.52 3.17 76.66 -0.05 1757 -46.04

and depth R-10 4ft 53.69 2.00 79.16 -055 17.91 -4654

R-10 8ft 36.52 1.67 54.16 -039 12.91 -46.21

Unheated Bsmt R-0 5.48 0.41 8.08 0.04 229 -233

(kBtusqftof fdn) R-ll fir 1.87 1.83 3.50 1.02 0.97. -111

underloorR-value R-19 fir 059 2.23 1.81 136 0.62 -0.85

R-30 fir -0.22 2.49 0.72 1.57 0.40 -0.69

Crawl Space R- 16.70 2.14 2534 1.43 9.21 -132

(kBtu/sqfoffndn) R-11 flr 330 3.20 636 2.17 234 -0.05

underfloorR-value R-19 fr 1.14 337 3.20 2.27 1.41 0.00

R.30 flr -0.13 3.50 1.23 2.41 1.03 0.02

R-38 fir -0.42 353 0.77 2.44 0.94 0.02

R.49 fir -1.26 3.62 -053 2.53 0.68 0.04

Infiltration 0.0007 12.69 1.44 19.78 0.67 4.19 2.72

kBtu/sqft of floor) 0.0005 8.60 1.05 13.55 0.45 2.21 1.88

ELF 0.0003 4.88 0.64 7.78 0.25 0.85 1.09

Window Conduction l-Pane (U=1.10) 96.07 -3.89 144.40 -1.65 39.09 -13.87

cBtu/sqftof window) 2-Fne (U=0.49) 38.40 -3.55 60.86 -1.93 12.18 -11A4

number of panes 3-Pane (U=031) 24.02 -235 38.28 -1.29 739 -754

R-10 (U=0.10) 7.11 -0.94 11.73 -054 1.76 -2.96

Window Solar 1.OD -54.82 40.34 -72.79 .30.40 -33.47 64.87

(kBtu/sqftofwindow) 0.80 -44.84 31.97 -59.42 23.94 -27.84 51.96

Shading coefficiet 0.60 -3437 23.75 -45.46 17.66 -21.68 39.01

Residual Load (MMBtu/unit) 128 4.18 1.25 2.56 3.22 10.78

1) Component loads are from DOE-2 simulations done in Huang et al. 1987b, in support of ASHRAE Special Project 53.

Component loads are additivc. Simulations assume thermostat setpoints of 70P for heating with no setback and 78F for

cooling with no setup, typical internal gains, and window shading coefficients of 0.80 during winter to account for framing

effects and 0.60 during sunmer for shades above the glazing SC given in the table.

2) For infiltation, air danges per hour (ACH) during heating season are Washington (0.83.0.58,0.35), Chicago

(0.89.0.660.40), and Charleston (0.740.53,032) for ELF=0.0007, 0.0005,0.0003. respectively.

3) Window solar loads given are for windows @ 12% of floor area, equally distributed on four sides of the building.

45

EE-RM-94-230 COMMENT 61C

T a e • bl 3 14 . B u Ddl nl~ C ornponent 1.0 a cis fI or M u III • F U B amJ, u IIdl n~ 

Component Component US (Wabington DC) North (Chicago IL) Soulh (OuIrlcslCn SC) 

Descriptions Lc:vcl Heating Cooling . Heating CoOlil!g Heating Coo~_ 

CeWog RoO 26.00 6.24 35.26 4.96· 14.70 7.10 

(kBtu/sqft of ceiling) R·7 9.92 2.26 13.62 1.88 5.27 2.45 

ailing insulDlitm R·ll 7.35 1.62 10.16' 1.39 3.76 1.71 

R-vtUu.e R-l!> 5.04 1.OS 7.06 0.94 2.41 . 1.04 

R-22. 4.25 0.87 5.96 0.79 2.01 0.87 

R-30 3.19 0.64 4.48 0.59 1.49 0.65 

R-38 ~ 0.49 3.59 0.47 1.17 0.51 

R-49 toOl 0.41" 2.85 0.38 0.94 0.40 

R-60 1.69 0.36 2.38 ·0.32 0.80 0.33 

Wan R-G 23.11 2.46 32.45 2.24 11.26 2.48 

(kBtu/sqft of wall) R-7 10.63 0.99 15.10 1.12 4.55 0.56 

WGll insulDlion R-ll 8.SS 0.78 12.63 0.96 3.60 0.29 

R·vaiu.e R-13 6.86 0.56 9.83 0.78 2.65 0.14 

R-19 5.87 0.45 8.45 0.69 2.18 0.07 

R~27 4 •. 22. 0.34 6.06 0.49 1.57 0.03 

R-34 3.21 0.26 4.60 '0.36 1.20 0.00 . 

Slab R-O S4.52 ·16.00 . 85.83 -13.22 24.07 -80.04 

(kBlU/lin. ft of slab) R-' 2ft 29.52. ·15.17 49.66 -11.39 12.74 -79.04 

puinraer R-vtUu.e R-' 4ft 22..8S ·14.00 39.33 -9.55 10.91 -78.88 

anddq,h R-102ft 25.19 ·14.67 43.00 -10.55 11.57 -79.71 

R-104ft 16.02 ·13.33 29.16 -8.72 9.41 -78.04 

HeatedBsmt R-D 109.69 8.17 161.66 0.78 45.74 -46.54-

(kBtuJlin. ft of bsmt) R-S 4ft 64.02 3.so 94.33 -0.39 21.41 -46.04 

1"5' ;..- R-valMe . R-S 8ft 51.52 3.17 76.66 -0.05 17.57 -46.04 

anddqlla R-I04ft 53.69 2.00 79.16 . -0.55 17.91 -46.54-

R-R08ft 36.52 1.67 54.16 -0.39 12.91 -46.21 

Unheated Bsmt R-o 5.48 0.41 8.08 0.04 2.29 ·2.33 

(kBNlsqft of £rlCin) R-U fir 1.87 1.83 3.so 1.02 0.97 . -U1 

IIIIIlDfI«n R-vallle . R-R9Or 0.59 2.23 1.81 1.36 0.62 -0.85 

R-30flr -0.22 2.49 0.72 1.57 0.40 -0:69 

CrawlSpace 'iW . . 16.70 2.14 25.34 1.43 9.21 -1.32 

(kBtu/sqCt of £ruin) R-UOr 3.30 3.20 6.36 2.17 2.34 -O.os. 

IlniMrfIoor R-vallle R-U9Or 1.14 3.37 3.20 2.27 1.41 0.00 

R-30flr -0.13 3.50 1.23 2.41 1.03 0.02 

R-38Or ~.42 3.53 0.77 2.44 0.94 0.02 

R-49Or -1.26 3.62 -0.53 2.53 0.68 0.04 

IDfUtratlon 0.0007 12.69 1.44 19.78 0.67 4.19 2.72 

(kBtuI":lft of floor) O.OOOS 8.60 I.OS . 13.55 0.45 2.21 1.88 

w: 0.0003 4.88 0.64 7.78 0.25 O.SS 1.09 

Wlbdow Conduction l·Pane (U-l.10) 96.07 -3.89 144.40 -1.65 39.09 -13.87 

(kBtulsqCtofwiftdow) 2-P'anc (U=O.49) 38.40 -3.5S 60.86 -1.93 12.18 -11M 

nuntber of panu 3-Pane (U=O.31) 24.02 -2.35 38.28 -1.29 7.39 -7.54 

R-IO (U=O.10) 7.11 -0.94 11.73 . -0.54 1.76 -2.96 

Wlbdow Solar 1.OD -54.82 40.34 -72.79 .30.40 -33.47 64.87 

(kBtulsqft of window) 0.80 -44.84 31.97 . -59.42 23.94 -27.84 51.96 

S~ coef/icienJ O.~ -34.37 23.7S . -45.46 17.66 -2Ui8 39.01 

Residual Load (MMBtuiunit) 1.28 4.18 1.25 2.56 3.22 .10.78 

1) Component loads m:c from DOE-2 simulations donem Huang Cl ale 1987b, in support of ASHRAE Special Project 53. 

Component loads are Itddilivc. Simulations assume Ihemostat lClpOints of 70P fOr heating with no setback and 78F for 

cooling with no setup, typical intanal gains. and window shading coefficients of 0.80 during winta'to accounl for framing 

cffecu and 0.60 durinl: summer for shades above the glaziDgSC given in the table. . 

2) For infillralion. air dwlges per hour (ACH) during heating season are Washington (0.83.0.58,0.35), Chicago 

(0.89,O~66,O.40). and Charleston (0.74,0.53.0.32) for ELF=O.OOO7, 0.0005, 0.0003, respc:ctively. . 

3) Window solar loads given are for windows@ 1290 of floor area, equally distributed on four sides of"the building. 

45 
Table 3.15. Building Component Loads Coefficients for Single-Family Buildings

(also used for Manufactured Homes)

National (Washington DC) North (Chicago IL) South (Charleston SC)

Component Coefficient Heaing Cooling Heating Cooling Heating Cooling

Roof slope

curve

intercept

Wall slope

curve

intercept

Slab slope

curve

intercept

Heated slope

Basement curve

intercept

Unheated

Basement

Crawl

Space

Infiltration

Window

Residual

Window

Solar

Coefficients

slope

curve

intercept

slope

curve

intercept

slope

curve

intercept

slope

curve

intercept

NAlpha

EAlpha

SAlpha

WAlpha

Beta

517037

-143.06

0.00

4831.60

-82.36

0.00

574595

-80.64

-1436

3146.97

-29.19

0.00

466051

-- 37750

-536

4421.03

-6533

-5.86

19.94

0.97

0.00

4739.24

-1833

0.00

1.98

-34.73

-56.48

-97.95

-54.82

0.0115

1544.34

-6034

0.00

809.06

-28.55

0.00

-610.01

32.28

-4.82

160.33

1.04

0.00

-1020.56

80.75

4.00

-185.43

-2.15

4.04

2.44

0.00

0.00

82.23

-0.12

0.00

-2.06

26.24

40.47

39.43

47.69

0.0088

6977.53

-19831

0.00

6627.85

-96.87

0.00

8407.39

-121.21

-15.72

4723.43

-45.21

0.00

6233.26

-520.25

-5.68

6450.79

-12931

-6.46

30.03

1.46

0.00

6453.57

-1753

0.00

2.79

-37.61

-74.98

-139.01

.-6930

0.0080

1111.40

-3336

0.00

560.40

-13.80

0.00

-98439

41.75

-1.93

14.13

1.16

0.00

-804.63

62.19

2.76

-80.06

-12.17

2.87

1.23

0.24

0.00

91.81

0.02

0.00

-1.96

1754

31.80

28.05

34.60

0.0213

2809.71

-62.75

0.00

2195.23

1539

0.00

1891.66

31.07

10.15

1414.18

-. 80

10.94

177659

-129.16

-0.02

176658

46.88

0.00

5.03

4.63

0.00

1054.92

25.92

0.00

-0.18

-23.19

-3931

-6336

-34.69

0.0287

1219.54

35.71

0.00

381.44

63.99

0.00

-756.41

40.97

-39.15

-44.29

1.73

-22.61

-64235

4133

-0.13

60.08

-33.96

0.00

5.42

-033

0.00

-770.65

18.74

0.00

938

46.22

76.09

68.17

70.55

-0.0006

Source: Huang et al. 1987b. For a description of how to use these coefficients, see Table 3.12.

46

EE-RM-94-230 COMMENT 61C

Table 3.15. Building Component Loads Coefficients for Single.Family Buildings 

(also used for Manufactured Homes) 

National (Washington .o:q North (Chicago ll.) 

Component Coefficient Heatine: Coolin~ Heatine: CooJjn~ 

Roof slope 5170.37 1544.34 6977.53 1111.40 

curve -143.06 -60.34 ·198.31 -33.36 

intercept 0.00 0.00 0.00 0.00 

Wall slope 4831.60 809.06 6627.85 560.40 

curve ·82.36 -28.55 -96.87 -13.80 

inEerCept 0.00 0.00 0.00 0.00 

Slab slope 5745.95 -610.01 8407.39 ·984.39 

curve -80.64 32.28 -121.21 41.75 

intercept -14.36 -4.82 -15.72 -1.93 

Heated slope 3146.97 160.33 4723.43 14.13 

Basement curve -29.19 1.04 -45.21 1.16 

inaen:epl 0.00 0.00 0.00 0.00 

Unheated slope 4660.51 -1020.56 6233.26 ·804.63 

Basement curve .. -377.50 80.75 ·520.25 62.19 

intercept -5.36 4.00 -5.68 2.76 

Crawl slope 4421.03 -185.43 6450.79 -80.06 

Space curve -6533 -2.15 -12931 -12.17 

intercept -5.86· 4.04 -6.46 2.87 

InfIltration slope 19.94 2.44 30.03 1.23 

curve 0.97 0.00 1.46 0.24 

intercept 0.00 0.00 0.00 0.00 

Window slope 4739.24 82.23 6453.57 91.81 

curve -18.33 -0.12 ·17.53 0.02 

intercept 0.00 0.00 0.00 0.00 

Residual 1.98 -2.06 2.79 -1.96 

Window NAlpha -34.73 26.24 ·37.61 17.54 

Solar EAJpha -56.48 40.47 -74.98 31.80 

Coefficients SAlpha . -97.95 39.43 -139.01 28.05 

WAlpha -54.82 47.69 .-69.30 34.60 

Beta O.OllS 0.0088 0.0080 0.0213 

South (Charleston SC) 

Heatine: Cooling 

2809.71 1219.54 

-62.75 35.71 

0.00 0.00 

2195.23 381.44 

1539 63.99 

0.00 0.00 

1891.66 -156.41 

31.07 40.97 

10.15 -39.15 

1414.18 -44.29 

-8.80 1.73 

10.94 -22.61 

1776.59 -642.35 

-129.16 41.33 

-0.02 -0.13 

1766.58 60.08 

46.88 -33.96 

0.00 0.00 

5.03 5.42 

4.63 -0.33 

0.00 0.00 

1054.92 ·770.65 

25.92 18.74 

0.00 0.00 

-0.18 9.38 

·23.19 46.22 

-3931 76.09 

. -63.36 68.17 

-34.69 70.5S 

0.0287 -0.0006 

Source: Huang et aI. 1987b. For a description of how to use these coefficients. see Table 3.12. 

46 
Table 3.16. Building Component Loads Coefficients for Multi-Family Buildings

National (Washington DCI North (Chicago IL) South (Charleston SC)

Component Coefficient Heatin Cooling Heaing Cooling Heating Cooling

Roof slope 4593.79 918.63 6477.18 855.31 2098.79 882.25

curve -35.12 22.45 -8941 -3.11 64.23 53.18

intercept 0.00 0.00 0.00 0.00 0.00 0.00

Wall slope 4076.50 297.48 5891.16 486.69 1399.24 -83.82

curve 40.97 29.60 26.53 -13.19 129.28 101.36

intercept 0.00 0.00 0.00 0.00 0.00 0.00

Slab slope 5257.64 -1212.12 8534.83 -1652.06 253.31 -970.47

curve -41.62 57.04 -9834 71.56 119.27 51.83

intercept -9.71 -10.94 0.60 -3.70 7.35 -75.59

Heated slope 3490.64 128.66 5337.97 -50.24 670.13 41.13

[Basement curve -19.71 1.75 -32.91 1.72 9.49 -1.01

intercept 0.00 0.00 0.00 0.00 4.95 -46.59

Unheated slope 3145.91 -943.48 4223.62 -821.95 812.27 -560.76

Basement crve -309.56 7838 -427.18 7925 -63.66 30.87

intercept -3.20 3.81 -2.34 2.17 -0.20 -0.26

Crawl slope 3918.12 -37334 6046.94 -317.14 1182.85 75.03

Space curve -6.36 10.13 -888 15.85 119.70 -64.78

intercept -3.78 4.13 -3.49 2.64 0.00 0.00

Infiltration slope 14.85 221 24.21 0.71 0.48 3.42

curve 4.69 -021 5.78 037 7.87 0.68

intercept 0.00 0.00 0.00 0.00 0.00 0.00

Window slope 2964.96 -42532 493834 -24539 67832 133157

curve 25.54 10.52 20.13 6.93 3039 30.54

intercept 0.00 0.00 0.00 0.00 0.00 0.00

Residual intercept 1.11 5.05 1.25 2.56 3.22 10.78

Window NAlpha -34.73 26.24 -37.61 17.54 -23.19 46.22

Solar EAlpha -56.48 40.47 -74.98 31.80 -3931 76.09

Coefficients SAlpha -97.95 39.43 -139.01 28.05 -6336 68.17

WAlpha -54.82 47.69 -6930 34.60 -34.69 70.55

Beta 0.0115 0.0088 0.0080 0.0213 0.0287 -0.0006

Source: Huang et aL 1987b. For a description of how to use these coefficients, see Table 3.12.

47

EE-RM-94-230 COMMENT 61C

Table 3.16. BUliidilllg Component Loads Coef)'ic:ients for Multi-Family Buildings 

National (Washington DC: North (Chicago n.). South (Charleston SC) 

CompOnent Coefficient Heating Cooling ·Heatil!K Coolin..!.. HeatinA Coolin~ 

Roof slope 4593.79 918.63 6477.18 855.31 2098.79 882.2S 

curve -35.12 22.45 -89.41 -3.11 64.23 53.18 

inrercept 0.00 0.00 0.00 ·.0.00 0.00 0.00 

Will .slope 4076.50 297.48 5891.16 486.69 1399.24 -83.82 

·curve 40.97 29.60 26.53 -13.19 129.28 101.36 

inrercept 0.00 0.00 0.00 0.00 0.00 0.00 

Slab :slope 5257.64 -1212.12 8534.83 ~1652.06 25331 -970.47 

t:urve -41.62 5.7.04 -9834 71.56 119.27 51.83 

intercept -9.11 -10.94 0.60 -3.70 735 -75.59 

Heated slope 3490.64 t28.66 5337.97 -50.24 670.13 41.13 

Basement curve . -19.71 1.75 -32.91 1.72 9.49 -1.01 

intercept 0.00' 0.00 0.00 0.00 4.95 -46.59 

Unheated slope 3145.91 . -943.48 4223.62 -821.95 812.27 -560.76 

Basement c:urve -309.56 78.38 -427.18 79.25 -63.66 30.87 

inlerlCept -3.20 3.81 -2.34 2.17 -0.20 -0.26' 

Crawl slope 3918.12 -373.34 6046.94 -317.14 1182.85 75.03 

Space c:arve -6.36 10.13 -82.88 . . 15.85 119.70 -64.78 

inrercepl -3.78 4.13 -3.49 2.64 .. 0.00 0.00 

. Infiltration slope 14.85 2.21 24.21 0.71 0.48 3.42 

curve 4.69 -0.21 5.78 0.37 7.87 0.68 

intercept 0.00 0.00 0.00 0.00 0.00 0.00 

Window slope 2964.96 -42532 493834 -24539 67832 -1331;57 

c:urve 25.54 10.52 20.13 6.93 3039 30.54 

iJarercept 0.00 . 0.00 0.00' . 0.00 0.00 0.00 

Residual illltercept 1.11 5.05 .1.25 2.56 3.22 10.78 

Window NAlpha -34.73 26.24 -37.61 . 17.54 -23.19 46.22 

Solar EWpha -56.48 40.47 -74.98 31.80 -3931 76.09 

CoeffICients SAlpha . ~97.95 39.43 -139.01 28.05 -6336 68.17 

WAlpha -54.82 -47.69 -6930 34.60 . -34.69 70.55 

Beta 0.0115 0.0088 0.0080 0.0213 0.0287 -0.0006 - . Source: Huang et al. 1987b. For a description of how to use these coefficients, see Table 3.12. 

47 
Table 3.17. Construction Type and U-value and Shading Coefficient Assumptions

Component Construction U-val SC Construction assumptions

Roof ROO 0.25 Uninsulated ceiling below attic

R07 0.09 R07 insulated ceiling below attic

R11 0.07 R 1 insulated ceiling below attic

R19 0.05 R19 insulated ceiling below attic

R22 0.04 R22 insulated ceiling below attic

R30 0.03 R30 insulated ceiling below attic

R38 0.02 R38 insulated ceiling below attic

R49 0.02 R49 insulated ceiling below attic

R60 0.02 R60 insulated ceiling below attic

Wall ROO 0.22 Uninsulated 2x4 wood frame wall

R07 0.11 R07 insulated 2x4 wood frame wall

Rl 0.09 Rll insulated 2x4 wood frame wall

R13 0.07 R13 insulated 2x4 wood frame wall

R19 0.06 R19 insulated 2x6 wood frame wall

R27 0.04 R19 insulated 2x6 wood frame wall with insulated sheathing

R34 0.03 R19 insulated 2x6 wood frame wall with insulated sheathing

Window 1.0-gla 1.10 0.90 Wood Frame Window, 80%glass, single clear glass

2.0-gla 0.48 0.66 Wood Frame Window, 80%glass, double clear glass, 1/2" air space

3.0-gla 0.30 0.61 Wood Frame Window, 80%glass, triple clear glass, 1/2" air space

2-gla oE 036 0.59 Wood Frame Window, 80%glass, low emissivity film

2-gla loEAr 0.30 0.59 Wood Frame Window, 80%glass, low emissivity film, argon fill

Spect 036 0.44 Wood Frame Window, 80%glass, spectrally selective double glass

Super 0.20 0.51 Wood Frame Window, 80%glass, superwindow

HMirror 0.29 0.39 Wood Frame Window, 80%glass, heat mirror surface

Floors ROO 021 Uninsulated 2x10 floor over basement or crawl space

(crawl or Rl1 0.07 R 1 insulated 2x10 floor over basement or crawl space

unheated R19 0.05 R19 insulated 2x10 floor over basement or crawl space

basement) R30 0.03 R30 insulated 2x10 floor over basement or crawl space

R38 - 0.03 R38 insulated 2x10 floor over basement or crawl space

R49 0.02 R49 insulated 2x10 floor over basement or crawl space

Slab R-0 0.48 Uninsulated Slab

R-5 2ft 0.25 Exterior vertical slab insulation to depth and R-value listed

R-10 2ft 0.21 Exterior vertical slab insulation to depth and R-value listed

R-5 4ft 020 Exterior vertical slab insulation to depth and R-value listed

R-10 4ft 0.14 Exterior vertical slab insulation to depth and R-value listed

Heated R-O 1.67 Uninsulated basement wall

Basement R-5 4ft 0.83 Exterior vertical basement wall insulation to depth and R-value listed

R-10 4ft 0.67 Exterior vertical basement wall insulation to depth and R-value listed

R-5 8ft 0.67 Exterior vertical basement wall insulation to depth and R-value listed

R-10 8ft 0.45 Exterior vertical basement wall insulation to depth and R-value listed

1) All U-value assumptions from SP53 project (Huang et al. 1987b) for insulated components. Foundation (Slab

and Heated Basement) U-values are the U-value of foundation concrete an insulation, if any, and are not the

effective U-value of the total foundation.

2) Window U-values and shading coefficients from Koomey et al. 1994a. Window U-values and shading

coefficients are for whole window unit, including the window frame.

48

EE-RM-94-230 COMMENT 61C

Table 3.17. Construction Type and U·value and ShadingCoeflicient Assumptions 

Component Construction U-val SC Construction assumptions 

Roof ROO .0.25 Uninsulated ceiling below awe 

R07 0.09 R07 insulated ceiling below attic 

Rll 0.07 RII insulated ceiling below atti~ 

RI9 O.OS RI9 insulated ceiling below attic 

R22 0.04 R22 insulated ceiling below attic 

R30 0.03 RJO insulated ceiling below attic 

R38 0.02 RJ8 insulated ceiling below attic 

R49 0.02 R49 insulated ceiling below attic 

R60 0.02 R60 insulated ceilin~ below attic 

Wall ROO 0.22 Un insulated 2x4. wood frame wall 

R07 0.11 R07 insulated 2x4 wood frame wall 

Rll 0.09 Ril insulated lx4 wood frame wall 

R13 0.07 Rl3 insulated 2x4 wood frame wall 

RI9 0.06 RI9 insulated lx6 wood frame wall 

R27 0.04 RI9 insulated 2x6 wood frame wall with insulated sheathing 

R34 0.03 Rl9 insulated 2x6 wood frame wall with insulated sheathin~ 

Window l.().gla 1.10 0.90 Wood Frame Window, SO%glass, single clear glass 

2.().gla 0.48 0.66 Wood Frame Window, 80%glass, double clear glass, 1/2" air space 

3.().gla ·0.30 0.61 Wood Frame Window, 80%glass' triple clear glass, l/2"air space 

2-gla IoE 0.36 0.59 Wood Frame Window. 80%glass. low emissivity film 

2-gla IoEAr 0.30 0.59 Wood Frame Window. 80%glass,low emissivity film, argon fiU 

Spect 0.36 0.44 Wood Frame Window, 80%glass, spectrally selective double glass 

Super . 0.20 0.51 WoOd Frame Window. 80%glass, superwindow 

HMirror 0.29 0.39 Wood Frame Window, SO%glass. heat mirror surface 

Floors ROO 0.21 Uninsulated 2x10 floor over basement or crawl space 

(crawl or R11 0.07 RII insulated lxlO floor over basement or crawl space 

unheated RI9 O.OS Rl9 insulated lxlO floor over basement or crawl space 

basement) R30 0.03 RJO insulated 2x10 floor ov~r basement or crawl space 

R38 - -0~03-- RJ8 msulated 2x10 floor over basement or crawl space 

R49 0.02 R49 insulated 2x 10 floor over basement or crawl space 

Slab R-O 0.48 Uninsulated Slab 

R-S2ft 0.25 Exterior vertical slab insulation to depth and R-value listed 

R-IO 2ft 0.21 Exterior vertical slab insulation to depth and R-value listed 

R-S4ft 0.20 Exterior vertical slab insulation to depth and R-value listed 

R-I04ft 0.14 Exterior vertical slab insulation to depth and R-value listed 

Heated R-O 1.67 Uninsulated basement wall 

. Basement R-S4!t 0.S3 Exterior vertical basement wall insulation to depth and R-value listed 

R-I04ft 0.67 Exterior vertical basement wall insulation to depth and R-value listed· 

R-S 8ft 0.67 Exterior vertical basement wall insulation to depth and R-value listed 

R·IO Sft 0.45 Exterior vertical basement wall insulation to depth and R-value listed 

I) AU U-value assumptions from SPS3 project (Huang et al. 19S7b) for insulated components. Foundation (Slab 

and Heated Basement) U-values are the U-value of foundation concrete an insulation, if any, and are not the 

effective U-value of the total foundation. 

2) Window U-values and shading coefficients from Koomey et ale 1994a. Window U-values and shading 

coefficients are for whole window unit, including the window frame. 

48 
Table 3.1L. Window Component Loads for Specific Glazing Types

Component Loads (kBtu/square foot of window)

Locaion/ Heating Cooling

Window Type U-value SC Conduction Solar Total Conduction Solar Total

Washington DC(national)

1.O-gla 1.10 0.90 112.3 48.S 63.9 2.1 36.6 38.7

2.0-gla 0.48 0.66 522 -36.8 15.4 0.9 26.4 27.4

3.0-gla 0.30 0.61 332 -34.3 -1.1 0.6 24.4 24.9

2-gla oE 0.36 0.59 39.6 -33.2 6.4 0.7 23.5 24.2

2-gla loEAr 030 059 332 -33.2 .0.1 0.6 23.5 24.1

Spect 0.36 0.44 39.6 -25.3 14.3 0.7 17.4 18.1

Super 020 0.51 22.3 -29.0 -6.7 0.4 20.2 20.6

HMirror 0.29 0.39 32.1 -22.6 9.5 0.6 15.4 15.9

Chicago IL (North)

1.0-gla 1.10 0.90 158.2 -64.5 93.7 2.4 27.7 30.1

2.0-gla 0.48 0.66 72.0 48.8 23.2 1.1 19.8 20.9

3.0-gla 030 0.61 45.6 -45.4 0.2 0.7 18.2 189

2-gla loE 036 0.59 54.5 -44.0 10.4 0.8 17.6 18.4

2-gla loEAr 030 0.59 45.6 -44.0 1.5 0.7 17.6 183

Spect 036 0.44 545 -33.5 21.0 0.8 12.9 13.7

Super 0.20 0.51 30.6 -38.4 -7.9 0.4 15.1 15.5

HMirror 0.29 0.39 44.1 -29.8 14.2 0.6 11.4 12.0

Charleston SC (South)

1.0-gla 1.10 0.90 459 -29.2 16.7 -73 583 51.0

2.0-gla 0.48 0.66 15.6 -22.8 -7.2 -64 42.9 365

3.0-gla 030 0.61 8.9 -213 -12.4 -4.6 39.6 35.0

2-gla oE 036 059 11.0 -20.7 -9.7 -53 38.3 33.1

2-glaloEAr 030 0.59 8.9 -20.7 -11.8 -4.6 383 33.8

Spect '036 0.44 11.0 -16.0 -5.0 -53 28.6 23.4

Super 0.20 0.51 5.7 -18.2 -12.6 -33 33.2 29.9

HMirror 0.29 0.39 8.6 -14.4 -5.8 -45 25.4 20.9

Based on methodollogy in Huang et al. 1987b.

Values calculated for One Story Prototype, 1540 square feeL

Window area assumed as 12% of floor area, equally distributed around four sides of building.

Window U-valies and shading coefficients are from Koomey et a. 1994a.

49

EE-RM-94-230 COMMENT 61C

Table 3.Ut. Window Component Loads for Spedftc Glazing Types ; 

- Component Loads (kBtulsquare foot of window) 

Locatioo/ HeadDg C()OliDg 

Window T!r1Je U-value SC Conduction Solar Total Conduclion Solar 

Wahlnflo" DC (nllllonal) 

1.O-gla 1.10 0.90 • 112.3 -48.5 63.9 21 36.6 

2.O-gla 0.48 0.66 522 -36.8 1S.4 0.9 26.4 

3.O-gla 0.30 0.61 33.2 -34.3 -1.1 0.6 24.4 

2-glaloE 0.36 0.59 39.6 -33.2 6.4 0.7 23.5 

2-glaloEAr 030 0.59 33.2 -33.2 -0.1 0.6 23.5 

Spect 0.36 0.44 39.6 -25.3 14.3 0.7 17.4 

Super 0.20 0.51 22.3 -29.0 -6.7 0.4 20.2 

HMirror 0.29 0.39 32.1 . -22.6 9.S 0.6 IS.4· 

ChIcago lL (North) 

1.00gta 1.10 0.90 158.2 -64.5 93.7 2.4 Xl.7 

2.O-gla 0.48 0.66 . 72.0 -48.8 23.2 1.1 19.8 

3.0-gla 0.30 0.61 45.6 -4S.4 0.2 0.7 18.2 

2-glaloB 0.36 0.59 S4.5 -44.0 10.4 0.8 17.6 

2-glaloEAr 0.30 0.59 45.6 -44.0 . 1.5 0.7 17.6 

Spea 0.36 0.44 54.5 -33.5 21.0 0.8 12.9 

Super 0.20 0.51 30.6 -38.4 -7.9 0.4 15.1 

HMirror 0.29 0.39 44.1 -29.8 14.2 0.6 11.4 

Cluzrlato" SC (Sollth) 

1.0-g1a 1.10 0.90 45.9 -29.2 16.7 -7.3 58.3 

2.O-gla 0.48 0.66 15.6 -22.8 -7.2 -6.4 42.9 

3.0-g1a 0.30 0.61 . 8.9 -21.3 -12.4 . .-4.6 39.6· 

2-glaloE 0.36 0.59 11.0 -W.7 -9.7 -5.3 38.3 

2-glaloEAr 030 0.59 8.9 -W.7 -11.8 -4.6 38.3 

Spea '0.36 0;44 11.0 -16.0 -5.0 -5.3 28.6 

Super 0.20 0.51 5.7. -18.2 -12.6 -33 33.2 

HMirror 0.29 0.39 8.6 -14.4 ·5.8 -4.5. 25.4 

Based on methodollogy in Huang et al. 1987b. 

Values calcula1led fOr One Story Prototype, 1540 square feet: 

Wmdow area assumed as 12% of floor area. equally distributed around foUr sides ofb~lding. 

Wmdow U-vahtes and shading coefficients are from Koomey et a1. 1994a. 

49 

TOIal 

38.7 

27.4 

24.9 

24.2 

24.1 

18.1 

20.6 

IS.9 

30.1 . 

20.9 

18.9 

18.4 

·18.3 

13.7 

15.5 

12.0 

51.0 

36.S 

35.0 

33.1 

33.8 

23.4 

29.9 

20.9 
Where there are analogous LBL/GRI prototype buildings, heating and cooling loads from

these prototypes are compared in Table 3.19 with the building loads from the prototypes in

the residential database. Note that the building loads given for the LBIJGRI prototypes are

also calculated using the SP53 methodology as described elsewhere (Hanford and Huang

1992).

The heating and cooling loads for the LBL/GRI prototypes calculated directy from DOE-2

simulations are typically lower in magnitude than those calculated using the SP53

methodology. The DOE-2 simulations assume different operating conditions (primarily a

nighttime thermostat setback of 6F) and are generally more detailed than the simulations

used to generate the SP53 loads database.

Building Heating and Cooling Energy Use Calibration

To complete the model of building heating and cooling energy use, we compare the UECs

estimated from measured data that were discussed in Section 3.1 with UECs calculated

from building heating and cooling loads and average stock equipment and distribution

system efficiencies using the generalized UEC equations shown in Section 3.1. Ideally,

the UECs determined from each of these two methods would be the same.

Using data for existing buildings, we define a calibration multiplier, which is the ratio of

the database UEC (that was estimated from measured and other utility data) to the calculated

UEC. This ratio is a measure of the amount of error in the model used to calculate UECs

from building loads and equipment data. This calibration multiplier is then applied to the

UEC calculated for new buildings to determine the database UEC for new buildings.

The calibration of the heating and cooling energy use model is shown in Tables 3.20 and

3.21. The magnitude of the calibration multiplier ranges from 0.4 to 3.1 for heating, from

0.5 to 2.0 for CAC and HP cooling systems, and from 0.2 to 0.7 for RAC cooling. The

low value for room air conditioning reflects the fact that with RAC. the entire building is

not typically cooled.

Because we have better knowledge of the characteristics of the heating and cooling

efficiencies, the distribution system efficiencies, and the UECs, the calibration multiplier is

assumed to apply in total to the building heating and cooling loads. Obviously, there are

unknowns in all of these areas. More work is required in this area to more fully

characterize the sector.

50

EE-RM-94-230 COMMENT 61C

Where there are analogous LBUGRI prototype buildings. heating and cooling loads from 

these prototypes are compared in Table 3.19 with the building loads from the prototypes in 

the residential database. Note that the building loads given for the LBUGRI prototypes are 

alSo calculated using the SP53 methodology as described elsewhere (Hanford and Huang 

1992). . 

The heating and cooling loads for the LBUGRI prototypes calculated directly from DOE-2 

simulations are typically lower in magnitude than those calculated using the SP53 

methodology. The DOE-2 simulations assume different operating conditions (primarily a 

nighttime thermostat setback of 6F) and are generally more detailed than the simulations 

used to generate the SP53.loads database. 

Building Heating and Cooling Energy Use Calibration 

To complete the model of building heating and cooling energy use. we compare the UECs 

estimated from measured data that were discussed in Section 3.1 with UECs calculated 

from building heating and cooling loads and average stock equipment and distribution 

system efficiencies using the generalized UEC equations shown in Section 3.1. Ideally, 

the UECs determined from each of these two methods would be the same. 

Using data for existing buildings. we define a calibration multiplier. which is the ratio of 

the database UEC (that was estimated from measured and other utility data) to the calculated 

UEC. This ratio is a measure of the amount of error in the model used to calculate UECs 

from building loads and equipment data. This calibration multiplier is then applied to the 

UEC calculated for new buildings to detennine the database UEC for new buildings. 

The calihration of the heating and cooling energy use model is shown in Tables 3.20 and 

3.21. The magnitude of the calibration multiplier ranges from 0.4 to 3.1 for heating, from 

0.5 to 2.0 for CAC and HP cooling systems. and from 0.2 to 0.7 for RACcooling. The 

low value for room air conditioning reflects the fact that with RAe. the entire building is 

not typically cooled. 

Because we have better knowledge of the characteristics of the heating and cooling 

efficiencies. the distribution system efficiencies. and the UECs. the calibration multiplier is 

assumed to apply in total to the building heating and cooling loads. Obviously, there are 

unknowns in all of these areas. More work is required in this area to more fully 

characterize the sector. 

50 
Table 3.19. Residential Forecasting Database (RFD) Building Prototype Populations

and Heating and Cooling Loads

(compison of RFD prototype Loads to LBUGRI prototype loads)

House Heat Heat Fuel Shell Shell Popin Heat Cool

Vintage Type Region Type Share Group Share (million) MMBtu MMBtu

Stock SF North Electric 0.08 Loose 0.231 0.64 89.4 7.6

Electric 0.08 Tight 0.769 2.12 66.9 6.5

Stock SF North Fuel 0.88 Loose 0.487 14.74 105.0 9.0

Fuel 0.88 Tight 0.513 15.53 81.5 7.3

Stock SF North HeatPump 0.04 Loose 0.028 0.04 120.0 11.5

Heat Pump 0.04 Tight 0.972 134 59.4 6.4

RFL 1990 prototypes 34.4 million wtd average 90.0 8.0

LBLUGRprototypes 34.1 million wtd average 81.4 11.4

% difference 10% .43%

Stock SF South Electric 0.13 Loose 0.288 1.04 31.5 20.7

Electric 0.13 Tight 0.712 258 26.5 18.8

Stock SF South Fuel 0.77 Loose 0.557 11.97 463 30.5

Fuel 0.77 Tight 0.443 9.52 36.9 26.3

Stock SF South HeatPump 0.10 Loose 0.142 0.40 40.0 24.7

Heat Pump 0.10 Tight 0.858 239 24.2 17.6

RFD 1990prototypes - 27.9 million wtd average 38.7 26.

LBUGRI prototypes 26.3 million wtd average 27.4 24.8

% difference 29% 6%

(%)

New SF North Electric 0.08 All 1 0.08 58.2 7.0

New SF North Fuel 0.78 All 1 0.78 73.0 9.0

New SF North Heat Pump 0.13 All 1 0.13 70.3 8.8

RFD 1990 prototypes 70.7 8.7

LLUGRI proiotypes 64.2 9.8

% difference 9% -12%

New SF South Electric 0.13 All 1 0.13 22.8 17.6

New SF South Fuel 0.57 All 1 0.57 24.3 17.9

New SF South Heat Pump 0.31 All 1 031 22.3 16.9

RFD 1990 prototypes 23.7 17.7

LBUGRI prototypes 19.7 22.2

% difference 17% -25%

1) RFD prototype populations from Appendix B.

2) LBLGRI prototype populations and population heating and cooling loads from Hanford and Huang 1992.

3) Heating and cooling loads calculated using ASHRAE SP53 loads database methodology (loads are

uncalibrated to actual field conditions).

51

EE-RM-94-230 COMMENT 61C

,{I" -

Table 3.19. Residential Forecasting Database (RFD) Building Prototype Populations 

and Heating and Cooling Loads 

(co-,."ri..nn ofRFD prolorype Loads to LBUGRI prOlOtYpe loads) 

. HOllse Heat Heat Fuel Shell Shell Popln 

Vintage T~ Region Type Share Group Share (million) 

: 

Stock SF Nonh EJecDic 0.08 Loose 0.231 0.64 

EJecCric 0.08 TIght 0.769 2.12 

Srock S]~ North Fuel 0.88 Loose 0.487 14.74' 

Fuel 0.88 Tight 0.513 15.s3 

Srock 51: Nonh Heat Pump 0.04 Loose 0.028 0.04 

Heat Pump 0.04 Tight 0.972 1.34 

RFI) 1990 prototypes 34.4 million wulaverage 

LBUGRl prototypes 34.1 million wtdaverage 

% difference 

- Srock SF South EJecDic 0.13 Loose . 0.288 1.04 

EJecDic 0.13 Tight 0.712 2.58 . 

Srock SF South Fuel 0.77 Loose 0.557 11.97 

Fuel 0.77 Tight 0.443 9.52 

SrocJc SF South Heat Pump 0.10 Loose 0.142 0.40 

Heat Pump 0.10 TIght 0.858 239 

RFD 1990prolOtypes 27.9 million wtdaverage 

UJUGRI prolorypu 26.3 million wtdaverage .. % difference 

(9&) 

New SF Nonh ElecDic 0.08 All 1 0.08 

New SF Nonh Fuel 0.78 All 1 0.78 

New SF Nonh Heat Pump 0.13 All 1 0.13 

RFD 1990 prototypes 

L8.UGRI proiotypes 

% difference 

- New SF South Electric 0.13 All 1 0.13 

New SF South Fuel 0.57 All 1 0.57 

New SF South Heat Pump 0.31 All 1 0.31 

RFD 1990 prototypes 

LBIJG1U prototypes 

% difference 

l)·RFD prototype populations from Appendix B. 

Heat Cool 

MMBtu .MMBtu 

89.4 7.6 

66.9 6.s 

105.0 9.0 

81.5 7.3 

120.0 11.5 

59.4 6.4 

90.0 8.0 

81.4 11.4 

10% -43% 

31.5 W.7 

26.5 18.8 

463 30.5 

36.9 26.3 

40.0 24.7 

24.2 17.6 

38.7 26.4 

27.4 24.8 

29% 6% 

58.2 7.0 

73.0 9.0 

70.3 8.8 

70.7 8.7 

64.2 9.8 

9% -12% .. 

22.8 17.6 

24.3 17.9 

22.3 16.9 

23.7 17.7 

19.7 22.2 

17% -15% 

2) LBUORl prototype populations and population heating and cooling loads from Hanford and Huang 1m. 

3) Heating and COOling loads calculated using ASHRAE SP53 loads database methodology (loads are 

uncalibrated to acllllal field conditions). -

51 
Table 3.19 (cont). RFD Building Prototype Populations and Heating and Cooling Loads

(comparison of RFD prototype Loads to LBUGRI prototype loads)

House Heat Heat Fuel Shell Shell Popln Heat Cool

Vintage Type Region Type Share Group Share (million) MMBtn MMBtu

Stock MF North Electric 0.15 1980s 0.201 0.46 213 4.6

Electric 0.15 pre-80s 0.799 1.85 48.3 7.2

Stock MF North Fuel 0.84 1980s 0.053 0.69 22.2 5.0

Fuel 0.84 pre-80s 0.947 12.25 51.7 7.8

Stock MF North Heat Pump 0.01 1980s 0.278 0.04 21.6 4.4

Heat Pump 0.01 pre-80s 0.722 0.11 34.4 6.0

RFD 1990 prototypes 15.4 million wtd average 48.9 7.5

LBLGRI prototypes 15.6 million wtd average 37.4 9.2

% difference 24% -22%

Stock MF South Electric 0.42 1980s 0.276 1.18 65 11.7

Electric 0.42 pre-80s 0.724 3.10 14.7 15.7

Stock MF South Fuel 0.53 1980s 0.106 0.57 6.3 11.6

Fuel 0.53 pre-80s 0.894 4.83 16.2 16.4

Stock MF South Heat Pump 0.06 1980s 0.224 0.14 63 11.5

Heat Pump 0.06 pre-80s 0.776 0.47 14.9 15.5

RFD 1990prototypes 102 million wtd average 14.0 15.4

LBL/GRIprototypes 93 million wtd average 123 15.3

% difference 13% 1%

New MF North Electric 0.23 All 1 0.23 213 4.6

New MF North Fuel 0.63 All 1 0.63 22.2 5.0

New MF North HeatPump 0.13 All 1 0.13 21.6 4.4

RFD 1990prototypes 21.7 4.8

LBUGRI prototypes 14.0 6.5

% difference 35% -36%

New MF South Electric 030 All 1 030 6.5 11.7

New MF South Fuel 0.34 All 1 034 63 11.6

New MF South HeatPump 035 All 1 035 63 11.5

RFD 1990 prototypes 63 11.5

LBUGRI prototypes 5.0 17.0

% difference 20% -48%

1) RFD prototype populations from Appendix B.

2) LBL/GRI prototype populations and population heating and cooling loads from Hanford and Huang 1992.

3) Heating and cooling loads calculated using ASHRAE SP53 loads database methodology (loads are

uncalibrated to actual field conditions).

52

EE-RM-94-230 COMMENT 61C

Table 3.19 (conL). RFD BuUding Prototype Populations and Heating and Cooling Loads 

(comparison 0/ RFD prototype Loads to LBUGRl prototype loads) 

House Heat Heat Fuel Shell Shell Popln Heat Cool 

Vintage T~ Region Type Share Gronp Share (million) MMBtu MMBbJ 

Stock MF North EleCtric O.IS 1980s 0.201 0.46 21.3 4.6 

Electric 0.15 pre-80s 0.799 . 1.85 48.3 7.2 

Stock MF North Fuel 0.84 1980s 0.053 0.69 22.2 5.0 

Fuel 0.84 pre-80s 0.947 12.25 51.7 7.8 

Stock MF North Heat Pump 0.01 1980s 0.278 0.04 21.6 4.4 

HcatPump 0.01 pre-80s 0.722 0.11 34.4 6.0 

RFD 1990 prototypes 15.4 million wtdaverage 48.9 7.5 

UllGRl prototypes 15.6 million wtdaverage 37.4 9.2 

% difference 24% -22% 

Stock MF South Electric 0.42 1980s 0.276 1.18 6.5 11.7 

Electric 0.42 pre-80s 0.724 3.10 14.7 15.7 

Stock MF South Fuel 0.53 1980s 0.106 0.57 6.3 11.6 

Fuel 0.53 pre-SOs 0.894 4.83 16.2 16.4 

Stock MF South Heat Pump 0.06 1980s 0.224 0.14 6.3 11.5 

Heat Pump 0.06 pre-80s 0.776 0.47 14.9 15.5 

RFD 1990 prototypes 10.2 million wtdaverage 14.0 15.4 

LBllGRl prototypu 9.3 million wtdaverage 12.3 15.3 

% difference • 13% 1% 

New MF North Electric 0.23 All 1 0.23 21.3 4.6 

New MF North Fuel 0.63 All 1 0.63 22.2 5.0 

New MF North Heat Pump 0.13 All 1 0.13 21.6 4.4 

RFD 1990 prototypes 21.7 4.8 

UUGRl prototypes 14.0 6.5 

% difference 35% -36% 

New MF South Electric 0.30 All 1 0.30 6.5 11.7 

New MF South Fuel 0.34 All 1 0.34 6.3· 11.6 

New MF South Heat Pump 0.35 All 1 0.35 6.3 11.5 

RFD 1990 prototypes 6.3 11.5 

UUGRl prototypes 5.0 17.0 

% difference 20% -48% 

1) RFD prototype populations from Appendix B. 

2) LBUGRI prototype populations and population heating and cooling loads from Hanford and Huang 1992 

3) Heating and cooling loads calculated using ASHRAE SP5310ads database methodology (loads are 

uncalibrated to actual field conditions). . 

.52 
Table 3.19 (cont). RFD Building Prototype Populations and Heating and Cooling Loads

(comparion of RFD prototype Loads to LBL/GRI prototype loads)

House Heat Heat Fuel Shell Shell PopIn Heat Cool

Vintage Type Region Type Share Group Share (million) MMBtu MMBtu

Stock MH North Eleiic 0.11 All 1 031 43.9 6.1

Stock MH North Fuel 0.88 All 1 2.46 35.8 4.4

Stock MH North Heat Pump 0.01 All 1 0.03 583 65

RFD 1990 prototypes 2.8 million wtd average 36.9 4.6

Stock MH. South Eectric 0.27 Al 1 0.73 20.7 20.8

Stock MH South Fuel 0.72 All 1 1.94 17.0 18.7

Stock MH South Heat Pump 0.02 All 1 0.05 11.2 16.5

RFD 1990prototypes 2.7 million wtd average 18.1 19.4

New MH North All AU 1 35.6 6.1

New MH South All AU 1 15.6 19.7

1) RFD prototype populations from Appendix B.

2) Heating and cooling loads calculated using ASHRAE SP53 loads database methodology (loads are

uncalibrated to acual field conditions).

EE-RM-94-230 COMMENT 61C

" 

Table 3.19 (.eont.). RFD Building Prototype Populations and HeatiDg and CooIiDg Loads 

(comparison of RFD prototype Loads to LBl/GRJ prototype Wads) 

House Heat Heat Fuel Shell Sbell Popln .Heat 

Vintage T~ ReJtion T~ Share Group Share (million) MMBtu 

Stock MH Nonh Electtic 0.11 All 1 0.31 43.9 

Stock MH North Fuel 0.88 All I 2.46 35.8 

Stock MH North .HeatPump 0.01 All I 0.03 58.3 

RFl) 1990 prototypes 2.8 million wtdavera~ 36.9 

Stock MH. South Electric 021 All I 0.73 W.7 

Stock MH South Fuel 0.72 All I 1.94 17.0 

Stock . MH South Heat Pump 0.02 All I 0.05 11.2 

RFD 1990 prototypes 2.7 million wtdaverage 18.1 

New Mfl North All All I 35.6 

New MH South All All I 15.6 

1) RFD protolypepopulatiODS from Appendix B. 

Cool 

MMBtD 

6.1 

4.4 

6.5 

4.6 

20.8 

18.7 

16.5 

19.4 

6.1 

19.7 

2) Heating and cooling loads calculated using ASHRAE SPS3 loads database methodology (loads are 

uncalibrated til ac&Ual field conditions).' . 
. Calibration of Forecasting Prototype Heating Loads with Database UECs

Region Prototype Prototype Database

Heat Bldg Heat Average UEC UEC

Heat Heat Heat Share Popln Load Effcincy () (MMBtu) (MMBtu) Calibation

gion Type Fuel Tech (%) (mill) (MMBtu) Eqmt Dist System (kWh) (kWh) Multiplicr

INGLE-FAMILY

J _______ ______________________ _____________________________

Fuel G FRN

Fuel G H20

Fuel G RM

avg.

Fuel 0 FRN

Fuel 0 H20

Fuel 0 RM

avg.

Fuel L FRN

Fuel L H20

Fuel L RM

avg.

Elec E FRN

Elec E H20

Eloc E RM

avg.

IHtPump E HP

9%

2%

58%

9%

9%9

1%

19%

3%

0%

1%

4%

2%

0%

7%

9%

2%

16.0

3.1

0.8

199

3.0

3.2

0.2

6.4

1.0

0.1

0.4

1.4

0.7

0.0

25

32

0.8

929

92.9

929

929

92.9

92.9

92.9

92.9

92.9

72.1

72.1

72.1

61.1 -I I J I .... I~

Fuel G FRN

Fuel G H20

Fuel G RM

avg.

Fuel 0 FRN

Fuel 0 H20

Fuel 0 RM

avg.

Fuel L FRN

Fuel L RM

avg.

Elec E FRN

Elec E RM

avg.

HtPump E HP

38%

1%

17%

56%

3%

1%

2%

5%

2%

3%

6%

10%

5%

14%

13%

10.7

03

4.7

15.7

0.8

0.1

0.6

15

0.6

1.0

1.5

2.7

13

4.0

42.1

42.1

42.1

42.1

42.1

42.1

42.1

42.1

27.9

27.9

3.6 26.4

68% 80% 54%

67% 90% 60%

65% 100% 65%

76% 80% 61%

76% 90% 68%

75% 1005 75%

67% 80% 54%

67% 90% 60%

65% 100% 65%

100% 80% 80%

100% 90% 90%

100% 100% 100%

6.6* 80%

68% 70% 47%

67% 90% 60%

65% 100% 65%

76% 70% 53%

76% 90% 68%

75% 100% 75%

67% 70% 47%

65% 100% 65%

100% 70% 70%

100% 100% 100%

--- ~ ~~~ 65* 70%-- a --

171

154

143

167

153

136

124

143

173

154

143

164

26406

23472

21125

22330

11660

89

70

65

81

79

62

56

68

90

65

74

11678

8175

10559

5758

93

111

83

96

83

112

79

97

74

116

59

73

14000

14000

14000

14000

9000

52

79

38

48

55

86

46

54

59

35

44

6000

6000

6000

5000

054

0.72

0.58

057

0.55 I

0.82

0.64

0.68

0.43

0.75

0.41

0.45

0.53

0.60

0.66

0.63

0.77

0.58

1.14

0.59

0.59

0.69

139

0.82

080

0.66

0.53

059

0.51

0.73

057

0.87

54

South

_ .

I

EE-RM-94-230 COMMENT 61C

. Calibration of Forecasting Prototype Heating Loads with Database UECs 

Region Prototype Prototype Database 

Heat Bldg Heal Ava-agc: UEC UEC 

Heat Heat Heal ~ Popln Load Efficiency (9'0) (MMBtu) (MMBtu) Calibration 

~ion T~ Fuel Tc:ch (9&) (mill) (MMBtu) Eqmt Dist System (kWh) (kWh) Mul!!p1icr 

INGLE·FAMILY 

nth 

Fuel 0 FRN 479'0 16.0 92..9 68% 8()'1, 54CJ, 171 93 0.54 

Fuel 0 H2O 9% 3.1 92.9 67% ~ 6OI.JO 154 III 0.72 

Fuel G RM 2% 0.8 92.9 65% 100% 659'0 143 83 0.58 

avg. j89f, 19.9 167 96 0.57 

Fuel 0 FRN 9% 3.0 92.9 76% 80'11 619'0 153 83 0.55 

Fuel 0 H2O 990 3.2 92..9 76% 9090 68% 136 112 0.82 

Fuel 0 RM 19'0 0.2 92..9 75% 100% 75% 124 79 0.64 

avg. 19% 6.4 143 97 0.68 

Fuel L FRN 3% 1.0 92.9 67% 80'11 549'0 173 74 0.43 

Fuel L H2O 09'0, 0.1 92.9 67% 9090 6()I1o 154 116 0.75 

Fuel L RM 1% 0.4 92.9 659'0 1009'0 659'0 143 59 0.41 

aYg. 49'0 1.4 164 73 0.45 

£lee: E FRN 2'10 0.7 72.1 1009'0 8()C1Q 809'0 26406 14000 0.53 

Elee: E H2O 090 0.0 72.1 100% 9()9l, 90% 23472 14000 0.60 

Elee: E RM 79'0 2.S 72.1 1009'0 10090 1()09'o 21125 14000 0.66 

avg. 9% 32 22330 14000 0.63 

HlPump E HP 2% 0.8 61.1 6.6- 80% 11660 9000 0.77 

South 

Fuc:1 G FRN 389'0 10.7 42.1 68% 70'10 479'0 89 52 0.58 

Fuc:1 G H2O 1% 03 42.1 67% 90% 6()CI, 70 79 1.14 

Fuel G RM 17% 4.7 42.1 65% 100% 65% 6S 38 0.59 

avg. 569'0 15.7 81 48 0.59 

Fuel .0 FRN 3% 0.8 42.1 769'0 70% 53% 79 55 0.69 

Fuel 0 H2O 19'0 0.1 42.1 769'0 90% 68% 62 86 139 

Fuel 0 RM 2% 0.6 42.1 759'0 100% 75% 56 46 0.82 

avg. 5'10 1.5 68 54 0.80 

. Fuc:1 L FRN 29'0 0.6 42.1 679'0 70% 47% 90 S9 0.66 

Fuel L RM 3% 1.0 42.1 659'0 100% 65% 6S 35 0.53 

avg. 69'0 1.5 74 44 0.59 

Elee: E FRN 10% 2.7 27.9 1009"0 70% 70% 11678 6000 0.51 

Elee: E RM 59'0 13 27.9 100% 100% 1009'0 8175 6000 0.73 

avg. 149'0 4.0 10559 6000 0.57 

HtPump E HP 13% 3.6 26.4 6.5· 70% 5758 5000 0.87 

54 
Table 3.20 (cont.). Calibration of Forecasting Prototype Heating Loads with Database UECs

Region Prototype Pototye Daabase

Heat Bldg Heat Avenge UEC UEC

Heat Heat Heat Share Popin Load Eiciency( (M)MBtu) (MMBtu) Calibration

Vinage Region Type Fuel Tech () (mill) (MMBtu)Eqmt Dist Syst (kWh) (kW Multiplier

EWSINGLE.FAMiLY

New North

Fuel G FRN 53% 73 78% 80% 62% 117 64 0.55

Fuel G H20 4% 73 80% 90% 72% 102 74 0.72

avg. 58% 116 65 056

Fud 0 FRN 4% 73 80% 80% 64% 114 62 0.55

Fuel 0 H20 6% 73 85% 90% 76% 96 79 0.82

avg. 100 103 73 0.71

Fuel L FRN 8% 73 82% 80% 65% 112 48 0.43

Fuel L H20 0% 73 82% 90% 73% 100 75 0.75

avg. 8%0 112 49 044

Elec E FRN 4% 58.2 100% 80% 80% 21316 11301 0.53

Elec E H20 0% 58.2 100% 90% 90% 18947 11301 0.60

Elec E RM 3% 58.2 100% 100 % 100% 17053 11301 0.66

avg. 8% 19372 11301 058

_____ tPump E HP 13% 70.3 7* 80% 12500 9648 0.77

New South

Fad G FRN 46% 24.3 78% 70% 55% 45 26 058

Fuel G H20 0%o 24.3 80% 90% 72% 34 39 1.14

avg. 46% 45 26 0.58

Fuel 0 FRN 1% 243 80% 70% 56% 44 30 0.69

avg. 1% 44 30 0.69

Fuel L FRN 7% 24.3 82% 70% 57% 43 28 0.66

avg. 79% 43 28 0.66

IElec E FRN 9% 2L8 100% 70% 70% 9543 4903 0.51

Elec E RM 3% 22.8 100% 100% 100% 6680 4903 0.73

avg. 12% 8886 4903 0.55

HtPum E HP 31% 223 7* 70% 4532 3935 0.87

55

EE-RM-94-230 COMMENT 61C

T bl 320 ( ) C r .. a e . cont. • a Ibrationo fF . orecastlOg Pro totype H eata~g L oa ds Wit • h D' a tab ase UECs - Region ProIotypc Prototyp;e Database 

Heat Bldg Heal Average UEC UEC 

Heat Heal Heat SbaJe PopJn Load El'fiQ=cy (9&) (MMBtu) (MMBtu) Calibration 

Vmrage Region Type Fuel Tech . (%) (mill) (MMBtu) Earnt Din Svst.em (lcWh) (kWh) MultiJJlicr 

NEW SINGLE·FAMILY 

New Nonh 

Fuel G FRN 5391. 73 78'11 8QIL 6291. 117 64 0.55 

. ",:1 G H2O 4% 73 8()'1, 9QII, 72'10 102 74 0.72 

atlg. SB90 116 6S 0.56 

fuc:1 0 FRN 4% 73 80% 8M, 64% 114' 62 0.55 

fuc:1 0 H2O 69'0 73 8S% 90110 769l, 96 79 0.82 

avg. 10lf0 103 73 0.1J 

FueJ L FRN 89'0 73 82% 8M, 6S9l, 112 48 0.43 

fueJ L H2O 0% 73 8291. 9QII, 739l, 100 75 0.75 

avg •. 8ifo. 112 49 OM 

Bee E FRN 49;, 58.2 100%· 8QIL 8091. 21316 11301 0.53 

Bee E H2O oq;, 58.2 100% 909Q 90'10 18947 11301 0.60 

Bee E RM 39'0 58.2 100% 10090 . 100'10 17053 11301 0.66 

avg. 890' 19312 llJOI 0.58 

HtI'urnu E HP 1390 70.3. 7· 80C1. 12500 9648 0.77 

New South 

hel G FRN 46C1o· 24.3 789l, 70C1. 55'10 4S 26 O.5S 

Fuel G H2O 0% 24.3 80% 9QII, 72'10 34 39 1.14 

avg. 46tfo 45 26 0.58 

Fuel .:0 FRN 190 24.3 8091. 70C1. 56'10 44 30 0.69 

avg. 1% 44 30 0.69 

:Fud L FRN 79'0 24.3 829l, 70C1. 57'10 43 28 0.66 

avg. 7% 43 28 0.66 

IElee E FRN 9t& 22.8 100% 70% . 70C10 9543 4903 0.51 

Elee E RM 3'" 22.8 100% l()()ll;, lOO'J> 6680 4903 0.73 

avg. 129'0 8886 4903 O.1S 

HtPuurp E' HP . 3190 22.3 7· 7Qt, 4532 .3935 . 0.87 

5S .. ' 
Table 3.20 (cont.). Calibration of Forecasting Prototype Heating Loads with Database UECs

Region Prototype Prototype Databse

Heat Bldg Heat Average UEC UEC

Heat Heat Heat Share Popn Load Efficiency (%) (MMBu) (MMBtu) Calibration

Vintage Region Type Fuel Tech (%) (mill) (MMBtu) Eqmt Dist System (kWh) (kWh) Multiplier

EXISTING MULTI-FAMILY

Existing North

Fuel FRN 23% 3.5 50.1 689% 80% 54% 92 69 0.75

Fuel G H20 32% 4.9 50.1 67% 90% 60% 83 65 0.78

Fuel G RM 3% 05 50.1 65% 100% 65% 77 63 0.82

avg. 58% 8.9 86 67 0.77

Fuel O FRN 2% 0.4 50.1 76% 80% 61% 82 66 0.79

Fuel 0 H20 16% 2.5 50.1 76% 90% 68% 73 66 0.90

Fuel 0 RM 1% 0.1 50.1 75% 100% 75% 67 60 0.90

avg. 19% 3.0 3 74 66 0.89

Elec E FRN 4% 0.6 42.9 100% 80% 80% 15712 8700 0.55

Elec E H20 0% 0.0 42.9 100% 90% 90% 13966 8700 0.62

Bec E RM 16% 2.4 42.9 100% 100% 1009 % 12570 8700 0.69

avg. 20% 3.0 13167 8700 0.66

HtPump E HP 2% 03 30.8 65* 809% 5878 4000 0.68

Existing South

Fuel G FRN 24% 2.4 15.2 68% 70% 47% 32 31 0.96

Fuel G H20 4% 0.4 15.2 67% 90% 60% 25 35 1.40

Fuel G RM 19% 1.9 15.2 65% 100% 65% 23 19 0.79

avg. 46% 4.8 28 26 0.94

Fuel 0 H20 1% 0.1 15.2 76% 90% 680 22 68 3.05

Fuel O RM 0% 0.0 15.2 75% 100% 75% 20 11 0.53

avg. 1% 0.1 23 40 1.76

Elec E FRN 24% 25 12.4 100% 70% 70% 5190 3700 0.71

Elec E RM 11% 1.1 12.4 100% 100% 100% 3633 3700 1.02

avg. 35% 3.6 4701 3700 0.79

Hdmp E HP 14% 1.4 13 6.6* 70% ' 2835 2100 0.74

56

EE-RM-94-230 COMMENT 61C

T a e bJ 3 . 20 ( con 1.) . C a I rb ra t' Ion 0 fF orecas f mg P t t ro 0 ype H ea t' mg Lo a cis WI 'th D a tab ase UECs 

Region Prototype Prototype Database 

Hcal Bldg Heat Average UEC VEe 

Heat Heat Heat Share PopIn Load Efficiency (9'0) (MMBw) (MMBtu) Calibration 

V"mtage ~egion Type Fuel Tc:ch (9'0) (miD) (MMBtu) Ec!mt Dist System (kWh) . (kWh) Multiplier 

EXISTING MULTI-FAMILY 

Existing North 

Fuel 0 FRN 23% 3.5 50.1 68% 80% 54'1, 92 69 0.75 

Fuel G H2O 32% 4.9 50.1 67% 90% 6O'L 83 6S 0.78 

Fuel G RM 3% 0.5 50.1 65% 100% 6S'L 77 63 0.82 

tZlIg. 58% 8.9 86 67 0.77 

Fuel 0 FRN 2% 0.4 50.1 76% 80% 61'1, 82 66 0.79 

Fuel 0 H2O 16% 2.5 50.1 76% 909'0 68'1, 73 66 . 0.90 

Fuel 0 RM 1% 0.1 50.1 75% 100% 7S% 67 60 0.90 

tZlIg. 19% 3.0 74 66 0.89 

Elec E FRN 4% 0.6 429 100'1, 80% 8O'L 15712 8700 0.55 

Elec E H2O 0% 0.0 429 1009'0 9Q9l, 9090. 13966 8700 0.62 

Elcc E RM 16% 2.4 42.9 100% 100% 10090 12570 ·8700 0.69 

tZlIg. 20'f0 3.0 13167 8700 0.66 

HtPump E HP 2% 0.3 30.8 6.5- 80% 5878 4000 0.68 

Existing South 

Fuel G FRN 24% 2.4 15.2 68% 70% 47~ 32 31 0.96 

Fuel G H2O 4% 0.4 15.2 67% 90% 6O'L 2S 35 1.40 

Fuel G RM 19% 1.9 15.2 65% 100% 6S~ 23 19 0.79 

tZlIg. 46% 4.8 28 26 0.94 

Fuel 0 H2O 1% 0.1 15.2 76% 90% 68% 22 68 3.05 

Fuel 0 RM 0% 0.0 15.2 75% . 100% 75~ 20 11 0.53 

tZlIg. 1% 0.1 23 40 1.76 

Elec E FRN 24% 2.5 124 100% 70% . 70% 5190 3700 0.71 

Elec E RM 11% 1.1 124 100% 100% 100% 3633 3700 1.02 

avg. 35% 3.6 4701 3700 0.79 

HlPump E HP 14% 1.4 13 6.6- 70% / 2835 2100 0.74 

56 
Table 3.20 (cont.). Calibration of Forecasting Prototype Heating Loads with Database UECs

Region Prototype Prototype Database

Heat Bldg Hat Average UEC UEC

Heat Heat Heat Shae Popln Load Efficincy (%) (MMBu) (MMBtu) Calbation

Vintage Region Type Fuel Tech (%) (mill) (MMBtu) Eqt Dist Systea (kWh) ( h Muplier

NEW MUTI-FAMLY

New North

Fuel G FRN 22% 222 '78% 80%. 62% 36 27 0.75

Fuel G H20 38% 22.2 80% 90% 72% 31 24 0.78

avg. 60% 33 25 0.77

Fuel O H20 1% 22.2 85% 90% 76% 29 26 0.90

avg. 1% 29 26 090

Elec E FRN 8% 21.3 100% 80% 80% 7801 4320 055

Elcc E RM 15% 213 100% 100% 100% 6241 4320 0.69

avg. 23% 6801 4320 0.64

__Hdup i E HP 13% 21.6 7* 80% 3841 2614 0.68

New South

Fuel G FRN 25% 63 78% 70% 55% 12 11 0.96

Fuel G H20 2% 63 80%- 90% 72% 9 12 i.40

Fuel G RM 5% 63 67% 100% 67% 9 8 0.80

avg. 32% 11 11 0.95

Fuel O H20 0% 63 85% 90% 76% 8 25 3.04

avg. 0% 8 25 3.04

IElec E FRN 28% 65 100% 70% 70% 2721 1940 0.71

1-oc E RM 2% 6.5 100% 100I 100% 1905 1940 1.02

avg. 30% 2671 1940 0.73

._____ . HtPump E HP 35% 63 7* 70% 1280 948 0.74

57

EE-RM-94-230 COMMENT 61C

T a bl e 3.20 ( cont. ) C • a rb I rationo fF orecasbne - Pro totype H eatmg Lo a cis WIt • hD a tab ase VEC s· 

Region Prototype Prototype Database 

Heat Bldg Heat Average UEC UEC 

Hcat Heat Heat Shale Popln Load Efficiency ('II) (MMBIU) (MMBtu) Calibration 

Vuttage Region Type Fuel Tech ('II) (miO) CMMBIU) .EQmt Disl Svstem {]eWh} (kWh) MullilJlie:' 

NEW MUr:rt-FAMILY 

New North 

Fuel G FRN 221& 22.2 '78% 8()C1, . 62., 36 27 0.75 

Fuel G H2O 381& 22.2 8090 9()CI, 72., 31 24 0.78 

avg. 6MI JJ .2J 0:17 , 

Fuel 0 H2O I'll 22.2 8S% 90% 76., 29 26 0.90 

avg. 1% 29 26, 0.90 

Elce E FRN 890 21.3 100% 8()C1, 80Cl0 7801 4320 0.55 

Elce E RM 151& 21.3 1~ I~ 10011! 6241 4320 0.69 

avg. 23'11 6801 4320 0.64 

H~ E HP 13% 21.6 7· 80Ck 3841 2614 0.68 

New South 

Fuel G FRN 25% 6.3 78% 70% SS90 12 11 0.96 

Fuel G H2O 2., 6.3 80%·, 9()CI, 7290 9 12 i.40 

Fuel G RM S., 6.3 67% 1009'0 67% 9 8 0.80 

avg. 32% 'Jl Jl 0.95 

lFuel O· H2O ()II, 6.3 85% 90% 76., 8 2S 3.04 

avg. 0tJ, 8 2J 3.04 

11;lec: E FRN 281& 6.S 100'11 7()C1, 70Cl0 2721 1940 0.71 

J:Jec: E RM 290 6.S 1()()CJ, I()()% 10011! 1905 1940 1.02 

avg. J(YI, 2671 1940 Oj3' 

HtPump E HP 351& 63 7· 70Ck 1280 948 0.74 
Table 3.20 (cont). Calibration of Forecasting Prototype Heating Loads with Database UECs

Region Prototype Prototype Database

Heat Bldg Heat Average UEC UEC

Heat Heat Heat Share Popin Load Efficiency (%) (MMBtu) (MMBtu) Calibration

Vintage Region Type Fuel Tech (%) (mill) (MMBtu) Eqmt Dist System (kWh) (kWh) Mulitplier

I

EISTvING MANUFACTURED HOME

Existing Nonh

Fuel G FRN 41% 1.1 35.8 68% 80% 54% 66 65 0.98

Fuel G RM 1% 0.0 35.8 65% 100% 65% 55 63 1.14

avg. 41% 12 66 65 0.99

Fuel 0 FRN 17% 0.5 35.8 76% 80% 61% 59 59 1.00

avg. 17% 05 59 59 1.00

Fuel L FRN 14% 0.4 35.8 67% 80%- 54% 67 51 0.77

Fuel L RM 1% 0.0 35.8 65% 100% 65% 55 55 1.00

avg. 16% 0.4 66 52 0.78

Elec E FRN 16% 0.4 43.9 100% 80% 80% 16078 8000 0.50

Elec E RM 4% 0.1 43.9 100% 100% 100% 12863 8000 0.62

avg. 19% 03 15494 8000 052

_HtPump E HP 1% 0.0 58.3 650 80% 11126 6300 0.57

Existing South

Fuel G FRN 31% 0.8 17 68% 70% 47% 36 36 1.00

Fuel G RM 3% 0.1 17 65% 100% 65% 26 28 1.07

avg. 34% 0.9 35 35 1.I

Fuel 0 FRN 8% 0.2 17 76% 70% 53% 32 61 1.91

Fuel O RM 2% 0.0 17 75% 100% 75% 23 18 0.78

avg. 10% 03 30 54 1.77

Fuel L FRN 23% 0.6 17 67% 70% 47% 36 32 0.87

Fuel L RM 8% 0.2 17 65% 100% 65% 26 13 0.48

avg. 31% 0.8 34 27 0.79

Elec E FRN 13% 03 20.7 100% 70% 70% 8664 4500 0.52

Elec E RM 7% 0.2 20.7 100 100% 100% 6065 4500 0.74

avg. 20% 05 7707 4500 0.58

_HtPump E HP 4% 0.1 11.2 6.5* 70% 2443 1500 0.61

58

EE-RM-94-230 COMMENT 61C

T a bl e 3.20 ( cont. ) C • a J"b I rabon 0 fF orecasting Pro totype H ea ti ng Lo a ds WI 'th D a tab ase VEe s 

Region . Prototype Prototype Database 

Heal Bldg Heal Average UEC UEC 

Heat Heat Heat Share Papin Load Efficiency (%) (MMBtu) (MMBtu) Calibration 

Vintage Region T~ Fuel Tech (%) (mill) (MMBtu) EQmt Dist System (kWh) (kWh) MulitpJier 

EXISTING MANUFAcruRED HOME 

Existing Nonh 

Fuel G FRN 41% 1.1 35.8 6890 8()t1, 5490 66 6S 0.98 

Fuel G RM 1% 0.0 35.8 65% I()()% 6590 5S 63 1.14 

avg. 41'10 1.2 66 65 0.99 

Fuel 0 FRN 17% 0.5 35.8 769'0 8()t1, 6190 59 59 1.00 

avg. 17% OS 59 59 IJ)() 

Fuel L FRN 14% 0.4 35.8 679'0 8()t1, - 5490 67 51 0.77 

Fuel L RM 190 0.0 35.8 65% 100% 65'10 55 55 1.00 

avg. 16'10 0.4 66 52 0.78 

Elcc E FRN 16% 0.4 43.9 100% 8()t1, 8090 16078 . 8000 O..so 

Elcc E RM 490 0.1 43.9 100% 100% 100% 12863 8000 0;62 

avg. 1990 0.5 15494 8000 0.52 

HtPump E HP 1% 0.0 58.3 6.50 8()t1, 11126 6300 0.57 

Existing Soulh 

Fuel G FRN 31% 0.8 17 68% 7()t1, 47% 36 36 1.00 

Fuel G RM 3% 0.1 17 65% I()()% 6590 26 28 . 1.07 

avg. 34% 0.9 35 35 1.01 

Fuel 0 FRN 8% 0.2 17 76% 7()t1, 5390 32 61 1.91 

Fuel 0 RM 2% 0.0 17 75% 1()()% 7590 23 18 0.78 

avg. 1090 0.3 30 54 1.77 

Fuel L FRN 23% 0.6 17 67% 7()t1, 47% 36 32 0.87 

Fuel L RM 8% 0.2 17 65% 100% 65% 26 13 0.48 

avg. Jl% 0.8 34 27 0.79 

Elcc E FRN 13% 0.3 20.7 100% 7()t1, 7090 8664 4500 0.52 

Elcc E RM 7% 0.2 20.7 100% I()()% 100% 6065 4500 0.74 

avg. 2090 OS 7707 4500. 0.58 

HtPump E HP 4% 0.1 11.2 6.5· 70% 2443 1500 0.61 

58 
Table 3.20 (cont.). Calibration of Forecasting Prototype Heating Loads with Database UECs

Region Prototype Prototype Database

Hut Bldg Heat Average UEC UEC

Heat Heat Heat Shae Popln Load Efficiency (%) (MMBt) (MMBIt) Calibration

Vintage Region Type Fuel Tech (%) (mi) (MMBtu) Eqmt Dist System (kWh) (kWh) Multplir

NEW MANUFACTURED HOME

New North

Fuel G FRN 349 35.6 78% 80% 62% 57 56 0.99

Fuel G RM 4% 35.6 67% 100% 67% 53 61 1.14

avg. 38%e 57 57 1.00

Fuel O FRN 20% 35.6 809% 80% 64% 56 56 1.00

avg. 20% 56 54 097

Fuel L FRN 20% 35.6 82% 80% 65% 55 42 0.77

Fuel L RM 5% 35.6 78% 100% 78% 46 46 1.00

. g. 25% 53 43 0.81

Elec E FRN 11% 35.6 100% 80% 80% 13038 6488 0.50

Elec E RM 7% 35.6 100% 1009 100% 10431 6488 0.62

av A. 18 o 12031 6488 054

New South

Fuel G FRN 6% 15.6 78% 70% 55% 29 29 1.00

Fuel G H20 3% 15.6 80% 90% 72% 22 22 1.00

avg. 9 . 26 26 1.00

Fuel L FRN 25% 15.6 82% 70% 57% 27 24 0.87

Fuel L RM 5% 15.6 78% 100% 78% 20 10 0.48

avg. 299% 26 22 082

Elec E FRN 55% 15.6 100% 70% 70% 6530 3391 052

avg. 55% 6530 3391 052

___ Hp E HP 6%0 15.6 7* 70% 3170 1947 0.61

Heat Pump values are in kBlukWh.

1) Existing HVAC shares ar from US DOE 1992. Data are segmented into North and South regions using census division and heating

degree day data.

2) Building populations ar based on US DOE 1994 total national building population estimates and HVAC shaes noted above.

3) Prototype heating loads are calculated from prototype descriptions using ASHRAE SP53 loads database methodology (see Table 3.19).

4) Existing buildings database UEC sources: Fuel heating UECs for all building types re from US DOE 1989a. UECs for existng

single-family electric heating in North arx estimated from Cohen et aL 1991 for post-retrofit houses at 6000 heating degree days (see

Fig. 3.8). UECs for single-family electric heating in South are estimated from utility survey data in the UEC database in South region

(see Fig. 3.6). Single-family heat pump heating UECs are estimated from averages of regional utility survey data in UEC database in

North and South regions (see Fig. 3.7). Electric and heat pump heating UECs for multi-family and manufactured home prototypes ar

estimated from fuel heating calibration multipliers and single-family UEC calibration multiplicrs for electric heat.

5) Database UECs for new buildings are calculated using the prototype heating load, equipment efficiency, and distribution system

cfficiency and the calibration multiplier from the existing vintage buildings.

6) Equipment efficiencies for the new vintage are taken from the most recent data in the database as shown in Figures 3.12 and 3.15.

For equipment not shown in these figures. or not covered by available data, efficiencies for new equipment are estimates.

7) Stock equipment efficiencies are calculated from historical shipment and efficiency data in the database with an assumed equipment

lifetime. For equipment not shown in these figures, or not covered by available data, efficiencies for stock equipment ae estimates.

8) Distribution system efficiencies are assumed base cases for stock and new building systems as shown in Table 3.4.

59

EE-RM-94-230 COMMENT 61C

Tb1320( a e . con t)Crb •• ..8 I ration 0 fF< areca sf ang Prot o t ype H ea ti ng Lo· a cis WI ·th D 8 tab ase. VEe 5 

Region Prototype Prototype Darabase 

Heal Bldg Heal Average UEC UEC 

Heat Hcal Heal Share Popln Load Efficiency (9:,) (MMBbl) (MMBQI) Calibration 

Vintage Region Type Fuel Tech 

NEW MANUFACTURED HOME 

New Nonh 

Fuel G fRN 

Fuel G RM 

avg. 

Fuel 0 fRN 

avg. 

:Fuel L FRN 

Fuel L RM 

avg. 

lEtec E fRN 

lac:c E RM 

avll. 

New· South 

Puel G FRN 

l::Uel G H2O 

avg. 

):Uel L fRN 

1:ueJ L RM 

avg. 

l:1ec E FRN 

avg. 

Htlf»urnp E HP 

• Heal Pump values lire in kBlU/kWh. 

Sources: 

('I,) . (miD) (MMBtu) Eqmc 

34% 35.6 7g% 

4% 35.6 67tfo 

389'0 

20% 35.6 80ff0 

20% 

2()CJ, 3S.6 g2tfo 

S% 35.6 78% 

2.5% 

11% 3S.6 100% 

7% 3S.6 100% 

189'0 

6% 15.6 78% 

3% 15.6 80ff0 

990 

2S% 15.6 .82% 

5tfo 15.6 78tfo 

29% 

SS9Q 15.6 100% 

SS% 

6% -15.6 7· 

Dist Svstem (kWh) (kWh) MultipJier 

g~ 62"- 57 56 0.99 

1()()% 61"- 53 61 1.14 

57 57 1.00 

B09'o 64% 56· S6 1.00 

56 54· 0.97 

BOffo 65"- 5S 42 0.77 

l()()% . 78'1. 46 46 1.00 

53 43 0.81 

80ff0 80% 13038 6488 0.50 

1()()% 100% 10431 6488 0.62 

12031 6488 0.54 . 

709'0 SStfo 29 29 1.00 

9Otfo 72% 22 22 1.00 

26 .26 1.00 

70ff0 S7% 27 24 0.87 

100% 78% 20 10 0.48 

·26 22 0.82 

70ff0 70% 6530 3391 0.52 

6530 3391 0.52 

70ff0 3170 1947 0.61 

1) &isbng HVAC shares ~ from US DOE 1992. Data ~ segmented into North and South regions using census division and heating 

degree day daIa. 

2) Building populatiOl'lS IR based on US DOE 1994lOtal natipnal building population estimllCS and HV AC shares DOted above. 

3) Prototype heating ItJads are c:alc:ulatcd from prototype descriptions wing ASHRAE SPS3 loads da1abase methodology (sec Table 3.19). 

4) Exisling buildings database VEe sources: Fuel heating UECs for all building typca 1ft from US DOE 19B9L 1JECa foremting 

single-family electric healing in North IlC estimated &om Cohen et a1. 1991 forpost-retrofit houses &16000 heating depe days (sec 

Fig. 3.8). UECs for single-family electric heatirlg in South an: estimated from utility survey data in the UEC database in South region 

(see Fig. 3.6). Single-family heat pump heating UECs are estirnarcd from averages of regional utility survey da1a in UEt database in 

North and South regions (see Fig. 3.7). Electric and heat pump heating UECs for multi-family and manufac:1UrCd home pioror.ypes are 

estimated from fuel heating c8Jibration multipliers and single-Camily UEC c~tion multipliers for electric heaL. 

S) Database UECs for new buildings are calculaled using the prolOtype heating load. equipment efficiency. and distribution system 

efficiency and the calilbration multiplier Crom the existing vincage buildings. . . 

6) equipment efficicnc:ies fer the new vintage an: taken from the most recent dita in the dacabase as shown in Figures 3.12 and 3.1S. 

For equipment not shawn in these figures. or not covered by available da1a.. efficiencies for new equipment are estimau:s. 

7) Stock equipment efticiencies an: calculated from hislOrica1 shipment and efflci~cy data in the database with an assumed equipment 

lifetime. For equipment not shown in these figmes. or not covered by available dala, efficiencies for stock equipment are estimaleS. 

8) Distribution system efficiencies are assumed base cases for srock and new building systems as shown in Table 3.4. 

59 
Table 3.21. Calibration of Forecasting Prototype Cooling Loads with Database UECs

Region Protoype Aveage

Cool Bldg Cool Efficiency Prototype Database

Cool Cool Cool Share Popn Load Eqmt Dist UEC UEC Caliration

Vintage Region Type Fuel Tech (%) (mill) (MMBtu) kB/Wh) (%) (kWh) (kWh) Muiplier

EXISTING SINGLE-FAMILY

Existing North

Central E CAC 0.289 9.9 8.0 8.2 80% 1230 1160 094

HPump E HP 0.022 0.8 6.5 8.4 80% 963 1176 1.22

Room E RAC 0.292 10.0 8.0 7.4 100% 1072 375 0.35

_____avia. _0.603 20.7 _____1)1143 781 0.68

Existing South

Central E CAC 0.402 11.3 262 8.2 70% 4588 3821 083

HPump E HP 0.128 3.6 18.6 8.4 70% 3148 4077 1.29

Room E RAC 0.246 6.9 27.4 7.4 100% 3692 1358 Q37

___avg.. 0.776 21.7 ___4067 3082 0.76

NEW SINGLE-FAMILY

New North Central E CAC 0.552 8.9 9.2 80% 1200 1132 094

HPump E HP 0.129 8.8 9.4 80% 1167 1425 1.22

Room E RAC 0084 8.7 8.7 100% 1004 352 0.35

avg. 0.765 __1173 1096 0.93

New South

Central E CAC 0.506 17.8 9.2 70% 2758 2297 083

HPump E HP 0.311 16.9 9.4 70% 2560 3316 1.30

Room E RAC 0056 17.9 8.7 100% 2055 756 0.37

_______0.873 _2643 2561 097

EXISTING MULTI-FAMILY

Existing North Central E CAC 0.168 2.6 7.3 8.2 80% 1114 515 046

HPump E HP 0.019 0.3 5.6 8.4 80% 829 517 062

Room E RAC 0.422 6.5 7.6 7.4 100% 1019 160 016

_______0.609 9.4 1039 269 026

Existing South

Central E CAC 0461 4.7 15.2 8.2 70% 2652 1366 052

HPiunp E HP 0136 1.4 14.6 8.4 70% 2471 1371 0.55

Room E RAC 0.152 1.6 15.5 7.4 100% 2085 424 0.20

__________ ave. 0.749 7.7 __. 2504 1176 0.47

60

EE-RM-94-230 COMMENT 61C

Table 3.21. Calibration of Foreasting Prototype Cooling Loads with Database UECs 

Region Prototype Avenge 

Cool Bldg Cool Efficiency Prototype Database 

Cool Cool Cool Share Popln Load Eqmt Dist UEC VEe CalibratiOa 

Vintage Region Type Fuel Tech (%) (mill) (MMBtu) KtBtulkWh) (CJD) (kWb) (kWh) Multiplier 

EXISTING SINGLE·FAMILY 

Existing North 

Central E CAC 0.289 9.9 8.0 8.2 8()1l. 1230 1160 0.94 

HPump E HP 0.022 0.8 6.S 8.4 80% 963 1176 1.22 

Room E RAe 0.292 10.0 8.0 7.4 100% 1072 37S 03S 

aYtl. 0.603 20.7 1143 781 0.68 

Existing South 

Ccotral E CAe 0.402 11.3 26.2 8.2 70C1. 4588 3821 0.83 

HPump E HP 0.128 3.6 18.6 8.4 7()1l. 3148 4fJ77 1.29 

Room E RAe 0.246 6.9 27.4 7.4 100% 3692 13S8 0.37 

aYtl. 0.776 21.7 4067 3082 0.76 

NEW SINGLE·FAMILY 

New North Ccmral E CAe o.sS2 8.9 9.2 80% 1200 1132 0.94 

HPump E HP 0.129 8.8 9.4 80% 1167 1425 1.22 

Room E RAe 0.084 8.7 8.7 100% 1004 . 352 Q.3S 

QVft. 0.765 1173 1096 0.93 

New SOUlh 

Ccnttal E CAC 0.506 17.8 9.2 70% 2758 2297 0.83 

HPwnp E HP 0.311 16.9 9.4 70% 2S6O 3316 1.30 

Room E RAe 0.056 17.9 8.7 100% lOSS 756 037 

aYlt. 0.873 2643 2561 0.97 

EXISTING MULTI·FAMILY 

Existing North Central E CAe 0.168 2.6 7.3 8.2 80% 1114 SIS 0.46 

HPump E HP 0.019 0.3 S.6 8.4 80C1. 829 517 0.62 

Room E RAe .Q.422 60S 7.6 7.4 100% 1019 160 0.16 

QVft. 0.609 9.4 1039 269 0.26 

Existing South 

Central E CAe 0.461 4.7 15.2 8.2 70% 2652 1366 Q.S2 

HPump E HP 0.136 1.4 14.6 8.4 70% 2471 1371 Q.SS 

Room E RAe 0.152 1.6 IS.s 7.4 100% 208S 424 0.20 

aYtl. 0749 7.7 2504 1176 0.47 

60 
Table 3.21 (cont). Calibration of Forecasting Prototype Cooling Loads with Database UECs

Region Prototype Average

Cool Bldg Cool Efficiency Prototype Database

Co ol Coo l Shar Popin Load Eqmt Dist UEC UEC Calibion

Vintge Region Tye Fud Tech () (mil) (MMBnu) (MM B) t(ku) UB/kWh) Multipier

NEW MULTI-FAMIlY

New North Centrl E CAC 0.225 49 9.2 80% 663 307 0.46

HPump E HP 0.129 4.4 9.0 90%. 548 342 0.62

Room E RAC 0.484 4.9 8.7 100% 565 89 016

avR. 0.838 _ 589 186 032

New South

Central E CAC 0.406 11.6 9.2 70% 1801 928 052

HPump E HP 0.352 11.5 8.9 90% 1457 808 0.55

Room E RAC 0.034 11.7 8.7 100% 1340 273 020

______ av. 0.792 __________1628 847 0.52

EXISTING MANUFACTURED HOME

Existing North Central E CAC 0.284 0.8 4.8 8.2 80% 731 1443 1.97

HPrunp E HP 0.008 0.0 6.5 8.4 80% 963 1544 1.60

Room E RAC 0263 0.7 4.7 7.4 100% 629 447 0.71

.________ . 0.555 1.6 ____ 686 sn 1.42

Existing South

Central E CAC 0.275 0.7 19.3 8.2 70% 3369 2988 0.89

HPurnp E HP 0.04 0.1 16.5 8.4 70% 2793 3175 1.14

Roon E RAC 0355 1.0 19.1 7.4 100% 2575 1007 039

________ 0.67 1- ____ 2914 1950 0.67

NEW MANUFACTURED HOME

New North Cental E CAC 0363 6.1 9.2 80% 825 1630 197

Room E RAC 0.351 6.1 8.7 100% 701 499 071

a_____ . _ 0.714 764 1074 1.40

New South

Ccntal E CAC 0.516 19.7 9.2 70% 3046 2702 089

HPump E HP 0.062 19.7 9.2 70% 3046 3463 1.14

Room E RAC 0.219 19.7 8.7 100% 2264 886 0.39

v. 0.797 2831 2262 0.80

1) Stock HVAC shares ue from US DOE 1992. Data are segmented into North and South regions using census division and

heating degree day data.

2) Building populations ae based on US DOE 1994 total national building population estimates and HVAC shares noted above

3) Database UECs for stock buildings are from LBL electricity supply crves (Kooy et aL 1991a), which ae derived from

prototype descriptions.

4) Database UECs for new buildings are calculated using the prototype coding load, equipment efficiency, and distribution system

efficiency and the calibration multiplier from the existing vintage buildings.

5) Equipment efficiencie; for the new vintage are taken from the most recent data in the daabase as shown in Figure 3.12 and 3.15.

For equipment not shown in these figures, or not covered by available data efficiencies for new equipment ae estimates.

6) Stock equipment efficiencies ar calculated from historical shipmnt and efficiency data in the database with an assumed equipment

lifetime. For equipment not shown in these figures, or not covered by available data, efficiencies for stock equipment are estimates

7) Distribution system cffiencicis ae assumed base cases for stock and new building systems as shown in Table 3.4.

8) Prototype heating loads are calculated from prototype descriptions using ASHRAE SPS3 loads database methodology (see Table 3.19)

61

EE-RM-94-230 COMMENT 61C

'. 

Table 3.21 (cont.). Calibration or Forecasting Prototype Cooling Loads with Database UECs 

Region Prototype Average 

Cool Bldg Cool Effici~ Pzototype , DaIabase 

Cool Cool Cool Shue Popin Load Eqm& Disc USC USC Ca1ibmsion 

Vintage Region Type Fuel Tech (IL) (mill) (MMBID) ~ID/kWh)(") (kWh) (kWb) MultbJlier 

, NEW MUU'I·FItMILY 

New NCIItb CenID1 E CAe 0.225 4.9 9.2 80'11 663 3f17 

HPump E UP 0.129 4.4 9.0 90'11 S4S 34l 

Rocl'm E RAe 0.484 4.9 8.7 1~ S65 89 

tIIIR. 0.B3B 589 186, 

New South 

Cenlral E CAe 0.406 11.6 9.2 70'11 1801 928 

HPUJnp E UP 0.352 11.5 8.9 90'11 1457 808 

Room E RAe 0.034 11.7 8.7 100'II 1340 273 

avg. 0.192 1628 847 

EXlSI'ING MANUFAC7'VRED HOME 

Emling Nonh Central - E CAe 0.284 0.8 4.8 8.2 80'11 731 1443 

HPurnp E UP O.O:OS 0.0 6.5 8.4 80'11 963 ' 1544 

ROO1l1l E RAe 0.263 0.7 4.7 7.4 100'II 629 447 

rlVfl. 0.555 1.6 686 972 

Emling SoUlb 

. CenD:al E CAe 0.275 0.7 19.3 8.2 70'11 3369 2988, 

HPw:np E UP 0.04 0.1 16.5 8.4 7~ 2793 317S 

Root:n E RAe O.3SS 1.0 19.1 , 7.4 100'II 2S7S '1007 

_tlVR· 0.67 1.8 29/4 1950 

NEW MANUFACIVREJ.) HOME 

- New North Centl'll E CAe 0.363 6.1 9.2 80'11 82S 1630 

ROOIn E RAe 0.351 6.1 8.7 100'II 701 499 

avg. 0.714 764 1074 

New South 

Central E CAe o.s16 19.7 9.2 7()C1, 3046 2702 

HPIln1P E UP 0.062 19.7 9.2 7"' 3046 , 3463 

ROOIn E RAe 0.219 19.7 8.7 100'II 2264 886 

1IIIt. 0.797 2831 2262 

Sources: 

1) SlOCIt HV AC sbares &l:e from US DOE 1992. Data are segmented inlO North and Soulh regions using ceosus division and 

healing degree da)' data. 

0..46 

Q.62 

0..16 

0.32 

o..s2 

o..sS 

0..20 

0.52 

1.97' 

1.60 

0..71 

1.42 

Q.89 

1.14 

0.39 

0.61 

L97 

0.71 

1.40 

0..89 

1.14 , 

0.39 

0.80 

2) Buding populabons lin: ba:sed Oft US DOE 1994 total national building population eslimales and HV AC shares noled above. 

3) Database UECs for stodc buildings are fromLBL e1ecuicUy supply curves (Koomey et aL 1991a). which are derived from 

prolOt)'JIe descriptions. . . 

4) Database UECs for new baildings are calculated using the prototype c:ocIing load, equipment effICiency, and dis~bation system 

effIciency and the caliimuion multiplier from the existing Yinlage buildings. 

S) Equipment efficiencic:1 for me new vintage are Iaken {rem the most icccnt data in the daIabase as shown ~ FigUIC 3.12 and 3.1S. 

Far equipment not shown in these figures. or not covered by available dala, efficiencies for new equipment are esDmares. 

6) Srock equipment efficiencies ue calculaled from historica1shipmentand efficiency data in the database With an assumed equipment 

lifetime. For eqllipmenlllOllhown in these figures. or nOI covered by available dala, efTu:ienc:ics for stoe.k equipmenl ue estimales. 

7} Distribution s),slCm efrlciencicsue assumed base c:asc:a for stocJt and new b,:,i1ding S)'SlCms as shown in Table 3.4. 

8) Prototype heating loads are calculalCd from prolOlype descriptions using ASHRAE SPS3 loads dalabase methodology (see Table 3.19) 

61 
3.6. Standards

Equipment Standards

Efficiency standards for space conditioning equipment were enacted in 1987 under the

National Appliance Energy Conservation Act (NAECA). The date of initial implementation

depends upon the type of equipment. The standards for heating equipment are given in

Table 3.22, while those for cooling are given in Table 3.23. All standards are based on an

efficiency (or energy factor) derived from a test procedure.

Table 3.22. Minimum Efficiency Standards for Residential

Heating Equipment

Database Year Minimum

Type I Code Fuel Effective Effiency

Heat Pump

Split System HP Elec 1992 6.8 HSPF

Single Package HP Elec 1993 6.6 HSPF

Furnace FRN Gas 1992 78 AFUE

Funace FRN Oil 1992 78 AFUE

Boiler H20 Gas 1992 80 AFUE

Boiler H120 Oil 1992 80 AFUE

Direct Heating

wall heater w/fan

<42000 Btu/hr RM Gas 1990 73 AFUE

>42000 Btulhr RM Gas 1990 74 AFUE

wall heater (gravity)

<10000 Btu/hr RM Gas 1990 59 AFUE

10-12000 Btu/hr RM Gas 1990 60 AFUE

12-15000 Btu/hr RM Gas 1990 61 AFUE

15-19000 Btu/hr RM Gas 1990 62 AFUE

19-27000 Btu/hr RM Gas 1990 63 AFUE

27-46000 Bu/hr RM Gas 1990 64 AFUE

>46000 Btu/hr RM Gas 1990 65 AFUE

floor heater

<37000 Btu/hr RM Gas 1990 56 AFUE

>37000 Btu/hr RM Gas 1990 57 AFUE

room heater

<18000 Btu/hr RM Gas 1990 57 AFUE

18-20000 Btu/hr RM Gas 1990 58 AFUE

20-27000 Btu/hr RM Gas 1990 63 AFUE

27-46000 Btu/hr RM Gas 1990 64 AFUE

>46000 Btu/hr RM Gas 1990 65 AFUE

1) Effective date is January 1 of year indicated.

2) All standards levels from NAECA 1987.

3) AFUE is Annual Fuel Utilization Efficiency (%).

4) HSPF is Heating Season Performance Factor (kBtu/kWh).

62

EE-RM-94-230 COMMENT 61C

3.6. Standards 

Equipment Standards 

Efficiency standards for space conditioning equipment were enacted in 1987 under the 

National Appliance Energy Conservation Act (NAECA). The date of initial implementation 

depends upon the type of equipment. The standards for heating equipment are given in 

Table 3.22, while those for cooling are given in Table 3.23. All standards are based on an 

efficiency (or energy factor) derived from a test procedure. 

Table 3.22. Minimum Efficiency Standards for· Residential 

H E eahne ~Qu.pmen i t 

DaIabase 

Type Code Fuel 

Heat Pump 

Split System HP Flee 

Single Package HP Flee 

Furnace FRN Gas 

Furnace FRN Oil 

Boiler H2O Gz 

Boiler H2O Oil 

Direct Heating 

wall hearer w/fan 

<42000 B tuIhr RM Gz 

>42000 BtuIhr RM Gz 

wall hearer (gravity) 

<10000 B tuIhr RM Gas 

10-12000 BtuIhr RM Gas 

12·15000 Btu/hr RM Gas 

IS-19000 BtuIhr RM Gas 

19-27000 BtuIhr RM Gas 

27-46000 lBtu/hr RM Gz 

>46000 BtuIhr RM Gz 

floor beater 

<37000 BtuIhr RM Gas 

>37000 BtuIhr RM Gas 

roombeatet 

< 18000 BtuIhr RM Gas 

18-20000 BtuIhr RM Gas 

20-27000 BtuIhr RM Gas 

27-46000 Btu/hr RM Gz 

>46000 BtuIhr RM Gas 

I} Effecuve date IS January 1 of year mdicated. 

2} All standards levels from NAECA 1987. 

Year 

Effective 

1992 

1993 

1992 

1992 

1992 

1992 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

1990 

3) AFUE is Annual Fuel Utilization Efficiency (%). 

4} HSPF is Heating Season Performance Factor (kBtulkWb). 

Minimum 

Effidency 

6.8 HSPF 

6.6 HSPF 

78 AFUE 

78 AFUE 

80 AFUE 

80 AFUE 

73 AFUE 

74 AFUE 

S9 ARJE 

60 ARJE 

61 AFUE 

62 ARJE 

63 ARJE 

64 AFUE 

6S AFUE 

S6 AFUE 

S7 AFUE 

57 AFUE 

S8 AFUE 

63 AFUE 

64 AFUE 

6S AFUE 
Table 3.23. Minimum Efficiency Standards for Residential

Cooling Equipment

Database Year Minimum

T1ype _Code Fuel Effec Efficiecy

Central Air Conditioner

Split System CAC lec 1992 10.0 SEER

Single Package CAC Eec 1993 9.7 SEER

Heat Pump

Split System HP Eec 1992 10.0 SEER

Single Package HP Ele 1993 9.7 SEER

Room Air Conditioner

w/o reverse cycle and w/louvers

<6000 Bt/hr RAC Elec 1990 8.0 EER

6000-7999 Btulhr RAC Elec 1990 8.5 EER

8000-13999 Btu/br RAC Elec 1990 9.0 EER

14000-19000 Btu/br RAC Elec 1990 8.8 EER

>20000 Btu/hr RAC Elec 1990 8.2 EER

w/o reverse cycle and w/o louvers

<6000 Btu/hr RAC Elec 1990 8.0 EER

6000-7999 Btulhr RAC Elec 1990 8.5 EER

8000-13999 Btu/br RAC Elec 1990 85 EER

14000-19000 Btu/br RAC Elec 1990 8.5 EER

>20000 Btu/br RAC Eec 1990 8.2 EER

wheverse cycle and wlouvers RAC Elec 1990 8.5 EER

w/reverse cycle and w/o louvers RAC Elec 1990 8.0 EER

1) Effective date is January 1 of year indicated.

2) All standards levels from NAECA 1987.

3) SEER is seasonal energy efficiency ratio. Units are kBtu/kWh.

4) EER is energy efficiency ratio. Units are kBt/kWb.

EE-RM-94-230 COMMENT 61C

Table 3.23. MIDlmum Emclency Standards for Residential 

C 00 II n~ E i ;qulpmen t 

Dalal&e Year Minimum 

Type Code • Fuel meet. Efficieacy 

Caural Air Conditioner 

Split System CAe Flee 1992 10.0 SEER 

Single Paclcage CAe Flee 1993 , 9.7 SEER ' 

Heat Pump 

Split System HP Flee 1992 10.0 SEER 

Single Package HP Flee 1993 9.7 SEER 

Room Air Conditioner 

wlo revezse cyde and wllouvers 

<6000 Btu/hr RAe Flee 1990 8.0 EER 

6000-7999 BtuJbr RAe Flee 1990 8.S EER 

8000.;13999 Btulbr' RAe Flee 1990 9.0 . FER 

14000-19000 Btu/br RAe Flee 1990 8.8 EER 

>20000 BbIIbr RAe Flee 1990 8.2 EER 

wlo reverse cycle and wlo louvers 

<6000 Btu/hr RAe Flee 1990 8.0 FER 

6000-7999 BtuIhr RAe Flee 1990 8.5 EER 8000-13~9 BtuIbr RAe Flee ' 1990 8.5 FER 

14000-19000 Btu/br RAe Flee 1990 8.5 ' FER 

>20000 BtuIbr RAe Flee 1990 8.2 ,FER 

w~ cycle and wllouvers RAe Elec 1990 8.5 FER 

, w/reverse cycle and wlo louvers' , RAe Flee , 1990, 8.0 FER 

1) Effective dare IS January 1 of year mdicated. 

2) All standards levels from NAECA 1987. 

3) SEER is seaonal energy efficiency ratio. Unirs are kBtulkWb. 

4) EER is energy efficieDcy ratio. Uaits are kBtulkWb. 
4. WATER HEATING END-USE DATA

Water heating accounts for approximately 15% of electricity usage and 25% of natural gas

consumption in residential buildings. Water heating is comparable to space heating in

terms of the complexity of the issues surrounding level of usage, behavioral impacts, and

climatic impacts. There is large variability in water heat energy use across households,

which is partly due to household size (Kempton 1984). In addition, there are subtle

climatic effects on water heating energy use, since colder areas of the country also have

colder inlet water temperatures and thus greater water heating requirements.

Water heating is a complex end-use because of the unknowns involved, including hot water

demand in gallons, incoming cold water temperatures, and the hot water temperature at the

point of use. These parameters are inter-related. For example, if the hot water temperature

of the storage water heater is higher, less hot water will be needed to meet a certain need

since it is usually mixed with cold water to achieve the desired temperature.

4.1. Water Heating UECs

Measured data on electric water heating UECs are plentiful, but show the large variability

previously described. Measured data on gas water heat energy use is limited and the RECS

conditional demand estimates and a few studies summarized by Usibelli (1984) provide

virtually the only estimates of national average gas water heating energy use. Water heating

UECs can easily be calculated according to engineering principles, and these are usually

used in models. However, these calculations require assumptions regarding key

parameters.

UEC equation

There are several different ways of incorporating usage and efficiency data in calculating

water heating UECs. The equations below show a simplified method that uses the Energy

Factor of a water heater determined from the DOE test procedure. This equation may not

be valid for levels of consumption that are far from the base test procedure usage, however.

Electric:. kWhyr = Use * TempRise * 8.2928 * 365

Electr kW/yr = 3413 Btu/kWh * (EF/100)

Fuel: MM[Btuyr _- Use * TempRise * 8.2928 * 365 Fuel: MMB tu/yr =- (EF/100)

where: Use is the household hot water use (gallons/day)

TempRise = temperature difference between incoming cold water and tank temperature (77 F)

8.2928 is the specific beat of water (Btu/gal-F)

365 is days per year

EF is the energy factor from the DOE test procedure (%)

Stock UECs

The UECs in the residential database are derived from weighted averages of other studies

and are 3750 kWh/yr (n=96) for electric water heating and 23.7 MMBtu/yr (n=22) for gas

water heating across all building types (Appendix B). We assume that oil water heater

UECs are the same as gas for stock units. There are few measured data specifically

addressing the difference between water heat usage between housing types, so the

residential database currently does not distinguish water heating UECs by house type.

64

EE-RM-94-230 COMMENT 61C

4. WATER HEATING END-USE DATA 

Water heating accounts for approximately 15% of electricity usage and 25% of natural gas 

consumption"in residential buildings. Water heating is comparable to space heating in 

terms of the complexity of the issues surrounding level of usage, behavioral impacts, and 

climatic impacts. There is large variability in water heat energy use across households. 

which is panly due to household size (Kempton 1984). In addition, there are subtle 

climatic.effects on water heating energy use, since colder areas of the country also have 

colder inlet water temperatures and thus greater water heating requirements. 

Water heating is a complex end-use because of the unknowns involved. including hot water 

demand in gallons. incoming cold water temperatures. and the hot water temperature at the 

point of use. These parameters are inter-related. For example. if the hot water temperature 

" of the storage water heater is higher. less hot water will be needed to meet a certain need 

since it is usually mixed with cold water to achieve the desired temperature. 

4.1. Water Heating UECs 

Measured data on electric water heating UECs are plentiful. but show the large variability 

previously described. Measured data on gas water heat energy use is limited and the RECS 

conditional demand estimates and a few studies summarized by Usibelli (1984) provide 

virtually the only estimates of national average gas water heating energy use. Water heating 

UECs can easily be calculated according to engineering principles. and these are usually 

used in models. However. these calculations require assumptions regarding key 

p~eters. 

UEC equation 

There are several" different ways of incorporating usage and efficiency data in calculating 

water heating UECs. The equations below show a simplified method that uses the Energy 

Factor of a water heater detennined from the DOE test procedure. "This equation may not 

re valid for levels of consumption that are far from the base test procedure usage, however. 

Electric: 

Fuel: 

where: 

Stock UECs 

kWh! _ Use· TempRise* 8.2928 • 365 

yr - 3413 Btu/kWh • (EFIlOO) 

MMB u/ _ Use· TempRise • B.2928 • 365 

t yr - (EF/lOO) 

Use is the household hot water use (gallons/day) 

TempRise = temperature difference between incoming cold water and tank tempenture (77 f) 

8.2928 is the specific beat of water (Btu/gal.f) 

365 is days per year 

EF is the energy fuetor from the OOE test procedure (%) 

The UECs in the residential database are derived from weighted averages of other studies 

and are 37$0 kWhlyr (n=96) for electric water heating and 23.7 MMBtu/yr (n=22) for gas 

water heating across all building types (Appendix B). We assume_that oil water heater 

UECs are the same as gas for stock units. There are few measured data specifically 

addressing the difference between water heat usage between housing types, so the 

residential database currently does not distinguish water heating UECs by house type. 

64 
New UECs

UECs for new water heaters are calculated based on the UEC for stock units adjusted for

the difference between the stock and the new energy factor derived from the historical

efficiency data (see below). We estimate that UECs for new water heaters are 3545

kWh/yr for electric, and 21.5 MMBtus for gas and oil Table 4.1 provides 1990 stock

andd new water heating UECs.

Table 4.1. Water Heatin UECs

1990 1990

Fuel Type Stock New

Electric (kWh/yr) 3750 3545

Gas (MMBt/yr) 23.7 21.5

Oil (MMBtu/yr) 23.7 21.5

1) Stock electric and gas UECs from Appendix B.

2) New electric and gas UECs estimated based on

changes in efficiency from 1990 stock and 1990 new

units.

4.2. Hot Water Usage

In a summary of hot water usage studies, Usibelli (1984) estimates that hot water

consumption averages 17.7 gal/person-day. Several different metered studies in the Pacific

Northwest estimate per capita water use between 16.5 and 21.0 gallons per day. Measured

data from the BPA REMP program (Taylor et aL 1991) specifically gives electric water

heating energy use across number of occupants (see Figure 4.1). Assuming standby losses

(energy use at zero usage) and a 77 F temperature rise, a quadratic fit through the kWh data

allows the calculation of gallons for the level of occupancy. These data are shown in

Figure 4.2. The quadratic curve means that the incremental hot water consumption drops

off with increasing numbers of persons per home. At the national average of 2.61

persons/household (US DOE 1992), these data give national average hot water

consumption of 45.3 gallons/day-household, or 17.5 gallons/capita-day, which compares well with the other estimates of per capita usage. Assuming a 77 F rise between the

incoming cold water temperature and the hot water setpoint temperature, 45.3 gallons/dayhousehold gives UECs that are similar to the estimated UECs shown above. A more recent

study shows total hot water use for average early 1990s dwellings of 59.5 gallons/day

(Koomey et al. 1994b).

These estimates are in disagreement with the usage assumed in the U.S. DOE test

procedure for water heaters, where the average usage is 64.3 gallons/day. A summary of

several available water heating studies for ASHRAE supported average usage levels near

the U.S. DOE test procedure level (ASHRAE 1991), but these were not necessarily

representative samples.

4.3. Water Heating Technology Data

Two different basic technology types are included in the residential database. These are

individual storage water heaters (STR), where water is heated in a tank for individual

households, and common storage water heating systems (CMN), which are found in multifamily buildings. Instantaneous water heaters are a small portion of the market and are not

included. Technology data on common systems is also not included, although the market

shares are represented in the shares database.

EE-RM-94-230 COMMENT 61C

New UECs 

UECs for new water heaters are calculated based on the UEC for stock units. adjusted for 

the difference, between the stock and the new energy factor derived from the historical 

efficienc::y data (see below). We. estimate that UECs' for new water heaters are 3545 

kWhlyr for electric. and 21.5 MMBtus for gas and oil. Table 4.1 provides 1990 stock 

andd new water heating UECs. 

T a hi e 4.1. w ater H eating UECs 

1990 1990 

Fuel Type Srock New 

Electric (kWblyr) 3750 3S45 

Gas (MMBtulyr) 23.7 21.5 

Oil (MMBruIyr) 23.7 21.5 

.. 1) Stock electrtc and gas UECs from Appendix B. 

2) New eiecuic and gas UECs estimaledbased on 

changes in efficiency from 1990 slock and 1990 new 

units. 

4.2 •. Hoi Water Usage 

In a surnmary ~f hot water usage studi~ Usibelli (1984) estimates that hot water 

consumption averages 17.7 gallperson-day. Several different metered studies in the Pacific 

Northwest estimate per capita water use between 16.5 and 21.0 gallons per day. Measured 

data from the BPA REMP prograIIl (Taylor et al. 1991) specifically gives electric water 

heating (~nergy use across number of occupants (see Figure 4.1). Assuming standby losses 

(energy use at zero usage) and a 77 F temperature rise. a quadratic fit ~ugh the kWh data 

allows the calculation of gallons for the level of occupancy; These data ~ shown in 

Figure 4 .. 2. The quadratic curve means that the incremental hot water .consumption drops 

off with increasing numbers of persons per home. At the national average of 2.61 

personslbousehold (US DOE 1992), these data give national average hot water 

consumption of 45.3 gallons/day-household. or 17.5 gallons/capita-day. which compares 

well with the other estimates of per capita usage. Assuming a 77 F rise between the 

incoming cold water temperature and the hot water setpoint temperature. 45.3 gallonsldayhousehold gives UECs that are similar to the estimated UECs shown above. A more reCent 

stu!iY shows total hot water use for average early 1990s dwellings of 59.5 gallons/day 

(Koomeyet aI. 1994b).. . . . 

These e.stimates are in disagreement with' the usage assumed in the U.S. DOE test 

procedure for water heaters. where the average usage is 64.3 gallons/day. A summary of 

several available water heating studies for ASHRAE supported average usage levels near 

the U.S. DOE test procedure level (ASHRAE 1991). but these were not necessarily 

representative samples. 

4.3. Water Heating TechmJlogy Data 

Two different basic technology types are included in' the residential database. These are 

indi vidual storage water heaters (STR). where water is heated in a. tank for individual 

households. and common storage water heating systems (CMN). which are found in multifamily buildings. Instantaneous water heaters are a -small portion of the market and are not 

included. Technology data on common systems is also not included. although the market 

shares are represented in the shares database. . 
Figure 4.1. Water Heating Energy vs Household Size, Raw Data and Quadratic Fit

1 2 3 4 5 6

Number of Occupants

Source: Taylor et al. 1991. Data includes 200 houses in sample (single-family only). Quadratic fit gives Rsquared of 0.983. Standby losses (usage at.zero occupancy) are estimated from vacation days during

monitoring period.

Figure 4.2. Hot Water Consumption vs Household Size

90

en

o

80

70

60

50

40

30

20

10

0

0 1 2 3 4 5 6

Number of Occupants

Source: Calculated from kWh vs. household size regression results assuming 77 F temperature rise. At national

average 2.61 persons/household (US DOE 1992), hot water consumption is 45.3 gal/day/household, or

17.5 gal/day-capita.

66

8000

' 7000

6000

P 5000

X 4000

I 3000

a 2000

; 1000

0

? . : --=-*

.~~~~~~~~~~~~~ i

ir~ ~.-1

; r __. .~- ~--~ ~----. ........ -.i

0

EE-RM-94-230 COMMENT 61C

F".gure 4.1. Water Heating Energy vs Household Size, Raw Data and Quadratic: Fit 

l! 2000 

~ ... 1000 

o 1 2 3 4 5 6 

Number orOc:cupants 

Source: Taylor et at. 1991. Data includes 200 houses in sample (single-family only). Quadratic fit gives Rsquared of 0.983. Standby losses (usage at.zeIO occupancy) are estimated from vacation days during 

monitoring period. 

Figure 4.2. Hot Water Consumption vs Household Size 

90 

80 

~ 70 ell 

e 

en 60 I: 

..5! 

-; 50 

c 

-= 40 Ci 

.c 

~ 30 

CI 

:c 20 

10 

0 

0 

===~+=:~=:=:==~=~=~=--=r-=~-==r-:=--­

---~--=~ =~=+=~=~--- T---~- ~:-==:~~~~==- : 

-_· .. ·_·--1-- -.... · .. --··l------.. --.. ·-.. -.. -r· .. -.. ·-----·l''·-.. · .. -.. ·-----··-··1 .. ··---.. · .... ·----· 

--==~====:==t=:===~====]=-====::==== 1 2 3 4 5 6 

Number of Occupants 

Source: Calculated from kWh vs: household size regression results assuming 77 F temperature rise. At national 

average 2.61 persons/household (US DOE 1992), hot water consumption is 45.3 galldayJbousehold. or 

17.s gaJIday-capil3. 

66 
Historical Efficiency Data

Shipments of each type of storage water heater are shown in Figure 4.3 and the energy

factor of new storage water heaters sold over time is shown in Figure 4.4. Efficiencies

have apparently changed little since 1980. Efficiencies associated with common water

heating systems in multi-family buildings are not well known.

Cost vs. Efficiency for New Equipment

The database includes estimates of cost vs. efficiency for new water heaters purchased for

electric and gas storage-type water heaters. These are shown in Figure 4.5 and 4.6. Heat

pump water heaters are still in small production volumes and are not currently available on

a wide basis.

Product Lifetimes

Estimates of storage water heater lifetimes are included in the database from several sources

and are listed in Table 4.2.

Table 4.2. Estimates of Residential Storage Water Heater Lifetimes

Lifetime in Years

Gas Oil Electric

Water Water Water

Source Estimate Heate Heater Heater

Low 5 n/a 8

Appliance Avg 10 n/a 12

High 13 n/a 17

Low 10 n/a 10

Lewis/Clark Point 10 n/a -12

High 15 n/a 15

Low 7 7 7

LBLREM Avg 13 13 13

High 19 19 19

Sources: Appliance 1992 (first owner lifetime only);

ASHRAE 1987; Lewis and Clarke 1990;:LBLREM

1991.

67

EE-RM-94-230 COMMENT 61C

Historic:ll Efficiency Data 

Shipments of each type. of storage water heater are shown in Figure 4.3 and the energy 

factor of new storage water heaters sold over time is shown in Figure 4.4., Efficiencies 

have app:uently changed little since 1980. Efficiencies associated with common water 

heating systems in multi-family buildings are not well known. ' ' , 

Cost vs. Emciency for New Equipment 

The database includes estimates of cost vs. efficiency for new water heaters purchased for 

e~ectric and gas storage-type water heaters. These are sho.wn in Figure 4.5· and 4.6. Heat 

pump water heaters are' still in small production volumes and are not currently available on 

a wide basis. 

Product Lifetimes 

Estimates of storage water heater lifetimes are included in the database from several sources 

and are listed in Table 4.2. 

Table 4.2. Estimates of Residential Storage 

Water Heater Llretimes " Ufetime in Years 

Gas OU' EIectti~ 

WaJJ:I Wau:r WaJt:l 

Source Estimate Heater Heater Healer 

Low S nla 8 

Appliance Avg 10 nla 12 

High 13 nla 17 

Low 10 nla 10 

Lewis/Clark Point 10 nla 12 

High 15 nla 15 

Low 7 7 7 

LBiJREM Avg 13 13· 13 

High 19 19 19 . Sources: Appliance 1992 (fll'St owner lifetime only); 

ASHRAE 1987: Lewis and Clarke 1990;LBLREM 

1991. 

67 
Figure 4.3. Annual Storage Water Heater Shipments, 1951-1990

4.5

4.0- -

0.0 ----- --------- 

i 2.5_____. ._S_ \______ 

.5 .-. .- _..

1.o- I _. ! 

3 - .-. ._ , l : ,-, ..._ _ ....-.,_ 

1.o - _ . --- ----- - _/--

1950 1955 1960 1965 1970 1975 1980

Souce: GMA 1991. Gas includes LPG appliances.

Figure 4A. -Shipment-Weighted Energy Factor for Storage Water Heaters, 1972-1990

100

9() 90

80

E 74) i

,& 60 I ..

8 50)

& 4i)

I 30

20

11)

0

1!

Source: US I

1985

. ._ ,,''-' i

. „ - -- ._ __ , . .

.- |-----s--_ : . I __.. 

.+I I I

972 1974 1976 1978 1980

O)E 1982b, GAMA 1991, NAECA 1987.

1982 1984 1986 1988 1990

1990

EE-RM-94-230 COMMENT 61C

Figure 4.3. Annual Storage Water Heater Shipments, 1951·1990 

----EJec 

----- G8s' 

----.~--- Oil 

1955 1965 1970 1975 1980 '1985 1990 

Source: GAMA 1991. Gas includes LPG appliances. 

Figure 4.4 .. Shlpment.Welghted Energy Factor for Storage Water Heaters, 1972·1990 

100 i' 

90 I 

i 

i 

I 

80 I' 

i 70 ~ 

,!. 60 

.. 

.s 50 u l 

~ ~ 40 ~. 

2." ., 

c: 30' ~ 

20 

10 

() 

i ~ . 

I 

, i 

I 

.--1 ! ._- - .. I '":"' : T. 

i + 1: '-'-+---j' ---"!---!-

i 2 i! 

____ +--_-_--__ ~i.------r----- ------1 

I I ------f------..;-

• 

-, 

EJec _ 

-O--Gas '--

-r---

j : " ; 

--f1 

1972 , 1974 1976 1978 1980 1982 1984 1986 1988 1990 

Source: US DOE 1982b. GAMA 1991. NAECA 1987. 
Figure 4.5. Cost Versus Efficiency for New Electric Storage Water Heater

Option Description

Baseline Electric water heater - 52 gallon unit

1 0 + Reduce Heat Leaks

2 1 + Heat Traps

3 2 + Add On Heat Pump

4 3 + R-25 Insulation

1990 Standard= 88% EF

0 200 400 600 800

Consumer Cost ($1990)

Source: US DOE 1993.

Figure 4.6. Cost Versus Efficiency for New Gas Storage Water Heater

Option Description

Baseline Gas water heater - 40 gallon unit, 1990 Standard

1 0 + HeatTraps

2 1 + Reduce Heat Leaks

3 2 + R-16 Insulation

4 3 + mprove Flue Baffle with Standard Venting

5 4 + Electronic Ignition w/Fluc Damper

6 0 + Submerged Combustion

0 200 400 600 800 1000

Consumer Cost ($1990)

Source: US DOE 1993. Annual electricity use is 137 kWh for option 5 and 356 kWh for option 6.

69

200

ISO 180

140

5 120

100

80

I.

o

'.

SP

100

95

90

85

so80

75

70

65

60

55

50

EE-RM-94-230 COMMENT 61C

Figure 4.5. Cost Versus Emciency for New Electric Storage Water Heater 

~ ~------~----~------~------~ 

180 ----"+----

I ~ 160 -t----~I!------'-

i .140----·-r--T- ·--·-r--·---· 

r:s 120 .. _ .. -_._.-r-.... _-. --_.---... ---

. .' . ·------1---- -+-------------

~ I f 

80 +-------~----~~----_+------~ 

100 

o 400 600 800 

CODSUIDer" Cost (51990) 

Source: US DOE 1993. 

. lion 

Elccttic Walei' heater - 52 gallon unit 

0+ Reduce Heat Leaks 

1 + Heat Traps 

3 2 + Add On Heat Pump 

4 3 + R-25lnsulation 

1990 Standard = 889'0 EF 

Flgure 4.6. Cost Versus Emciency for New Gas Storage Water Heater 

100 

9S 

90 

- 8S * ..... 

.. 80 0 

1: 

r: 7S 

~ 

~ 70 

• 

~ 6S 

60 

SS 

SO 

-----r--

o 200 400 600 800 

Consumer Cost ($1990) 

1000 

. tion 

Gas water hearer· 40 gallon Wlit. 1990 Standard 

. 0 + Heat Traps 

1 + Reduce Heat Leaks 

2 + R-16 Insulation 

4 3 + Improve fluc Baffle with Standard Venting 

S 4 + Electronic Ignition w/Fluc,Dampcr 

6 0 + Subm cd Combustion 

Source: US DOE 1993. Annual electricity use is 137 kWh for option 5 and 356 kWh fO{ option 6. 

69 
4.4. Shares

The database includes fuel and technology shares for water heating at a national level It

includes stock shares from the RECS data for 1981, 1984, 1987 and 1990. It also includes

shares in new buildings from the RECS data for buildings built during the previous 5-7

years from the same data sets. Stock shares over time, as well as new shares by housing

type from the 1990 RECS data are shown in Figures 4.7 and 4.8. According to the RECS

data, electric water heating gained market share from 1981 to 1990 while fuel-fired systems

shares have been dropping. Share for new units in new homes favor electricity over gas by about 2 to 1.

4.5. Standards

Efficiency standards for water heaters were enacted in 1987 under the National Appliance

Energy Conservation Act (NAECA) and were implemented in 1990. The standard

specifies a minimum energy factor for storage water heaters based on water heater size.

The energy factor is based on the U.S. DOE test procedure mentioned above. The standard

and estimated UECs associated with the standard are shown in Table 4.3.

Table 4.3. Minimum Efficiency Standards for Residential Storaee Water Heaters

Calculated Calclated

Year Minimum Efficiency Average SndS Standard

Fuel 'Eff. Standard Equation Volume EF UEC

Gas 1990 EF=0.62-(0.0019 * Volume) ( 40 gallons 054 19.4 MMBuyr

Oil 1990 EF=059-(0.0019 * Volume) 40 gallons 0.51 205 MBtu/yr

Eectric 1990 EF=0.95-(0.00132 * Volume) 52 gallons 0.88 3510 kWh/yr

1) Effective date is January I of year indicated.

2) Standards level from NAECA 1987. Volume is rated storage volume in gallons.

3) Average volume is for typical size unit from LBL Appliance Energy Conservation

Database (LBL i990).

4) UEfC based on usage of 453 gal/day and at 77F temperature rise.

7n

EE-RM-94-230 COMMENT 61C

4.4. Shares 

The dtltabase includes fuel and tecbnologyshares for water heating at a national level It 

includes stock shares from the RECS data for 1981,1984,1987 and 1990. It also includes 

shares in new buildings from the RECS data for buildings built during the previous 5-7· 

years from the same data sets. Stock shares over time, as well as new shares by housing 

type fr~m the 1990 RECS data are shown in Figures 4.7 and 4.8. According to the RECS 

data, electric water heating gained madcet share from 1981 to 1990 while fuel-fired systems 

shares have been dropping. Share for new units in new homes favor electricity over gas by 

about :~ to 1. 

4.5. StandariIs 

·EfficiencY standards for water heaters were enacted in 1987 under the National Appliance 

Energy Conservation Act (NAECA) and were implemented in 1990. The standard 

specifies at minimum energy factor for. storage water heaters based on water heater size. 

The enl~gy factor is based on the U.S. DOE test procedure mentioned above. The standard 

and estimated UECs associated with the standard are shown in Table 4.3. 

Tabne 4.3. Minimum Efficiency Standards lor Residential Storage Water Heaters 

, Qllcuiated Cak:uJared 

Year Minimum Efficiency Average Standard Standard 

Fuel ·EIr. Standard Equation Volume . EF UEC 

Gas 1990 EF=O.62-(0.OO19· Volume) 40 galloils 0.54 19.4 MMBtuIyr 

Oil 1990 EF=O.59-(0.OOI9 • Volume) 40 gallons 0.51 20.5 MMBtuIyr 

Electric 1990 EF=O.95-(0.OO132 • Volmne) 52 gallons 0.88 3510 kWbJyr . . 1) Elfectivc dale IS January 1 of year mdlcated • 

2) Standards level fromNAECA 1987. Volume is rated storage volume in gallons. 

3) Average volume is Cor typical size unit fromLBL Appliance Energy Conservation 

Database (LBL i990). 

4) tme based on usage of 45.3 galIday and at 77F temperature riSe. 

70 
Figure 4.7. Water Heating Fuel Shares in Total Housing Stock, 1981-1990

- - l 

p~~~~~~~

1982 1984 1986 1988

--- Electric

----- Gas

Oil

-- 0-LPG

1990

Source: US DOE 1982a, 1986, 1989a, 1992.

4% of units with Electric have common systems, 15 to 20% of units with Gas have common systems, and

40 to 50% of units with Oil have common systems (multifamily units only).

Figure 4.. Water-Heating Fuel Shares for New Construction by Housing Type

0.9

0.8

0.7

= 0.6

.o

u OA

.03

0.2

0.1

0

SF

is~· l-A

4·- -- ··fl- ----------

,,~~~~~~~~~~~~~~~. ..... ..

MH

Ei Electric

c Gas

0oil

LPG

ALL

Source: US DOE 1992 data for buildings built between 1985 and 1990.

36% of Multifamily units with Gas have common systems.

71

0.6

05

c 0.4

*3

u

I 0.3

0. oA 0.2

0.1

v19

1980

-iI , „ I . I

____

E- ::§ i

'M.....

511111111 __1111

..R. E.

nA

EE-RM-94-230 COMMENT 61C

F"JgUre 4.7. Water Heating Fuel Shares in Total Housing Stock, 1981·1990 

-

c 

0 .. :: 

u 

III 

~ 

-4U 

"-III 

~ 

en 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

l' 1 - i J ---__ ---.... ~:::.----.-... ------+i -_ ..-

: I,' ':.: 

~ 

+-------~--------~------·~l-------t-------­ .i. 

-- - ; - ; ·------'t---····-.. -t"'-·-······-···-·r----·-···-~---····-........ -

I I ' I --.. -·--r--···· .. ·-.. -·-··· u

1·-· .. ·-·-.. -·-.. -···r·--··----·r-·····_ .. _· __ ..... : : : : 

~ f ~ ~ ': : : : +-----~. :: -- -- - --~, ---.,._-- ! 

o +-------~------~------_r------_+------~ 

1980 1982 1984 1986 1988 1990 

Source: US DOE 1982a. 1986. 1989a. 1992-

• Electtic 

-D--aas 

--+-- Oil 

---O-LPG 

4CJ{, of unilS with Electric have common systems. IS to 20% of units with Gas have common systems. and 

40 ro 50% of units with Oil have common systemS (multifamily units only). 

F"agure 4.8. Water-Heating Fuel Shares for New Construction by Housing Type 

0.9 .,------------------'--.:....-------, 

0.8 +--.-.. -.------

0.7 +-.-.------.--.~ 

'2 0.6 -r-.,-------.---+ 

.2 

i 0.5 ~ 

-2: 0.4 III 

~ 0.3 

0.2 

0.1 

o 

SF MF MH 

Source: US DOE 1992 data for buildings built between 1985 and 1990. 

36% of MultifamilyunilS with Gas have common systems. 

71 

All. 

ID] Eleea1c . 

DOss 

II Oil 

~LPG 
5. REFRIGERATOR END-USE DATA

Refrigerators are the single largest consumer of electricity among the typical household

appliances. Refrigerators use approximately 125 TWh or 15% of residential electricity

consumption. This is due to the fact that refrigerators are present in almost all households,

a large percentage of households have multiple refrigerators, and each unit uses a

significant amount of electricity. Refrigerators have been extensively studied, and occupant

behavior has relatively small effects, so refrigerator energy consumption is well

understood. Refrigerator UEC depends slightly on ambient temperature.

5.1. Refrigerator UECs

Refrigerator UECs for new units are measured using a laboratory test procedure from U.S.

DOE. Research has shown that this test provides a reasonably good estimate of actual field

usage, but it is not exact (Meier and Heinemeier 1990). The UEC database of measured

and estimated data on field energy usage of refrigerators contains 112 records, and the

estimates show large variability (Appendix B). This variability may be partly due to large

improvements in efficiency of the refrigerators entering the appliance stock. In addition,

there are several different classes of refrigerators (manual defrost, automatic defrost), size

differences, and variations in features that affect the energy consumption of the unit.

UEC equation

The equation below shows the relationship between efficiency, capacity (volume), and

energy consumption for refrigerators used in standards setting procedures and in the U.S.

DOE test procedure.

uEWhyr = 365 * Capacity EC: WMh/yr = HEF

where: 365 is days per year

Capacity is adjusted volume (cubic feet)

Adjusted volume = 1.63 x freezer volume (cubic feet) + refrigerator volume (cubic feet)

EF is the energy factor from the DOE test procedure (cubic feet-day/kWh)

Stock UECs

We estimate the 1990 stock test UEC for refrigerators to be 1270 kWh/yr. based on

historical shipment data of test UECs for refrigerators (AHAM 1991) and a straight line

decay function with a minimum lifetime of 7 years and a maximum lifetime of 29 years.

The analysis of available data for refrigerators in the UEC database (n=50 for studies that

are generally representative of all product classes) suggests that the UEC may be lower, at

around 1150 kWh/yr (Appendix B). For automatic defrost units only, which represent the

majority of the stock, the UEC database analysis results are 1350 kWh/yr. Overall, the

UEC database results are slightly lower than the test values, which is consistent with earlier

findings (Meier and Heinemeier 1990). To maintain consistency with the AHAM historical

data, we include the estimate based on the AHAM data in the database.

72

EE-RM-94-230 COMMENT 61C

s. ,REF~IGERATOR END-USE DATA 

RefrigeratorS are the single largest consumer of electricity among the typical household 

appliances. Refrigerators use approximately 125 1Wb or 15% of residential electricity 

consumption. This is due to the fact that refrigerators are present in almost all households, 

a larige percentage of households have multiple refrigerators, and each unit uses a 

signUicant amount of electricity. Refrigerators have been extensively studied, and occupant beha~"ior has relatively small effects, so refrigerator energy consumption is well 

unde!'Stood. Refrigerator UEC depends slightly on ambient temperature. 

S.l. Refrigerator UE~s' 

Refrigemtor UECs for new units ate measured using a laboratory test procedure from U.S •. 

DOE. Research has shown that this test provides a reasonably good estimate of actual field 

usage, but it is not exact (Meier and Heinemeier 1990). The UEC database of measured 

and estimated data on field energy usage of refrigerators contains 112 records, and the 

estimntes show large variability (Appendix B). This variability may be partly due to large 

improvements in efficiency of the refrigerators entering the appliance stocL In adcUtio~ 

there are several different classes of refrigerators (manual defrost. automatic defrost), size diffen:~ces, and v.ariations in features that affect the energy consumption of the unit. . 

VEe equation 

The ecluation below shows the relationship between efficiency, capacity (volume), and , energy consumption for refrigerators,used in standards setting procedures and in th~ U.S. 

DOE test procedure. '.' ' 

UEe: 

wbe:re: 

StockUECs 

1.:nrL I _ 365 • Capacity A.nwyr - SF 

365 is days per year 

Capacity is adjusted volume (cubic feet) 

Adjusted volume = 1.63 x freezer volume (cubic feet) + refrigerDlOr volume (cubic feet) 

EF is the energy factor from the DOE test procedure (cubic feet.<fayIkWb) 

We estimate the 1990 stock test UEC for refrigerators to be 1270 kWh/yr. based on 

historic:al shipment data of test UECs for refrigerators (AHAM 1991) and a straight line 

decay function with a minimum lifetime of 7 years and a maximum lifetime of 29 years. 

The amllysis of available data for refrigerators in the UEC database (n=50 for studies that 

are gem~rally representative of all product classes) suggests that the UEC may be lower, at 

around 1150 kWh/yr (Appendix B). For automatic defrost units only, which represent the 

majority of the stock. the UEC database analysis results are 1350 kWh/yr. Overall. the 

UEC database results are slightly lower than the test values, which is consistent with earlier 

findings (Meier and Heinemeier 1990). To maintain consistency with the AHAM historical 

data, wc:~ include the estimate based on the AHAM data in the database. 

72 
New UECs

New unit average UEC derived from the laboratory tests and reported by the industry for

1990 is 916 kWh/yr for the overall average sales of new refrigerators. This is similar to the

average for top-freezer automatic defrost units, which comprise approximately 67% of the

new refrigerator market (AHAM 1991).

5.2. Refrigerator Usage

The energy usage of refrigerators will vary in the field with number of door openings as

well as the ambient temperature in the vicinity of the refrigerator, the internal temperature of

the refrigerator, and the level of maintenance. These factors have not been characterized on

a large scale, however.

5.3. Refrigerator Technology Data

As previously stated, there are seven different classes of refrigerators that each have

specific performance characteristics related to energy use. In the residential database, we

include technology data that best represent the entire refrigerator market. For some

measures, we include data that are an average across all refrigerator types. For other

measures, we include data on top-mounted auto defrost refrigerators, which accounts for

approximately two-thirds of the unit sales and has characteristics that approximate the

market average.

Historical Efficiency Data

Annual refrigerator shipments from 1951 to 1990 are shown in Figure 5.1 and the overall

average efficiency of new refrigerators sold over time is shown in Figure 5.2 along with

the average size (capacity, or adjusted volume, in cubic feet). Efficiencies have risen

dramatically since the first recorded data in 1972. Technological changes (such as the

transition from fiberglass to polyurethane foam insulation in the 1970s) and minimum

efficiency standards (in California in 1978 and nationally since 1990) are the major factors

influencing this trend.

Cost vs. Efficiency for New Equipment

The database includes estimates of cost vs. efficiency for new top-mounted automatic

defrost refrigerators. These are shown in Figure 5.3. The values are based on data from

the U.S. DOE appliance standards analysis (US DOE 1989b) and estimates in the LBL

Electricity Conservation Supply Curves (Koomey et al. 1991a), adjusted to 1990$.

71

EE-RM-94-230 COMMENT 61C

New UECs 

New unit average VEe derived from the laboratory tests and reported by the industry for 

1990 is 916 kWhlyr for the overall average sales of new refrigerators. This is similar to the 

average for top-freezer automatic defrost units, whicll comprise approximately 67% of the 

new refrigerator market (AHAM 1991). . 

5.2. Refrigerator Usage 

The energy usage of refrigerators will vary in the field with number of door openings as 

well as the ambient temperature in the vicinity of the refrigerator. the internal temperature of 

the refrigerator, and the level of maintenance. These factors have not been characterized on 

a large scale, however. 

5.3. Refrigerator Technology Data 

As previously stated, there are seven different class~ of refrigerators that each have 

specific performance characteristics related to energy use. In the residential database, we 

include technology data that best represent the entire refrigerator market. For some 

measures. we include data that are an average across all refrigerator types. For other 

measures, we include data on top-mounted auto defrost refrigerators. which accounts for 

approximately two-thirds of the unit sales and has characteristics that approximate the 

market average. 

Historical Efficiency Data 

Annual refrIgerator shipments from 1951 to 1990 are shown in Figure 5.1 and the overall 

average efficiency of new refrigerators sold over time is shown in Figure 5.2 along with 

the average size (capacity, or adjusted volume. in cubic feet). Efficiencies have risen 

dramatically since the first recorded.data in 1972. Technological changes (such as the 

transition from fiberglass to polyurethane foam insulation in the 19705) and minimum 

efficiency standards (in California in 1978 and nationally since 1990) are the major factors 

influencing this trend. 

Costvs. Efficiency for New Equipment 

The database includes estimates of cost vs. efficiency for new top-mounted automatic 

defrost refrigerators. These are shown in Figure 5.3. The values are based on data from 

the U.S. DOE appliance standards analysis (US DOE 1989b) and estimates in the LBL 

Electricity Conservation Supply Curves (Koomey et al. 1991a), adjusted to 1990$. 
Figure 5.1. Annual Refrigerator Shipments, 1951-1990

8

7

6

U

a

{

I

§

5

4

3

2

1

0

1965 1970 1975 1980 1985 1990

Source: AHAM data given in USDOE 1989b.

Figure 5.2. Shipment-Weighted Energy Factor and Capacity for Refrigerators, 1972-1990

9

g

C

on

U

a§

8

7

6

5

4

21

20 A

19 f

lg

18 |

Is9

167

16 9'

a.

3 -. I - - I I I | i 1. 15

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990

Source: AHAM 1991.

74

Ii a

i I I

i~~~~~~~~ i~~ I * ~~~i

_ _ _ _ I_

4. ~~~~~~~~~~~~~~~~~~.~~~~

1950 1955 1960

I

EE-RM-94-230 COMMENT 61C

FIgure S.l. Annual Refrigerator Shipments, 1951-1990 

8 

7 

6 

.. 

I 5 f 

'a 

~ 4 

II: 

~ 3" ~ 

2 

1 

0 

I 

! --'-r- : . 

I r-' +-----!------ii---~-_t,----+-----+.---.'!---.--.i---

--~--. 1--·- ! !.! - i , ----; , 

i 

I 

i i ::' •i, ! i • • 

·-~~--'---j----·T-- -----I··---I-··--j .. . 

19!5O 1955 1960 1965 1970 1975 1980 1985 1990 

Somce: AHAM data given in USDOE 1989b. 

FIgure Sol. Shlpment-Wei'ghled Energy Factor and Capacity ror Refrigerators, 1972-1990 

9~--~~--~----~----~----~--~~--~----~----~ 21 

! ~ i _ 8 +----+.---t---i------+-~=::.::tI"" 20.1 

t 7 

~ 

.! 

i 

~ 

~ S 

c 

fI1 

I 

I 

--+--~I---t---'--j~ 

I £ 

.--i--.-t----,-l--.--'- '"'--~..,..- ---t-

! -1---' I 

! ! • : I 

I 

i 

j 

-.......;..---+-- : 

~ 

! ---f- 1-. -- -0-- EffICiency 

i : 

- ..... --~--... ---~-.. -~ .. -.. --- Iii 

! i i 

----- CapaCity 

i .: : 

19 

18 

17 

16 

3~~--~----~----+---~~---+----~----~---+----~15 

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 

Source: AllAM n991. 

74 

~ 

i 

u 

I 

i 

'B 

.. 

"" :t 

:;;' 

~ 

l:-

-U 

81 

Co 

III 

U 
Figure 53. Cost Versus Efficiency for New Refrigerator-Freezer

20

S 16

t12..- _..... ... ..... .... .................... _ ....

I l2o1 ""~ 1 0 """'"" ~~^ ^ " - **..............................._-._._

. ~= i i

a

Baseline

500 550 600 650 700 750 800 850

Consumer Cost (S1990)

Source: US DOE 1989b for baseline and options 1-3. Koomey et al. 1991a for options 4 and 5. Models 4 and 5

are available after the year 2000. Costs adjusted from $1987 using a CPI multiplier for household

appliances of 1.024.

Ontion Descrtinn

75

Baseline 20.8 cuft adjusted volume. Top freezer auto defrost Meets 1990 Standard. No CFCi

1 1993 Standard. Enhanced heat transfer + foam door + 5.05 EER compressor + 2 in. door insulation.

2 1 + Evacuated Panels

3 2 + Two-Compressor System

4 3 + Recycle Condensor Heat to Replace Anti-Sweat Heaters

4 + Refrierator Compressor EER to 53

_r____ _____r_____

EE-RM-94-230 COMMENT 61C

Figure 5.3. Cost Versus Efficiency for New Refrigerator-Freezer 

~ ,-------~------~------;-------~------~------~------~ 

s 

18 ----1--- I-----T---- '---T----:-

. : 3 j . 

2 

---r----T--- .. _--_._-.... -................ _-_ .... -_ .. -

~ ~ 

8 

BaseIiDe 

6 ~------~--------~------~--------4-------~--------~------~ 

500 550 600 700 750 800 8SO 

Consumer Cost ($1990) 

Source: US DOE 1989b for baseline and options 1-3. Koomey et aI. 1991a for options 4 and S. Models 4 and 5 

are available aCte{ the year 2000. Costs adjusted from S 1987 using a CPI multiplier for household 

appliances of 1.024 •. 

Option Ion 

Baseline 20.8 c:u.ft. adjusted volwne. Top freezer auto dcirosL Meers 1990 Standard. No CFCs. 

1 1993 Standard. Enhanced heat InInSCc:r + foam door + 5.05 EER compressor + 2 in. door insulation. 

2 1 + EvacualCd Pmels 

3 2 + Two-Compn:ssot: System 

4 3 + Recycle Condensor Heat to Replace Anti-Sweat Heaters . 

5 4 + Refrigc:raIOrConrpressor EER to S3 

75 
Product Lifetimes

The database contains estimates of the lifetimes of refrigerators, as listed in Table 5.1.

Table 5.1. Estimates of

Residential Refrigerator

Lifetimes

Source Estimate Years

Low 10

Appliance Avg 16

1High 20

Low 13

LBLUREM Avg 19

High 25

Sources: Appliance 1992 (first

owner lifetime only); LBLREM

1991.

5.4. Shares

The database includes shares for refrigerators for stock buildings by housing type at a

national level It includes total shares and specific shares for manual defrost and automatic

defrost from the RECS data (US DOE 1982a, 1986, 1989a, 1992). It also includes shares

in new buildings from the RECS data for buildings built during the last 5-7 years from the

same data sets. Some of these data are shown in Figures 5.4 and 5.5. Note that these are

total "saturations" of refrigerators (shares x housing stock = refrigerator stock) to account

for multiple refrigerators per household. The share of houses with no refrigerators is

virtually zero.

According to the RECS data, the number of refrigerators per household is growing over

time. In comparing the stock shares with new shares, we see that this growth is not

necessarily due to greater-re'frige'rator.saturations in newly constructed homes since the

"new" shares are essen'tiallUyItiesame as for the stock as a whole. This suggests that the

growth in refrigerator saturations is mainly due to the acquisition of second refrigerators in

existing houses.

5.5. Standards

Efficiency standards for refrigerators were enacted under the National Appliance Energy

Conservation Act (NAECA) 'difirst implemented in 1990. The standard specifies a

maximum energy use for refrigrators based on the type of refrigerator and the size. The

energy usage is based on the U.S. DOE test procedure. More stringent standards were

implemented in 1993. These are summarized in Table 5.2.

_,

EE-RM-94-230 COMMENT 61C

Product Lifetimes 

The database contains estimateS of the lifetimes of refrigerators. as listed in Table 5.1. 

5.4 . .shares 

Table 5.1. Estimates of 

Residential Refrigerator . 

Lifetimes 

Source Fslimate Years' 

lDw 10 

Appliance Avg 16 

Higb 20 

Low 13 

LBUREM Avg 19 

High 2S . Sources: Appbance 1992 (first 

. owner liCetil:ne only); LBLREM 

1991. 

The databaSe includes shares for refrigerators for stock buildings by housing type at a 

nationalle.veL It includes total shares and specific shares for manual defrost and automatic 

defrost from the RECS data (US DOE 1982a, 1986. 1989a, 1992). It also includes shares . in new buildings from the RECS data for buildings built during the last 5-7 years from the 

same data sets. Some oftbese data are'shown in Figures 5.4 and 50S.. Note that these are 

total "saturations" of refrigerators (shares x housing stock = refrigerator stock) to account· 

for mTlltiple refrigerators per household. The share of houses with no refrigerators is 

virtually zero. . 

According to the RECS data. the. number of refrigerators per household is growing over 

time. In comparing the s~Q¢k ,shares with new shares. we see that this growth is not 

n~:arily due. to g~ll.~er::~tij·~~~t~rs·at~atio~~ newly construc~ homes since the 

"new" slull'eS are essennally'TtHe'same asforthe'stock as a whole. 'TIus suggests that the 

growth in refrigerator saturations is mainly due to the acquisition of second refrigerators in 

existinlg houses. .. 

5.5. Standards 

\ - < -- _. , 

Emcie~ncy standards f9trefrig~~tors were eriactedcunderthe National Appliance Energy 

Conservation Act (NAEGAr~d!tfirst implemented in 1990.'. The standard specifies a 

maximum energy use lor refrig~rators baSeCI on the type of refrigerator and the size. The 

energy usage is based on the U.S. DOE test procedure. More stringent standards were 

implemented in 1993. These are summarized in Table 5.2. . . 
Figure 5.4. Refrigerator Ownership Shares by Housing Type, 1981-1990

1.25

1.20

1.15

1.10

1.05

1.00

1980

i

.j "- ^ ^ ^ _ ^ -- SF

__ __ ----- sF

! _ I ,_=--- _ _ ALL

= i „ j___~~~~~~~~~~~~~~~~~I i _ I "

1982 1984 1986 1988 1990

Source: US DOE 1982a, 1986, 1989a. 1992.

Shares are for total saturations across households (includes multiple refrigerators per household).

Figure 5.5. Refrigerator Ownership Shares for New Construction by Housing Type

1.25

1.00 --

0

= 0.75

0- a 0.50

c.U)

0.25

.-.-.

0.00 - l

SF

---

MF MH ALL

Source: US DOE 1992 data for buildings built between 1985 and 1990.

Shares are for total saturations across households (includes multiple refrigerators per household).

.0

I

u

ta

EE-RM-94-230 COMMENT 61C

Figure 5.4. Refrigerator Ownership Shares by Housing Type, 1981·1990 

c 

~ 

-

u 

~ 

-

1.25 

1.20 

1.15 

I 

I 

I 

I 

i 

. I 

~ 1.10 . 

"'" r.tJ 

1.05 

i 

I 

I 

i 

--------~i--------~ ~ 

1.00 +-----+-----+----+------+-~----1 

1980 1982 1984 1986 1988 1990 

Source: US DOE 1982a. 1986, 1989a. 1992. 

• SF 

• MH 

--<>-ALL 

Shares are for total sarurations aaoss households [mcludes multiple refrigerators ~ household). 

Figure 5.5. Refrigerator Ownership Shares for New Construction by Housing Type 

1.25T---------------------------------------------------~ 

1.00 

c 

~ 0.7S u . 

~. 

-

~ 0.50 

"'" r.tJ 

0.25 

0.00 +-----JO 

SF MF 

Source: US DOE 1992 data for buildings built between 1985 and 1990. 

MH AU. 

Shares are for total saturations across households (includes multiple refrigerators per household) . 

..,.., 
Table 5.2. Minimum Efficiency Standards for Residential Refrigerators

Type:

MND

PAD

TAD

SAD

BAD

TADI

SADI

Refrigerators and Rfrigerator-Freezers with manual defrost

Rcf;gerator-Frtc cr - patal automatic defrost

Refrigerator-Freezs - automatic defrst with: Top-mounted freezer w/o hrough-the-door ice service

Refigerator-Freee - automatic defrost with: Side-mounted freezer w/o through-the-door ice service

Refigerator-Freezes - automatic defrost with: Bottom-mounted freezer w/o through-the-door ice service

Refigerator-Fezes - automatic defrost with: Top-mounted fezer w through-the-door ice service

Refigrator-Freezer - automatic defrost with: Sid-mounted freezer w/ drough-the-door ice service

1) Effective date is January 1 of year indicated.

2) 1990 Standards level equation from NAECA 1987. 1993 Standards level equation from US DOE 1989b.

Capacity measure is adjusted volume (AV), where AV-refrigerator volume + 1.63 * freezer volume.

3) Average volume for different product classes from AHAM 1991 for shipments in year 1990.

4) EF calculaed from UEC as 365*Capacity/UEC. Units are cuft-day/kWh.

5) Sales by product class are from US DOE 1989b, and are data from 1988.

6) Weighted average across entire product category is similar to data for the TAD product class.

78

Average Fraction

Year Calculated aCapacity Cal. of

Type Ef. Maximum UEC Equation (Adj. Vol.) UEC EF Saes

MND 1990 UEC= 163 Capacity + 316 5.0 cuft 398 kWh/yr 4.60 4.7%

PAD 1990 UEC= 21.8 Capacity + 429 14.6 cuft 747 kWhyr 7.13 5.6

TAD 1990 UEC= 23.5 Capacity + 471 20.6 cuft 956 kWhyr 7.88 72.9%

SAD 1990 UEC= 27.7 *Capacity + 488 27.2 cuft 1243 kWh/yr 8.00 6.2%

BAD 1990 UEC= 27.7 Capacity + 488 27.2 cuft 1243 kWh/yr 8.00 2S%

TADI 1990 UEC= 26.4 Capacity + 535 20.6 cuft 1079 kWh/yr 6.97 0.7%

SADI 1990 UEC= 30.9 * Capacity + 547 27.2 cuft 1389 kWh/yr 7.16 7.4%

Average 1990 _ . n/a 20.6 cuft 976 kWhyr 7.71 100.0%

MND 1993 UEC= 19.9 * Capacity + 98 5.0 cuft 198 kWh/yr 9.25 4.7%

PAD 1993 UEC= 10.4 *Capacity + 398 14.6 cuft 550 kWh/yr 9.69 5.6%

TAD 1993 UEC= 16.0 * Capacity + 355 20.6 cuft 685 kWh/yr 10.99 72.9%

SAD 1993. UEC= 11.8 * Capacity + 501 27.2 cuft 822 kWh/yr 12.09 6.2%

BAD 1993 UEC= 14.2 * Capacity + 364 27.2 cuft 751 kWh/yr 13.24 2.5%

TADI 1993 UEC= 17.6 * Capacity + 391 20.6 cuft 754 kWh/yr 9.98 0.7%

SADI 1993 UEC= 16.3 Capacity + 527 27.2 cuft 971 kWh/yr 10.24 7.4%

Average 199'3 _ . n/a 20.6 cuft 686 kWh/yr 10.96 100.0%

EE-RM-94-230 COMMENT 61C

Table 5.1. Minimum Efficiency Standards ror Residential Refrigerators 

Type 

MNti 

PAD 

TAD 

SAD 

BAD 

TADI 

SADI 

Average 

MND 

PAD 

TAD 

SAD 

BAD 

TADI 

SADI 

Average 

Type: 

Average 

Year Capacity 

Eff. Maximum UEC Equa~on (Adj. Vol.) 

19CJO UEC= 16.3" Capacity + 316 S.O cuft 

1~)() UEC= 21.8 • Capacity + 429 14.6 cuft 

19'.10 UEC= 23.s • Capacity + 471 2O.6.cuft 

19'.10 UEC= 27.7" Capacity + 488 27.2 cuft 

1990 UEC= 27.7" Capacity + 488 27.2 cuft 

1990 UEC= 26.4 .. Capacity + 535 20.6 cuft 

19~1O UEC= 30.9" Capacity + 547 27.2 cuft 

1990 n/a 2O.6cuft 

1993 UEC= 19.9" Capacity + 98 . 5.0 cuft·. 

1993· UEC= 10.4" Capacity + 398 14.6 cuft 

19513 UEC:: 16.0" Capacity + 3S5 20.6 cuft 

-1993· UEC= 11.8" Capacity + SOl 27.2 cuft 

1993 UEC= 14.2" Capacity + 364 27.2 cuft 

1993 UEC= 17.6" Capacity + 391 20.6 cuft 

1993 UEC= 16.3 "Capacity + 527 27.2cuft· 

19913 nla 20.6 cuft 

Rcmgcrar.ars and Refrigerator-Freezers with manual defrost 

Rd~crazor-Fn:czc:r - panial awoma1ic de&ost 

, Calculated . 

UEC 

398 kWb/yr 

747 kWblyr 

956 kWblyr 

1243 kWh/yr 

1243 JcWh/yr 

1079kWh/yr 

1389 kWh/yr 

976 kWh,lyr 

198 kWh/yr 

5S0kWblyr 

685 kWblyr 

822 kWh/yr 

751 kWh/yr 

754 kWh/yr 

971 kWh/yr 

686kWh,lyr 

Fraction 

Calc. of 

EF Sales 

·4.60 4.19& 

7.13 S.6.9D 

7.88 72.9% 

8.00 . 6.29& 

8.00 2.59& 

6.97 0.79& 

7.16 7.4% 

7.71 100.09& 

9.25 4.79& 

9.69 5.69& 

10.99 72.99& 

12.09 6.29& 

13.24 2.59& 

9.98 .0.7% 

10.24 7.4CJb 

10.96 100.0% 

MND 

PAD 

TAD 

SAD 

'BAD 

TADI 

SADI 

R.efrigcrazor-Frcczers - auromalic defrost with: Top-mounted freezer wlo lhrough-the~ ice scrvic:c 

Rcfr.igcrazor-Fn:ezc:rs - aUlOmalic defrost with: Side-mounted f'teczz:r w/o thJough-lhe~ icc service 

Rc6.igc:rator-Freezcrs - IUIOmalic deCtoStwith: Bottom-mourued freezer wlo through-thC-door ice service 

Rcfi.ige:r.uar-FRczen - auromatic defrost with: Top-mounted fr=zer wI through-thc-door ice service 

RcfJigcnror-Freez=s - aUIOmatic deltast with: SidC-mo1Dltcd freezer wI dtrough-the-door ice ~cc 

1) Effective diate lis January 1 oCyear indicaled. 

2) 1990 Slandard.<; level equation from NAECA 1987. 1993 Srandards level equation from USDOE 1989b. 

Capacity measure is adjUSled volume (A V), where A V=refrigerator volume + 1.63 .. freezer volume. 

3) Average volume for different product classes from AHAM 1991 for shipments in year 1990. 

4) EF caIculaJted from UEC as 36S"Capacity/UEC. Units are cuft-dayIkWh. 

S) Sales by product class are from US DOE 1989b. and are data from 1988. 

6) Weighted Ilverage across entire product category is similar to data for Ihe TAD product class. 

78 
6. FREEZER END-USE DATA

Freezers, specifically those that are separate from the freezer compartment of the

refrigerator, are a relatively large consumer of electricity among the typical household

appliances, using approximately 33 TWh, or 5% of sector electricity consumption. Like

refrigerators, freezer energy consumption is well understood because of extensive research

and the relatively small effect of occupant behavior on appliance performance.

6.1. Freezer UECs

Freezer UECs for new units are measured using a laboratory test procedure such as for

refrigerators. These provide estimates of the UECs of new units entering the market. The

UEC database of measured and estimated data on energy usage of the freezer stock contains

89 records, but the estimates show large variability (Appendix B). This variability may be

partly due to large improvements in efficiency for freezers, but also reflects the problems

with estimating field usage of appliances. In addition, there are many different sizes and

several different classes of freezers (upright and chest types, manual defrost and automatic

defrost types) which vary widely in energy consumption.

UEC equation

The relationship between freezer UEC, efficiency, and capacity is the same as that for

refrigerators, and is as follows:

_UEC: lckWyr . 365 * Capacity UEC: kWh/yr= =----p-EF

where: 365 is days per year

Capacity is adjusted volume (cubic feet)

Adjusted volume = 1.73 x freezer volume (cubic feet)

EP is the energy factor from the DOE test procedure (cubic feet-day/kWh)

Stock UECs

Based on historical shipment data of test UECs for freezers (AHAM 1991) and a straight

line decay function with a minimum lifetime of 11 years and a maximum lifetime of 31

years, we estimate the 1990 stock test UEC for freezers to be 1025 kWh/yr. The analysis

of available data for freezers in the UEC database (n=52 for studies that are generally

representative of all product classes) also gives results of 1025 kWh/yr for a freezer UEC.

The residential database includes this value for stock UECs of freezers.

New UECs

New unit UECs derived from the U.S. DOE test procedure and reported by the industry for

1990 are 600 kWh/yr for the overall average, 471 kWh/yr for chest, manual defrost (54%

of sales), 679 kWh/yr for upright, manual defrost (37% of sales), and 1030 kWh/yr for

upright, automatic defrost (9% of sales). The current sales are best described by an

average of the two manual defrost classes.

'T7

EE-RM-94-230 COMMENT 61C

6. FREEZER END-USE DATA 

Freezers. specifically those that are separate from the freezer compartment of the 

refrigerator. are a ~latively large consumer of electricity among the typical household 

appliances. using approximately 33 1Wh. or 5% of sector electricity consumption. Like 

refrigerators. freezer energy consumption is well understood because of extensive research 

and the relatively small effect of occupant behavior on appliance performance. 

6.1. Freezer UECs 

Freezer UECs for new units are measured using a laboratory test procedure such as for 

refrigerators. These provide estimates of the UECs of new units entering the market. The 

UEC database of measured and estimated data on energy usage of the freezer stock contains 

89 records. but the estimateS show large variability (Appendix B). This variability may be 

partly due to large improvements in efficiency for freezers. but also reflects the problems 

with. estimating field usage of appliances. In addition •. there are many different sizeS and 

several different classes of freezers (upright and chest types. manual defrost and automatic 

defrost types) which vary widely in energy consumption. 

VEe equation 

The relationship between freezer UEC, efficiency. and capacity is the same as that for 

refrigerators. and is as follows: .. 

DEC: t.:WbJ _ 365 • Capacity ~ yr - EF 

wbere: 365 is days per year 

Capacity is adjusted volume (cubic feet) 

Adjusted volume = 1.73 x fRezer volume (cubic feel) 

EP is the eDergy factor from the DOE leSt procedure (cubic feet-dayIkWb) 

Stock UECs 

Based on historical shipment data of test UECs for freezers (AHAM 1991) and a straight 

line decay function with a minimum lifetime of 11 years and a maximum lifetime of 31 

years. we estimate the 1990 stock test UEC for freezers to be 1025 kWh/yr. The analysis 

of available data for freezers in the UEC database (n=52 for studies that are generally 

representative of all product classes) also gives results of 1025 kWhlyr for a freezer VEC. 

The. residential database includes this value for stock UECs of freezers. 

New UECs 

New unit UECs derived from the U.S. DOE test procedure and reported by the industry for 

1990 are 600 kWhlyr for the overall average, 471 kWhlyr for chest. manual defrost (54% 

of sales), 679 kWhlyr for upright. manual defrost (37% of sales), and 1030 kWhlyr for 

upright. automatic defrost (9% of sales). The current sales are best described by an 

average of the two manual defrost classes. . 

"0 
6.2. Freezer Usage

The energy usage of freezers will vary in the field with number of door openings as well as

the ambient temperature in the vicinity of the freezer and the level of maintenance.

6.3. Freezer Technology Data

There are three different classes of freezers that each have specific performance

characteristics related to energy use. In the residential database, we include technology data

that best represent the entire freezer market. For some measures, we include data that are

averages across product classes. For other measures, we include data on chest manual and

upright manual freezers, which together comprise over 90% of the unit sales and, together,

have characteristics that approximate the market average. The automatic defrost units use

significantly more energy but are only a small portion of current sales.

Historical Efficiency Data

Annual freezer shipments from 1951 to 1990 are shown in Figure 6.1 and the overall

average efficiency of new freezers sold over time is shown in Figure 6.2 along with the

average size (capacity, or adjusted volume, in cubic feet). Efficiencies have risen

dramatically since the first recorded data in 1972. National efficiency standards took affect

in 1990. In addition, average freezer size has been decreasing over time.

Cost vs. Efficiency for New Equipment

The database includes estimates of cost vs. efficiency for new chest manual and upright

manual freezers as well as an average of the two types. These are shown in Figure 63.

The values are based on estimates in the U.S. DOE appliance standards analysis (US DOE

1989b) as well as LBL estimates for future freezer technologies from the LBL Electricity Conservation Supply Curves (Koomey et al. 1991a), adjusted to $1990.

Product Lifetimes

The database contains estimates from two sources of the lifetimes of freezers. These are

listed in Table 6.1.

Table 6.1. Estimates of

Residential Freezer

Lifetimes

Source Esimate Years

Low 10

Appliance Avg 15

High 20

Low 17

LBIJREM Avg 21

High 25

Appliance 1992 (first owner

lifetime only); LBLREM 1991.

EE-RM-94-230 COMMENT 61C

6.2. Freezer Usage 

The energy usage of freezers will vary in the field with number of door openings as well as 

the ambient temperature in the vicinity of the freezer and the level of maintenance. 

6.3 •. Freez.er Technology Data 

There: are three different classes of freezers that each have specific performance 

charac:teristics related to energy use. In the residential database, we include technology data 

that best represent the entire freezer market. For some measure~ we include data that are averaJ~es across product classes. For other measures, we include data on chest manual and 

upright manual freezers, which together comprise over 90% of the unit sales and, together, 

have characteristics that approximate the market average. The automatic defrost units use . signifi.cantly more energy but are only a small portion of current sales. 

IDstorical Efficiency Data 

Annu~ll freezer shipments from 1951 to 1990 are shown in Figure 6.1 and the overall 

average efficiency of new freezers sold over time is shown in Figure 6.2 along with the 

averaJ:e size (capacity, or adjusted volume, in cubic feet). Efficiencies have risen 

dramatically since the fast recorded data in 1972. National efficiency standards took affect 

in 1990. In addition, average freezer size has been decreasing over time. 

Cost vs. Efficiency for New Equipment 

The database inCludes estimates of cost vs. efficiency for new chest manual and upright 

manual freezers as well as an average of the two types. These are shown in Figure 6.3. 

The va~ues are based on estimates in the U.S. DOE appliance standards analysis (US DOE 

1989b) as well as LBL estimates for fu~re freezer technologies from the LBL Electticity 

Conservation Supply Curves (Koorney et al1991a), adjusted to $1990. 

Produ.ct Lifetimes 

The da.tabase contains estimates from two sources of the lifetimes of freezers. These are 

listed in Table 6.1. 

Table 6.1. Estimates of 

Residential Freezer 

Llretlmes 

Source Estimate Years 

Low 10 

Appliance Avg 15 

High 20 

Low 17 

LBIJREM Avg 21 

High 2S . Appbance 1992 (fU'St owner 

lifetime only); LBLREM 1991. 

n" 
Figure 6.1. Annual Freezer Shipments, 1951-1990

3.5

3

a

o2

010

.2

2.5

2

1.5

1

0.5

0

1955 1960 1965 1970 1975 1980 1985

Source: US DOE 1989b.

Figure 6.2. Shipment-Weighted Energy Factor and Capacity for Freezers, 1972-1990

-i I II i i 20

1974 1976 1978 1980 1982 1984 1986 1988 1990

Source: AHAM 1991.

Q1

_ _ I _ _ _ _ _ I _ _ _ __

1950 1990

0-%

A

aJ

I

i

4=

1

U

to

a

w4

1.

El a

w

16 -

15 -^

14

13

12

11 -

10

9 -__

8

7-

6

1972

30

29

28

27

26

25

24

23

22

21

.

U

aI

I

*S

*S5

Zs

a%la

EE-RM-94-230 COMMENT 61C

Figure 6.1. Annual Freezer Shipments, 1951·1990 

----+-----i----__ o __ 

1970 1975 1980 1985 1990 

Figure 6.2. Shipment-Weighted Energy Factor and Capacity for Freezers, 1972-1990 

.a 

-:t 

t 

II 

i' 

.. ~ 

::I 

~ 

.. 

0 -Col 

II 

~ 

;..,. 

ff 

w 

c 

~ 

16 

15 

14 

13 

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9 

8 

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6 

~--~~--~----~----~----r---~----~----~----~30 

--+ 29 ~ 

.! 28 ~ 

.c 

27 B 

+---~----+I----o+----~~--~--~--~~--~~----+26 f _0-1°--·~--- 2S i 

+-0--0 .=:..;_~-+ 24 :s I ~ 

_o __ ~_oo_oo_o i 00_00_0 ______ 40 --0- -+----+-0-0 23 ~ l ! Efficiency l l ~ _0_-+_00-0 ---+ • Capacity -I -1-- 22!. 

_0-01-0--[0_0-- o_-t T: 21 ~ 

+-----r---~-----4-----+----~----~----~----+-----+20 

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 

Source: AHAM 1991. 
Figure 6,3. Cost Versus Efficiency for New Freezers

·_ · · _· ·~~~~~~~~~~~

i. -.* Chest Manual Defrost

(CHM)

-0-- hMidpoint

- Upright Manual Defrost

(UPM) X-M . 4

3Z 3

1 1 T~~~~

350 400 450 500

Consumer Cost ($1990)

Source: US DOE 1989b for baseline and options and 2. Koomey et aL 1991a options 3 and 4. Models 3 and 4

are available after the year 2000. Costs adjusted from $1987 using a CPI multiplier for household

appliances of 1.024. The midpoint of the two major classes approximates the freezer market

50

45' ---

40 ---

A 35

t.

S

It20

6.

= 15 M

10

5

0

---

300 550

Option Descrittton Manual Defrost Freezer

Baseline Upright Manual Defrost Freezer. 26.1 cubic feet adjusted volume. 1990 Standard.

Chest Manual Defrost Freezer. 225 cubic feet adjusted volume. 1990 Standard.

1 1993 Standard. 5.05 EER compressor. 2.5 in door and side insulation.

22 + Evacuated panels

3 3 + Condenser to EER =5.3

4 4 + Freezer Condenser Gas Heat

I

i`

.i

I._

,,

EE-RM-94-230 COMMENT 61C

FIgure 6':3. Cost Versus Emdency for New Freezers 

40 

,... 

t I, 

• Cheat Manual Defrost 

(CUM) 

---0-- Midpoint 

.a 3S 

I» I 

, i 2S -+-------, .-----+---r-----i 

• Upright MIQual Defrost 

(UPM) 

tJ i i w -t-_________ +l_' _____ ~~-~---~~~_; r: 

~ 

~ 1S 

10 -t-----

s 

4 

o ~--------_+--------~----------~--------~--------~ 

300 350 400 450 soo sso 

Consumer Cost ($1990) 

Source: US DOE 1989b for baseline and options 1 and 2. Koomey et al. 1991a options 3 and 4. Models 3 and 4 

as:e available aflU the year 2000. CosIS adjusted from $1987 using a CPI multiplier, for household 

8))J)WIDCCS of 1.024. The midpoint of the two major classes approximates the ~ marltet. 

Option Description 

Baseline U,pright Manual Defrost Freezer. 26.1 cubic feet adjusted volume. 1990 Standard. 

, Ollest Manual Defrost Freezer. 22.5 cubic feet adjusted volume. 1990 StandaId. 

1 H,g3 Standard. 5.05 EER compress~. 2.5 in door and side insulation. 

2 2 + Evacuated panels 

3 3 ,+Condenser to EER = S.3 

4 4 .+ Freezer Condenser Gas Heat ' 
6.4. Shares

The database includes shares for freezers for existing buildings by housing type at the

national level It includes total shares and specific shares for manual defrost and automatic

defrost from the RECS data (US DOE 1982a, 1986, 1989a, 1992). It also includes shares

in new buildings from the RECS data for buildings built during the last 5 to 7 years from

each of the above data sets. Some of these data are shown in Figures 6.4 and 6.5. The

shares of manual defrost and automatic defrost units, particularly for new buildings, does

not agree with the shipments data reported by the industry. The RECS data show a much

larger portion of automatic defrost units. The RECS data may be less accurate since the

type of freezer is determined during a quick survey of the household. Note that these are

total "saturations" of freezers (shares x housing stock = freezer stock) to account for

multiple freezers per household (except for the 1990 data).

According to the RECS data, the number of freezers per household is decreasing over time,

although because the 1990 data does not include multiple freezers in some households the

final data point should be considered slightly low. In comparing the stock shares with

shares in new construction, we see that the new shares are generally less than those for the

stock as a whole. Thus, the decrease in overall shares may be partly due to fewer freezers

in new households, but may also be due to retired freezers not being replaced. Note that

the shares for manufactured homes (MH) grew from 1987 to 1990, but since the RECS

sample for this housing type is relatively small, the change is not statistically significant.

6.5. Standards

Efficiency standards for freezers were enacted under the National Appliance Energy Conservation Act (NAECA) and first implemented in 1990. The standard specifies a

maximum energy use for freezers based on type and size. The energy usage is based on

the U.S. DOE test procedure mentioned above. More stringent standards were

implemented at the start of 1993. These are summarized in Table 6.2.

83

EE-RM-94-230 COMMENT 61C

6.4. Shares 

The datab~ includes shares for freezers for existing buildings by housing type at the 

national level It includes total shares and specific shares for manual defrost and automatic 

defrost from the RECS data (US DOE 1982~ 1986, 1989a, 1992). It also includes shares 

in new buildings from the RECS data for buildings built during the last 5 to 7 years from 

each of the above data sets. Some of these data are shown in Figures 6.4 and 6.5. The 

shares of manual defrost and automatic defrost units. particularly for new buildings. does 

not agree with the shipments data reported by the industry. The RECS data show a much 

larger portion of automatic defrost units. The RECS data may be less accurate since the 

type of freezer is detennined during a quick survey of the household. Note that these are 

total "saturations" of freezers (shares x housing stock = freezer stock) to account for 

multiple freezers per household (except for the 1990 data). 

According to the RECS data, the-number of freezers per household is decreasing over time, 

although because the 1990".data does not include multiple freezers in some households the 

fmal data point should be considered slightly low. In comparing the stock shares with 

shares in new construction. we see that the new shares are generally less than those for the 

stock as a whole. Thus. the decrease in overall shares may be partly due to fewer freezers 

in new households, but may also be due to retired freezers not being replaced. Note that 

the shares for manufactured homes (MH) grew from 1987 to 1990. but since the RECS 

sample for this housing type is relatively small, the change is not statistically significant 

6.S. Standards 

Efficiency standards for freezers were enacted under the National Appliance Energy 

Conservation Act (NAECA) and first implemented in 1990. The standard specifies a 

maximum energy use for freezers based on type and size. The energy usage is based on 

the U.S. DOE test procedure mentioned above. More stringent standards were 

implemented at the start of 1993. These are summarized in Table 6.2. 

83 
Figure 6.4. Freezer Ownership Shares by Housing Type, 1981-1990

1982 1984 1986 1988

-!- SF

-- MF

-- -MH

----- ALL

1990

Source: US DOE 1982a, 1986, 1989a, 1992.

Shares are for total saturations across households (includes multiple freezers per household). 1990 data

does not include multiple freezers. (Not part of 1990 survey. Approximately 1% in earlier years).

Figure 6.5. Freezer Ownership Shares for New Construction by Housing Type

0.4

0.3

T

I

!

.0dW

0.2 --

0.1 ---

0.0 4-- -

SF

---

MF MH ALL

Source: US DOE 1992 data for buildings built between 1985 and 1990.

0.6 --

0.5

0.4 --

e 0.3

m ..

0.2

0.1 -.

0.0 -

'1980

-r-

--- · -· ---^" "-·-------"-------·

---

EE-RM-94-230 COMMENT 61C

FIgu~ 6.4. Freezer Ownership Shares by Housing Type, 1981-1990 

0.6 

0.5 

Ii 0.4 ~ 

-

u 

-. 

.-

~ 0.3 

f 

f---~ 

ca 

-= (Il 0.2 

0.1 r·-- ..... -

0.0 

'1980 

. -

1982 

, 

i ! 

I 1 I 

J 

I -

I I 

I 

i I I I -+--_.- r --- : I 

I 1 i 

.--- i I ! I 

..:.. ... .... _. -i 

i 

i ! 

1 i 

1984 '1986 

Source: US IOOE 1982a. 1986. 1989a, 1992. 

-

~ 

~ 

---

..... -~ 

.. ._----

. - 1 

1988 1990 

• SF 

-O---MF 

• MH 

ShaI-es are for total saturations across households (includes multiple freezers per household). 1990 data 

~; not include multiple freezers. (Not part of 1990 survey.' Approximately 1% in earlier years). 

FIgure 6.5. l?reezer Ownership Shares for New CoDStructlon by Housing Type 

0.4,----------------------------, 

0.3 

0.1 

0.0 +----= 

SF MF MH ALL 

Source: US DOE 1992 data for buildings built between 1985 and 1990.' 
Table 6.2. Efficiency Standards for Residential Freezers

Average Fraction

Year Capacity Calculated Calc. of

Type Eff. Maximum UEC Equation (Adj. Vol.) UEC EF Sales

UPM 1990 UEC= 10.9 * Capacity + 422 26.3 cuft 709 kWh/yr 13.55 36.6%

UAD 1990 UEC= 16.0 * Capacity + 623 29.4 cuft 1093 kWh/yr 9.81 9.5%

CHT 1990 UEC= 14.8 * Capacity + 223 20.2 cuft 522 kWh/yr 14.13 53.9%

Average 1990 n/a 23.3 cuft 645 kWh/yr 13.20 100.0%

UPM 1993 UEC= 103 * Capacity + 264 26.3 cuft 535 kWh/yr 17.95 36.6%

UAD 1993 UEC= 14.9 * Capacity + 391 29.4 cuft 828 kWh/yr 12.94 9.5%

CHT 1993 UEC= 11.0 * Capacity + 160 20.2 cuft 382 kWh/yr 19.29 53.9%

Average 1993 n/a 23.3 cuft 481 kWh/yr 17.70 100.0%

Type: UPM Upright Freezers with Manual Defrost

UAD Upright Freezers with Automatic Defrost

CHT Chest Freezers and al other freezers

1) Effective date is January 1 of year indicated.

2) Standards level equation from NAECA 1987. 1993 Standards level equation from US DOE 1989b.

Capacity measure is adjusted volume (AV), where AV = 1.73 * freezer volume.

3) Average volume for different product classes from AHAM 1991 data for shipments in year 1990.

4) EF calculated from UEC as 365*Capacity/UEC. Units are cuft-day/kWh.

5) Sales by product class are from AHAM 1991 data for year 1990.

6) Weighted average across entire product category is approximately midway between UPM and CHT

product classes85

EE-RM-94-230 COMMENT 61C

T a bl e . 62. Em IClency_ • Sta n d d ar 5 Ii or R·d e51 entia . IF reezers 

Average 

Year Capacity 

Type Elf. Maximum UEC Equation (Adj. Vol.) 

UPM 1990 UEC= 10.9 ... Capacity + 422 

UAD 1990 UEe= 16.0· Capacity + 623 

CHr 1990 UEC= 14.8· Capacity + 223 

Average 1990 nla 

UPM 1993 UEC= 10.3 • Capacity + 264 

UAD 1993 UEC= 14.9'" Capacity + 391 

CIIT 1993 UEC= 11.0· Capacity + 160 

Average 1993 nla 

Type: UPM Upright fRczc:rs with Manual Defrost 

UAD Upright fRczc:rs with Automatic Defrost 

ClIT Chest Freezers and aD other freez=s 

1) Effective date is JanUary I of year indicated. 

26.3 cuft 

29.4 cuft 

20.2 cuft 

23.3 cuft 

26.3 cuft 

29.4 cuft 

20.2 cuft 

23.3 cuft 

Fraction 

Calcula1ed Calc. of 

UEC EF Sales 

709 kWh/yr 13.55 36.6% 

1093 kWh/yr 9.81 9.5% 

522 kWh/yr 14.13 53.9% 

645~ 13.20 100.0% 

535 kWh/yr 17.95 36.6% 

828 kWh/yr 12.94 9.5% 

382 kWh/yr 19.29 53.9% 

481 kWh/yr 17.70 100.0% 

2) Standards level equation from NAECA 1987. 1993 Standards level equation from US DOE 1989b. 

Capacity measure is adjusted volume (A V), where A V = 1.73 ... freezer volume. 

3) Average volume for different product classes from AHAM 1991 data for shipments in year 1990. 

4) EF calculated from UEC as 365*Capacity/UEC. Units are cuft-dayIkWh. 

5) Sales by product class are from AHAM 1991 data for year 1990. 

6) Weighted average across entire product category is approximately midway between UPM and CHT 

product classes:-

85 
7. DISHWASHER END-USE DATA

Dishwashers use energy primarily by increasing the water heating use for a residence.

Thus., they can be major energy consumers for a typical household.

7.1. Dishwasher UECs

Dishwasher UECs for new units are measured using a laboratory test procedure from U.S.

DOE. This procedure determines the total energy use - both for the motor, dryer, booster

heater, if present, and for the hot water required from the water heater. These UECs are

typically calculated assuming electric water heat, although some households' hot water will

be supplied by a gas water heater. Obviously, the UEC of a dishwasher in the field will be

directly proportional to the amount the appliance is used. Recent research has shown that

average field usage of dishwashers is approximately 229 cycles per year (US DOE 1990b).

Currently, however, the U.S. DOE test procedure is based on a usage estimate of 322

cycles per year.

Average energy use for stock dishwashers is difficult to estimate without direct metering of

the appliance as well as the water heater. In collecting data for the UEC database, we

found that it was difficult to determine if the water heat portion of the dishwasher was

included in the UEC estimate, even where the source may have explicitly stated whether or

not it was included, as shown by some incredibly high values given for the non-water heat

portion. The UEC database contains 31 estimates of the total dishwasher energy use and

45 estimates of the non-water heat portion only (see Appendix B).

UEC equation

The equation below shows the relationship between dishwasher efficiency and energy

consumption. The energy factor (EF) includes the hot water usage of the dishwasher,

calculated using electric water heating at 100% efficiency (i.e. standby losses of the electric

water heater are not included in the accounting for the dishwashing appliance). However,

the question remains whether ornot the efficiency of the water heater used to heat incoming

hot water should be included in the hot water energy of the dishwasher.

Use

UEC kWh/yr = E- = Use (Motor + Dryer + Booster Heater + Hot Water Energy)

where. Ue is in cyclesyear

EF is the energy factor (cycleskWh)

Motor, Dryer, Booster Heater. and Hot Water Energy are components of the UEC (kWh/cycle)

Stock lECs

The best estimate of the UEC for dishwashers resulting from weighted averaging of the

UECs in the UEC database is 250 kWh/yr for the non-water heater portion and 1050 kWh

for the total. However, these estimates are primarily from utility conditional demand

studies, which are not well suited to differentiating between various points of hot water

usage. Thus, we base the UECs in the residential database on the baseline "Standard Water

Heating Dishwasher" unit used in the U.S. DOE appliance standards analysis (US DOE

1990b). This assumes that: 1) the typical unit sold in 1988 is representative of the entire

85

EE-RM-94-230 COMMENT 61C

1. .DISHWASHER END"USE DATA 

Dishwashers use energy primarily by increasing the water heating use for a residence . . Thus" they can be major energy consumers for a typical household. . 

7.1. Dishwasher' UEC, 

Dishwasher UECs for new units are measured using a laboratory test procedure from U.S. 

DOE. nus procedure detennines the total energy use - both for the motor, dryer, booster 

heater, if present, and for the' hot water required from the water heater. These UECs are 

typically calculated assuming electric water heat, although some households' hot water will 

be sUI,plied by a gas water·heater. Obviously, the UEC of a dishwasher in the field will be 

directly proportional to the amount the appliance is used. Recent research has shown that 

average field usage of dishwashers is approximately 229 cycles per year (US DOE 1990b). 

Currelrltly, however. the U.S. DOE test procedure is based on a. usage estimate of 322 . cycles per year. . . 

Average energy use for stock dishwashers is difficult to estimate without diJ:ectmetering of 

the appliance as well as the water heater. In collecting data for the VEe database, we 

found that it was difficult to determine if the water heat portion of the dishwasher was . 

includt~ in the UEC estimate, even where the source may have explicitly stated whether or 

not it was included. as shown by some incredibly high values given for the non-water heat 

portiOJjI. The VEe database contains 31 estimates of the total dishwasher energy use and 

45 estimates of the non-water heat portion only (see Appendix B). 

UEe equation 

The equation below shows the relationship between dishwasher efficiency and energy 

consumption. The energy factor (EF) includes the hot water usage of the dishwasher, . calculated using electric water heating at 100% efficiency (i.e. standby losses of the electric 

water heater are not included in the accounting for the dishwashing appliance). Howeve~, 

the question remains whether or not the efficiency of the water heater used to heat incoming hot water should be included in the hot water energy of the dishwasher. 

UEC: U" . kWblyr = EF = Use • (Motor + Dryer + Booster Heater + Hot Watt:t Energy) 

wilen::· . UIO is in cyc1cs1year 

EP is the eoeqy factor (cycJe&lkWb) 

Motor. Dryer. Booster Heater. aDd Hot WarM Energy &Ie components ot. the UEC (kWblcycle) 

Stock liECs 

The best estimate of the UEC for dishwashers' resulting from weighted averaging of the 

UECs in the UEC database is 250 kWhlyr for the non-water heater portion and 1050 kWh 

for the total. However. these estimates are primarily. from utility conditional demand 

studies. which are not well suited to differentiating between various points of hot water 

usage. Thus. we base the UECs in the residential database on the baseline "Standard Water 

Heating Dishwasher" unit used in the U.S. DOE appliance standards analysis (US DOE 

1990b). 'This assumes that 1) the typical unit sold in 1988 is representative of the entire 

85 
stock in 1990, which may be a reasonable assumption since efficiencies have been

changing very little over time, and 2) that the assumed usage is representative across all

dishwashers. The data for this baseline unit are summarized in Table 7:1.

Table 7.1. 1990 Stock and New Dishwasher UECs

Wa Heater Efficiency

Description 100% 85% Units

Per Cycle Usage

Motor + Heater + Dryer Energy 0.78 0.78 kWh/cyce

Hot Water Demand 11.90 11.90 gal/cycle

Hot Water Load 2.04 2.04 kWh/cyle

Hot Water Energy 2.04 2.40 kWh/cyle

Total Energy 2.82 3.18 kWh/cycle

Annual Usage

Motor + Heater + Dryer Energy 179 179 kWh/yr

Hot Water Demand 2725 2725 gal/yr

Hot Water Load 467 . 467 kW yr

Hot Water Energy 467 549 kWh/yr

Total Energy 645 728 kWh/yr

Energy Factor 0.35 0.31 load/Wh

Sourcec US DOE 1989c, baseline Standard Water Heating Dishwasher

Hot water load calculated at 70F temperture rise

Annual energy use calculated assuming 229 cycles/yr

New UECs

The database UEC for new units (circa 1990) is estimated to be the same as for the stock,

since we base the stock value on the typical unit sold in 1988. Since appliance standards

will not impact sales until 1994, this assumption is reasonable.

7.2. Dishwasher Usage

The energy usage of dishwashers will vary in the field with number of cycles the appliance

is used as well as the temperature settings (hot wash, hot rinse; cold wash, cold rinse; etc.)

for each of those cycles. The most recent estimate of number of cycles is from Proctor and

Gamble and is 229 cycles per year (US DOE 1990b). Homeowner usage of various

temperature and drying options is difficult to ascertain. Estimates of these impacts are used

in the standards analysis for dishwashers, but are not included in the database.

7.3. Dishwasher Technology Data

There are three different classes of dishwashers: the standard dishwasher, the standard

water heating dishwasher (which has a small booster heater in the appliance) and the

compact dishwasher. The standard water heating dishwasher accounts for 62% of new

sales, and it is the only appliance considered in this residential database (US DOE 1990).

87

EE-RM-94-230 COMMENT 61C

stock in 1990~ which may be a reasonable assumption since efficiencies have been 

changing very little over time. and 2) that the assumed usage is representative across all 

dishwashers. The data for this baseline unit are summarized in Table 7 :1. 

Table 7.1. 1990 Stock and New Dishwasber UECs 

Water Healer EfiicieDcy 

Desaiption 100% 85% Units 

Pe, Cycle Usage 

. Motor + Heater + Dryer Energy 0.78 0.78 kWh/cycle 

Hot Water Demand 11.90 11.90 galIcycle 

Hot Water Load 2.04 2.04 kWh/cycle 

Hot Wau:r Energy 2.04 2.40 kWh/cycle 

Total Energy 2.82 3.18 kWh/cycle 

Annutll Ultlge 

Motor + Heater + Dryer Energy 179 179 kWblyr 

Hot Water Demand 2725 2725 galIyr 

Hot Water Load 467 401 kWblyr 

Hot Wau:r Energy 467 549 kWblyr 

Total Energy 645 728 kWblyr 

.cuergy Factor 0.35 0.31 IoadJkWb . Source: US DOE 1989<:. baseline Standard Wau:r Heaung Dishwasher 

Hot water load c:a1colated at 70F temperature rise 

AmmaI energy use calaUated assuming 229 c:ycles/yr 

New UECs 

The database UEC for new units (circa 1990) is estimated to be the same as· for the stock, 

since we base the stock value on the typical unit sold in 1988. Since appliance standards 

will not impact sales until 1994. this assumption is reasonable. 

7.2. Dishwasher Usage 

The energy usage of dishwashers will vary in the field with number of cycles the appliance 

is Used as well as the temperature settings (hot wash. hot rinse; cold wash. cold rinse; etc.) 

for each of those cycles. The most recent estimate of number of cycles is from Proctor and 

Gamble and is 229 cycles per year (US DOE 1990b). Homeowner usage of various 

temperature and drying options is difficult to ascertain. Estimates of these impacts are used 

in the standards analysis for dishwashers, but are not included in the database. 

7.3. Dishwasher Technology Data 

There are three different classes of dishwashers: the standard dishwasher. the standard 

water heating dishwasher (which has a small booster heater in the appliance) and the 

compact dishwasher. The standard water heating dishwasher accounts for 62% of new 

sales, and it is the only appliance considered in th~ residential database (US DOE 1990). 

87 
Historical Efficiency Data

Annual dishwasher shipments and the annual sales and overall average efficiency of new

dishwashers sold over time are shown in Figures 7.1 and 7.2. Note that the efficiencies

are largely determined by hot water demand since the hot water use is the greatest portion

of the total energy use. However, these historical data do not specify the motor and water

heat portions separately. In addition, the efficiency is calculated assuming electric water

heating. The average efficiency of new units sold increased between 1972 and 1980, but

has remained stable since that time.

Cost vs. Efficiency for New Equipment

The database includes estimates of cost vs. efficiency for new dishwashers. These are

shown in Figure 7.3. The values are based on estimates in the U.S. DOE appliance standards analysis (US DOE 1990b). Efficiency improvements come primarily from

reducing hot water energy demand. At the upper end, improvements only minimally affect

the hot water use and thus the efficiency.

Product Lifetimes

The daitabase contains estimates from two sources of the lifetimes of dishwashers, which

are summarized in Table 7.2.

Table 7.2. Estimates of Residential

Dishwasher Lifetimes

Soure Estimate Years

Low 7

Appliance Avg 10

High 14

Low 1

LBL/REvM Avg 13

High 20

Appliance 1992 (first owner lifetime only);

LBLREM 1991.

7.4. Shares

The database includes shares for dishwashers by housing type at a national level. It

includes total shares from the RECS data (US DOE 1982a, 1986, 1989a, 1992). It also

includes shares in new buildings from the RECS data for buildings built during the last 5 to

7 years. Some of these data are shown in Figures 7.4 and 7.5. Figure 7.4 shows that

dishwasher shares are increasing only slightly overall, with shares in SF and MF housing

growing and shares remaining flat in MH housing. Shares in new buildings are

significantly greater than in the building stock except for the MH building types. These

data suggest that the share of households in the stock with dishwashers will continue to

grow over time.

88

EE-RM-94-230 COMMENT 61C

Histolrical Efficiency Data 

Annual dishwasher shipments and the annual sales and overall average efficiency of new 

dishwflshc::rs sold over time are shown in Figures 7.1 ~d 7.2. Note that the efficiencies 

are largely determined by hot water demand s4ice the hot water use is the greatest portion 

of the ItOtal energy use. However, these historical data do not specify the motor and water 

heat portions separately. In addition, the efficiency is calculated assuming electric water 

heatinl~. The average efficiency of new units sold increased between 1972 and 1980. but 

has reD:lair!ed stable since that time. . . 

Cost 'vs. Efficiency Cor New Equipment 

The da.tabase includes estimates of cost vs. efficiency for new dishwashers. These are 

shown in Figure 7.3. The values are based on estimates in the U.S. DOE appliance 

standards analysis (US DOE 1990b). Efficiency improvements come primarilyfrom 

red~cing bot:waterenergy demand. ·At the upper end. improvements only minimally affect 

the bot water use.and thus the efficiency. . 

Produtt:t Lifetimes 

The daltabase contains estimates from two sources of the lifetimes of dishwashers. which 

are summarized in Table 7.2. .' 

7.4. Shares 

Table 7.2. Estimates or Residential 

Dishwasher Lifetimes 

Source Estimate Years 

Low 7 

AppliaDce Avg 10 

High 14 

Low 1 

LBUREM Avg 13 

High .20 . . Appliance 1992 (first owner lifetime only); 

LBLREM 1991. 

The databue includes shares for dishwashers by housing type at a national level. It 

includes total shares from the RECS data (US DOE 1984 1986, 1989a. 1992). It also 

includes; shares in new buildings from the RECS data for buildings built during the last 5 to . 

7 years. SlOme of these data·are shown in Figures 7.4 and 7.S. Figure 7.4 shows that 

dishwasher shares are increasing 'only slightly overall, with shares in SF and MF housing 

growing and shares remaining flat in MH ·housing. Shares in new buildings are 

significnntly greater than in the building stock except for the MH building types. These 

data suggest that the share of households in the stock with dishwashers will continue to 

grow over time. . . 

RR 
Figure 7.1. Annual Dishwasher Shipments, 1951-1990

4.5 ................ - .....

4.0 -

3.5

3.0 -

2.5 2.0- . ......... --------.- . g 2.0

1.5

0.5 o.o0

1950 1955 1960 1965 1970 1975 1980 1985

Source: Fechtel et al. 1980 (1951-1956); Appliance Magazine (1976-90); US DOE 1990b (1951-1975).

Figure 7.2. Shipment-Weighted Efficiency for Dishwashers, 1972-1990

0.40 OAO

03.5 ---------------------- -----

0.30

0.25

O 0.20

0.15

= 0.10 -- - ............ ........ ........................................................

............... ...................................................- .... 

0.00 'i

1972 1974 1976 1978 1980 1982 1984 1986 1988 1

Source: AHAM 1991. EF calculated assuming electric water heating @ 100% efficiency.

1990

I

990

89

EE-RM-94-230 COMMENT 61C

rtgUre 7.1. Annual Dishwasher Shipments, 1951·1990 

4~~------------~----~------------~------------~----~ 

4.0 +------;.- --.;-----

3~+-------;.------~----~------~--+~· 

~ 3.0 

i' •• 2.5 

I::: 

:I 

~ 2.0 

---........ -.. -----f--

~ 1.5 +---

+-__ .. 1 __ .1 ____ .. L .... _._._._ .. L_ ... _ ......... i __ . ___ L ___ ._.l.._._ .... __ .. 

:: --.t-----L---t-··· L-+_~---+--- no +-----~-------+------+-----~-------+------+-----~------~ 

1950 1955 1960 1965 1970 1975 1980 1985 1990 

Source: Feebtel et ale 1980 (1951-1956); Appliance Magazine (1976-90); US DOE 1990b (1951-1975). 

Figure 7.2. Shipment-Weighted Emcienc:y ror Disbwashers, 1972-1990 

0.40 ,----.,.---..-----------,.----,-----.-----.----;-----, 

035 

~ 030 

.:.: 1 0.25 =--:::=0---

oS 

-t; 0.20 

-... 

~ ~ 0.15 ~ Q.I 

~:: -==.r.=~.·~T~·=l==r=t--~=~~·=!==r= 0.00 +------t-----t--.---t------t------i-----t------t------i"-----'i 

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 

SoUrce: AllAM 1991. EF calculated assuming electric water heating @ 100% efficiency. 

89 
Figure 73. Cost Versus Efficiency for New Dishwasher

_ .0.48_ . . 1

i0.46; 4 - _ _ _ = 4 _

OA6i

0.46 ------. *--....... ... ---------------..... ' 

0.44 -- - - - 2- 

0.42.-------

034------

0.32-

0.30 -

310 315 320 325 330 335 340 345 350

Consumer Cost ($1990)

Source: US DOE 1990. Standatd Water Heating Dishwasher (includes booster heater) chosen to represent all

dishwasers. UEC and EF calculated assuming electric water heat at 100% efficiency. Database

includes hot water energy separately to calculate costs for gas water heat. Converted from S1988 using

CPI multiplier for major household appliances (CPI data for major household appliances, stoves, ovens,

DW. AC) of 0.979.

Option Description

Baseline Water Heating Dishwasher. 229 Cycles per Year. Water Heater efficiency = 100% (Electric).

1 Reduce Water Use

2 1 +Reduce Booster Use

3 2 + Improved Motor

4 3 + Fill Control

90

EE-RM-94-230 COMMENT 61C

Yagure 7.3" Cost Versus Efficiency for New Dishwasher 

0.48 

OAC; 

O.44~ 

_ 0.42 

.c: ' 

~ ~. 0.40 

~ &: 0.38-

i 

W 0.36 

0.34 

0.32 

0.30 

: . 

f i' 

-' i, i 4 ___ ..l-_-L... "t.:. u_--+! --: ._. ----4,.-----;:----

i ~ .i:::::: II 1 3 , i -.. __ ·_·_"i:-.-._·-_·-_··_~.:: .. _·--._ -i--2 j -- ._1 _ . .-:._-l ___ . __ L ____ ._j_ .. ___ -+-____ I 

:: i ~ ~ 

;.~ .~.~ i ~ ! j ~. ~ 

,--+-~ ----;--~---.----...:------

I I I 

-+----i~ + I . 1-- I· I I 

I I : : ',;, .1 : 

310 315 320 325 330 335 340 . 345 3S0 

Consumer Cost ($1990) 

Source: US J>OE 1990. Slalldaid Water Heating Dishwasher (inCludes booster hearer) choSen to represent all 

dish1N8SIlers. USC and EF' calculated assuming electric walei' heat at 100% efficiency. DatabaSe 

inclllidcs bOI water energy separare1y to Calculate costs for gas warei' heSt. Convened from $1988 using 

CPI llllwtipUer for major household appliances (CPl data for major household appliances. stoves. ovens • 

. OW, AC) of 0.979. 

Option Descriotion .'. . 

Baseline Water Healing Dishwasher. 229 Cycles per Year. Water Hearer effICiency = 100% (Electric). -

1 . Reduce Water ~se . 

2 1 + Reduce Booster Use 

. 3 2 + Improved Motor 

4 3 + Fill Control 

90 
Figure 7.4. Dishwasher Ownership Shares by Housing Type, 1981-1990

0.6

0 . .

e 0.4

. 03

0.11 ........... ... . ..- "_.

0.0

1980 1982 1984

Source: US DOE 1982a, 1986, 1989a, 1992.

1986 1988

-*-- SF

----- MF

-MH

-°-- ALL

1990

Figure 7.5. Dishwasher Ownership Shares for New Construction by Housing Type

is

10U

I-~41

a

is

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

SF MF MH

Source: US DOE 1992 data for buildings built between 1985 and 1990.

91

ALL

_ ________------

............... :·:·: · ·:

·2r' · ............

:--_I

·-- ·- ·------

EE-RM-94-230 COMMENT 61C

F"agure 7.4. DishwaSber Ownership Shares by Housing Type, 1981·1990 

0.6 .~---"",!",,"-----,------,------,------, 

. " 005 .-.-.--• .;.----.---'--- if: .. -.-... ---: --=--II-...-..!_ ........ ==I!I 

~ : 

= 0.4 +-.----- • SF 

.9 ~--~--------Q_-

-

u 

= S 0.3 

1-----· 

·-···-·--1-··-·----+--··-···· ···_···-r---···-···-····· 

· ··-.. ···j·-·-···-··--r-"----~----i-·--

--D-MF 

--+--MH 

---<>-AU 

0.1 ···-----r--- j----:---T------

O.O+-----+-----+-----+-----+-------i 

1980 1982 1984 1986 1988 1990 

Source: US DOE 1982a. 1986, 1989a, 1992. 

Figure 7.s. Dishwasber Ownership Shares for New Construction by Housing Type 

0.9 

0.8 

0.7 

-c:: 0.6 

.g -. u 

CIII 0.5 ~ 

-t! 0.4 CIII 

.c 

til 0.3 

0.2" 

0.1 

0.0 

SF MF MH ALL 

Source: US DOE 1992"data for buildings built between 1985 and 1990. 

91 
7.5.. Standards

Efficiency standards for dishwashers were first enacted under the National Appliance

Energy Conservation Act (NAECA) and implemented in 1988. These standards required

only that dishwashers have the option to dry without heat. Further efficiency standards

will become effective in 1994, as shown in Table 73.

Table 7.3. Minimum Efficiency Standards for Residential Dishwashers

Hot Water Motor, Booster, Total

Database Year Min. Energy & Dryer Energy UEC

Type Code Effective EF (kWhbycl W(kWhhcycle) e) 

Stadard . DW 1994 0.46 1.60 0.58 2.17

Standard Water Heating DW 1994 0.46 1.60 0.58 2.17

Co act r(Water Heating) .DW 1994 0.62 1.11 0.51 1.61

Soure US DOE 1990b. Hot water energy and motor, booster and dryer energy do not add to

total energy due to rounding emrrs.

1) Effective date is May 14 of year indicated.

2) Standards specified in NAECA 1987 and effective starting 1990 for dishwashers required

dishwashers to be equipped with an option to dry without heat

3)EF units are load/kWh.

4) UEC per cycle calculated as 1/EF. Includes assumption of electric water heating @ 100%

efficiency. Hot water use portion from US DOE 1990b. Other energy use is for Motor,

Booster Heater and Dryer within the machine itself.

Mandated efficiency level for standard dishwasher essentially makes ita water heating

dishwasher. The standard specifies only the EF, and in practice manufactuers may not use

dte specific design options rading off motor, booster heater, dryer, and hot water energies shown

above.

9o

EE-RM-94-230 COMMENT 61C

7.5.. Standards 

Efficiency standards for dishwashers were fust enacted under the National Appliance 

Energy Conservation Act (NAECA) and implemented in 1988. These standards required 

only .that dishwashers have the option to dry without heat. Further efficiency standards . will become effective in 1994, as shown in Table 7.3. .. . 

Tllblt: 7.3. Minimum Emclency Standards ror Residential Dishwashers 

. HotWattr Motor, Booster, TOIal 

DaIabase Yea' Min. Energy . Ii Dr}'er Energy' UEC 

Type Code Effective EF (kWblcycle) (kWh/cycle) (kWJL'C)'CIe) . 

Slalldard DW 1994 0.46 . 1.60 0.58 2.17 

ScandaId Wa1I!t Healing DW 1994. 0.46 1.60 0.58 2.17 

£!!!I!£t (W~ Heating) .OW 1994 0.62 1.11 0.51 1.61 

Swra:: US DOE 1990b. Hot water energy and motor, booster and dryer energy do not add ro 

rolal eoergy due to rounding emn. 

1) Effective date is May 14 of year iDdicared. 

2) Standards specifred ill NAECA 1987 and drective starting 1990 for dish~ required 

di..~wasbers to be equipped with an option to dry without heaL 

3). EF unics are loadlkWh. 

4) 1tJEC per cycle calculated as lIEF. Includes assumptiOD of elearic water beating @ 100% 

etrtciency. Hot water use poniOD from US DOE 1990b. Other energy use is for Motor, 

BooSter Hearer aDd Dryer within abe machine itself. 

Mandar.ed efficiency level for standan1 di$hwasber essentially makes ita water beadng 

disbwasher. The standard specifies only the EF~ aDd in pmcIioe manufacturers may Dot use 

die specific design options ttading offmotDt. booster heater, c:bler, and bot water energies shown . 

abo,ve. 

9' 
8. CLOTHES WASHER END-USE DATA

Clothes washers use energy primarily by increasing the water heating use for a residence. Thus, they can be major energy consumers for a typical household.

8.1. Clothes Washer UECs

Clothes 

U.S. DOE 

washer 

This 

UECs 

procedure 

for new units are measured using a laboratory test procedure from determines the total energy use - both for the motor and other items in the washer and for the hot water required from the water heater. These UECs are

be 

typically calculated assuming electric water heat, although some households' hot water will supplied by a gas water heater. Obviously, the UECofa clothes washer in the field will be directly proportional to the amount the appliance is used.

of 

Average energy use for stock clothes washers is difficult to estimate without direct metering the appliance as well as the water heater. In collecting data for the UEC database, we found that it was difficult to determine if the water heat portion of the clothes washer was

not 

included in the UEC estimate, even where the source may have explicitly stated whether or

portion. 

it was 

The 

included, 

UEC 

as shown by some incredibly high values given for the non-water heat database contains 21 estimates of the total cotheswasher energy use and 35 estimates of the non-water heat portion only (Appendix B).

UEC equation

The equation below shows the relationship between clothes washer efficiency and energy cosumption. The energy factor (EF) includes the hot water usage of the clothes washer, calculated using electric water heating. A major question is whether or not the efficiency of the water heater used to heat incoming hot water should be included in the hot water energy of the clothes washer.

UEC: kWb4r-- v Use * Capacity ~UEQC kWb/yr Us-e-F * - = Use * (Motor + Hot Water Energy)

where: Use is in cycles/year

Capacity is volume (cubic feet)

EF is the energy factor from the DOE test procedure (cubic feet/kWh)

Motor and Hot Water Energy are the components of the UEC (kWh/cycle)

Stock UECs

The best estimate of the UEC for clothes washers resulting from weighted averaging of the UECs in the UEC database is 100 kWh/yr for the motor portion (n=30) and 612 kWh for the 

utility 

total 

conditional including 

demand 

water heating (n=15). However, these estimates are primarily from

various studies, which are not well suited to differentiating between

the baseline 

points of hot water usage. Thus, we base the UECs in the residential database on "Standard Clothes Washer" unit used in the U.S. DOE appliance standards analysis 

usage 

(US DOE 1990b), where at a usage of 380 cycles per year, the annual energy

typical 

is 103 kWh for the motor and 1148 kWh/yr for the total. This assumes that the unit sold in 1988 is representative of the entire stock in 1990, which may be a

93

EE-RM-94-230 COMMENT 61C

8. CLOTH,ES WASHER END-USE DATA 

. Clothes washers use energy primarily by increasing the water heating use for a residence. . Thus. they can be major energy consumers for a typical household. 

8.1. ·Clothes Wuher UECs 

Oothes washer UECs for new units are measured using a laboratory test procedure from 

U.S. DOE. This procedure detennines the total energy use - both for the motor and other 

items in the washer and for the hot water required from the water heater. These UECs are 

typically calculated assuming electric water heat. although some households'. hot water will 

be supplied by a gas water heater. Obviously; the UECof a clothes washer in the field will 

be direcdy proportional to the amount.the appliance is used. 

Average energy use for stock clothes washers Is difficult to estimate without direct metering 

of the appliance as well ~ the water heater. In collecting data for the UEC databas~ we 

found that it was difficult to determine if the water heat portion of the clothes washer was 

included in the VEC estimate. even where the source may have explicidy stated whether or 

not it was included, as shown by some incredibly high values given for·the non-water heat 

portion. The DEC database CQntains 21 estimates of the total cotheswasher energy Use and 

35 estimates of the non-water heat portion only (Appendix B). 

UEe equaltion 

The equation below shows the relationship between clothes washer efficiency and energy 

consumptiOIll. The energy factor (EF) includes the hot water usage of the clothes washer, 

calculated using ·electric water heating. A .major question is whether or not the efficiency of 

the water heater used to heat incoming hot water should be included in the hot water energy 

of the clothes washer. 

UEC: 

where: 

Stock UECs 

Use • Capacity kWblyr = EF = Use • (Motor + Hot Water Energy) 

Use is ill cycleslyear 

Capacity is volume (cubic feet) 

EF is the energy factor from the DOE lest procedure (cubic fcetJkWb) 

Motor ud Hot Wa. Energy are the components of Ibe VEe (kWblcyc1e) 

The best estimate of the UEC for clothes washers resulting from weighted averaging of the 

UECs in the UEe database is 100 kWhlyr for the motor portion (n=30) and 612 kWh for 

the total including water heating (n=15). However, these estimates are primarily from· 

utility conditional demand studies, which are not well.suited to differentiating between 

various points of hot water usage. Thus, we base the UECs in the residential database on 

the baseline "Standard Clothes Washer" unit used iIi the U.S. DOE appliance standards 

analysis (US DOE 1990b). where at a usage of 380 cycles per year. the annual energy 

usage is 103 kWh for the motor and 1148 kWhlyr for the total. This assumes that the 

typical Wlit sold in 1988 is representative of the entire stock in 1990. which may be a 

93 
reasonable assumption since efficiencies have not been changing since 1979 (see Figure

8.2). The data for this baseline unit are summarized in Table 8.1.

Table 8.1. 1990 Stock and New Clotheswasher UECs

Water Hear Efficiency

Descaipri 100% 85% Units

Per Cycle Usage

Motor Energy 0.27 kWhcye

Hot Water Demand 12.80 12.80 ga/cycle

Hot Water Load 2.82 2.82 kWh/cyde

Hot Water Energy 2.82 332 kWh/cyce

Total Energy 3.09 359 kWh/cyde

Annual Usage

Motor Energy 103 103 kWh/yr

Hot Water Demand 4864 4864 gal/yr

Hot Water Load 1071 1071 kWh/yr

HotWater Energy 1071 1260 kWh/yr

Total Energy 1173 1362 kWh/yr

Energy Factor 0.84 0.73 ca ft/kWh

Source US DOE 1989c, baseline standard clothes washer

Hot water load calculated at 90F temperatre rise

EF calculated for capacity of 2.60 cubic feet

Annual energy use calculated assuming 380 cyclesyr

New ITECs

The database UEC for new units (circa 1990) is estimated to be the same as for the stock,

since we base the stock value on the typical unit sold in 1988. Since appliance standards

will not affect technology choices until 1994, this assumption is reasonable.

8.2. Clothes Washer Usage

The energy usage of clothes washers will vary in the field with number of cycles the

appliance is used as well as the temperature settings (hot wash, hot rinse; cold wash, cold

rinse; etc.) for each of those cycles. The most recent estimate of number of cycles is from

Proctor and Gamble (US DOE 1990b) and is 380 cycles per year. Currently, however, the

U.S. DOE test procedure is based on a usage estimate of 416 cycles per year. Clothes

washers often have many different options that would also affect energy usage such as hot

vs. cold rinse. These various temperature settings are included in the appliance standards

analysis and the UECs given above.

8.3. Clothes Washer Technology Data

There are two different classes of clothes washers: the standard clothes washer and the

compact clothes washer. The standard washer accounts for 96% of new sales, and it is the

only appliance considered in the database.

EE-RM-94-230 COMMENT 61C

reasonable assumption since efficiencies have not been changing since 1979 (see Figure 

8.2). TIle data for this baseline unit are summarized in Table 8.1. 

Table 8.1. 1990 Stock and New Cloiheswasher UECs 

New UEes 

Wau:r Heartr fJficieucy 

DescripciOll 1009& 85$ 

Per Cycle Usage 

Motex' Energy , '0:1.7 0.27 

Hat WaI,U Demand 12.80 12.80 

Hat Wa1J:Z Load 2.82 2.82 

Hot Warer &ergy 2.82 332 

TOlal Eoergy 3.09 359 

IAnnual Usage' 

MoUX' Energy 103 103 

Hat W~ Demand 4864 4864 

Hat Wau:r Load 1071 1071 

Hot Wa1J:Z Energy 1071 1260 

TOlal Energy 1173 1362 

Eoergy Factor 0.84 0.73 

Soma:: US DOE 1989c, baseline standanl clothes washer 

Hot water load caJcu1ate4 at 90F temperature rise 

EF calcu1ate.d for capacity of 2.60 cubic feet 

AmliiaI euergy usc calculated assuming 380 cYclesJyr 

, 

Units 

kWblcyde 

gal/cycle 

kWblcyde 

kWblcyde 

kWblcyde 

kWblyr 

gaJJyr 

kWblyr 

kWblyr 

kWblyr 

cu. ftlkWh , , 

The database UEC for new units (circa 1990) is estimated to be the same as fot the stock. 

since w,e base the stock value on the typical unit sold in 1988. Since appliance standards 

will not affect technology choices until 1994. this assumption is reasonable. , 

8.2. Clothes Washer Usage 

The eo(:rgy usage of clothes washers Will vary in the field with number of cycles the 

appliance is used as well as the temperature settings (hot wash. hot rinse; cold wash. cold 

rinse; etc.) for each of those cycles. The most recent estimate of number of cycles is from 

Proctor and Gamble (US DOE 1990b) and is 380 Cycles per year. Currently, however. the 

U.S. DOE test procedure is based on a usage estimate of 416 cycles per year. Clothes 

washers often have many different options that would also affect energy usage such as hot 

vs. cold. rinse. These various temperature settings are included in the appliance standards 

analysis and the UECs given above. 

'8.3. Cl.othes Washer Technology Data 

There are two different classes of clothes washers: the standard clothes washer and the 

compact clothes washer. The standard washer accounts for 96% of new sales, and it is the 

only appliance considered in the database. -' 
Historical Efficiency Data

Annual clothes washer shipments from 1957 to 1990 are shown in Figure 8.1 and the

overall average efficiency of new clothes washers sold over time is shown in Figure 8.2

along with the average size (capacity in cubic feet). Note that the efficiencies are largely determined by hot water demand since the hot water use is the greatest portion of the total

energy use. In addition, the efficiency is calculated assuming electric water heating. The

average efficiency of new units sold increased between 1972 and 1980, but has remained

stable since that time. The average size of clothes washers has increased slightly over the

last several years.

Cost vs. Efficiency for New Equipment

The database includes estimates of cost vs. efficiency for new standard clothes washers.

These are shown in Figure 8.3. The values are based on estimates in the U.S. DOE

appliance standards analysis (US DOE 1990b) adjusted to $1990. Efficiency improvements at the lower end come from elimination of hot and warm water rinse cycles

at virtually no cost. At the upper end, improvements only minimally effect the hot water

use and thus the efficiency. The primary means of efficiency improvement is to move to a

horizontal axis clothes washer, which uses significantly less hot water.

Product Lifetimes

The database contains estimates from two sources of the lifetimes of clothes washers,

which are listed in Table 8.2.

Table 8.2. Estimates of

Residential Clothes Washer

Lifetimes

Source Estimate Years

Low 12

Appliance Avg 13

High 14

LOw 1

LBL/REM Avg 14

High 25

Appliance 1992 (first owner

lifetime only); LBLREM 1991.

8.4. Shares

The database includes shares for clothes washers by housing type at a national level. It

includes total shares from the RECS data (US DOE 1982a, 1986, 1989a, 1992), and

includes a small amount of wringer washing machines which are slightly different than

automatic washers. It also includes shares in new buildings built during the previous 5 to 7

years from the RECS data from the same data sets. Some of these data are shown in

Figures 8.4 and 8.5. Figure 8.4 shows that clothes washer shares are increasing only

slightly overall, with shares in SF and MH housing growing and shares remaining flat in

MF housing. Shares in new buildings are virtually the same as in the building stock.

Approximately 91% of new housing units have clothes washers.

95

EE-RM-94-230 COMMENT 61C

" 

Historical Emciency' Data 

Annual clothes washer shipments from 1957 to 1990 are shown in Figure 8.1 and the 

overall average efficiency of new clothes washers sold over time is shown in Figure 8.2 

along 'with the average size (capacity in cubic feet). Note that the efficiencies are largely 

determined by hot water demand since the hot water use is the greatest portion of the total 

energy use. In addition, the efficiency is calculated assuming electric w~ter heating. The 

average efficiency of new units sold increased between 1972 and 1980, but has remained ' 

stable .sin('..e that time. The average size of clothes washers has increased slightly over the 

last seveml years. ' 

Cost '\'5. Emclency for New Equipment 

The da\tabase includes estimates of cost vs. efficiency for new standard clothes washers. 

These are shown in Figure 8.3. , The values are based on estimates in the U.S. DOE 

appliaJllce standards analysis (US DOE 1990b) adjUsted to $1990. Efficiency 

improvements at the lower end come from elimination of hot and wann water rinse cycles 

at Virttl8lly no cost. At the 'upper end, improvements only minimally effect the hot water 

use and thus the efficiency. The primary means of efficiency improvement is to move to a 

horizontal axis clothes washer, which uses significantly less hot water. 

Product Lifetimes 

The database contains estimates from two sources of the lifetin;les of clothes washers, 

which are listed in Table 8.2. 

8.4. 'Shares 

Table 8.2. Estimates of 

Residential Clothes Washer 

Lifetimes 

Source Eslimate Years 

Low 12 

Appliance Avg 13 

High 14 

LOw 1 

LBUREM Avg, 14 

High 2S . Appliance 1992 (first owner 

lifetime only); LBLRFM 1991. 

The database includes shares for clothes washers by housing type at a national level. It 

includes total shares from the RECS data (US DOE 198~ 1986, 1989a. 1992),' and 

includes a small amount of wringer washing machines which are slightly different than 

automatic washers. It also includes shares in new buildings built during the previous 5 to 7 

years fl'Om the RECS data from the same data sets. Some of these data are shown in 

Figures 8.4 and 8.5. Figure 8.4 shows that clothes washer shares are increasing only 

slightly overall. with shares in SF and MH housing growing and shares remaining flat in 

MF housing. Shares in new buildings are virtually the same as in the building stock. 

Approximately 91 % of new housing units have cloth~ washers. ' 

95 
Figure 8.1. Annual Clothes Washer Shipments, 1957-1990

7 _ ___._ . .___ . ____ E ._ __ _.______.. _ 6

5

-........... _ _._.... ___ .... __ ____ ___ _ ..... ............

0 - ._._ --- .__.--_------- ---- I--------- I------------

0

1950 1955 1960 1965 1970 1975 1980 1985 1990

Source: US DOE 1990b (1957-75); Appliance Magazine (1976-90).

Figure 8.2. Shipment-Weighted Efficiency for Clothes Washers, 1972-1990

12 - 2.7

1.0 ------ 2.6

0.8 . 2.5

0. ..i i i i.. 2.3

*- 1972 1974 1976 - 1978 1980 1982 1984 1986 1988 1990

Source: A AM 1991. Efficiency calculated ass ing electric water heat at 100% efficiency.

- E-Iiciency

0.4- -' - Capacity

0.2 -- I -I -- i -- 242

1972 1974 1976 1978 1980 1982 1984 1986 - 1988 1990

Source: AHAM 1991. Efficiency calculated assuming electric water heat at 100% efficiency.

96

EE-RM-94-230 COMMENT 61C

Figure 8.1. Annual Clothes Washer Shipments, 1957·1990 

lllS 

CII 

... 

~ ·S 

= 

c 

~ 

~ 

7 

6 

5 

4 

3 

2 

1 

. . . . . : : 

·-.. ---"',r------.l-.---1---1 -- !.' -·-._-J._-.. --··-·r- . - -;:.:-- .. ---- ! ------+------+.-- ,------t------! ----~.--.. -- :: :: 

===I=:=:L=J==-r=-~~.J==t=-=~-==­ ---+1----i ---l----t---- !--------~---+--- o +-------~----~~-----+------~------~------~------+-----~ 

1950 1955 1960 1965 1970 1975 1980 1985 1990 

Source: US DOE 1990b (1957-75); Appliance Magazine (1976-90). 

Figure 8.2. Shipment.Weighted Efficiency ror Clothes Washers, 1972·1990 

12.-----~--------~----------~----~----:-----~--_.27 

le 

~ 

&! 0.6 ... 

---.-t.----... -.;-... -.-~-... -. ---.. -----J.--... -_ .... -.--_. __ .... -.... ----1.-- - ! ; o--!'""--...~-y-_o__{:J.__F_._,..;.,_ 

_ -- .--------1----r---r-----t---

1.0 2.6 

CII 

ICII 

C 

~ 

--0- Efficiency 

0.4 

--Capacity 

2.3 

0.2 +------1-----+------4-----+----1----+---+-----+----+ 2.2 

1972 1974 1976 1978 1980 1982 1984 1986. 1988 1990 

Source: AHAM 1991. Efficiency calculated assuming electric water heat at 100% efficiency. 

96 
Figure 83. Cost Versus Efficiency for New Clothes Washer

33

2.8 - --.- . _.......... __ .....__ _

1. . . ........................ ............................ ........ ...........

U 23

15t; r . · y t/ . *-- - ~- Horizontal Axis Washer

E / . --- . Standani Clothes Washer l.4* ........ ...........

4 S

0.8 ---..--- Baselin ................... ------------- ......

03

350 400 450 500 50 60 650

Consumer Cost ($1990)

Source: US 

includes 

DOE 

hot 

1990. 

water 

UEC 

energy 

and EF calculated assuming electric water heat at 100% efficiency. Database

CPI multiplier for laundry separately 

products 

to calculate costs for gas water heat. Converned from $1988 using of 1.02.

Level Dsion

Baselie 

1 

Standard 

Eliminate 

clothes 

WamV/Warm 

wsher . 2.60 cuft capacity. 380 Cycles per Year. WH efficiency = 100% (Electric). Set

2 Eliminate Warm Rinse (1994 Standard) 3 2 + Improve Motor Efficiency

4 3 + Thermal Mixing Valve

5 4 + Plastic Tub

6 2 + Horizontal Axis

7 6 + Thermal Mixing Valve

8 7 + Plastic Tub .

97

EE-RM-94-230 COMMENT 61C

Figure 8.3. Cost Versus Efficiency (or New Clothes Washer 

3.3 

2.8 

-

~ 2.3 

~ 

r! 

.!:! 

~ 

:::I 

S:!. 1.8 .. 

0 

~ 

~ .. 

fI 

.. 

c: 1.3-

(oj 2 

0.8 

0.3 

400 450 

I 8 

. 6 --1-_···· __ 

·-. 

• Horizonlal Axis Washer 

~ Sl8I'Idard Oothes Washer 

SOO 

Consumer Cost (51990) 

sso 600 6SO 

Source: US DOE 1990. UEC and EFcalculated assuming electric water heat at 100% efficiency. Database 

includes hot waterenezgy separately to calculate costs for gas water heaL Converted from $1988 using 

CPI multipliez for laundry products of 1.02. 

Baseline Standard clOlhes WISher. 2.60 cuit capacity. 380 Cycles pel' Year. WH efficiency = 100% (Electric). 

I Eliminarc Wmn/Warm Set. 

. 2 EIiminmte Warm Rinse (1994 Standard) 

3 2 + lmpruve Motor EffICiency 

4 3 + Thcmta! Mixing Valve 

5 4 + Plaslic Tub 

6 2 + HorizOntal Axis 

7 6 + Thema! Mixing Valve 

8 7 + Plaslic Tub 

------------------------------------------------------------------~ 

97 
Figure 8.4. Clothes Washer Ownership Shares by Housing Type, 1981-1990

1982 1984 1986 1988

*--" SF

-0-- MF

*- MH

-- <- ^ALL

1990

Source: US DOE 1982a, 1986, 1989a, 1992.

Approximately 2% of all clothes washers are "wringer" type in 1981-87 surveys.

Figure 85. Clothes Washer Ownership Shares for New Construction by Housing Type

SF MF MH ALL

Source: US DOE 1992 data for buildings built between 1985 and 1990.

98

L.

a

1.0 -

0.9

0.8 -

0.7

0.6

0.5 -

0.4 ...

0.3 ...

0.2 ...

0.1 .

0.0

1980

-E

.20

V,

I1

Mn

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 l E ..... : . : I l

.......... _. ....

_........_..._.

......_

.-- -_ -. -_

...... .... ..........

...... ,_ - __

............. _--

............ .....

......

......- _

..........

.. ..... . .........

a i j:

:i

a :::

,il · 9··-··-··

::::i':::::l:l:i':::':i:':::

sislaiI1 iiisri

jiii"llisiiaiI

j-

iii:::::,:::i:·I:::-·::::::i :::::-::-:·:·:Iit

EE-RM-94-230 COMMENT 61C

Figure 8.4. Clothes Washer Ownership Shares by Housing Type, 1981·1990 

- 1.0 -r-----r-----:-----:------r----~ 

~ . . -- T ~ _ r----- -.. _.... ~ ... -.... --.... -n --_ .............. _ .......... . 

0.9 ~-..... . ........ . :-

0.8 

. f-. . ... i~ .. ~.-.-.. ---:- -...... "- '.'- '2' 0.7... i i 

~ 0.6 '.-.'-"-"-' .. -.-.-.--+-... -.-.-.-+ ........ -....... -............ _ .. _.-

• SF 

U i j ~ 0.5 .. _ ... -_ ... _ .. _·······_··_·-t···_·-····-········l···················'-' ...................... - .. 

. ~ 0.4 ...•........... _ ........ _··············_··1-···_················1"············· .......... _ ........................... . 

0.3 t-.:.········u .... ....•.........•. ·····T········ ············1······ .. · ... -..... . ........................ .J. 

--G--MF 

---+-- MH 

0.2 ~ ............. ---. ···············_·······t·························t-···· ............ -... -.................... _.-

0 .. 1 _. __ ._ .. __ . -···-············l·-························f········· ................................... -... . 

0.0 +----.f-----.f-----.f-----+---_ 

1980 1982 1984 1986 1988 1990 

Source: US DOE 1982a. 1986. 1989a. 1992. 

Approximalely 2% of all clothes washers are "wringer" type in 1981-87 surveys. 

Figure 8.5. Clothes Washer Ownership Shares for New Construction by Housing Type 

1.0 .----------------------------; 

0.9 +-··-UtmgnJ 

0.8 

_ 0,7 

c 

~ 0.6 +···-.:H: U $ 0.5 +·····-it:::::m::: 

t = 0.4 .c 

~ 

0.3 

0.2 

0.1 t-···········lf::;::::::;::': 

0.0 -+----'~==""""---+--........ ==~---+--~"""""'~=~--+--

SF MF MH 

Source: US DOE 1992 data for buildings built between 1985 and 1990. 

98 

ALL 
8.5. Standards

Efficiency standards for clothes washers were first enacted under the National Appliance

Energy Conservation Act (NAECA). These standards required only that clothes washers

have an unheated water option for the rinse cycle. New standards will become effective in

1994, and are shown in Table 8.3.

Table 83. Efficiency Standards for Residential Clothes Washers

Hot Water Motor Total

Databse Year Min. Energy egy UEC

Type Cod Effective EF (k cycle) c(Wh/cycle) Wh/cycle) 

Using DOE Test "

Pocedme

Stndard, Top-Loading CW 1994 1.18 1.94 0.27 2.21

Compact Top-Loading CW . 1994 0.90 .1.36 .025 1.61

Using P&G Data

Stdandd, Top-Loading CW 1994 1.18 1.50 0.27 1.77

Compact, Top-Loading CW 1994 0.90 1.05 0.25 1.30

Source: US DOE 1990b.

1) Effective date is May 14 of year indicated.

2) Standards specified in NAECA 1987 and effective starting 1990 for clothes washers required

clothes washers to be equipped with an unheated water rinse option.

3) EF units are capacity (cuft)/kWh.

4) UEC per cycle calculated as capacity/EF, using 2.60 cu.ft for standard size and 1.45 cult for

compact size. Includes assumption of electric water heating at 100% efficiency. Hot water use

portion from US DOE 1990b. Other energy use for motor. The standard specifies only the EF, and

in practice manufacturers may not use the specific design options trading off motor and hot water

energies shown above.

5) Other (top loading, semiautomatic; front loading; and suds saving) are not regulated under the

1994 standards but must have unheated water rinse option.

99

EE-RM-94-230 COMMENT 61C

- 8.5'. Standards 

Efficiency standards for clothes washers-were rust enacted under the National Appliance , Eru:rgy Conservation Act (NAECA). These standards required only that clothes washers 

have an unheated water option for the rinse cycle. New standards will beCome effective in 

19914. and are shown in Table 8.3. -

T'able 8.3. Emciency Standards for 'Residential Clothes Washers 

HotWatt:r Motor Total 

Dalabase Year Min. Energy Eaergy UEC 

T)pe Code Effective EF (kWh/cycle) (1cWblcycle) (kWblcycle) _ 

Using DOE Test 

fttX%dure 

Standard, Top-Loading CW 1994 1.18 1.94 0.27 2.21 

Co,mpac:t. Top-Loading CW' ,1994 0.90 1.36 0.25 1.61 

Using P&G Data 

Slandard, Top-Loading CW 1994 1.18 1.50 0.27 1.77 

Compact. Top-Loading CW 1994 . 0.90 1.05 0.25 1.30 

Soun::e: US DOE 1990b. 

1) Effective dale is May 14 of year imtic:ated. 

2) Standalds specified in NAECA 1987 and effective starting 1990 for clothes washers required 

clothes washers to be equipped With an unbeated water rinse option. 

3) EF units are capacity (CulL)jkWh. 

4) VEe pet cycle·CaIallaled as capacitylEF. using 2.60 CUlL for standard size and 1045 cu.fL for 

compact size. Includes assumption of electric water healing at 100% efficiency. Hot water use 

ponion from US DOE 199Ob. Other energy use for motor. The standard specifleS only the EF. and 

in. practice manufacturers may not use the specific design options trading off motor and hot water 

eoergifS shown above. 

5) Other (top loading. semiautomatic; frOnt loading; and suds saving) are not regulated under the1994 siandards but must bve unheated water rinse option. 

• 

99 
9. CLOTHES DRYER END-USE DATA

Clothes dryers account for about 6% of total electricity usage and 2% of total natural gas

usage in the residential sector. Dryers are a relatively well understood end-use and have

been studied as part of the U.S. DOE appliance standards process.

9.1. Clothes Dryer UECs

Clothes dryer UECs for new units are measured using a laboratory test procedure from

U.S. DOE. This regime determines the total energy use for a cycle of drying using a

standard quantity of wet clothing. The UEC of a clothes dryer in the field will be directly

proportional to the amount the appliance is used.

Average energy use for stock clothes dryers is estimated by utilities and other groups

through direct metering or statistical techniques. In the UEC database, there are 4 metered

estimates (76 total estimates) for electric clothes dryers only 1 for gas dryers (12 total).

However, there are more than 40 statistically-derived estimates of electric dryer UECs

(Appendix B).

UEC Equation

The U.S. DOE test procedure is used to determine per-cycle energy consumption, or UEC,

from which the energy factor is derived. The relationship between the UEC and the energy

factor is as follows.

UEC (electric): kWhyr Use * Capacit

EF

UEC (gas MMBtu ._Use * Capacity * 0.003412

where: Use is in cycles/year

Capacity is unit size (Ib/load, or 7 Ibs for standard dryer)

EF is the energy factor from the DOE test procedure (lb/kWh)

0.003412 is the kWh to MMBtu conversion factor

Stock UECs

The analysis of the clothes dryer UECs in the UEC database resulted in estimates of 1000

kWh/yr for electric (n=67) and 3.9 MMBtu/yr for gas (n=9) dryers. These values are close

to the baseline new unit energy consumption in the U.S. DOE appliance standard analysis

(967 kWh/yr and 3.73 MMBtu/yr). The similarities suggest that both the assumption for

cycles in U.S. DOE 1990b (based on Proctor & Gamble data) is reasonable and that the

efficiencies have not been changing over time. Efficiencies for electric dryers have changed

very little since 1972, whereas gas dryer efficiency has increased 20% (from EF = 2.0 to

2.4). For simplicity, the residential database includes the UEC of the appliance standards

base unit as the stock UEC.

New UECs

The UEC for new dryers is assumed to be the same as for stock units, since the stock UEC

is a new unit average.

Inn

EE-RM-94-230 COMMENT 61C

9. CLOTHES DRYER END-USE DATA 

Clothes dryers account for about 6% of total electricity usage and 2% of total natural gas 

usage in the residential sector. Dryers are a relatively well understood end-use and have 

been studied as part of the U.S. DOE appliance standards process. 

9.1. Clothes Dryer UECs 

Clothes dryer UECs for new units are measured using a laboratory test procedure from 

U.S. DOE. This regime detennines the total energy use for a cycle of drying using a 

standard quantity of wet clothing. The UEC of a clothes dryer in the field will be directly 

proportional to the amount the appliance is used. . 

A verage energy use for stock clothes dryers is estimated by utilities and other groups 

through direct metering or statistical techniques. In the VEC database, there are 4 metered 

estimates (76 total estimates) for electric clothes dryers only 1 for gas dryers (12 total). 

However, there are more than 40 statistically-derived estimates of electric dryer. UECs 

(Appendix B). 

UEC Equation 

The U.S. DOE test procedure is used to determine per-cycle energy consumption, or VEC, 

from which the energy factor is derived. The relationship between the UEC and the energy 

factor is as follows. 

UEC (electric); 

UEC(gas): 

where: 

Stock UECs 

'"WhI Use • Capacity !'- yr - EF 

MMB _ Use· Capacity • 0.003412 

tu - EF 

Use is in cycles/year 

Capacity is unit size (lblload. or 7 Ibs for standard dryer) 

EF is the energy factor from the DOE test procedure (IblkWh) 

0.003412 is the kWh to MMBtu conversion factor 

The analysis oCthe clothes dryer UECs in the UEC database resulted in estimates of 1000 

kWhlyr for electric (n=67) and 3.9 MMBtulyr for gas (n=9) dryers. These values are close 

to the baseline new unit energy consumption in the U.S. DOE appliance standard analysis 

(967 kWh/yr and 3~73 MMBtulyr). The similarities suggest that both the assumption for 

cycles in U.S. DOE 1990b (based on Proctor & Gamble data) is reasonable and that the 

efficiencies have not been changing over time. Efficiencies for electric dryers have changed 

very little since 1972, whereas gas dryer efficiency has increased 20% (from EF = 2.0 to 

2.4). For simplicity, the residential database includes the UEC of the appliance standards 

base unit as the stock DEC. . 

New UECs 

The UEC for new dryers is assumed to be the same as for ·stock units, since the stock UEC 

is a new unit average. 

l ()(l 
9.2. Clothes Dryer Usage

The energy usage of clothes dryers will vary in the field with number of cycles the

appliance is used. The most recent estimate of number of cycles is from Proctor and

Gamble (US DOE 1990b) and is 359 cycles per year. Currently, however, the U.S. DOE

test procedure assumes that usage averages 416 cycles per year.

9.3. Clothes Dryer Technology Data

There are three different classes of electric clothes dryers: the standard clothes dryer and

two types of compact clothes dryers. Since the standard dryer accounts for 94% of new

sales it is the only electric dryer considered in the database. There is only one class of gas

clothes dryers.

Historical Efficiency Data

Annual clothes dryer shipments from 1957 to 1990 are shown in Figure 9.1 and the

average efficiency of new clothes dryers sold over time is shown in Figure 9.2. Note that

the efficiencies for gas units are given in terms of Ibs/kWh, where the gas energy is

converted to kWh at 3412 Btu/kWh. The average efficiency of new electric units sold has

changed only marginally since 1972, while the elimination of pilot lights has improved the

efficiency of new gas dryers.

Cost vs. Efficiency for New Equipment

Estinates of cost vs. efficiency for new standard clothes dryers are shown in Figure 93.

The values are based on estimates in the U.S. DOE appliance standards analysis (US DOE

1990b), adjusted to 1990$. Efficiency improvements are relatively minor except for the

major new technologies which may become available for electric clothes dryers.

Product Lifetimes

Table 9.2 shows three different estimates of the lifetimes of clothes dryers.

Table 9.2. Estimates of Residential

Dryer Lifetimes

Lifetime in Years

Gas Electric

Source Dryer Dryer

Low 12 11

Appliance Avg 14 13

High 16 16

Low 13 13

Lewis/Clark Point 15 15

High 18 18

Low 6 6

LBIRBEM Avg 17 17

High 30 30

Sources: Appliance 1992 (first owner lifetime

only); Lewis and Clarke 1990; LBLREM

1991.

101

EE-RM-94-230 COMMENT 61C

9.2. Clothes Dryer Usage 

Th.e energy usage of clothes dryers will vary in the field with number of cycles the 

appliance is used. The most recent estimate of number of cycles is from Proctor and . Gamble (US DOE 1990b) and is 359 cycles per year. Currently. however. the U.S. DOE 

test procedure assumes that usage averages 416 cycles per year. 

9.3~. Clothes Dryer Technology Data 

Thl~re are three different classes of electric clothes dryers: the standard clothes dryer and 

two types of compact clothes dryers. Since the standard dryer accounts for 94% of new 

sales it is the only electric dryer considered in the database. ,There is only one class of gas 

clorhes dryers. ' 

IDstorical Efficiency, Data 

Annual clothes dryer shipments from 1957 to 1990 are shown in Figure 9.1 and the 

avelrage efficiency of new clothes dryers sold over time is shown in Figure 9.2. Note that 

the efficiencies for gas units are given in terms of IbslkWh. where the' gas energy is 

converted to kWh at 3412 BtulkWh. The average efficiency of new electric units sold has 

changed only marginally since 1972, while the elimination of pilot lights has improved the 

eftic:iency of new gas dryers. 

Cost vs. Efficiency for New Equipment 

Estimates of cost vs. efficiency for new standard clothes dryers are shown in Figure 9.3. 

The values are based on estimates in the U.S. DOE appliance standards analysis (US DOE 

1990b). adjusted to 1990$. Efficiency improvements are relatively minor except for the 

major new technologies which may become available for electric clothes dryers. 

Product Lifetimes 

Tabk~ 9.2 shows three different estimates of the lifetimes of clothes dryers. 

Table 9.2. Estimates of Residential 

D ryer LU eames' t· 

.' Lifetime in Years 

Gas Electric 

Source Dryer DJyez 

Low 12 11 

Appliance Avg 14 13 

High 16 16 

Low 13 13 

Lewis/Clark Point 15 15 

High 18 18 

Low 6 6 

LBlJREM Avg 17 17 

High 30 30 

Sources: Appliance 1992 (first owner lifeume 

only); Lewis and ClaIke 1990; LBLREM 

1991. . 

101 
Figure 9.1. Annual Clothes Dryer Shipments, 1957-1990

1960 1965 1970 1975 1980 1985 1990

Source: US DOE 1990b (1951-1975); Appliance Magazine (1976-90).

Figure 9.2. Shipment-Weighted Efficiency for Clothes Dryers, 1972-1990

*-- IElectcic _ .._.__.

:--}-- Gas

'- ........-..-. --_--__ .... .. . . . . '.__-... .... _._7 ...

.. .. ... . . .. . .. . . . . .. .. . . ... . . . . .. .. .. .. . . .. . . . . ... . . . . . . .. .. .. . . .. .. ..? .. . . . . . . .... . . . .

1974 1976 1978 1980 1982 1984 1986 1988 1990

Source: AHAM 1991. Gas energy converted to kWh @ 3412 Btu/kWh to calculate energy factor.

102

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

15

e

L.

a

I

= ---- Electric

_.„ -.... „.. ..... . .... s. Z^.......... ..... ........... _ -_. ..... 

Gas

I I I I1

950 1955

3.0

2.5

2.0

i 1.5 U

4

1.0

0.5

0.0

1972

I I -I _ __--IEE-RM-94-230 COMMENT 61C

Figure 9.1. Annual Clothes Dryer Shipments, 1957·1990 

4.0 .------:---:---:---~--~---.,...---:_--~--__. 

. 3.5 . 

3.0 

1.0 

[ 2.5 

~ 'c =2.0 

= 

~ ~ 1.5 

1.0 

0.5 

-----·+----~-----·-t--········--t---___t··-···-·-·-t-·-····-··-··t·-··· ._-..... . 

~== -----:mc F--=t=~ r~:o=:-i=_-~I~~~=: 

0.0 +----+---+----+----+----+---+-----'--+---~ 

1950 1955 1960 1965 1970 1975 1980 1985 1990 

Source: US DOE 1990b (1951-1975): Appliance Magazine (1976-90), 

Fagure 9.2. Shipment-Weighted Emciency for Clothes Dryers, 1972-1990 

3.0 ........-------,...----,....---'--,..------:---------------, 

2.5 

~ .~ 2.0 

1 

..J 

-

~ 1.5 -

y 

&! 

~ 

f.'l 1.0 cu 

= 

~ 

0.5 

. : • 

-.-............. -t ........ -.......... ~ .. -... -.......... ·r···················1 ............. - .•.... ; ...... -.................................. ~................... . ........ -.... -.. 

~=J=-=l=~=:==l:-=~~:~=:=~I~-=~ 

---r---ro--r---o-'-----+--c- T -

o--'--;-----

···················]""···················1··-·················j······-_··-····_·;····················r····················r····················t···················-< ...............•. _ .. 

. : 

0.0 +----+----+----+----+----+------j--~~--j-----f 

1972 1974 1976 1978 1980 1982 1984 1988 1990 

Source: AHAM 1991. Gas energy convened to kWh @ 3412 BtuJ1cWh to calculate energy factor~ 

102 
Figure 93. Cost Versus Efficiency for New Dryers

9

7

6

aU

Ga

4

3

2

1

0

300 400 500 600

Consumer Cost ($1990)

Source: USDOE 1990b. Converted from $1988 using CPI multiplier for laundry products of 1.02. Gas energy

factor represents gas consumption convened to kWh @ 3413 Btu/kWh. Test procedure uses 71bs/cycle

and the test is run until the moisture content of the test load is between 25 and 5.0% of the bone dry

wieght of the test load.

Option Description

Baseline Standard Electric Dryer. 5.9 cubic feet 359 cycles/year.

1 Automatic termination

2 1 + insulation

3 2 + recycle exhaust

4 2 + microwave

5 2 + heat pump

Baseline Standard Gas Dryer. 5.9 cubic feet. 359 cycles/year.

1 Automatic termination

2 i + insulation

3 2 + recycle exhaust

103

:200 700 800

EE-RM-94-230 COMMENT 61C

Figure 9.3. Cost VerSus EFficiency for New Dryers 

9 

8 

7 

6 -.c 

:t 

~ .,Q s e 

.. 

oS 

u 

~ 4 

tl 

.. 

QI 

c:: 

~ 

3 

2 

1 

0 

__ . __ . __ .. _w .... ..i-.•.... __ ... ___ ._ •. ...;... •. ___ •...•..• _._ • 

! i 

··-------1---·--·-+-----

:200 300 400 

r--··· l----

500 

Consumer Cost ($1990) 

s 

--a- Heat Pwnp (E) 

---0-- Microwave (E) 

----- Standard Elec 

~ Standard Gas 

600 700 800 

Source: USDOE 1990b. Conveited fromS1988 using CPI multiplier for laundry prOduclS of 1.02. Gas energy 

fac:tor represenlS gas consumption converted to kWh @ 3413 Btu/kWh. Test Procedure uses 71bs/cycle 

and the leSt is run until the moisture content of the test load is between 2.5 and 5.0% of the bone dry 

wieght of the test load. 

Option Descliption 

Baseline Standard Electric Dryer. 5.9 cubic feeL 359 cycles/year. 

-1 Automatic termination 

2 1 + insulation 

3 2 + recycle exhaust 

4 2 + microwave 

5 2+ heat pump 

Baseline Sltandard Gas Dryer. 5.9 cubic feeL 359 cycles/year. 

1 Automatic lermination 

2 i + insulation 

3 2 + recycle exhaust 

103 
9.4. Shares

The database includes ownership shares for electric and gas clothes dryers at a national

level. It includes total shares from the RECS data (US DOE 1982a, 1986, 1989a, 1992).

It also includes shares in new buildings from the RECS data for buildings built during the

previous 5 to 7 years from the same data sets. Some of these data are shown in Figures 9.4 and 9.5. Figure 9.4 shows that clothes dryer shares are increasing slightly overall,

with the growth coming from electric dryers. Shares in new buildings are approximately 75% for electric clothes dryers, whereas the stock share is 52%, so increases in the stock

may be due primarily to dryers in new buildings.

9.5. Standards

Efficiency standards for clothes dryers were first enacted in 1988 under the National

Appliance Energy Conservation Act (NAECA), and required only that gas clothes dryers

not have a constantly burning pilot light. Table 9.3 shows minimum efficiency standards

for residential dryers.

Table 9.3. Minimum Efficiency Standards for Residential

Dryers.

DDatabe Year Min. Total

Type o Fuel Code Effectiv EF UEC

Standard Electric DR 1994 3.01 2.33 kWh/cycle

Compact (120V) Electric DR 1994 3.13 0.96 kWh/cycle

Compact (240V) Electric DR 1994 2.90 1.03 kWh/cycie

Standard Gas DR 1994 2.67 8.95 kBtu/cycle

1) Effective date is May 14 of year indicated.

2) Standards specified in NAECA 1987 and effective starting 1990 for gas

dryers required that gas dryers shall not be equipped with a pilot light 1994

standards levels from US DOE 1990b.

3) EF units are Ibs/kWh. Gas dryer EF are also Ibs/kWh at a conversion of

3412 Btu/kWh.

4) UEC per cycle calculated as capacity Obs)/EF, using 7 Ibs for standard

dryers and 3 Ibs for compact size.

104

EE-RM-94-230 COMMENT 61C

/ 

9.4. Shares 

The database includes ownership shares for electric and· gas clothes dryers at a national 

level. It includes total shares from the RECS data (US DOE 1982a. 1986, 1989a, 1992). 

It also includes shares in new buildings from the RECS data for buildings built during the 

previous 5 to 7 years from the same data sets. Some of these data are shown in Figures 

9.4 and 9.5. Figure 9.4 shows that clothes dryer shares are increasing slightly overall. 

with the growth coming from electric dryers. Shares in new buildings are approximately 

75% for electric clothes dryers. whereas the stock share is 52%. so increases in the stock 

may be due primarily to dryers in new buildings. 

9.5. Standards 

Efficiency standards for clothes dryers were rust enacted in 1988 under the National 

Appliance Energy Conservation Act (NAECA). and required only that gas clothes dryers 

not have a constantly burning pilot light. Table 9.3 shows minimum efficiency standards 

for residential dryers. 

Table 9.3. Minimum Emciency Standards for Residential 

D 'ryers 

Database Year Min. Total 

~ype Fuel Code Effective EF UEC 

Slandaai Electric DR 1994 3.01 2.33 kWh/cycle 

Compact (120V) Electric DR 1994 3.13 0.96 kWh/cycle 

Compact (240V) Electric DR 1994 2.90 1.03 kWh/cycle 

Stmdard ~ DR 1994 2.67 8.95 kBtulcyde 

1) Effective dale is May 14 of year indicated. 

2) Standards specified in NAECA 1987 and effective starting 1990 for gas 

dryers required that gas dryers shall not be equipped with a pilot ligbL 1994 

slaDdards levels from US DOE 1990b. 

3) EF units are IbslkWb. Gas dryer EF are also IbslkWb at a conversion of 

3412 BtulkWb. . 

4) UEC per cycle calculated as capacity (lbs)/EF, using 7 Ibs for standard 

dryers and 3 Ibs for compact size. 

104 
Figure 9.4. Clothes Dryer Fuel and Total Ownership Shares by HousingType, 1981-1990

1982 1984 1986 1988

-- -- Eecric

-- 0-- Gas

* Total

1990

Source: US DOE 1982a, 1986, 1989a, 1992.

Figure 9.5. Clothes Dryer Fuel and Total Ownership Shares for New Construction by Housing Type

C

A

a

w0

1.0

0.9

0.8

0.7

0.6

0.5

0.4

03

0.2

0.1

0.0

SF MF MH

Source: US DOE 1992a data for buildings built between 1985 and 1990.

.r -

0.7

0.6

0.5

· OS 0.4

. 0.3 0

.0

0.2

0 4-

1980

Q Electic

[ Gas

* Total

ALL

I

EE-RM-94-230 COMMENT 61C

Figure 9.4. Clothes Dryer Fuel and Total OwnecsblpShares,by Rousing Type, 1981·1990 

0 .. 7 

0 .. 6 

O.S 

i 

= '" 0.4 ~ 

-f 0.:3 

J! 

U> 

0.2 

0.1l 

(I 

1980 

-

.... 

... 

I 

i 

I 

t I 

I i 

I I 

j .L 

i T t i I I j. 

i 

1982 1984 

Source: US DOE 19823. 1986,19898, 1992. 

--

• EIecuic 

I 

I 

i • Tow 

j ... ! I . ... i 

I I ! i j j. 

1986 '1988 1990 

Flgure.9.5. Clotbes Dryer Fuel and Total Ownership Shares for New Construction by Housing Type 

1.0 ,..--------------------,.-----, 

0.9 +--,----

0.8 +-----

_ 0.7 

I: 

o 

~ 0.6 

~ 0.5· -:! 

III 0.4 

.c: 

(I) 0.3 

0.2 

0.1 

0.0 

SF MF MH 

Source: US DOE 1992a data for buildings buill between 1985 and 1990. 

• Tocal 
10. LIGHTING END-USE DATA

Residential lighting accounts for between 10 and 15% of residential electricity

consumption, and is thus a major end-use. However there has only recently been an effort

by researchers, utilities, and policymakers to characterize the lighting end-use in the

residential sector. In this section, we present the methodology used to create a detailed

disaggregation of energy use in residences.

Average energy use for lighting in the building stock is difficult to measure by metering

because of the spatially diffuse nature of lighting. It is also difficult to estimate UECs from

other statistical techniques. In the UEC database, there are no metered estimates for

lighting and only 2 conditional demand estimates (Appendix B).

Residential lighting exhibits a great deal of diversity in usage and equipment size (i.e.,

wattage of bulbs). This situation is further complicated by the fact that the usage level

affects the service life of the device. For instance, an incandescent bulb used one hour per

day will last approximately three years, while the same bulb operated three hours per day

will last less than one year. The usage level is important because it largely influences the

cost-effectiveness of energy-efficient lighting technologies.

We use the results of detailed lighting surveys to create a breakdown of usage and wattage

for incandescent bulbs. We use this breakdown to calculate total electricity use and use per

household for residential lighting. We also present a summary of costs and lifetimes for

standard incandescent bulbs and their more efficient replacements.

10.1. Baseline Lighting Usage

We divide the current stock of indoor and outdoor light sockets into six usage bins: less

than 1 hour, 1 to 2, 2 to 3, 3 to 4, 4 to 5, and greater than 5 hours per day. The fraction of

sockets assigned to each bin (third column in Table 10.1) is adapted from monitored

residential lighting usage in Washington state (Manclark 1991). We assume, in the absence

of better data, that this usage distribution is representative of residential lighting usage in

the U.S.

10.2. Distribution of Installed Wattage

We focus mainly on incandescent lamps because they comprise the vast majority of lighting in the residential sector. We base the relative frequency of each incandescent lamp wattage

on data collected in a survey of homes in New York and New Jersey (Robinson 1992), as

shown in the top three rows of Table 10.1.

10.3. Energy Consumption per Socket

Table 10.1 also shows the calculation of average socket UEC, based on the usage and

wattage distributions discussed above. Each combination of lamp wattage and daily usage

leads to a unique annual socket UEC, ranging from 5 to 352 kWh per socket per year.

These individual UECs are then weighted according to their frequency of occurrence in the

housing stock to calculate a UEC for the average socket. The average UEC per socket

based on our data is 51.5 kWh/socket/year. The column and row marked "% of total"

show the percent of total incandescent lighting energy consumption attributable to each

usage bin and wattage bin, respectively.

106

EE-RM-94-230 COMMENT 61C

10. UGHTING END-USE. DATA 

Residential lighting accounts for between 10 and 15% of residential electricity 

consumption, and is thus a major end-use. However there has only recently been an effort 

by researchers, utilities, and policymakers .to characterize the lighting end-use in the 

residential sector. In thiS section, we present the methodology used to create a detailed 

disagl:regation of energy use in residences. . . 

A vera.ge energy .use for lighting in the building stock is difficult to measure by metering 

becau')e of the spatially diffuse nature of lighting. It is also difficult to estimate UECs from 

other statistical techniques. In the UEC database, there are no metered estimates for 

lighting and only 2 conditional dem3:Dd estimates (Appendix B). 

Residl~ntial·lighting exhibits a great deal of diversity in usage and equipment size (Le., wattal~e of bulbs). 'This situation is further complicated by the fact that the usage level 

affect:; the service life of the device. For instance, an incandescent bulb used one hour per 

day will last approximately three years, while the same bulb operated three hours per day 

will last less than one year. The usage level is important because it largely influences the 

cost-effectiveness of energy-efficient lighting technologies. 

We use the results of detailed lighting surveys to create a breakdown of usage and wattage 

for inc:andescent bulbs. We use this breakdown to calculate total electricity use and use per 

housellloid for residential lighting. We also present a summary of costs and lifetimes for 

standard incandescent bulbs and their more efficient replacements. . 

10.1. BaseUne Lighting Usage 

We divide the current stock of indoor and outdoor light sockets into six usage bins: less 

than 1 hour. 1 to 2, 2 to 3, 3 to 4, 4 to 5, and greater than 5 hours per day. The fraction of 

sockets assigned to each bin (third column in Table 10.1) is adapted from monitored· 

residelntiallighting usage in Washington state (MancIark 1991). We assume, in the absence 

of betJter data, that this usage distribution is representative of residential lighting usage in 

the U.S. 

10.2. Distribution of Installed Wattage 

We focus mainly on incandescent lamps because they comprise the vast majority of lighting 

in the residential sector. We base the relative frequency of each incandescent lamp wauage 

on data collected in a survey of homes in New York and New Jersey (Robinson 1992), as 

ShOWII in the. top three rows of Table 10.1. 

10.3. Energy Consumption per Socket 

Table 10.1 also shows the calculation of average socket UEC, based on the usage and 

wattage distributions discussed above. Each combination of lamp wattage and daily usage 

leads to a unique annual socket UEC, ranging from 5 to 352 kWh per socket per year. 

These individual UECs are then weighted according.to their frequency of occurrence in the 

housing stock to calculate a UEC for the average socket" Th~ average UEC per socket 

based on our data is 51.5 kWh/socket/year. The column and row marked "% of total" 

show the p~rcent of total incandescent lighting energy consumption attributable to each 

usage bin and wattage bin, respectively. . . 

106 . 
Table 10.1: Usage and wattage distributions for incandescent sockets in US residences

(1) Usage distribution adopted from Manclark (1991). Wattage distribution from Robinson (1992)

(2) Assumes that usage distribution applies in an identical manner across all wattage bins.

(3) Hours in >5 hour/day bin adjusted to result in Manclark's average hourly usage of 2.1 hours per socket.

10.4. Energy Consumption per Household

Table 10.2 shows how we use the installed wattage per square foot from PG&E's recent

lighting survey (Kelsey and Richardson 1992) and the estimate of lighting usage from

Table 10.1 to estimate average lighting UECs per household by housetype. The average

UEC per household is about 1300 kWh/year.

10.5. Total Energy Consumption by Housetype

Table 10.3 shows total incandescent electricity consumption in US residences,

disaggregated by house type, usage bin, and wattage bin. Total annual consumption for

residential incandescent bulbs is slightly more than 120 TWh. If the PG&E survey's

estimate of fluorescent (not compact fluorescent) penetration per household accurately

reflects households throughout the nation, electricity use for fluorescent lamps in

residences would add another 15 TWh to this total. Our total (including fluorescents) is

more than 50% higher than the estimate of 1990 lighting energy use contained in US DOE

(1994), but it is closer to the 122 TWh for 1990 calculated by Atkinson et al. (1992). More

than 80% of incandescent lighting energy is found in single-family homes, with most of the

rest found in multifamily buildings.

107

Wattage bin <40 W 40 W 60 W 75 W 100 W 150 W >150 W Wtd avg

Avg watts in bin 25 40 60 75 100 150 175 67.1

% of bulbs in bin 9% 16% 37% 20% 12% 5% 1%

Daily Mean Bulb % of

Usage in Bin Fraction Electricity use per socket by combined usage total

Hrs/day Hrs/day % of total and wattage bin (kWh/yr)

0-1 hrs 0.5 40% 5 7 11 14 18 27 32 4.9 10%

1-2 hrs 1.5 20% 14 22 33 41 55 82 96 7.4 14%

2-3 hrs 2.5 10% 23 37 55 68 91 137 160 6.1 12%

3-4 hrs 3.5 10% 32 51 77 96 128 192 224 8.6 17%

4-5 hrs 4.5 10% 41 66 99 123 164 247 288 11.0 21%

>5 hrs 5.5 10% 50 80 121 151 201 301 352 13.5 26%

Sum/Avg 2.10 100% 1.7 4.9 17.0 11.5 9.2 5.8 1.3 51.5 100%

%of total 3% 10% 339 22% 18% 11% 3% 100%

EE-RM-94-230 COMMENT 61C

Table 10.1: Usage and wattage distributions for incandescent sockets in US residences 

Wattage bin <40 W 40 W 60 W 7S W 100 W 150 W >150 W Wtd avg 

A vg watts in bin 2S 40 60 75 100 150 175 67.1 

~ of bulbs in bin 9% 16% 3'~ 20% 12% 5% 1% 

Daily Mean Bulb 

Usage in Bin Fraction Electricity use per socket by combined usage 

Hrs/day Hrs/day 'Ioo/total and wattage bin (kWhlyrJ 

0-1 brs O.S 40% 5 7 11 14 18 27 32 4.9 

1-2 brs I.S 20% 14 22 33 41 SS 82 96 7.4 

2-3 brs 2.5 10% 23 37 5S 68 91 137 160 6.1 

3-4 brs 3.5 10% 32 51 77 96 128 192 224 8.6 

4-5 brs 4.5 10% 41 66 99 123 164 247 288 11.0 

>Shrs 5.S 10% 50 80 121 151 201 301 3S2 13.5 

SumJAvg 2.10 1(X)lfo 1.7 4.9 17.0 11.5 9.2 5.8 1.3 51.5 

%o/total 3% 10% 33% 22% 189'0 11% 3% 100% 

(1) Usage distnbution adopted from Manclark (1991). Waltage distribution from Robinson (1992) 

(2) Assumes that osage distribution applies in an identical manner aaoss all wattage bins . 

%0/ 

total 

10% 

14% 

12% 

17% 

21% 

26% 

100% 

. (3) Hours in >5 hour/day bin adjusted to result in Manclarlc's average hourly usage of 2.1 hours per socket. 

10.4. Energy Consumption per Household 

Table 10.2 shows how we use the installed wattage per square foot from PG&E's recent 

lighting survey (Kelsey and Richardson 1992) and the estimate of lighting usage from 

Table 10.1 to estimate average lighting UECs per household by housetype. The average 

UEC per household is about 1300 kWh/year. 

10.5. Total Energy Consumption by Housetype 

Table 10.3 shows total incandescent electricity consumption in US residences, 

disaggregated by house type, usage bin, and wattage bin. Total annual consumption for 

residential incandescent bulbs is slightly more than 120 TWh. If the PG&E survey's 

estimate of. fluorescent (not compact fluorescent) penetration per household accurately 

reflects households throughout the nation, electricity use for fluorescent lamps in 

residences would add another 151Wh to this total. Our total (including fluorescents) is 

more than 50% higher than the estimate of 1990 lighting energy use contained in US DOE 

(1994), but it is closer to the 1221Wh for 1990 calculated by Atkinson et al. (1992). More 

than 80% of incandescent lighting energy is found in single-family homes, with most of the 

rest found in multifamily buildings. 

107 
(1) Source for % of 1990 households: RECS (US DOE 1992)

(2) Results of PG&E Lighting Survey are documented in Kelsey & Richardson (1992).

(3) Lighting UEC in first row includes incandescent and fluorescents together. Incandescent UEC

is net of tube fluorescent lamps. Fluorescent UEC calculated based on Kelsey & Richardson (1992),

32 lamps per house@ 41.1 Watts/lamp used 3.8 hrs. day for5 out of 6 days a year.

(4) PG&.E floor area from survey. Floor area by house type from US DOE (1992)

(5) Installed waaage/sf based on PG&E survey, 1.25 W/sf for single-family and mobile home, 1.18 W/sf for

multi-family, reduced by 12% to account for the fact that incandescent lamps are 88% of installed wattage.

Total wattage for US homes calculated as the product of PG&E wattage/sf and floor area.

(6) PG&E average usage value based on customer-reported usage; US value from Table 10.1.

(7) Annual UEC (kWh/yr) equals average usage * installed watts/1000

(8) PG&E incandescent UEC per socket based on survey results; US value from Table 10.1.

(9) PG&E value for sockets/house based on survey data; US values = annual UEC+UEC per socket

108

Table 10.2: Calibration of Annual Consumption to PG&E survey

Housing Type

Parmeter PG&E (2) Single-family Multifamily Mobile Homes Total

% of 1990 households 69% 26% 6% 100I

Lighting UEC (kWh/yr) 1,274

Fluorescent UEC (kWh/yr) 152 - -

Incandescent UEC (kWh/yr) 1,118 .

Existing home floor area (sq ft) 1,400 1,865 928 921 1569

Installed incandescent wa 1,552 2,052 964 1,013 1712

Avg. incandescent usage (h/day) 1.94 2.10 2.10 2.10 2.10

Annual incandescent UEC (kWh/yr) 1,098 1,574 739 777 1313

Inc UEC per t WsocketWhsoet/yr) 44.7 51.5 51.5 51.5 51.5

Socketsbouse 25 31 14 15 26

i~~~~~ i i.

EE-RM-94-230 COMMENT 61C

--

Tablt~ 10.l: Calibration of Alinual Consumption to PG&E survey 

Housing' Type 

Parameter PG&E(2) Single-family Multifamily Mobi~Homes Total , '---

% 0/1990 Iwuselwlds 6990 26% 6% llX1fo 

~-

Ugbtililg UEC (kWhlyr) 1,274 - - - - Fluorescent UEC (kWblyr) 152 - - - Incandescent VEC {kWblyr) 1.118 - - -

EXistilllg home floor area (sq ft) 1,400 1,865 928 921 1569 

Installed incandescent watts 1,552 2,052 964 1,013 1712 

.A vg. incandescent usage (hrIday) 1.94 .. 2.10 2.10 2.10 2.10 

Alinual incandescent UEC {kWblyr) 1,098 . 1,574 739 Tn 1313 

IDe. UEC per socket (kWblsockellyr) 44.7 51.S .51.5 51.5 51.5 

Socketslhouse 2S 31 14 15 26 

(1) Sowrce for % of 1990 housebolds: RECS (US DOE 1992) 

(2) Resulcs of PG&E Lighting Survey are documented in Kelsey & Richardson (1992). 

(3) Uglllling UEC in fIrSt row includes incandescent and fluorescents together. Incandescent VEC 

is net of tuoo fluorescent lamps. ~uorescent UEC calculated based on Kelsey & Rich;irdson (1992), 

32lamj)s per house@ 4U W:ausJ1amp used 3.8 brs. day for 5 out of 6 days a· year. '. 

(4) PG&£ Boer area from: survey. floor area by bouse type from US DOE (1992) 

(5) Insuilled wattage/sf based on PG&E survey; 1.2:5 WiSt for single-family and mobile home, 1.18 Wlsf for 

multi-family, reduced by 12% to accxnmt for the tact· that incandescent lamps are 88% of installed wattage. 

Total ~lttage for US homes calculated as the. product ofPG&E wattage/sf and floor area. 

(6) PG&E average usage value based on customer~repotted usage; US value from Table 10.1. 

(7) Annual UEC (kWh/yr) equals avemge usage • installed waUS/l000 

(8) PG&E incandescent UEC per socket based on survey results; US value from Table 10.1. 

(9) PG&E ydlue for sockets/bouse based on survey data; US values = annual UEC+UEC per SockeL 

108 

-

-
Table 10.3: 1990 residential incandescent 

usage lighting electricity use by house type, bin, and 'wattage bin (TWh/yr)-

Bulb wattage bin <40 W 40 W 60W 75W 100W 150W >150W SUm %ofo Sing family . 0-1 ha/day 0.3 0.9 32 2' . 1 7 . 1-2 his/day

2-3 his/day

3-4 hts/day

4-5 hrs/day

>5 hs/day

Sum

Multifamily

0-1 hrs/day

1-2 hrs/day

2-3 hia/day

3-4 brs/day

4-5 hrs/day

>5 hs/day

Sum

Mobile home

0-l hrsday

1-2 hrs/day

2-3 hrs/day

3-4 hs/day

4-5 hrs/day

>5 hs/day

Sum

Total

0-1 hsday

1-2 hrs/day

2-3 hisJday

3-4 brsday

4-5 hrs/day

>5 hs/day

Sum

0.5 1.4 4.8 3.2 2.6 1.6 0.4 0.4 1.2 4.0 2.7 2.2 1.3 0.3 0.6 1.6 5.6 3.8 3.0 1.9 0.4 0.7 2.1 7.2 4.9 3.9 2.4 0.6 0.9 2.5 8.8 5.9 4.7 3.0 0.7

3.4 9.7 33.5 22.7 18.1 11.3 2.6

0.1 0.2 0.6 0.4 0.3 0.2 0.0 0.1 0.2 0.9 0.6 0.5 0.3 0.1 0.1 0.2 0.7 0.5 0.4 0.2 0.1 0.1 0.3 1.0 0.7 0.5 0.3 0.1 0.1 0.4 1.3 0.9 0.7 0.4 0.1 0.2 0.5 1.6 1.1 0.8 0.5 0.1

0.6 1.7 6.0 4.0 3.2 2.0 0.5

0.0 0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.1 0.2 '0.1 0.1 0.1 0.0 0.0 0.0 0.2 0.1 0.1 0.1 0.0 0.0 0.1 0.2 0.2 0.1 0.1 0.0 0.0 0.1 0.3 0.2 0.2 0.1 0.0 0.0 0.1 0.4 0.2 0.2 0.1 0.0

0.1 0.4 1.3 0.9 0.7 0.5 0.1

0.4 1.1 3.9 2.6 2.1 1.3 0.3 0.6 1.7 5.8 3.9 3.2 2.0 0.5 0.5 1.4 4.9 3.3 2.6 1.6 0.4 0.7 2.0 6.8 4.6 3.7 2.3 0.5 0.9 2.5 8.7 5.9 4.7 3.0 0.7 1.1 3.1 10.7 7.2 5.8 3.6 0.8

4 12 41 28 22 14 3

4

9.7

14.'

12.:

16.5

21.,

26.5

101

'Al

1.7

2.6

2.1

3.0

3.9

4.7

I1_R

0.4

0.6

0.5

0.7

0.9

1.1

4

4

12

18

15

21

26

32

123

5

1

(1) Total 1990 households (94 million) from 1990 RECS (US DOE 1992). (2) Total TW calculated using number of households by house type and usage/wattage breakdowns from Tables 10.1 and 10.2.

8%

12%

10%

14%

18%

22%

82%

1%

2%

2%

2%

3%

4%

15%

0%

0%

0%

1%

1%

1%

3%

10%

14%

12%

17%

21%

26%

100%

109

-

-

.

-

I

I

EE-RM-94-230 COMMENT 61C

/ 

Table 10.3: 1990 residential incandescent ligbting electr4city use by bouse type, 

usage bin, and ·wattage bin (TWblyr)· .. 

Bulb wattage bin 

<4() W 40 W 60W 7S-W 100 W _ISO W >lS0W Sum 

Single-family 

O-lbis1day 0.3 0.9 3.2 2.2 1.7 1.1 0.3 ·9.7 

1-2hrs1day 0.5 1.4· 4.8 3.2 2.6 1.6 0.4 14.5 

2-3 brsIday 0.4 1.2 4.0 2.7 2.2 1.3 0.3 12.1 

3-4 brsIday 0.6 1.6 5.6 3.8 3.0 1.9 0.4 16.9 

4-5 brsIday 0.7 2.1 7.2 4.9 3.9 2.4 0.6 21.7 

>Shrslday 0.9 2.5 8.8 5.9 4.7 3.0 0.7 26.5 

Sum 3.4 9.7 33.5 22.7 18.1 11.3 2.6 101 

Multifamily 

0-1 ht:sIday 0.1 0.2 0.6 0.4 0.3· 0.2 ·0.0 1.7 

1-2 ht:sIday 0.1 0.2 0.9 0.6 0.5 . 0.3 0.1 2.6 

2-3 hrsIday 0.1 0.2 0.7 0.5 0.4 0.2 0.1· Z.1 

3-4 brslday ·0.1 0.3 1.0 0.7· 0.5 0.3 0.1 3.0 

4-Sbrslday 0.1 0.4 1:3 0.9 0.7 0.4 0.1 3.9 

>5 hrsIday 0.2 0.5 1.6 1.1 0.8 0.5 0.1 4.7 

Sum 0.6 1.7 6.0 4.0 3.2 2.0. 0.5 18 

Mobile home 

O-lhrs1day 0.0 0.0 0.1 0.1 0.1 0.0 0.0 0.4 

1-2 brsIday 0.0 0.1 0.2 '0.1 0.1 0.1 0.0 0.6 

2-3 hrsIday : 0.0 0.0 0.2 0.1 0.1 0.1 0.0 0.5 

3-4 brsIday 0.0 0.1 0.2 0.2 0.1 0.1 0.0 0.7 

4-5 hrsIday 0.0 0.1 0.3 0.2 0.2 0.1 0.0 0.9 

>5 hrsIday 0.0 0.1 0.4 0.2 0.2 0.1 0.0 1.1 

Sum 0.1 0.4 1.3 0.9 0.7 0.5 0.1 4 

Total. 

O-lbrsiday 0.4 1.1 3.9 2.6 2.1 1.3 0.3 12 

1-2 hrsJday 0.6 1.7 5.8 3.9 3.2 2.0 0.5 18 

2-3 hnJday 0.5 1.4 4.9 3.3 2.6 1.6 0.4 15 

3-4bWday 0.7 2.0 6.8 4.6 3.7 2.3 0.5 ·21 

4-5hrs1day 0.9 2.5 8.7 5.9 4.7 3.0 0.7 26 

>5 brsIday 1.1 3.1 10.7 7.2 5.8 3.6 0.8 32 

Sum 4 12 41 28 22 14 3 123 

(1) Total 1990 households (94 million) ~m 1990 RECS (US DOE 1992). 

(2)-Total1Wb calculated using number of households by house type and usage/wattage 

breakdowns from Tables 10.1 and 10.2. 

109 

%o/total 

8% 

·12% 

10% 

14% 

18% 

22% 

82% 

1% 

2% 

2% 

2% 

3% 

4% 

lS% 

fRo 

fRo 

(11'0 

1% 

J% 

J% 

3% 

10% 

14% 

12% 

17% 

21% 

26% 

JOO% . 
10.6. Costs of Efficiency Improvements in Lighting

Table 10.4 shows costs and lifetimes for typical incandescent bulbs and more efficient

replacements for those bulbs.

(1) Source for standard and reflector incandescents and halogens: Atkinson et a. 1992.

(2) Source for compact fluorescents: Koomey et al. 1994a.

(3) Prices are to the end user, not including utility rebates.

110

Table 10.4: Cost and lifetimes for incandescent and compact fluorescent bulbs

Approximate

Incandescent Rated Lamp

Lamp type Style Lamp Equivalent Life Cost

Wattage Watts Hours 1990 $

Incandmect General service 60 60 1000 $0.48

75 75 750 $0.48

100 100 750 $0.48

CompactFluorescent Capsule 15 60 9,000 $14

Capsule 18 75 9,000 $20

Globe 15 60 9,000 $14

Twin Tube 7 40 10,000 $24

Twin Tube 11 40+ 10,000 $24

Twin Tube 15 60 10,000 $24

Twin Tube 20 75 10,000 $24

Quad Tube 20 75+ 9,000 $20

Quad Tube 27 100 9,000 $22

Incanden reflector PAR 38 Flood 150 150 2,000 $3.66

Halogen reflector PAR 38 Flood 90 150 2,000 $4.91

EE-RM-94-230 COMMENT 61C

10.6. Costs of· Efficiency Improvements in Lighting 

Tablc:: 10.4 shows. costs and lifetimes for' typical incandescent bulbs and more efficient . repla(:ements for those bulbs. 

Table 10.4: Cost and lifetimes for incandescent and compact Ouorescent bulbs 

r 

Approximate 

Incandescent Rated 

I.amp type' Style Lamp Equivalent Life 

Wattage Watts Hours 

IDcandf:scent General service 60 60 1000 

75 75 750 

100 100 750 

Compact Flluorescent Capsule . 15 60 9,000 

Capsule 18 75 9,000 

Globe 15 60 9,000 

TwiDTube 7 40 10,000 

Twin Tube 11 40+ . 10,000 

TwiDTube 15 60 10,000 

Twin Tube 20 75 10,000 

Quad Tube 20 75+ 9,000 

QuadThbe 1:1 100 9,000 

InCande:scent reflector PAR 38 Rood 150 150 2,000 

HalOgelll reflector PAR 38 Flood 90 ISO 2,000 

(l)Sourc:e for standard and retleaoriocandesceats and halogens: Atkinson et al. 1992. 

(2) SOlUce for compact tluorescents: Koomey et aI. 1994a. 

(3) Pricc:s aceta dle end user, not including utility rebates. 

110 

Lamp 

Cost 

1990 $ 

$0.48 

$0.48 

$0.48 

$14 

S20 

$14 

S24 

$24 

S24 

$24 

S20 

$22 

$3.66 

$4.91 
11. COOKING END-USE DATA

Cooking, or the combined total for cooktops, ovens, and microwave ovens, accounts for

of 

about: 

LPG 

7% 

consumption. 

of residential 

The 

electricity 

primary consumption, 

consideration 

4% of natural gas consumption, and 10%

may for forecasting of cooking energy use be changes in usage as people cook more with microwave ovens and utilize more prepared foods. Both of these structural changes could decrease residential energy use for cooking over time. The residential forecasting database includes data on cooktops, ovens and microwaves. The cooking end-use is made more complicated by the smaller devices

provided 

such as toaster 

in Table 

ovens 

13.1 in 

and 

Chapter 

coffee makers. UECs for these miscellaneous devices are 13, Miscellaneous End-Use Data

11.1. Cooking UECs

Cooking UECs for new cooktops, ovens, and microwaves are measured using a laboratory test procedure from U.S. DOE. The UEC of a cooking appliance in the field will be directly proportional to the amount the appliance is used.

Average energy use for cooking appliances in the stock is estimated by utilities and other groups through direct metering or statistical techniques. In the UEC database, metered estimates for electric cooking, there are 6 However, there are 50 derived 3 for microwaves, and only 1 for gas cooking. from statistical techniques. In only a few cases are the cooking UECs split between cooktops and ovens (Appendix B).

Stock UECs

The UECs for cooking in the residential forecasting database are 815 kWh/yr for electric cooktops and ovens, 5.6 MMBtu/yr for gas and LPG cooktops and ovens, and 130 kWh/yr 

the UEC 

for 

database 

microwave 

(Appendix 

ovens. These are taken from weighted averages of the records in B).

New UECs

New UECs are assumed to be the same as for the existing stock

11.2. Cooking Technology Data

ovens, 

There is 

and 

very 

microwaves.

little data currently available on the technology characteristics of cooktops,

Historical Efficiency Data

There is no historical efficiency data for cooking appliances in the database, but shipments are included and are shown in Figure 1.1 for standard cooking equipment and Figure 11.2 for microwave ovens.

111

EE-RM-94-230 COMMENT 61C

11. COOKING END-USE DATA 

Cooking, or the combined total for cooktops, ovens. and microwave ovens. accounts for 

about 7% of residential electricity consumption. 4% of natural gas consumption, and 10% 

of LPG consumption. The primary consideration for forecasting of 'cooking energy use 

may be changes in usage as people cook more With microwave ovens and utilize more 

prepared foods. Both of these structural changes could decre~e residential energy use for 

cooking over time. The residential forecasting database includes data on cooktops,ovens 

and microwaves. The cooking end-use is made more complicated by the' smaller devices 

such as toaster· ovens and coffee makers .. UECs for these miscellaneous devices are 

provided in Table 13.1 in Chapter 13, Miscellaneous End-Use Data. 

11.1. Cooking UECs 

Cooking UECs for new cooktops, ovens. and microwave.s are measured using a laboratOIY 

test procedure from U.S. DOE. The UEC of a cooking appliance in the field will be . 

directly proportional to the amount the appliance is used. 

Average energy use for cooking appliances in the stock is' estimated by utilities and other 

groups. through direct metering or statistical techniques. In the UEC database. there are 6 

metered estimates for elecuic cooking. 3 for microwaves, and only I for gas cooking. 

However, there are .50 derived from statistical techniques. In only a few cases are the 

cooking UECs split between cooktops and ovens (Appendix B). 

Stock UECs 

The UECs for cooking in the residential forecasting database are 815 kWh/yr for electric 

cooktops and ovens, 5.6 MMBtu/yr'for gas and LPG .cooktops and, ovens, and. 130 

kWh/yr for microwave ovens. These are taken from weighted averages of the records in 

the UEC database (Appendix B). . 

New UECs 

New UECs are assumed to be the same as for the existing stock. 

11.2. Cooking Technology Data 

There is very little data currently available on the technology characteristics of cooktops, 

ovens, and microwaves. ' 

~istorical Efficiency Data 

There is no historical efficiency data for cooking appliances in the database. but shipments 

are included and are shown in Figure 11.1 for standard cooking equipment and Figure 11.2 

for microwave ovens. 

111 
Figure 111.1. Annual Cooking Range Shipments, 1951-1990

:3.5 e_.

3.0. - i

1.0 - Electric

----- Gas

0.5

0.0.

1950 1955 1960 1965 1970 1975

Source: US DOE (1951-69); GAMA (1970-85); Appliance Magazine (1986-90).

Figure 11.2. Annual Microwave Oven Shipments, 1976-1990

14

12

10

S. § 6

4

.2 -_ -- .........

0

1975

Source: Appliance Magazine.

1980 1985

112

77

\ . , - ....... ....... .V.........._„.

-i----I---

i ' i

1980 1985 1990

1990

EE-RM-94-230 COMMENT 61C

Figure 1ll.1 .. Annual Cooking Range Shipments, 1951-1990 

:3.5 -r----~--~-----.---~--_r_--~---:___-__, ! 1 ! i :s.o ---~-.-.-.-.-- --~!--~-;'. 

i , 

2.5 !! I 

... 

!. l 2.0 

1· 

---:.;.: _._- (--I-~ 

: i \ _ 

.- '-'r-." : .:.-.-.. _- -·_··_ .. _-·t .. ·/·:;; "-·I-.. --·_ .. t,·t .. _··_-- : ._.-.. -- l /f~_ -

\ '/ \: ,- , :" ! . 1/ ..... - - \ : " -1' 

c 

.g 1.5 \ ,\:" " ...... ./, i ~ ~ / 1 

~. 

Electric 1.0 

0.5 ----- Gas 

0.0 +------+------4----+-----+---+-----+---~--___I 

1950 1955 1960 1965 1970 1975 1980 1985 1990 

Source: US DOE (1951-69); GAMA (1970-85); Appliance Magazine (1986-90). 

Figure 11..2. Annual Microwave Oven Shipments, 1976-1990 

14 ~--------------~----------~------~----------------~ 

I 12 -----.-----... -.. -- -·-~----···-··--····--l- --.----... _ ... .-.. __ .---

.. 10 .-_ ... -._ .... -._ ........... _._ ... --.- .. -.-.-----.-.----.. - .. --... --. ·-.. r ........ ·---.. · ........ -.. · .._--

u f S; '-"-r--- ~. 

c: 

6 -_ ...... -._ .. _--...... _ .. - --'''-'''--'''--- ,: -.. -· .. -·-.. · .. l---·-----·-----

4 -t--- ......; ____ m_._. ___ _ 

2 ........... _ .. -..... -_ .... _. ··-· .... ·---··-··-·-.. ····_·· ...... ·-·····--.... -·-f··· .. --.. ·-·---.. -...... -.. -_ .. __ .. --.-

o +----------+-~----------+------------~ 

1.975 1980 1985 1990 

Source: Appliance Magazine. 

112 
Cost vs. Efficiency for New Equipment

Estimates of cost vs. efficiency for new electric cooktops (coil element), gas cooktops, electric oven (non self-cleaning), gas oven (non self-cleaning), and microwave ovens are provided in figures 11.3, 11.4, 11.5, 11.6, and 11.7. The values are based on estimates in the U.S. DOE appliance standards analysis (US DOE 1993).

Product Lifetimes

The database contains several estimates for the lifetime of cooking equipment, which are shown in Table 11.1.

Table 11.1. Estimates of Residential

Cooking Equipment Lifetimes

Lifetime in Years

Gas Electric

Source Range Range

Low 11 13

Appliance Avg 15 15

High 18 19

Low 15 15

Lewis/Clark Point 15 15

High 20 20

Low 16 16

LBL/REM Avg 18 18

High 21 21

Sources: Appliance 1992 (first owner lifetime

only); Lewis and Clarke 1990; LBLREM

1991.

11.3. Shares

The database includes shares for main cooking fuel for the standard cooking appliances at a national level It includes total shares from the RECS data for 1981, 1984, 1987 and 1990. It also includes shares in new buildings from the RECS data for buildings built during the previous 5 to 7 years from the same data sets. Some of these data are shown in Figures 11.8 and 11.9. There is a clear movement towards electric cooking in both the building stock and in new construction. Figure 11.10 shows that microwave ovens have reached almost an 80% share in the housing stock, and as shown in the shipments data, may have saturated the market.

11.4 Efficiency Standards

Starting in 1990, gas cooktops and ovens were no longer allowed to have a constantly burning pilot light. Thus, all new gas cooktops and ovens must have electric or electronic ignition systems, which will increase electricity usage for gas ranges.

113

EE-RM-94-230 COMMENT 61C

Cost vs. Efficiency for New Equipment . 

Estimates of cost vs. efficiency for new electric cooktops (coil element). gas cooktops. 

elecoic oven (non self-cleaning), gas oven (non self-deaning), and microwave ovens are 

provided in figures 11.3, 11.4, 11.5. 11.6, and 11.7. The values are based on estimates in 

the U.S. DOE appliance standards analysis (US DOE 1993). 

Product Lifetimes , The database contains several estimates for the lifetime of cooking equipment, which are 

shown in Table 11.1. . 

11.3. Shares 

Table 11.1. Estimates or Residential 

C 00 k' In! E' ~gulpment L'r: I etlmes . 

Lifetime in· Years 

Gas Eleclric 

Source Range Range 

Low 11 13 

Appliance Avg 15 IS 

High 18 19 

Low IS IS 

Lewis/Clark Point 15 IS 

High 20 20 

Low 16 16 

LBLJREM Avg 18 18 

High 21 21 ., Sources: Apphance 1992 (first owner lifetime 

only); Lewis and Clarke 1990: LBLREM 

1991. 

The database includes shares for main cooking fuel for ~e standard cooking appliances at a 

national level It includes total shares from the RECS data for 1981. 1984,1987 and 1990. 

It also includes shares in new buildings from the RECS data for buildings built during the 

previous 5 to 7 years from the same data sets. Some of these data are shown in Figures 

11.8 and 11.9. There is a clear movement towai'ds electric cooking in both the building 

stock and in new construction. Figure. 11.10 shows that microwave ovens have reached 

almost an 80% share in the housing stock, and as shown in the shipments data, may have. 

saturated the market. 

11.4 Efficiency Standards 

Starting in 1990, gas cooktops and ovens were ·no longer allowect to have a constantly 

burning pilot lighL Thus, all new gas cooktops and ovens must have electric or electronic 

ignition systems, which will increase electricity usage for gas ranges. 

113 
Figure 113. Cost Versus Efficiency for New Electric Cooktops with a Coil Element

ISO 185 190

Consumer Cost ($1990)

195 200

Source: US DOE 1993.

Figure 11.4. Cost Versus Efficiency for New Gas Cooktops

i

* 2*34(

-.-- -_____ ____ _ ___ i I 5

Option -Description

Baseline lectric Coo oc Coil Elenm t

I 0 + Impoved Conact Coodui

2 I +Rdlecive Srfaces

Option Descriptioa

Baseline Conventional Cooktop

1 0 + Reduce Excess Air at Buer

2 1 + Elecronic Ignition

3 2 + Sealed Burners

4 3 + Relcaive.Sufaces

5 44 + Thermostaically Controlled Burn

0 i:0 200 300 400 500

Consumer Cost (S 1990)

Source: US DOE 1993.

114

100

90

80

70

5-60

2 so50

c 40

30

20

10

0

175

_x ___ j_

_ --- ~ . ~ " . I

E. ,_._. 7~ __ _ ..,,. i

J---------j -- j --

100

90

80

ci70

60

50

40

30

20

10

0

EE-RM-94-230 COMMENT 61C

Figure 11.3. Cost Y crsuS Efficiency for New Electric Cooktops "ith a Coil Element 

100 

90 ~. 

i ~ 2 

80 f-. B~iac Ii • --- , 

70 ! ~ 

~. r ..... 60 .. i' 0 

~ ~ s:! SO r-' 

J. ~ ... 'f .. 40 1-'--- c: 

IIJ 

30 r-' j 

2°f .1: . . 

'175 180 185 190 195 

Consucner Cost ($1990) 

Source: US DO:E 1993. 

Figure 11.4. OJ'st Versus Efficiency for New Gas Cooktops 

l00r 

90 ,-'-

, 

. 

1-

200 

80 --·---------··-.. ··---.-·.;-.... ·· .. · .. · .. ·-·1-.··-··--;-·--.. ------

~ :: l~:=~=~~_·~ .. -=:.=~=~r~=·--· .. -r--i---·--· ~ . so _ .. ________ 3415 

~ i2 -+- ~ 40';'" - ..... _--.... - .. ···-----.. -.. ·-i-------.. --1- "'---'1-'--"'''''-''--

30 ,·--·--.. ·--·--.. t----- ·-·t-·----4--

! jl l . 

:: 1- :==:'><1- -l-----T-----.... -

o ,:0 200 300 sao 

Consumer Cost ($1990) 

Source: US DOE 1993. 

114 

Ootion 

BasdiDc 

I 

2' 

3 

4 

S 

. Descriotion 

E1cclric Coo~ Coil E.1eaal 

o + Improved CoIdaCt Coo"'''..".rr 

1 + Rdleaive SUrfaces 

4 + Thennostmicall 
Figure 11.5. Cost Versus Efficiency for New Electric Ovens (Non Self-Cleaning)

25

20

.__si+ .

U

.-- _^ --- ~~~~~

- , -------- - ----- iin- _

0 100 200 300 400 500 600 700

Consumer Cost ($1990)

Source: US DOE 1993.

Figure 11.6. Cost Versus Effciency for New Gas Ovens (Non Self-Cleaning)

12

10

8 -

6 -

4 -

400 450 500 550 600 650

Consumer Cost ($1990)

Source: US DOE 1993.

115

0

cL

Ca

cc

15

10

Opotoo Desciption

BaselO El ic Oven No Sfeanig

I 0 + Improved Door Seals2 1 +Reduad Vent

3 2 + Reflctive Surface

4 3 + Add nulaion

5 4+ Conveaion

6 + Reduce Thermal Mass

7 6 + Improved Insulation

8 7 + Biradiant Oven

9 8I+Separnor

10 9 * Reduce Conducion Loss 5

0

I.

-2

U6 /

... ... ..... 5 .......... _

I

I

. .... . . ......... ........ .. ...

Bascline. I *

Option Descipoon

Baseline Gas Oven. Non Sclf-Cleanng

I 0 + No Window

2 I + Electronic Ignition

3 2 + Reduced Vent

4 3 + Impred Door Seals

5 4 + Reflecive Surface

6 5 +Add Inslation

7 6 +Convection

8 7 + Redced Thermal Mass + Improved Insulaacn

9 8 +Separator

2

0

1

A

EE-RM-94-230 COMMENT 61C

Figure 11.5. Cost Versus Efficiency for New Electric Ovens (Non Self·Cleaning) L 

25 

, 910 

20 

IRA, .~oa~OD 

....... 

~ 15 .. 

Q 

U 

'" "" >-

cc 

.. 

.. 10 c 

I..: 

5 ~. i ----; ____ ~ ... _-;_.-.-.. ..L-.... -~ ..... -.--. 

o +-----+-----~--~----_+----~----~--~ 

o 100 200 300 400 560 600 700 

Consumer Cost ($1990) 

SoUrce: US DOE 1993. 

Fagure 11.6. Cost Versus Efficiency for New Gas Ovens (Non Self-Cleaning) 

12 

10 

.i ~"!-9 I 

i ~: : 

-~-~-~-;-:/'-'-"- ._, 'I - 8 ~ 

E 

~ 

~ 6 

............ ··· .. ··· .. ··I·~·5 /., ................................... , .......................... , ................ i 3 ,. 

.-.-....... :.--2............. . ....... -._. . ........ , ................ , ......... - ..... - .. , ......... , .......... . , I 

2 

... 

; 

I:,; 4 

I 

/ . I I 

B~·ti·~'~~··,i .... ·m,.- ... ........... _-_ ........................................................ _ ................ · .... 1 

: -+---:-. _. ""-".+-----,--... .. ..... '" -' .. - .............. _;---- ........................... ~......- ...... j 

400 450 500 550 600 650 

Consumer Cost (SI99O) 

Source: US DOE 1993. 

115 

I 

2 

3 

'4 

5 

6 

7 

8 

9 

10 

3 

4 

5 

6 

7 

8 

9 

o + Impro¥ed Door ScaIs_ - 1 + Reduced Veal 

2 + Rcf1caive Surface 

3 + Add Iasulalioa 

4 + Couveaioo 

S + Rcduoc 1benDal Mass 

6 + Improved lusulatiou 

7 + Bir3di.aIu Oveu 

8 +Separzor 

9 + Reduce Conduaioo Loss 

0+ NoWmdow . 

I + Elecaonic Ignition 

2 + Reduced Vall 

3 + ImPfO''cd Door ScaIs 

4 + Refleaive Surface 

5 + Add Ins::lalion 

6 + Convection i 

i + RedUced ibcnnal Mass + Impro"cd Insula:lC':l' 

8+ or 
Figure 11.7 Cost Versus Efficiency for New Microwave Ovens

100

to

L.

£

t3

0 50 100 1I0 200 250

Consumer Cost ($1990)

Source: US DOE 1993. The energy factor is calculated by dividing output by the the microwave power electric power input

116

EE-RM-94-230 COMMENT 61C

", 

Figure 11.7 Cost Versus Efficiency for New Microwa\'e O,'ens 

100 

90 

80 

- 70 ~ 

-Lo 60 <:I 

-

U 

: SO >. 

., 

~ 

, 

. , 

i· 

i 

1 

i· Basel1J)e 

... 

c.o 

L. 40 GI 

I 

---------~---------I C 

~ .30 

20 1--' 

10 

0 

0 SO 100 ISO 200. 250 

Consumer Cost (51990) 

Source; US DOE 1993. The energy factor is calculated by dividing the microwave power 

output by the electric power input. 

Option Description 

Baseline Microwave oven 

. 0 + More Efficient Power Supply 

2 1 + More E:fficient Fan 

3 2 + Modify Wave Guide 

4 3 + [mproved MagnelrOn 

5 4 + Reflective Surfaces 

116 
Figure 118. Cooking Fuel Shares in Total Housing Stock, 1981-1990

---- Electric

--- G-- Gas

-- MLPG

0.6

0.5

5 0.4 .

2

~ 0.3.

a

us 02 ....

0.1 ...

0

1980

Source: IS DOE 1982a, 1986, 1989a, 1992.

Fuel shares are for "main cooking fuel" only. Not all houses will have both rangetops and ovens.

Figure 11.9. Cooking Fuel Shares for New Construction by Housing Type

1982 1984 1986 1988 1990

I=

C

aem

,,

1.0

0.9

0.8

0:7

0.6

0.5

0.4

0.3

0.2

0.1'

0.0

r

":"''

xi

:i

SF

................................ ............

............

.............. .. ........................ .... ..... --------------------- - ---------------------- ----------- - ------- -------- - -------- . ..........

.. .. .... .............. . f~~oW,2e ,_,,,__,,,,,,,,,_,,S jC,$?,~~~.:::_,,,,,,,_,_.:*.:.::,_,_,_

MF

...- _..

.....................-.... _

........................

_ ...........

ALL

. ...........~~~

m--------

MH

E Electric

D Gas

0 LPG

Source: US DOE 1992 data for buildings built between 1985 and 1990.

Fuel shares are for "main cooking fuel" only. Not all houses will have both rangetops and ovens.

117

I I l ... i -I- ... ..

..........................

I......................... ...

..................

.... ... ' ..... '. ........................

.......................

EE-RM-94-230 COMMENT 61C

F"agure 11.8. Cooking Fuel Shares in Total Housing Stock. 1981·1990 

0.6 r~.:=I. ===:.r.; ==.==:.:::::ii==.~==1 (J1.5 .. ·······_ .. _ .. ·_·r .. ·· .... ·· ...... ·· ...... ··r .. ··· .. · .. _·· .... ·· .. --r .. _ .. ·._ .. _ ............ r--._ .. _ ............ _. 

'i: 0.4 

.2 

-

Cot 

ca € 0.3 

f 

ca 

.c 

rn 0.2 

0 . .1 

~ ~ ~ ~ : : : : 

.--. ...,..,..-.... -.J--.. -.............. -. .l.-.-.... -... -.. ~ .. -.. --.----t---. -"--'-" - : ~ i 

- . . . . ...... _ .._ ... __ .. _.r--....................... --t-... _ .. _ .... _.-.~-.-... --... -.... '!'.-•. ---•. -..... --.-. 

~ ~ ~ ~ : : : : 

: : : : : : :. : 

....... _ ....... _ .... _u •• _y .. u .............. n········r· ...... _··· ....... _u·······r· .. _·· .. u······_ ...... ·_····r·····_··· __ .. ·_ ......... . 

~ ~ i ~ 

~ l ! ~ ···_·················_·t·_· .. ··· .. · .. ······ .. ·······t···_· .. _ ........... n···t·_· .. ·············· .. ·····;u._ ..................... . 

.: t :. I • 

o +-------~-------+------~--------~----~ 

1980 1982 1984 1986 1988 1990 

Source: US DOE 1982a.1986. 1989a, 1992. 

• Eleclric 

--G-aas 

-+--LPG 

Fuel shares are for "main cooking fuel" only. Not all houses will have both rangetops and ovens. . 

F"tgure 11.9. Cooking Fuel Shares for New Construction by Housing Type 

1.0 .,......------------------------------------, 

0.9 + .... ··-·-· .. --·· .. ·-· ........ -........ l:~~ 

o.:~ t--.. · ........ -· .............. · ...... ·· .... -·f;I,;X:::"I ...... · .. · .. -.... -· .... _· ...... · .... _· ........ • __ .... · .. · .. ·_ .. · .... _ .. ··· .. ·_· .... ·_ .. _ ...... ··" .. -·1 

_ 0.'1 

c: 

~ 0.15 Cot 

ca € O.S 

f 

ca 0.4 

.c 

rn 0.3 

0.2 

0.1 

0.0 

SF MF MH 

Source: US DOE 1992 data for buildings built between 1985 and 1990.· 

ALL 

[ill EJecbic 

o Gas 

~LPG 

Fu:el.shares are for "main cooking fuel" only. Not aU houses will have both rangetops and ovens. 

117 
Figure 11.10. Microwave Oven Shares in Total Housing Stock, 1981-1990

0.8

0.7 -. ...... -i..-.. ----_

0. .......... 3 .......... ...... .. ..... ... ....................................

0.4 --... . .................................

Source: US .DOE 1982a, 1986,1989 1992.

* 0.2

0

1980 1982 1984 1986 1988 1990

Source: US DOE 1982a, 1986, 1989a, 1992.

118

EE-RM-94-230 COMMENT 61C

Figure 11.10. Microwave Oven Shares in Total Housing Stock, 1981·1990 

0.8 ,.---.,.----,-------:--------,-----;-----__ 

o. 7 _ ..... _._ .. _ .... u ................. ~_ ••••••••••• _ ••••••••.. •••• .. •••••••••• ...... •••••• .. •• .. ··_····· .. • .. • .. ······f·· .... · ................................... -t._ ... . 

_ 0.6 ....... -.. -----............. ;-......................... ············r··--·········-······-············:····-··........ ··-··········-·1··'····························-··-· 

c . : . .: ~ 0.5 ----·-·-·-·················r-······························-·1····-···-·---··-·········· .. j-....... -......................... -........................... -.. -.. -. 

S 0.4 -.-.----................... , .................. -........... -.... ~ .... -.... . .......... -.... -... -: .... -.... - ...... -.... -........ ~ ......... -.. -................. -.. . 

OIl . : : i "-

~ 0.3 ·-·····--·--···············-r·····-·············· ············1······································\·· ............................. -... """( .. -........................... -.-.. {'-l 

0.2 ... -... -..... -.-...... ······r····-··············-··············~····-······· ... -.-..... -.......... 1' .................. , ...................................................... -.. 

0.1 .............. _ •••• _ ••••••••• _ ••••••• ~_ ••••••••••••• _._ ••• __ .... __ ••••••• _ ••• ~ ••••••••• _ ••••••• _._ •• _ •••• _ ••••••••• _~ .................................. __ ••• ~ •••••••• •••• 0_ •••••••••••••••••••••••• 

0+------r-------~--------~---------4--------~ 

1980 1982 1984 1986 1988 1990 

Source: US DOE 1982a. 1986. 1989a. 1992. 

118 
12. TELEVISION END-USE DATA

Televisions account for about 5% of total electricity usage in the residential sector because

of the number of appliances in the stock and the large number of daily hours of usage per set. Televisions have been studied as pan of the U.S. DOE appliance standards process.

12.1. Television UECs

Television UECs for new units are measured using a laboratory test procedure from U.S.

DOE, The UEC of a television in the field will be directly proportional to the amount the

appliance is used.

Average energy use for televisions in the stock is estimated by utilities and other groups through metered estimates or statistical techniques. In the UEC database, there are no

metered estimates for televisions but more than 30 derived from statistical techniques.

Typically, these UECs estimate the total household UEC for televisions and not the unit

UEC (Appendix B).

UEC Equation

Energy usage by television sets is a function of the "on-time" and the "off-time".

Televisions typically consume power while off, which is termed the standby load. This

relationship is as follows (US DOE 1993):

UEC: kWh/yr = PT hours on + P * hours off

where: P totl power (Po + PS)

Po operaing power (W)

P standby power (kW)

hours on and hours off e in (r/yr). and

hours on + hors off = 8760 hour per yer.

For the U.S. DOE appliance standards analysis, hours on = 2200 hours (6.0 hours per day

per set) and hours off= 6560 hours (18.0 hours per day).

Stock UECs

The UECs for televisions in the residential forecasting database are 500 kWh/yr for color

and 190 kWh/yr for black and white. These are taken from weighted averages of the

records in the UEC database, and represent household usage for televisions, not usage per

set.

New UECs

The UECs for new televisions are assumed to be the same as for-stock units.

12.2. Television Usage

Estimates from 1985-1986 data are that households have at least one television set in

operation 7 hours and 10 minutes per day (Neilsen 1987).

119

_·__ __·

EE-RM-94-230 COMMENT 61C

------------.~"-.---------------

12. TE'LEVISION END·USE DATA 

. - TelevAsions account for about 5% of toUll electricity usage in the residential sector because 

of the number of appliances in the stock and the large nombel' of daily hoUlS of usage per 

set. Televisions have been studied as pan ofebe U.s. DOE appliance standards process. . 

12.1. T4rieyisioll UECs 

Television UECs for new units ale measured using a laboratory test procedure from U.S~ 

DOE: The UEe of a television in the field will be directly proportional to the amount the 

appliaulce is used. " . " .... . 

Average energy use for televisions in the stock is estimated by utilities and other groUps 

through metered estimates or statistical techniques. In the VEe database, "there are no. 

meterc:d estimates for televisions but more than 30 derived from statistical techniques. 

TypicaJly ~ these UECs estimate the total household UEC for televisions' and not the unit 

UEC (Appendix B). 

UEe ;Equation 

Energy uSage by television" sets is a function of the "on-time" and' the "off-~e". 

Televisions typically consume power while off, which is termed the standby load. This 

relatioIllShip is as follows (US DOE 1993): 

UEC: kWhlyr = PT • hours em + PI- hours off 

where: PF coW power (Po + PS> 

operating power (tW) 

standby power (kW) 

hours on and hours off are in (brlyr). and 

hours on + hours off = 8760 hours per )'Ut. 

For the U.S. "DOE appliance standards analysi~, hours" on = 2200 hours (6.0 hours per day 

per set) and hours off = 6560 hours (18.0 hours per day). 

Stock UECs 

The UECs for televisions in the residential forecasting database are 500 kWh/yr for color" 

and 190 kVlb/yr for black and white. These arc" taken from weighted averages of the 

records iin the UEC database, and represent household usage for televisions, not usage per 

set. 

New UECs 

The UECs for new televisio~s are alSsumed to be the same as for-stOCk unitS. 

12.2. l'elevision Usage 

Estimates from 1985-1986 data are that households have at least one television set in 

operation.7 hours and 10 minutes per day (Neilsen 1987). 
12.3. Television Technology Data

The main difference between different television technologies is between color and black

and white television sets. Clearly, color televisions are the most important, since black and

white'televisions are becoming much less prevalent. The database includes shipments of

color and black and white televisions, technology data for standard sizes of televisions, and

shares of each type and the average number of televisions per household. Changes in the

market, such as increasing numbers of projection televisions or other large units, may affect the energy use of televisions in the future but are not addressed here.

Historical Efficiency Data

Ther are no historical efficiency data for televisions in the residential forecasting database,

but shipments are included and are shown in Figure 12.1.

Cost vs. Efficiency for New Equipment

The database includes estimates of cost vs. efficiency for new color and black and white

televisions. These are shown in Figure 12.2 and 12.3. The values are based on estimates

in.the U.S. DOE appliance standards analysis (US DOE 1988, 1993) adjusted to $1990.

The energy usage values are based on 2200 hours of operation per year.

Product Lifetimes

The average lifetime for 19" and 20" color televisions is estimated be 11.5 years (US DOE

1993).

12.4. Shares

The database includes shars for color and black and white televisions for stock buildings

by housing type at a national level. It includes total shares from the RECS data for 1981,

1984, 1987 and 1990. It also includes shares in new buildings from the RECS data for

buildings built during the previous 5 to 7 years from the same data sets. The shares of

televisions in the housing stock are shown in Figure 12.4. Clearly, the penetration of color

televisions is almost 100%, while the share of households with B&W televisions is

dropping. In addition, Figure 12.5 shows that the number of color televisions per

household is increasing to almost 2 per household.

12.5. Standards

None applicable at this time.

120

EE-RM-94-230 COMMENT 61C

· __ .. ----.----_ .... _ .. _. __ .:.-.----

12.3.. Television Technology Data 

The lllam difference between different televiSion technologies is between color and black: 

and white· television setS. Ccarly, color televisions are the DlOst important. since black and 

white' televisions arc becoming much less prevalent. The database includes shipments of 

color and black and white televisions, teChnology data for standard sizes of televisions, and 

share.~ of each type and the average Dumber of televisions per household Ctan~es in the marb~t, such as increasing Dumbers of projection televiSIons or other large units, may 

affcctthe energy usc of televisions in the future but are not addressed here. 

Historical Efficiency Data 

There are no historical efficiency data for televisions in the residential forecasting database, 

but shipments are included and are shown in Figure 12.l. . 

Cost vs. Efficiency '(or New Equipment 

The d~ltabase includes estimates of cOst vs. efficiency for new color and black and white 

televisions. These arc shown in Figure 122 and 12.3. The values are based on estimates 

in.the U.S. DOE·appliance standards analysiS (US DOE 1988, 1993) adjusted to $1990. 

The en1crgy usage values are based on 2200 hours of operation ver year. 

ProdutCt Lifetimes 

The avt:rage lifetime for 19" and 20" color televisions is estimated be 11.5 years (US DoE 

1993).· , 

12.4. . Shares 

The database includes shares for colOr and black and white televisions for stoCk: buildings 

by housing type at a national level. . It includes total shares from the RECS data. for 1981, 

1984, 1987 and 1990. It also includes shares in new buildings from the RECS data for 

buildings built during the previous 5 to 7 years from the same data sets. The shares of 

televisions in the housing stock are shown in .Figure 12A. Clearly. the peneaation of color 

televisions is almost 100%, while the share of households with B&W televisions is 

dropping. In addition, Figure 12.5 shows that the number of color televisions p~ 

household il; increasing to almost 2 per household. . .. 

.. : 12.S. Sta'l.dards 

None applicable at this time. 

. 120 
Figure 12.1. Annual Television Shipments, 1976 to 1990

25

21)

5

S. 10

0 -i

1975 1980 1985

Source: Appliance Magazine. Shipments for B&W not reported after 1987.

.% I

1990

_ __i·_____

EE-RM-94-230 COMMENT 61C

" -----,---,._-----.•.. _ ... _ ....... . 

Figure 12.1. A~ual Television Shipments, 1976 to 1990 

2S 

21) 

... 

:J l' l!i 

'2 

::J 

S 

a· 10 

:i 

5 

o 

1~97S 

I I 

i 

Color I 

1 

----- B8tW 

~ ! . 

! 

/ i 

~ ; 

.. ....- ~ 

~ i 

, 

1 

i 

---:.----- - . 

--- .... _----....... _- ~ - . .... 1--- -- I 

1980 1985 

Source: Apt,liance Magazine. Shipments for B&W not reported after 1987 • 

..... 

~ 

i990 
Figure 12.. Cost Versus Energy Use for New Color Televisions

COLOR TELEVISION

Option Description

Baseline 19 to 20 inch color television.

I 0+Reduce sndby power o 2W

2 1 + Reduce W/B scram power by 6W F 3 2+ Reduce W/B seen pow ry 73/41W

365 370 375 380

Consumer Cost (S1990)

Source: US DOE 1993. Energy use calculated using 2200 hours of operation and 8760 hours of

stuadby per year.

Figure 123. Cost Versus Energy Use for New Black and White Televisions

62 63

Consumer Cost ($1990)

BLACK AND WHITE TELEVISION

Opton Discnripon

Baseline 13 to 14 inch monochrme TV

1 Reduc scren power 5%

2 Reduce screen power 7%

64

Somce: US DOE 1988. Costs convened from $1988 using CPI multiplier for video and audio products of 0.936.

Energy use calculated using 2200 hours of operation and 8760 hours of standby per year.

122

250

-- 1

3 lSO

a

loo

50

SO

0

Basine

-3

J. 4

360

50

49

g 48

Oa

47

46

61

1

~~~~~~- - - -

EE-RM-94-230 COMMENT 61C

Figure U..l. Cost Versus Energy Use for New Color Televisions 

~ T-----~------~------~----~ 

1-·--

so 

o ~---~~------~------+-----~ 

36(1 365 370 375 380 

Consumer Cost (SI99O) 

COLOR TELEVISION 

Option ~ticm 

Basdillc 19 to 20 inch color television. 

1 0 + Reduce ItIDdby pow~ to 2W , 

2 1 + Reduce W/B screca power by 6W 

3 2 + Reduc:c WIB saa:n""" ~ 73141W 

Source: US DOE 1993. Enezgy use calculated using 2200 boors of operation and 8760 hours of 

srandby per year. 

Figure 12.3. Cost Versus Energy Use for New Black and White Televisions 

-.. 

i 

C!!-

50~--------~--------~--------~ 

49 .. ------r ·1;··e&-·-·--·~ -----.--

:: .48 ~ 

11 

_.- ! 

I 

47 ..... -... -----....... -. .. ~ 

. . ·---······--1-----···----· 

2 

~+-~------~--------~--~----~ 

61 62 63 64 

Consumer Cost (SI951O) 

BLACK AND WIUTE TELEVISION 

~on . ·on 

Baseline 1310 14 inch monochrome TV 

1 Reduce screen power 5% 

2 Reduce screen ~wa 7% 

s~: US DOE 1988. Costs convened from $1988 using CPI multipliec for video and audio products of 0.936 •. . Energy use caJculared using 2200 hours of operation and 8760 hours of srandby per year. 

122 
Figure 12.4. Television Ownership Shares, 1981-1990

I

*:------- -- I j -----

---- t

.S ~ ~ ~ . I I

~i i

1984 1986 1988

-* -- Color

1990

Source: US DOE 1982a, 1986,1989a, 1992.

Data is fraction of households with at least one television of the type indicated.

Figure 12.5. Average Number of Televisions for Houses with Televisions, 1981-1990

-- - Color

---- B & W

1982 1984 1986 1988 1990

Source: US DOE 1982a, 1986, 1989a. 1992.

DaLa is average number of television sets for those with televisions for the type indicated.

123

0

&I

.0IU

1.0

0.9

OS

0.7

0.6

0.5

0.4

03

0.2

0.1

0.0

1980 1982

1.80

1.70

1.60

I

= 1.40

1.30

j.4

1.20

1.10

1.00 4

1980

EE-RM-94-230 COMMENT 61C

Figure 12.4. Television Ownersh~p Slaares, 1981-1990 

1.0 

0.9 

0.8 

_ 0.1 

c 

~ 0.6 Col S 0.5 

t 

• 0.4 ~ 

CIl 0.3 

0.2 

. 0.1 -' 

0.0 

1980 

, 

--

~ 

-v-- - ! 

J 

i i 

1982 1984 

Source: US DOE 1982a. 1986. 1989a. 1992. 

I 

, 

I 

i 

--,..- • , . 

, 

I 

! 

I 

1 

i 

1986 

.. 

-

1988 1990 

. Data is fraction of households with at lean one television of the type indicated. 

---.,-- Color 

--a--:B&W 

FIgure 1~~.s. Average Number of Televisions for Houses with Televisions, 1981·1990 

l.WT-------~----~------~i-------r----~ 

i 

1.70 '-"'-- : 

1 !i l 

------.-.t------i;-----~-- 00 

! I 

U50 ~ 

"0 

it 1.S0· ------t-o---+---...,.~---__f---.__t 

:I '" 

o 

= 1.4~ .. 

!. 

• 1.30 ~. 

fool 

1.20 

1.10 

I 

l.~)+-------+-------r-----~--------r-----~ 

1980 1982 1984 ·1986 1988 

. Source: US DOE 1982a. 1986. 198930 1992. 

• Color 

---'-O--B&W 

DaLl is average number of television sets for those wilb televisions for the type indicated. " . 

t23 
13. MISCELLANEOUS END-USE DATA

We estimate that the miscellaneous end-use category accounts for about 13% of the

residential sector electricity consumption. The residential forecasting database includes

estimates of stocks and UECs for these miscellaneous end-uses (Meier et al. 1992). These

are shown in Table 13.1.

Table 13.1 Stocks, UECs, and Sector Energy Consumption of

Miscellaneous Electric End-Uses

Furnace Fan 45 500 22.5

Watebed Heater 14 900 12.6

Pool Pump 4 1500 6.0

Aquariufm/errarium 10 548 5.5

Crankcase Heater 27 200 5.4

Spa/Hot Tub 2 2300 4.6

Clock 180 25 4.5

Well Pump 11 400 4.4

Dehumidifier 11 400 4.4

Toasterfroaster Oven 86 50 4.3

Audio System 81 50 4.1

Ha Dryer 85 40 3.4

Blanket 27 120 3.2

Vacuum Cleaner 90 30 2.7

Ceiling Fan 54 50 2.7

Grow-Lights and Ace. 3 800 2.4

VCR 59 40 2.4

Coffee Make 36 50 1.8

Computer 13 130 1.7

Ion 32 50 1.6

umidifier 11 100 1.1

Engine Heater 4 250 1.0

Exhaust Fan 54 15 0.8

Whole House Fan 8 80 0.6

Sump/Sewage Pump 13 40 0.5

Garbage Disposer 40 10 0.4

HeatTape 3 100 0.3

Botled Water Dispenser I 300 0.3

Window Fan 9 20 0.2

Mower 5 10 0.1

Instant Hot Water 0.5 160 0.1

Total Miscellaneous Electric 106

124

National

Stock UEC Consumption

End-Use (millions) (kWh/yr) (TWh/yr)

Source: Adapted from Meier et al. 1992. End-uses already included

in the database have been removed from the list

I

EE-RM-94-230 COMMENT 61C

13. ItllSCELLANEOUS END-USE DATA 

.We estimate that the'miscellaneous end-use category accounts for about 13% of the 

residential sector electricity consumption. The residential forecasting database includes estima~es of stocks and UECs for these miscellaneous end-uses (Meier et ale 1992). These : 

are shown in Table 13.L 

.... ~ 

Table 13.1 Stocks, VECs, and Sector Energy CODSumption or 

Miscellaneous Electric End·Uses 

Stock DEC 

End-Use (millions) . (kWh/yr) 

FumaceFan 4S SOO 

Warerbed Hea1u 14 900 

. Pool Pump 4 1500 

Aquarlum{I'enarium 10 S48 

Crankcase Heater 27 ·200 

Spa/Hot Tub . 2 2300 

Clock 180 25 

Well Pump 11 400 

Dehumidifier 11 400 

l'03Saerlfoaster Oven 86 50 

AlI;dio System 81 SO 

Hair Dryer 8S 40 

Blanket 27 120 

Vacuum Oeaner . 90 30 

Celling Fan 54 50 

Grow-Lights and Arx.. 3 800 

VCR S9 40 

CoffeeMakei 36 50 

Computer 13 130 

Iron 32 SO 

Humidifier 11 100 

Engine l:Ieaaer 4 250 

Exhaust Fan S4 15 

Whole House Fan 8 80 

Sump/Sew8ge Pump 13 . 40 

Garbage Disposer 40 10 

Heat Tape 3 100 

Bottled Water Dispenser 1 300 

\VmdowFan 9 20 

Mower S 10 

Instant Hot Wa~r O.S 160 

Total Miscellaneous Electric 

Source: Adapted from Meier et a1. 1992. End-uses already mcluded 

in the database have been removed from the liSL 

124 

National 

Consumption 

(IWh/yr) 

22.5 

12.6 

6.0 

S.S 

S.4 

4.6 

4.5 

4.4 

4.4 

4.3 . 

4.1 

3.4 

3.2 

2.7 

2.7 

2.4 

2.4 

1.8 

1.7 

1.6 

1.1 

1.0 

0.8 

0.6 

O.S 

0.4 

0.3 

0.3 

0.2 

0.1 

0.1 

106 
14. DEMOGRAPHIC AND PRICE DATA

Table 14.1 provides other data related to residential sector forecasting, including 1990 data

on housing stocks, housing starts, and energy prices, and forecasts for 2000, 2005, and

2010.

Table 14.1. Residential Sector

200S. and 2010. Forecasting Demographic and Price Data 1990, 2000,

1990 2000 2005 2010

Households (millions)

Single family 64.36 71.64 75.09 78.70

Multi-family 24.42 24.72 25.43 26.37

Mobile homes 5.21 5.31 5.38 5.39

Total 93.99 101.67 105.90 110.46

Housing Starts (millions)

Single family 0.90 1.05 1.08 1.04

Multi-family 0.30 0.38 0.39 0.46

Mobile homes 0.19 0.22 0.22 0.21

Total 1.39 1.66 1.69 1.71

Energy prices (1992$ per MBtu)

Electricity 24.98 25.39 26.66 28.58

Natural Gas 6.00 7.05 7.62 8.30

Distillate Fuel 8.55 7.51 8.34 8.94

Liuified Petroleum Gas 11.67 11.13 12.30 13.49

Source: US DOE 1994.

125

EE-RM-94-230 COMMENT 61C

i4. DEMOGRAPHIC AND PRICE DATA 

Table 14.1 provides other data related to residential sectorforecasting, including 1990 data 

on housing stocks, housing starts, and energy prices, and forecasts for 2000, 2005. and 

2010. 

Table 14.1. Residential Sector Forecasting Demographic and Price Data 1990, 2000, 

200S and 2010 '. . 

1990 2000 2005 2010 

Households (millions) 

Single family 64.36 71.64 75.09 78.70 

Multi -family 24.42 24.72 25.43 26.37 

Mobile homes 5.21 5.31 5.38 5.39 

Total 93.99 101.67 105.90 110.46 

Housing Starts (millions) 

Single family 0.90 1.05 1.08 1.04 

Multi-family 0.30 0.38 0.39 0.46 

Mobile homes 0.19 0.22 0.22 0.21 

Tow 1.39 1.66 1.69 1.71 

Energy prices (1992$ per MBru) 

Electricity 24.98 25.39 26.66 28.58 

NanualGas . 6.00 7.05 7.62 8.30 

Distillate Fuel 8.55 7.51 8.34 8.94 

LiQuified Petroleum Gas 11.67 11.13 12.30 13.49 

Source: US DOE 1994. 

125 
15. FUTURE WORK

We have identified several areas that need further work in order to fully support our

residential sector analyses. The greatest need is for a database of calibrated building

prototypes complete with an analysis of shell measure savings based on real-life conditions

and applicable building technologies. We have building models that have been compared to

measured data showing fairly good agreement (e.g. the LBLGRI prototypes), but the

analytical work to estimate the impact of potential thermal shell improvements on these

buildings has not been done. Building shell measure conservation potential databases

developed at LBL and other places have made no attempt to calibrate the models to actual

residential sector data, and typically have been used in analyzing design energy use in new

construction.

The RECS databases, as well as other data collected by utilities, contain a wealth of

information on efficiency measures already in place in the residential sector which are not

well-represented in the residential forecasting database described in this report. These data

include measures such as water heater wraps, storm windows, shade trees for cooling load

reduction, occupant behavior such as building zoning, and others. These types of data

would be useful for researchers in evaluating future potential for these types of measures

(and thus avoiding double-counting of savings) and other related issues.

LBL has collected a great deal of data from utilities that could supplement the RECS

surveys which form a major part of the work here. These data are currently being

compiled, and will provide much greater regional detail, as well as error-checking, on the

RECS data, and should be included in the future.

Finally, in its current form, the residential forecasting database and associated programs act

primarily as a repository of information. The only programs we have developed that

actually manipulate the data are 1) the appliance vintaging routine, and 2) the heating and

cooling load calculation routine. The functions of the database should be expanded in the

future to 1) calculate savings for building prototype shell improvements, 2) calibrate the

heating and cooling loads with the database UECs, 3) calibrate the appliance efficiency data

with the estimated UECs, and 4) provide estimates of sector energy consumption based on

the data in the database. These are just a few of the potential functions for the residential

forecasting database.

126

EE-RM-94-230 COMMENT 61C

15. FUTURE WORK 

We have identified sevenu areas that need further work in order to fully support our 

resid.ential sector analyses. The greatest need is for a database of calibrated building 

prototypes complete with an analysis of shell measure savings based on real~1ife conditions 

· and ~lPplicable building technologies. We have building models that have been compared to 

measured data showing fairly good agreement (e.g. the LBUGRI prototypes). but the 

analytical work to estimate the impact of potential thermal shell improvements on these 

build!ings has not been done. Building shell measure conservation potential databases 

devellop(:d at LBI,. and other places have made no attempt to calibrate the models to actual 

residential sector dat~ and typically have been used in analyzing design energy use in new 

cons1ruction . 

. The RECS databases, as well as other data collected by utilities, contain a wealth of . 

infonllation on efficiency measures already in place in the residential sector which are not 

well-represented in the residential forecasting databas.e described in this report· These data 

include measures such as water heater wraps, stonn windows, shade nees for cooling load 

· reduction, occupant behavior such as building zoning, and others; These types of data 

would be useful for researchers in evaluating future potential for these types of measures 

(and thus avoiding double-counting of savings) and other related issues. . 

LBL has collected a great deal of data from utilities that could supplement the RECS 

surveys which form a major part of the work here. These data are currently being 

compiled. and will provide much greater regional detail. as well as error-checking, on the 

RECS. data. and should be included in the future.' 

Finally, ill its CUlTCnt form, the residential forecasting database and associated programs act 

primarily as a repository of information. The only programs we have developed that 

actually manipulate the data are 1) the appliance vintaging routine, and 2) the heating and 

cooling IQad calculation routine. The functions of the database .should be expanded in the 

· future to 1) .calculate savings for bu.ilding prototYPe shell. improvements. 2) calibrate the 

heating and cooling loads with the database UECs. 3) calibrate the appliance efficiency data 

with the estimated UECs, and' 4) provide estimates of sector energy consumption based on 

the data in the database. ·Theseare just a few of the potential functions for the residential 

forecasting database. . 

126 
REFERENCES

ADM. 1987. Costing Analysis of Design Options for Residential Appliances and Space Conditioning Equipment. Arthur D. Little Associates, Inc. Final Contractor Report to Lawrence Berkeley Laboratory, Sacramento, CA.

AGA, American Gas Association. 1991. Gas Househeating Survey.

AHAM, Association of Home Appliance Manufacturers. 1991. Refrigerator, Freezer, Clothes Washer, and Room Air Conditioner Energy Efficiency and Consumption Trends. Chicago: AHAM.

Andrews, J. W. and Mark P. Modera. 1991. Energy Savings Potential for Advanced Thermal Distribution Technology in Residential and Small Commercial Buildings. Lawrence Berkeley Laboratory. LBL-31042.

Appliance. 1992. "A Portrait of the U.S. Appliance Industry 1992: The Life Expectancy/Replacement Picture." Appliance 49:9 (46-47).

ARI, American Refrigeration Institute. 1991. Comparative Study of Energy Efficiency Ratios. Arlington, VA: ARL

ASHRAE, American Society of Heating, Refrigeration, and Air-Conditioning Engineers. 1987. HVAC Systems and Applications. Atlanta, GA: ASHRAE.

ASHRAE, American Society of Heating, Refrigeration, and Air-Conditioning Engineers. 1991. "Service Water Heating," in ASHRAE Handbook of HVAC Applications. Atlanta: ASHRAE.

Atkinson, Barbara, James E. McMahon, Evan Mills, Peter Chan, Terry Chan, Joseph H. Eto, Judith D. Jennings, Jonathan G. Koomey, Kenny W. Lo, Matthew Lecar, Lynn Price, Francis Rubinstein, Osman Sezgen, and Tom Wenzel. 1992. Analysis of Federal Policy Options for Improving U.S. Lighting Energy Efficiency: Commercial and Residential Buildings. Lawrence Berkeley Laboratory. LBL- 31469. December.

Boghosian, Stan. 1991. Description of the Shell Thermal Characteristics of U.S. Residences and Calculation of Shell Retrofit Option Costs. Lawrence Berkeley Laboratory. DRAFTLBL-29417. April.

Cohen, S.D., C.A. Goldman, and J.P. Harris. 1991. Measured Energy Savings and Economics of Retrofitting Existing Single-Family Homes: An Update of the BECA-B Database. Lawrence Berkeley Laboratory. LBL-28147, volumes I and II. February.

EEI, Edison Electric Institute. 1983. Annual Energy Requirements of Electric Household Appliances. Washington, DC: EEI.

Eley Associates. 1991. Life-Cycle Cost Analysis: Volume I, Low-Rise Residential Confidence and Sensitivity Study. California Energy Commission. January 26.

EPACT, Energy Policy Act. 1992. Energy Policy Act of 1992 (P.L. 102-486).

127

EE-RM-94-230 COMMENT 61C

REFERENCES 

ADM. 1987. Cos~ing Analysis of Design Optionsfor Residential Appliances and Space 

Conditioning Equipment.Anhur D. Little Associates" Inc. Final Contractor Repon 

to Lawrence Berkeley Laboratory. Sacramento, CA. . . 

AGA, American Gas Association. 1991. Gas Househeating Survey. 

AHAM. Association of Home Appliance Manufacturers. 1991. Refrigerator, Freezer, 

Clothes Washer, and Room Air Conditiorzer Energy Efficiency and ConsiDnption :Trends. Chicago: AHAM. . 

Andrews, J. W. and Mark P. Modera. 1991. Energy Savings Potential for Advanced 

Thermal Distribution Technology in Residential and Small Commercial Buildings. 

lL.awrence Berkeley Laboratory. LBL-31042. 

Appliance. 1992. "A Portrait of the U.S. Appliance Industry 1992: The Life 

Expectancy/Replacement Picture." Appliance 49:9 (46-47). 

ARI, American Refrigeration Institute. 1991. Comparative Study of Energy EffiCiency 

Ratios. Arlington, VA: ARI. . 

ASHRAE, American Society ,of Heating, Refrigeration, and Air-Conditioning Engineers. 

X 987. HV AC Systems and Applications. Atlanta, GA: ASHRAE. . 

ASHRAE, American Society of Heating, Refrigeration, and Air-Conditioning Engineers. 

1991. "Service Water Heating," in ASHRAE Handbook of HVAC Applications. Atlanta: ASHRAE. . 

Atkinson, Barbara, James R McMahon, Evan Mills, Peter Chan, Teny Chan, Joseph H. 

Eto, Judith D. Jennings. Ionathan G. Koomey, Kenny W. Lo, Matthew Lecar, 

Lynn Price. Francis Rubinstein, Osman Sezgen, and Tom Wenzel. 1992. Analysis 

of Federal Policy Options for Improving U.S. Lighting Energy E!fic{ency: 

Commercial and Residential Buildings. Lawrence Berkeley Laboratory .. LBL31469. December. . 

Boghosian, Stan. 1991. Description of the Shell Thermal Characteristics of U.S. 

Residences and Calculation of Shell Retrofit Option Costs. Lawrence Berkeley 

Laboratory. DRAFTLBL-29417. April. . 

Cohen, S.D., C.A. Goldman. and J.P. Harris. 1991. Measured Energy Savings and 

Economics of Retrofitting Existing Single-Family Homes: An Update of the 

BECA-B Database. Lawrence Berkeley Laboratory. LBL-28147, volumes I and II. F~ebruary. 

EEl, Edison Electric Institute. 1983. Annual Energy Requirements of Electric HouseJwid 

Appliances. Washington, DC: EEL 

Eley Associates. 1991. Life-Cycle Cost Analysis: Volume I, Low-Rise Residential 

Confidence and Sensitivity Study. California Energy Commission. January 26. 

EPACf, Energy Policy Act. 1992. Energy Policy Act of 1992 (P.L. 102-486). 

127 
Fechtel, R. B., E.R. Novicky, and A.R. Wusterbarth. 1980. Energy Capital in the US.

Economy. Bethesda, MD: MTSC, Management Technology Division.

GAMA, Gas Appliance Manufacturer's Association. 1991. Statistical Highlights,

Arlington, VA: GAMA.

Granda, C. 1992. "A Statistically Based Impact Evaluation of a Direct Install Compact

Fluorescent Distribution Program" in Proceedings of the ACEEE 1992 Summer

Study on Energy Efficiency in Buildings. Washington DC: American Council for

an Energy-Efficient Economy

Grays Harbor PUD. 1992. The Grays Harbor PUD Compact Fluorescent Maximization

Study. Grays Harbor, Washington.

Hanford, J.W. and Y. J. Huang. 1992. "Residential Heating and Cooling Loads

Component Analysis," Lawrence Berkeley Laboratory. LBL-33101.

Huang, Y.J., R. Ritschard, J. Bull, S. Byrne, I. Turiel, D. Wilson, C. Hsui, and D.

Foley. 1987a. Methodology and Assumptions for Evaluating Heating and Cooling

Energy Requirements in New Single-Family Residential Buildings. Technical

Support Document for the PEAR Microcomputer Program. Lawrence Berkeley

Laboratory. LBL-19128. January.

Huang,, YJ., J.C. Bull and R.L. Ritschard. 1987b. Technical Documentation for a

Residential Energy use Data Base Developed in Support of ASHRAE Special

Project 53. Lawrence Berkeley Laboratory. LBL-24306.

Jansky, R. and M.P. Modera. 1994. Sensitivity Analysis of Residential Duct System

Efficiency in California. LBL-34085. Lawrence Berkeley Laboratory.

Kelsey, J. and Richardson, Valarie. 1992. "1991 Residential Appliance Saturation Survey -- A Profile of the Residential Lighting Load in Northern California," in

Proceedings of the ACEEE 1992 Summer Study on Energy Efficiency in

Buildings. Washington DC.: American Council for an Energy-Efficient Economy

Kempton, W., 1984. "Residential Hot Water: A Behaviorally-Driven System," in

Proceedings of the ACEEE 1984 Summer Study on Energy Efficiency in

Buildings. Washington DC: American Council for an Energy-Efficient Economy.

Koomey, J., C. Atkinson, A. Meier, J.E. McMahon, S. Boghosian, B. Atkinson, I.

Turiel, M.D. Levine, B. Nordman, and P. Chan. 1991a. The Potential for

Electricity Efficiency Improvements in the U.S. Residential Sector. Lawrence - Berkeley Laboratory. LBL-30477.

Koomey, J., J.E. McMahon, and C. Wodley. 1991b. Improving the Thermal Integrity of

New Single-Family Detached Residential Buildings: A Regional Assessment of

Capital Costs and Energy Savings. Lawrence Berkeley Laboratory. LBL-29416.

Koomey, J.G., F.X. Johnson, J.E. McMahon, M.C. Orland, M.D. Levine, P. Chan, and

F. Krause. 1993. An Assessment of Future Energy Use and Carbon Emissions

from U.S. Residences. LBL-32183. Lawrence Berkeley Laboratory.

Koomey, J.G., F.X. Johnson, J. Schuman, E. Franconi, S. Greenberg, J.D. Lutz, B.T.

Griffith, D. Arasteh, C. Atkinson, K. Heinemeier, Y.J. Huang, L. Price, G.

128

EE-RM-94-230 COMMENT 61C

Fecht/el, R. B. t E.R. Novicky t and 'A.R. Wusterbanh. 1980. Energy Capital in the U.s. 

,Economy. Bethesda, MD: MTSC. Management Technology Division. 

GAMA. Gas Appliance Manufacturer's Association. 1991. Statistical Highlights. 

Arlington, VA: GAMA. 

Granda, C. 1992. "A Statistically Based Impact Evaluation of a Direct Install Compact 

Fluorescent Disnibution Program" in Proceedings of the ACEEE 1992 Summer 

Study on Energy Efficiency in Buildings. Washington DC: American Council for 

an Energy-Efficient Economy 

Grays Harbor PUD. 1992. The Grays Harbor PUD Compact Fluorescent Maximization 

Study. Grays Harbor, Washington. " 

Hanford., J.W. and Y. J. Huang. 1992. uResidential Heating and Cooling Loads 

Component Analysis," Lawrence Berkeley Laboratory. LBL-33101. , , 

Huang, Y.I .• R. Ritschard. J. BUll, S. Byrne.!. Turiel, D. Wilson, C. Hsui, and D. 

Foley. 1987a. Methodology and Assumptionsjor Evaluating Heating and Cooling 

Energy Requirements in New Single-Family Residential Buildings. Tech,zical 

Support Document for the'PEAR Microcomputer Program. Lawrence Berkeley 

Laboratory. LBL-19128. January. 

Huang" Y J., J.C. Bull and R.L. Ritschard. 1987b. Technical Documentation JOT a , Residential Energy use Data Base Developed in Support of ASHRAE Special 

Project53. Lawrence Berkeley Laboratory. LBL-24306.' 

Jansky" R. and M.P. Madera. 1994. Sensitivity Analysis of Residential Duct System 

Efficiency in California. LBL-3408S. Lawrence Berkeley Laboratory. 

Kelsey, I. and Richardson, Valarie. 1992. "1991 Residential AppliaJlce Saturation Survey 

-- A Profile of the Residential Lighting Load in Nonhern California," in 

Proceedings of the ACEEE 1992 Summer Study on Energy Efficiency in 

.Buildings. Washington DC.: American Council for an Energy-Efficient Economy 

Kempton. W .• 1984. "Residential Hot Water: A Behaviorally-Driven System," in 

Proceedings of the ACEEE 1984 Summer Study on Energy Efficiency in 

Buildings. Washington DC: American Council for an ~nergy:-Efficient Economy. 

Koomey, .1 •• C. Atkinson. A.Meier, I.E. McMahon, S. Boghosian, B. Atkinson, I. 

Turiel, M.D. Levine. B. Nordman, and P. Chan. 1991a. The, Potential Jor 

Electricity Efficiency Improvements in the U.S. Residential Sector. Lawrence - 13erkeley Laboratory. LBL-30477. ' 

Koomey, J., J.E. McMahon. and C. Wodley. 1991b.lmproving lhe Thermal Integrity oj 

New Single-Family Detached Residential Buildings: A Regional Assessment of 

Capital Costs and Energy Savings. Lawrence Berkeley Laboratory. LBL-29416. 

Koomey, J.G .• F.X. Johnson, J.E. McMahon, M.C. Orland, M.D. Levine, P. Chan, and 

F. Krause. 1993. An Assessment of Future Energy Use and Carbon Emissions 

from U.S. Residences. LBL-.J2183. Lawrence Berkeley Laboratory. 

Koomey, J.G., F.X. Johnson. J. Schuman. E. Franconi, S. Greenberg, J.D. Lutz. B.T. 

Griffith" D. Arasteh, C. Atkinson, K. Heinemeier, Y.l. Huang. L. Price, G. 

128 
Rosenquist, S. Selkowitz, H. Taha, L Turiel. 1994a. Buildings Sector Demand- Side Efficiency Technology Summaries. Lawrence Berkeley Laboratory. LBL- 33887.

Koomey, J.G., C. Dunham, and J.D. Lutz. 1994b. The Effect of Efficiency Standards on Water Use and Water Heating Energy Use in the U.S.: A Detailed End-use Treatment.. Lawrence Berkeley Laboratory. LBL-35475. A shortened version of this report will be published in the Proceedings of the ACEEE 1994 Summer Study on Energy Efficiency in Buildings. American Council for an Energy-Efficient Economy, Washington DC.

LBL, Lawrence Berkeley Laboratory, Energy Analysis Program. 1987. Program for Energy Analysis of Residences (PEAR 2.1): User's Manual. Lawrence Berkeley Laboratory. PUB-610. January.

LBL, Lawrence Berkeley Laboratory. 1990. Appliance Energy Conservation Database. Lawrence Berkeley Laboratory.

LBLREM. 1991. LBL's Residential Energy Model, which includes a database of elasticities, saturations, unit energy consumptions, conservation measures, capital costs, and other parameters. Some of this information is contained in US DOE 1989b, McMahon 1986, and Koomey et al. 1993.

Lewis, J. E., and A. Clarke. 1990. Replacement Market for Selected Commercial Energy Service Equipment (Topical Report: Phase B--Commercial). Gas Research Institute. GRI-89/0204.02. June.

Manclark, B. 1991. "Of Sockets, Housecalls, and Hardware," Home Energy, November/December.

McMahon, J.E. 1986. The LBL Residential Energy Model. Lawrence Berkeley Laboratory. LBL-18622. January.

MEANS. 1989. Building Construction Cost Data 1989: 47th Annual Edition. Kingston, MA.: R.S. Means Construction Inc.

MEANS. 1992. Residential Cost Data. 11th Annual Edition and Mechanical Cost Data: 15th Annual Edition. Kingston, MA.: RS. Means Construction Inc.

Meier, A. and K. Heinemeier. 1990. "Refrigerator Energy Use: Label Versus Actual," in End-Use Load Information and Its Role in DSM (Conference Proceedings). Berkeley, CA. July.

Meier, A., L. Rainer, and S. Greenberg. 1992. "Miscellaneous Electrical Energy use in Homes," Energy 17:5 (509-518).

MHI, Manufactured Housing Institute. 1991. Quick Facts About the Manufactured Housing Industry 1990/91. Manufactured Housing Institute.

Modera, M. 1993. "Characterizing the Performance of Residential Air Distribution Systems," Energy and Buildings 20:1 (65-75).

NAECA 1987. National Appliance Energy Conservation Act of 1987.

129

EE-RM-94-230 COMMENT 61C

Rosenquist. S. Selkowitz, H. Taha. L Turie!. 1994a. Buildings Sector DenumdSide Efficiency Technology Summaries. Lawrence Berkeley Laborat<;>ry. LBL33887. 

Koomey, J.G., C. Dunham, and J~D. Lutz. 1994b. The Effect ofEfJiciency Standards on 

Water Use and Water Heating Energy Use in the U.S.: A Detailed End-use 

Treatment.. Lawrence Berkeley Laboratory. LBL-3S47S. A shortened version of 

this report will be published in the Proceedings of the ACEEEI994 Summer Study 

on Energy EfJiciency in Buildings. American Council for an Energy-Efficient 

Economy, Washington DC. . 

LBL, Lawrence Berkeley Laboratory, Energy Analysis Program. 1987. Program for 

Energy Analysis of Residences (PEAR 2.1): User's Manual. Lawrence Berkeley 

Laboratory. PUB-610. January. 

LBL, Lawrence Berkeley Laboratory. 1990.·~ppliance Energy Conservation Database. 

Lawrence Berkeley Laboratory. 

. . . 

LBLREM. 1991. LBL's Residential Energy Model, which includes· a database of 

elasticities, saturations, unit energy consumptions, conservation measures, capital ' 

costs, and other parameters. Some of this information is contained in US DOE 

.l989b, McMahon 1986, and Koomeyet ale 1993. 

Lewis, 1. E., and A. Clarke. 1990. Replacement Market for Selected Commercial Energy 

Service Equipment (Topical Report: Phase IB--Commercial). Gas Research 

Institute. GRI-89/0204.02. lune. -

Manclark. B. 1991. "Of Sockets, Houseca,lls, aDdHardware~~ Home Energy, . NovemberlDecember.' 

McMahon, I.E. 1986. The LBL Residential Energy Model. Lawrence Berkeley. 

Laboratory. LBL-18622. lanuary. 

MEANS. 1989. Building Construction Cost Data 1989: 47th Annual Edition. Kingston, 

MA.: RS. Means Construction Inc. . 

MEANS. 1992 Residential Cost Data: ·11 th Anlzual Edition' and Mechanical Cost Data: 

15th Annual Edition. Kingston. MA.: R.S. Means. Construction Inc. 

Meier, A. and K. Heinemeier. 1990. "Refrigerator Energy Use: Label Versus Actual," in . 

End-Use Load Information·and Its Role in DSM (Conference Proceedings). 

Berkeley, CA. luly. 

Meier, A., L. Rainer, and S. Greenberg. 1992. "Miscellaneous Electrical Energy use in 

Homes." Energy 17:5 (509-518). 

MHI, Manufactured Housing Institute. 1991. Quick Facts About the Manufactured 

Housing Industry 1990/91. Manufactured Housing Institute. 

Modera, M. 1993. "Characterizing the Performance of Residential Air Distribution 

Systems," Energy andBuildings 20:1 (65-75). 

NAECA 1987. National Appliance Energy Conservation Act of 1987 . 

. 129 
NAHB, National Association of Home Builders, NRC, National Research Center. 1986.

An Economic Data Base in Support of SPC 902: Costs of Residential Energy,

Thermal Envelope and HVAC Equipment. ASHRAE Research Project 494-RP.

Association of Heating, Refrigeration, and Air-conditioning Engineers (ASHRAE).

NAHB, National Association of Home Builders, NRC, National Research Center. 1989.

1987 Builder Practice Survey Data. No. CR#5023, PO #4556710. National

Association of Home Builders.

Neilsen. 1987. The 1987Neilsen Report. Neilsen Media Research Company.

Proctor, J. 1992a. Los Angeles Department of Water and Power Heat Pump/Air Conditioner System Efficiency Study: Summary of Baseline Data. Larkspur, California.

Proctor, J. 1992b. Personal communication with John Proctor, Proctor Engineering.

Ritschard, R. L. and YJ. Huang. 1989. Multifamily Heating and Cooling Requirements:

Assumptions, Methods, and Summary Results. GRI-88/0239. Gas Research

Institute.

Ritschard, R.L. and Y.J. Huang. 1990. "Estimating Water Heating and Aggregate Electricity Loads in Multifamily Buildings," ASHRAE Transactions, Vol. 97.

Ritschard, R. L, J.W. Hanford, and A.O. Sezgen. 1992a. Topical Report: Single-Family

Heating and Cooling Requirements: Assumptions, Methods, and Summary

Results. LBL-30377, GRI-91/0236. Lawrence Berkeley Laboratory, for the Gas

Research Institute.

Ritschard, R. L., J.W. Hanford, and A.O. Sezgen. 1992b. Topical Report: Analysis of

Energy Conservation Codes in Single-Family Homes. LBL-30376, GRI-91/0158.

Lawrence Berkeley Laboratory, for the Gas Research Institute.

Robinson, Donna M. 1992. "Comprehensive Residential Lighting Retrofits: A Case

Study," in Proceedings of the ACEEE 1992 Summer Study on Energy Efficiency in

Buildings. Washington,DC: American Council for an Energy-Efficient Economy.

Taylor, ME., K.G. Ritland and R.G. Pratt 1991. "Hot Water.Energy Use in SingleFamily Residences in the Pacific Northwest: Regional End-Use Metering Project

(REMP)," Office of Energy Resources, Bonneville Power Administration,

Portland, OR. DOE/BP-13795-27.

Treidler, E.B. and M. P. Modera. 1993. Thermal Performance of Residential Duct

Systems in Basements. LBL-33962. Lawrence Berkeley Laboratory.

U.S. Bureau of the Census. 1988. American Housing Survey for the United States in

1985, U.S. Department of Commerce.

U.S. Bureau of the Census. 1990a. Current Construction Reports, Characteristics of New

Housing; Series C25. U.S. Department of Commerce.

U.S. Bureau of the Census. 1990b. American Housing Survey for the United States in

1985, U.S. Department of Commerce.

130

EE-RM-94-230 COMMENT 61C

NAHB. National Association of Home Builders. NRC. National Research Center. 1986. 

An Economic Data Base in Support of SPC 90.2: Costs of Residential Energy, 

Thermal Envelope and HVAC Equipmenr. ASHRAE Research Project 494-RP. 

Association of Heating. Refrigeration, and Air-conditioning Engineers (ASHRAE). 

- NMm, National Association of Home Builders, NRC. National Research Center. 1989. 

1987 Builder Practice Survey Data. No. CR#5023, PO #4556710. National 

Association of Home Builders. 

Neilsen. 1987. The 1987 Neilsen Report. Neilsen Media Research Company. 

Proctor, J. 1992a~ Los Angeles Department of Water and Power Heat Pump/Air 

Conditioner System, Efficiency Study: Summary of Baseline Data. Larkspur,-

California. _ 

ProcltOr, J. 1992b. Personal communication with John Proctor, Proctor Engineering.' 

Ri~chard, R. L. and Y J. Huang. 1989. Multifamily Heating and Cooling Requirements: 

AssUmptions, Methods, and Summary Results. GRI-88/0239. Gas Research 

Institute. 

Ritschard, R.L. -and Y.J. Huang. 1990~ "Estimating Water Heating and Aggregate 

Electricity Loads in Multifamily ~uildings," ASHRAE Transactions, Vol. 97. 

Ritschard, R. L., J.W. Hanford, and A.O. Sezgen. 1992a. Topical Report: Single-Family 

Heating and Cooling Requirements: Assumptions, Methods, and Summary 

Resulrs. LBL-30377, GRI-91/0236. Lawrence Berkeley Laboratory, for the Gas 

, Research Institute. 

Ritschard, R. L., J.W. Hanford, and A.O. Sezgen. 1992b. Topical Report: Analysis of 

Energy Conservation Codes in Single-Family Homes. LBL-30376, GRI-91/0158. 

Lawrence Berkeley Laboratory, for the Gas Research Institute. 

Robinson, Donna M. 1992. "Comprehensive Residentiai Lighting Retrofits: A Case 

Study," in Proceedings of the ACEEE 1992 Summer Study on Energy Efficie.ncy in 

Buildings. Washington.DC: American Council for an Energy-Efficient Economy. 

, 

Taylc'r. M.E., K.G. Ritland and R.O. Pratt. 1991. "Hot Water_:Energy Use in SingleFamily Residences in the Pacific Nonhwest: Regional End-Use Metering Project 

(REMP)," Office of Energy Resources. Bonneville Power Administration. 

Poniand, OR. DOElBP-13795-27. 

Treidler, E.B. and M. P. Madera. 1993. Thermal Performance of Residential Duct 

Systems in Basements. LBL-33962. Lawrence Berkeley Laboratory. 

U.S. lBureau of the Census. 1988. American Housing Survey for the United States in 

1985, U.S. Department of Commerce. -

U.S. Bureau of the Census. 19903.0 Currenr Construction Repons, Characleristics of New 

Housing; Series C25. U.S. Department of Commerce. 

U.S. Bureau, of the Census. 1990b. American -Housing Survey for the United Stares in 

1985, U.S. Depanment of Commerce. 

130 
U.S. Bureau of the Census. 1992. American Housing Survey for the United States in 1985, U.S. Department of Commerce.

US DOE, U.S. Department of Energy. 1982a. Residential Energy Consumption Survey: Housing Characteristics 1981 and 1981 Public Use Data Tape. DOE/EIA-0314 (81). EIA, Energy Information Administration.

US DOE, U.S. Department of Energy. 1982b. Consumer Products Efficiency Standards Economic Analysis Document. DOE/CE-0029. March.

US DOE, 

Housing 

U.S. Department of Energy. 1983. Residential Energy Consumption Survey: Characteristics 1982. DOE/EIA-0314 (82). EIA, Energy Information Administration.

US DOE, U.S. Department of Energy. 1986. Residential Energy Consumption Survey: Housing Characteristics 1984 and 1984 Public Use Data Tape. DOE/EIA-0314 (84). EIA, Energy Information Administration.

US DOE, U.S. Department of Energy. 1988. Technical Support Document: Energy Conservation Standards for Consumer Products: Refrigerators, Furnaces, and Television Sets. DOE/CE-0239. U.S. Department of Energy, Assistant Secretary, Conservation and Renewable Energy, Building Equipment Division.

US DOE, U.S. Department of Energy. 1989a. Residential Energy Consumption Survey: Housing Characteristics 1987 and 1987 Public Use Data Tape. DOE/EIA-0314 (87). EIA, Energy Information Administration.

US DOE, U.S. Department of Energy. 1989b. Technical Support Document: Energy Conservation Standards for Consumer Products: Refrigerators and Furnaces. DOE/CE-0277. U.S. Department of Energy, Assistant Secretary, Conservation and Renewable Energy, Building Equipment Division.

US DOE, U.S. Department of Energy. 1989c. Technical Support Document: Energy Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, and Clothes Dryers. DOE/CE-0267. U.S. Department of Energy, Assistant Secretary, Conservation and Renewable Energy, Building Equipment Division.

US DOE, U.S. Department of Energy. 1990a. State Energy Data Report: Consumption Estimates 1960-1988. Energy Information Administration. DOE/EIA-0214(88). April

US DOE, U.S. Department of Energy. 1990b. Technical Support Document: Energy Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, and Clothes Dryers. DOE/CE-0299P. U.S. Department of Energy, Assistant Secretary, Conservation and Renewable Energy, Building Equipment Division.

US DOE, U.S. Department of Energy. 1992. Residential Energy Consumption Survey: Housing Characteristics 1990 and 1990 Public Use Data Tape. DOE/EIA-0314 (90). EIA, Energy Information Administration.

US DOE, U.S. Department of Energy. 1993. Technical Support Document: Energy Efficiency Standards for Consumer Products: Room Air Conditioners, Water Heaters, Direct Heating Equipment, Mobile Home Furnaces, Kitchen Ranges and Ovens, Pool Heaters, Fluorescent Lamp Ballasts, and Television Sets. DOE/EE131

EE-RM-94-230 COMMENT 61C

U.S. Bureau of 'the Census. ,1992. American Housing. Survey for: the United States in 

1985. U.S. Department of Commerce. 

US DOE, U.S. Department of Energy. 1982a. Residential Energy Consumption Survey: 

Housing Characteristics 1981 and 1981 Public Use Data Tape. DOElElA-0314 

(81). EIA. Energy Information Administration. . 

US DOE. U.S. Department of Energy. 1982b. Consumer Products Efficiency Standards' 

Economic Analysis Document. DOE/CE-0029. March. ' 

US DOE. U.S.'Department of Energy. 1983. Residential Energy Consumption Survey: , Housing Characteristics 1982. DOEJEIA-0314 (82). EIA. Energy Infonnation 

Administration. 

US DOE~ U.S. Department of Energy. 1986. Residential Energy Consumption Survey: 

Housing Characteristics 1984 and 1984 Public Use Data Tape. DOE/EIA-0314 

(84). EIA. Energy Information Administration. 

US DOE. U.S. Department of Energy. 1988. Technical Support Document: Energy 

Conservation Standards for: Consumer Products: Refrigerators, Furnaces, and 

Television Sets. DOE/CE-0239. U.S. Depanment of Energy, Assistant Secretary, 

Conservation and Renewable Energy, Building Equipment Division. 

US DOE, U.S. Department of Energy. 1989a. Res.idential Energy Consumption Survey: 

Housing Characteristics 1987 and 1987 Public Use Data Tape. DOE/EIA-0314 

(87). EIA. Energy Information Administration. ' 

US DOE. U.S. Department of Energy. 1989b. Technical Support Document:' Energy 

Conservation Standards for Consumer Products: Refrigerators and Furnaces. 

DOFJCE-0277. U.S. Department of Energy, Assistant Secretary, Conservation and 

lRenewable Energy. Building Equipment Division. 

US DOE. U.S. Department of Energy. 1989c. Technical Support Document: Energy 

Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, 

and Clothes Dryers. DOE/CE-0267. U.S. Depanment of Energy, Assistant 

Secretary, Conservation and Renewable Energy, Buildi,ng Eq~pment Division. 

US DOE. U.S. Department of Energy. 1990a. State Energy Data Report: Consumption 

Estimates 1960-,1988. Energy Infonnation Administration. DOElEIA-0214(88). J\priL , 

US DOE, U.S. Depanment of Energy. 1990b. Technical Support Document: Energy 

Conservation Standizrdsfor Consumer Products: Dishwashers, Clothes Washers, 

and Clothes Dryers. DOE/CE-029.9P. U.S. Dep~ent of Energy, Assistant 

Secretary, Conservation and Renewable Energy, Building Equipment Division. 

. ' 

US DOE, U.S. Department of Energy. 1992. Residential Energy Consumption Survey: 

Housing Characteristics 1990 and 1990 Public Use Data Tape. DOEIEIA-O~ 14 

(90). EIA. Energy Infonnation Administration. 

, - US DOE, U.S. Depanment of Energy. 1993. Technical Support Document: Energy , Efficiency Standards for Consumer Products: Room Air Conditioners, Water 

Heaters, Direct Heating Equipment, Mobile Home Furnaces, Kitchen Ranges and 

Ovens, Pool Heaters, Fluorescent Lamp Ballasis, and Television Sets. DOEIEE131 
0009. U.S. Department of Energy, Assistant Secretary, Energy Efficiency and Renewable Energy, Office of Codes and Standards.

US DOE, U.S. Department of Energy. 1994. Annual Energy Outlook 1994 With Projections to 2010. DOE/EIA-0383(94). Energy Information Administration. January.

Usibelli, A. 1984. Monitored Energy Use of Residential Water Heaters. Lawrence Berkeley Laboratory. LBL- 17873.

132

EE-RM-94-230 COMMENT 61C

0009. u.s. Department of Energy. Assistant Secretary. Energy Efficiency and 

Renewable Energy. Office of Codes and Standards. 

US DOE, U.S. Department of Energy. 1994. Annual Energy Outlook 1994 With 

Projections to 20ID. DOE/EIA-0383(94). Energy Infonnation Administration. 

January. 

Usibelli~ A. 1984. Monitored Energy Use 0/ Residential Water Heaters. Lawrence 

Berkeley Laboratory. LBL-17873. . 

'132 
APPENDIX A. RESIDENTIAL FORECASTING

DATABASE DESCRIPTION

The Lawrence Berkeley Laboratory residential forecasting database is programmed in Foxbase+/Mac and can output reports in a variety of formats. Figure A1 shows the report options screen for the database.

The residential forecasting database is organized in separate files so that similar types of data are contained in the same file. Each of the files is listed in Table A. 1.

The residential forecasting database program creates printed reports in tabular form, and writes headers on the files. The available reports are listed in Table A.2.

Figure A.2 is an example of how the data appears in the database. Table A.3 lists the various categories of technologies contained in the database.

. 

133

EE-RM-94-230 COMMENT 61C

APPENDIX A. RESIDENTIAL FORECASTING 

DA TABASE DESCRIPTION 

The Lawrence Berkeley Laboratory residential forecaSting database is programmed in . Foxbase+lMac and can output reports in a .variety of fonnats. Figure A.I shows the repon 

options screen for the database. . - . 

The residential forecasting database is organized in separate flIes so that similar types of 

data are contained in the same fIle. Each of the files is listed in Table AI.' 

The residential forecasting database program creates printed repons in tabular fonn. and 

writes headers on the files .. The available reports are listed in Table A.2. 

Figure A.2 is an example of how the data appears' in the databas.e. Table. A.3 lists the 

various categories of technologies contained in the database~ 

133 
Figure A.I. Residential Forecasting Database Report Options Screen

r* File Edit

Residential Forecast Options Report 

Residential Forecasting Database Incs&sbn Auary r'/iyLa 

® I New TchnoIgy Efficiency Characteristies

0 2 Nev HVAC Equipment Cost, Effictncy, and Capacity

0 3 Building Thermal Characteristics

0 4 Buiming Prototype Charcteristics O S Technobew Characterizatiou - Histeore Sales

0 6 Sector Descriptor by Husing Type - 1990 UEC o0 Sector Descriptor by Housimg Typ. - 1990 UEC

0 8 Sector tsecrptor by Housing Type - 1990 Applianc Share

0 9 Setor Desriptor by Housing Type - 1990 Appiance Sh re O 10 Sector Descriptor by Housing Type - 1990 HVAC Share O 1 Total Building Heatin und Cooling Loads

0 12 Retrofrt Shel Costs

0 13 Nev Building Shell Costs

0 4 1990 Residential Enrgy Data

0 15 Historical Housing Completions

0 16 Average Floor Arr for New Construction

0 17 Housing Starts Foreaist O 8 Appiatce Lifetime Estnmates

O Rdd Bibliography

O Rdd Comments

O Update Databases

POUtDU ODtfons

) Printer

0 TeNt File

0 Spreadsheet

0 Screen

O Generate Report...

O Exit Program

134

EE-RM-94-230 COMMENT 61C

Figure A.t. Residential Forecasting Database Report Options Screen 

,..... file Edit 

Residential Forecast ReportOpUons 

Residential Forecasting Database 

uwrenl."e &1rh/~ ulw'a1D/y 

® I Nt>. T.chnolow Ef'flct.1\CII Cha,.Actfristic. 

o 2 Nil. ON At £qv;pm."t Cost, Efftct~ftC.'J ..... CAplettll 

0:5 BUI~ TII.nnII CMr.etHistics 

o 4 Bl.ItldWIeJ Pr'ototyp. OIAr'Aettristicr 

o 5 hetlnoloty Cha,.aet.mations - H;.toric Sal .. 

o 6 SKter DuCt'~tOP' bll Hou.WIq Til p.- - 1990 UEC 

o 7 SKW DUCt'iptOP' by Hou.WIq Til"', - 1990 UEC 

a 8 SKw OucrIDtor bll Hou.VIq T",p. - 1990 App~ Sha,.. 

0, S~tor OtSCt'~tor' till Hou.n, TVPf' - 1990 App1Wlc. SMr-. 

o 10 Stetor OtSCr'~tor' by Hourn, TVPf' - 1990 HYAC SlIv. 

a II Tobl But1clin9 H .. t~ and CooIin9 Load. 

a 12 R.frof"rt!hn Cost. 

a I! Nt. Bu;JdWIeJ SMn Costs 

o 14 1990 R.nltntifl tn.,.VV O.t. 

o 1:Ii Histoneal Hou''''9 CompittiOft. 

a 1 ,. A."'IQ' FIooP' 10,. .. fo" N.y Construction 

o 17 HoI.Isi1'19 St.wtr FOP'toart 

a 18 A4>P~t Lif.tin. Estim.tts 

134 

o Add Bibliography 

o Add Comments' 

o Update Databases 

Q.y!P.ut Opttons 

® Printer 

o TeNt File 

o Spreadsheet 

o Screen 

o Generate Report .... 

o EHlt' fr'ogrom 
Table A.1. Residential Forecasting Database Titles and Contents

Database File

Number Name Description

I BYUECO1 Base Year (1990) UECs

2 BYApShO2 Appliance and equipment shams

3 HstShlO3 . Historical shipments, efficiency, and capacity data

4 TchEff04 Cost vs. efficiency data for appliances 5 BYHShrOS Base Year (1990) HVAC system shares

6 empty

7 HVACEqO7 Cost vs. effiency and cost vs capacity data for heating and cooling equipment 8 Units08 Efficiency, capacity, usage, and UEC units for each end use 9 BldPrtO9 Basic building prototype descriptions 10 UVWkSO U-values and shading coefficients of building shell components 11 BdCmpl Building prototypeshell component dimensions and thermal integrity 12 d1b112 SP53 regression coefficients for building components 13 SlrTbl13 Solar load regression coefficients

14 HsStckl4 Housing stock data, 1990 (will be 1980-90) 15 Fuel5 Fuel prices and income - historical and forecasts

16 empty Housing starts forecast

17 empty

18 empty

19 empty

20 ShlCst20 Shell measure costs for new buildings 21 RkCst2I Shell measure costs for building retrofits (SF only) 22 HstCmp22 Completions of new construction annually, 1980-90 23 sArea23 Conditioned floor area of new construction, 1980-90

24 Hscst24 Housing starts forecast

25AplLft25 Appliance lifetime estimates

Table A2. Residential Forecasting Database Report Titles and Contents

Report File

Number Name

I UECTIbI0

2 BYApSh02

3 TchTbl03

4 TchEfc04

5 BYHShSO5'

7 HVACEq07

9 PrtTb09

11 CmpTblll

20 ShlCst20

21 RtrCst2l

22 HstCmp22

23HsArca23

24 HsFcst24

.25 ApLft25

LoadCalc

Description

Base Year (1990) UECs

Appliance and equipment shars

Historical shipments, efficiency, and capacity data

Cost vs. efficiency data for appliances

Base Year (1990) HVAC system shares

Cost vs. effiency and cost vs capacity data for heating and cooling equipment Basic building prototype descriptions

Building prototype shell component dimensions and thermal integrity Shell measure costs for new buildings

Shell measure costs for building retrofits (SF only) Completions of new construction annually, 1980-90

Conditioned floor area of new construction, 1980-90

Housing starts forecast

Appliance lifetime estimates

Baseline heating and cooling loads for prototypes (calculated by database)

135

.-

EE-RM-94-230 COMMENT 61C

Table A.I. Residential Forecasting Database TItles and Contents 

Database File 

. Nw:nbcr Name Description 

I BYUECOI Base Ycar(I990) UECs 

2 BYApSh02 Appliance and equipment shares 

3 HstShl03, Historical shipments. effICiency, and capacity data 

4 TchECf04 . Cost VI. efficiency dam. for appliances· 

S BYHShrOS BaseYcar(l990) HVAC system shares 

6 empty 

7 HVACEq07 Cost vs. effiency and cost vs capacity data for heating and cooling equipment 

8 UniL'108 Efficlcucy, capacity, usage, and UEC wUts.forcach end use 

9 BldPrt09 Basic building prototype descriptions 

10 UVWkSIO U-values and sbadiag coefficients of tJuildiug shell components 

11 BldCmpll Building prototype. shell component dimensions and thermal integrity 

12 Ld1b1l2 SPS3 regression coefficients for building componcuts 

13 SlrTh113 Solar load regression coefficients 

14 HsStcld4 Housing stock data, 1990 (will be 1980-90) 

IS lFueUS Fuel prices and income - historical and fo~ts 

. 16 empty Housing starts forecast 

17 empty 

18 empty 

19 empty 

20 ShlCst20 Shell measure costs for new buildings 

21 1RtrCst21 Shell measure. costs for building retrofits (SF Only) 

22 HstCmp22 Completions of new construction annually, 1980-90 

23 HsArca23 Conditioned floor area of new construction, 1980-90 

24 HsFcst24 Housing starts forecast 

2S AplUt25 Appliance lifetime estimates 

Table A.l. Residential Forecasting Database Report TItles and Contents 

Report Yale 

Number Name Description 

I UECI'bIOI Base Year (1990) UECs 

2 J3YApSh02 Appliance and equipment sbares 

3 TchThl03 Historical shipments, efficiency, and ·capacity data 

4 TchEfc04 Cost vs. efficiency data for appliances 

S J3YHShxOS· Base Year (1990) HV AC system shares 

7 HVACEq07 Cost vs. efficncy and cOst vs capacity data for heating and cooling equipment 

9 PrtTbI09 BaSic building prototype descriptions 

11 CmpTbUl Building prototype shell component dimensions and thcnnal integrio/ 

20 ShlCst20 Shell measure costs for new buildings 

21 RtrCst21 Shell measure costs for building retrofits (SF only) 

22 HstCmp22 Completions of new construCtion annually. 1980-90 

23 HsArca23 Conditioned floor area of new construction, 1980-90 

24 HsFcst24 Housing starts forecast 

.25 AplUt2S . Appliance lifetime estimates 

LoadCalc Baseline heating and cooling loads for prototypes (calculated by database) 

135 
Figure A.2. Sample Page from the Residential Forecasting Database

Resldenltal Forcasting Database

Lawrence Berkceley Laboratory

Fud: Technology: HP- Heat Pup STR-Storgae

E - Electric B&W -Blk & Wht TV MND - Manual Defhot TAD -Top Auto. Defrost

Vintae: House Type: G -G* CAC -Centrl Air Cndt NON -None UAD - Upriht Auto. Dfrost

S-Stock S - Single Family L-LPC CHM -Chest Man. Defrost OTH -Omt UMD - Upright Man. Dedtot

N -New MP - MuiFamily N - one COL -Color RAC - Room Alr Cndtn

R -Rcplacement MH - Manufactured Home O-Oil FRN - FumKe M -Room

T Other H20- Hydronic 'SOL -Solar .~~~~mi, J .i1990 Residential Energy Data

122Q UEC Eml Dt

HomefjIjttjajp kg CSn 2O Hea Heat C-- Heat Cool

3%g 3p4 BUgtc= Fe t cIcch t(mliiol IMMBALIMw Efficnc

N MF North E FRN E CAC 0847 4100.00 412D0 10000 924 0.60 0.80

N MF Noth E FRN E RAC . 410000 128.00 100.00 8.0 080 1.00

N MF Noft E FRN N NON 0.131 410000 10000 080 1.00

N M North E H20 E CAC 412D0 924 1.00 0.80

N MF North E H20 E RAC 128.00 8.70 1JD0 1.00

N MF North E H20 N NON 1.00 1.00

N MF North E HP E CAC 0.939 416.00 412O0 924 0.80 0.80

N MF North E HP E RAC 1D47 416.00 128.00 8.70 0.80 1.00

N MF *North E HP N NON' 416.00 0.80 1.00

N MF North- E RM E CAC 4100.00 41200 9.24 1.00 080

N MF North E RM .E RAC 1.632 4100.00 128D0 8.70 100 1.00

N MF North E RM N NON 0A47 41000 100 1.00

N MF North G FRN E CAC 2.618 25.00 412D0 78.00 924 0.80 0.80

N MF North G FRN E RAC 25.00 1280 78.00 8.70 .080 1.00

N MF North G FRN N ON 0193 25.00 7800 0.80 1.00

N MF North G H20 E CAC 21.00 41200 79.60 924 1.00 0.80

N MF North G H20 E RAC 5.698 21.00 128.00 79.60 8.70 100 1.00

0411393

UEC

Heat Coo

MMBtulvr Kwhl?

13

13

13

13

13

13

13

13

13

13

13

13

13

13

1.3

13

13

2.6

2.6

2.6

2.6

26

2.6

2.6

2.6

2.6

2.6

2.6

2.6

2.6

2.6

2.6

2.6

2.6

12862.58

12862.58

12862.58

974.03

57217

974.03

57217

8900.76

8900.76

8900.76

12862.58

1286258

12862.58

52.77

52.77

52.77

2537

2537

974.03

572.17

974.03

57217

974.03

57217

974.03

572.17

papgI

HMatbng Crong

_MMBtul

EE-RM-94-230 COMMENT 61C

-

W 

0\ 

Figure· A.2. Sample Page .from the Residential Forecasting Database 

Fud: 

V1nll,e: HouscType: E -Etectrie 

c·e .. L·LPC S -SIDCk 

N-Nrw 

. R - Repllcement 

SF - Sinlle Flmlly 

MF - MuhiFlmily 

MH - MlnuflCturcd Home N -None 

O·Oil 

T-Odler 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

QmIDI 

fuellcdt 

MF North E FRN E CAC 

·MF 

Mf 

North E FRN E RAe 

North E FaN N NON· 

MF North E mo E CAC 

Mf NDI1h E mo E RAC 

MF NDI1h E H20 N NON 

MF North E UP E CAC 

MF NDI1h E HP E RAC 

. MF' North E HP N NON· 

MF NDI1h . E RM E CAC 

MF North E RM . E RAC 

MF North E RM N NON 

tAr North G FRN E CAC 

tAr North G FRN E RAC 

MF North G FRN N . NON 

MF North G H2O E CAC 

tAr North G H20 E RAC 

1!Il 

SIaIt 

(millions) 

0.847 

0.'" 

0.939 

1.017 

1.632 

G.647 

2.618 

0.693 

5.698 

ResldenUa' Forecasttns Oatabate 

Lawrence Berfceley uboratory 

TKhnololTBlW - Blit A WIlt TV 

CAe -Cmlrll Air Cndtn 

CHM -Chest Min. Defrost 

COL-Color 

FRN·FumKc 

H20 - Hydronlc . 

1990 Reslden" ... Energy DOIIbl 

DilItaba:,,; 

UP· Helt Pump . MND· Mlnull Ddrosf 

NON -None 

OTH-Odln 

RAC • Room Air Cndtn 

RM·Room 

SOL-Sol .. 

llEC 

Ueal CpgI 

rMMPtu or Kwh! 

Eqmt Dbl 

Heat Cool Uut Cogi 

Emden~ 

CaJga\atcd wads 

Huttal Cpolln. 

4100.00 412.00 100.00 924 0.80 0.80 

4100.00 

410000 

128.00 100.00 8.70 0.80 1.00 

412.00 

128.00 

100.00 0.80 1.00 

924 1.00 0.80 

8.70 1.00 1.00 

1.00 1.00 

416.00 412.00· 924 0.80 0.80 

8.70 0.80 1.00 

0.80 1.00 

416.00 128.00 

416.00 

4100.00 412.00 924 1.00 Q80 

8.70 1.00 1.00 

1.00 1.00 

412.00 78.00 924 0.80 0.80 

4100.00 128.00 

4100.00 

25.00 

2S.OO 

25.00 

Z1.00 

21.00 

121..00 78.00 8.70 .0.80 I.DO 

78.00 0.80 1.00 

412.00 79.60 9.24 1.00 0.80 

128.00 79.60 8.70 1.00 1.00 

papS 

1l 

1.3 

1.3 

1.3 

1.3 

1.3 

1.3 

13 

1.3 

1.3 

13 

13 

13 

13 

13 

1.3 

1.3 

IMMBtul 

. 2.6 

2.6 

2.6 

·2.6 

2.6 

2.6 

2.6 

2.6 

2.6 

1.6 

2.6 

2.6 

2.6 

2.6 

2.6 

2.6 

2.6 

.STR -Stofl,C 

TAD· Top Auto. Defrosf 

UAD • Uprilht Auto. Ddrolt 

UMD - Upright Min. Ddton 

IM/1S/" 

p!lCulatcd 

11EC 

Hcal Cool 

MMPtu/yr Kwblyt 

12862.58 

12862.S8 

12862.58 

8900.76 

8900.76 

89110.76 

12861.58 

12862.58 

12852.58 

52.17 

52.77 

52.17 

25.37 

2S.37 

974.03 

Sn,17 

974.m 

Sn,17 

974.03 

'5n,17 

974.03 

57117 

. 974.03 

Sn,17 

974.03 

Sn,17 
Table A3. Database Technology Categories

Index

Fuel

E Electric 

G Gas

L LPG

N None

O Oil

_T Other

Heatfuel (same options as Fuel)

E Electric

G Gas

L LPG

N None

O Oil

T Other

Coolfuel (same options as Fuel)

E Electric

G Gas

L LPG

N None

O Oil

T Other

Enduse

AC Air Conditioning

CK Cooking

CW Clothes Washer

DR Dryer

DW Dishwasher

FZ Freezer

HT Space Heating

LT Lighting

MS Miscellaneous

MW Microwave

RF Refrigerator

TV Television

WH Water Heating

137

EE-RM-94-230 COMMENT 61C

Table A.3. Database Technology Categories 

Index 

Fuel 

E Electric 

G Gas 

L LPG 

N None 

0 Oil 

T Other 

Heatfuel (same options as Fuel) 

E Electric 

G Gas 

L LPG 

N None 

0 Oil 

T Other 

Coolfuel (same options as Fuel) 

E Electric 

G Gas . 

L LPG 

N None 

0 Oil 

T Other 

Enduse 

AC Air Conditioning 

CK Cooking 

CW Clothes Washer 

DR Dryer 

DW Dishwasher 

FZ Freezer 

lIT Space Heating 

LT Lighting 

MS Miscellaneous 

MW Microwave 

RF Refrigerator 

TV Television 

WH Water Heating 

137 
Table A3. Database Technology Categories (cont.)

Applicable

Technology (entries specific to enduse) Enduse

B&W Black & White TV

CAC Central Air Conditioning AC

CHM Chest Manual Defrost FZ

COL Color TV

FRN Fumace HT

H20 HydronicHT

HP Heat Pump HT.AC

MND Manual Defrost RF

NON None any

OTH Other any

RAC Room Air Conditioning AC

RM Room HT

SOL Solar HT

STR Storage WH

TAD Top Automatic Defrost RF

UAD Upright Automatic Defrost FZ _ UMD Upright Manual Defrost FZ

Heattech (Subset of "Technology" field)

FRN Furnace

H.20 Hydronic

HP Heat Pump

NON None

OTH Other

RM Room

SOL Solar'

Cooltech (Subset of "Technology" field)

I CAC Central Air Conditioning

HP Heat Pump

NO None

RAC Room Air Conditioning

Region ___

0 National

I North Region

2 South Region

Housetype _______

SF Single Family

MF Multifamily

MH Manufactured Home

Vintage

S Stock

N New

R Replacement

YEAR (actual year value)

138

EE-RM-94-230 COMMENT 61C

Table A.3. Datllb.se TechllOlogy Categories (conL) 

I Applicable . Technololg:or (entries specific to end use) Enduse 

·B&.W Black &. White TV 

CAC Central Air Conditioning AC 

CHM Chest Manual Defrost FZ 

COL Color TV 

lFRN Furnace HI' 

]H2O Hydronic HI' 

HP Heat Pump HT,AC 

MND Manual Defrost RF 

NON None , any 

am Other any 

RAC Room Air Conditioning AC 

RM Room HT 

SOL Solar HI' 

., STR Storage WH 

TAD Top Automatic Defrost RF 

UAn Upright Automatic Defrost FZ 

UMD Upright Manual Defrost FZ 

Heattech (Subset of "Tedmology" field) 

FRN Fwnace 

H2O Hydronic 

HP Heat Pump 

NON None 

OTH Other 

RM Room 

SOL Solar 

Cooltech (Subset of "Technology" field) 

I CAe Central Air Conditioning 

HP Heat Pump 

NO . None 

RAC Room Air -Conditioning 

~egion 

---0 National 

[ North Region· 

2 South Region 

Housetype -SF Single Famil y MF Multifamily 

MH Manufactured Home 

Vintage 

S Stock 

N New 

R Replacement 

YEAR (actlltal year value) 

138 
UEC DATABASE DESCRIPTION

INTRODUCTION

The 

Energy purpose 

Consumption 

of this appendix is to review and assimilate all available estimates of Unit

of a larger effort 

values (UECs) for the major residential end-uses. This project is part

forecasting models 

to develop baseline data for use in residential sector energy demand and to document the source of each element within a database structure. UECs are among the most 

examination and documentation. important inputs to forecasting models and thus require careful

individual 

Data on UECs 

appliances, 

have traditionally come from a variety of sources, including sub-metering of

previous model 

conditional demand regression analyses, engineering estimates,

these methods inputs, and other utility and industry figures. Our analysis shows that

problems in estimating can produce UECs UEC 

from available 

estimates of vastly different magnitudes. Further data occur when considering regional data, end-uses 

technologies 

that 

within 

interact with other end-uses, appliances or equipment that use different

that suggest different 

the same end-use, vintage of equipment, and different housing types

UEC inputs that vary widely.

usage patterns. Not surprisingly, different researchers tend to use

residential 

The primary 

end-use 

goal of this project is to collect and systematically analyze existing data on

data. A secondary goal 

unit energy consumption and to derive UEC estimates based on that

various 

is to understand the level of uncertainty in UEC estimates for the'

UEC inputs 

residential 

in 

end-uses. The results of this analysis will be used to critically assess the

Berkeley Laboratory 

the residential energy demand forecasting models used at Lawrence (LBL) and to suggest improvements database allows us 

in these UEC inputs. Lastly, the

described above and 

to compare UEC estimates from the different analysis techniques specific end-uses. We 

to make observations about the applicability of those techniques for

conclusions present the results of the analysis in this report, along with

data. A bibliography 

about the nature of the data and the best UEC estimates based on'the collected including all data sources in the UEC database is provided.

DATA COLLECTION AND ANALYSIS METHODOLOGY

The 

unpublished 

data collection 

data, 

effort consisted of gathering all published data, as well as some

did not attempt 

collected by various researchers at LBL over the last several years. We

house 

to obtain a representative distribution of sources across utilities, regions,

In total, types, 

over 

or 

1300 

study UEC types. The sources include only those known to researchers at LBL.

may 

records were extracted from a list of 98 sources. While the data not be statistically representative, information. they include the. majority of the available

We 

contains 

entered 

the 

each 

UEC 

of the 1300 UEC estimates into a computerized database. Each record

the study useful in 

estimate 

understanding along with documentation of the source, other information from

quality of the estimate 

the reliability of the estimate, and an indicator of the

could' analyze it at different 

as well 

levels 

as other 

of 

notes. Our goal was to organize the data so that we disaggregation, depending on the number of records for a given end-use category.

139

APPENDIX B.

EE-RM-94-230 COMMENT 61C

APPENDIX B. UEe DATABASE DESCRIPTION 

INTRODUCTION 

The purpose ,of this appendix is to review and assimilate all available estimates of Unit 

Energy Consumption ,values (UECs) for the major residential end-uses. This project is part 

of a larger effon to develop baseline data for use in residential sector energy demand 

forecasting models and to document the source of each element within a database structure. 

UECs are among the most imponant' inputs to forecasting models and thus require careful 

examination and documentation. 

Data on UECs have traditionally come from a variety of sources, including sub-metering of 

individual appliances, conditional demand regression analyses, engineering estimates, 

previous model inputs, and other utility and industry figures. Our analysis shows that 

'these methods can produce UEC estimates of vastly different magnitudes. Further 

problems in estimating UECs from available data occur when considering regional data, 

end-uses that interact with other end-uses, appliances or equipment that use different 

technologies within the same end-use; vintage of equipment, and different housing types 

that suggest different usage patterns. Not surprisingly, different researchers tend to use 

UEC inputs that vary widely. 

The primary goal of this project is to collect and systematically analyze existjog data on 

residential end-use unit energy consumption and to derive UEC estimates based on that, 

data. A secondary goal is to understand the level of uncertainty in UEC estimates for the 

various residential end-uses. The results of this analysis will be used to critically assess the 

UEC inputs in the residential energy~emandforecasting models used at Lawrence 

Berkeley Laboratory (LBL) and to suggest improvements in these UEC inputs. Lastly, the 

,databnse aIlows us to compare UEC estimates from the different analysis techniques 

described above and to make observations about the applicability of those techniques for 

specific end-uses. We present the results of the analysis in this report, along with 

conclusions about the nature of.the data and the best UEC estimates based on 'the collected 

data. A bibliography including all data sources in the UEC database is provided. 

DATA, COLLECTION AND ANALYSIS METHODOLOGY -

The data collection effon consisted of gathering all published data, as well as some 

unpublished data, collected by various researchers at LBL over the last several years. We 

did not attempt to obtain a representative distribution of sources across utilities, regions, 

house typ~, or study types. The sources include only those known to researchers at LBL. 

In total, ove,r 1300 UEC records were extracted from a list of 98 sources. While the data 

may not be, statistically representative, they include the, majority of the available , information. ' 

We entered each of the 1300 UEC estimates into a computerized database. Each record 

contains the UEC estimate along with documentation of the source, other information from 

the study useful in understanding the reliability of the estimate, ~d an indicator of the 

quality of the estimate as well as other notes. Our goal was to organize the data so that we 

could'analyze it at different levels of disaggregation, depending on the number of records 

for a gi~en end-use category. 
For example, data on UECs come from a variety of different sources including submetering of individual appliances, conditional demand regression analyses, engineering

estimates, previous model inputs, and other utility and industry figures. In addition,

studies may contain information only for certain appliance classes, housing types, vintages,

or regions, and may have been performed in different years. Previous attempts at UEC

aggregation have either failed to account for these differences at all or have not examined

their effects systematically. Thus, we retain as much information about each study as is

necessary to understand the methodology and applicability of the data for further analysis.

We summarize the important fields in the UEC database in Table B.1 below and discuss

how we make use of these supporting data in the following sections.

Table B.I. Description of UEC Database Fields

Field I Description

We developed procedures for selectively aggregating the observations. Where appropriate,

weighting factors were used in the analysis based on data quality, historical efficiency

trends and the study type. By weighting and disaggregating as much as possible, we

sought to generate 1990 stock UECs that best represent the data in the database. Because

we had litte UEC data for the new vintage (e.g. recently purchased refrigerators or heating

energy use in recently constructed buildings), the results presented in this paper include

only those for the stock vintage. Data for new vintage equipment, appliances and buildings will not be discussed further.

End Use and Appliance Class

The 17 end-use and fuel type combinations included in the UEC database are gas and

electric heating, cooking, water heating, and clothes drying; electric air-conditioning;

refrigerator-freezers; stand-alone freezers; clothes washers; dishwashers; microwaves;

lighting; and color and black-and-white TVs. Additionally, we subdivide several of these

end-uses into their most important product classes wherever energy use varies significantly between classes and the data allow for it. The end-uses and appliance classes are

summarized in Table B.2.

140

End Use: a code for one of the seventeen end-uses included in the database (e.g. heating, cooling,

water heating)

Class the appliance class or technology under consideration, if specified (e.g. auto-defrost vs.

manual defrost refrigerators, central vs. room air conditioning)

Study Type one of six categories, including metered, conditional demand, engineering, model or other

previously aggregated value, utility, or industry (defined in detail below)

Vintage representative of either stock or new appliances, equipment, or buildings

House type single-family, multi-family, manufactured home, or all/not-specified

Year the yearin which the data were collected or the estimate made

Region area of the U.S. that the data represent

Quality a subjective rating of data quality assigned to each record

Source the report authors and title, or other documentation

Notes anecdotal information about the piece of data

EE-RM-94-230 COMMENT 61C

For example, data on UECs come from a variety of different sources including submetering of individual appliances, conditional demand regression analyses, engineering 

estimates, previous model inputs. and other utility and industry figures. In addition, 

studies may contain infonnation only for certain appliance classes. housing types, vintages, 

or region4l. and may have been perfonned in different years. Previous attempts at UEC 

aggregation have either failed to account for these differences at all or have not examined 

their effects systematically. Thus. we retain as much infonnation about each study as is 

necess;ary to understand the methodology and applicability of the data for further analysis. 

We summarize the important fields in the UEC database in Table B.l below and discuss 

how we make use of these supponing. data in the following sections~ 

Table B.l. Description of UEe Database Fields 

Field Description 

End.USC\ a code for Ol)e of the seventeen end·uses included in the database (e.g. heating. cooliIig, 

warer beating) 

Class the appliance class or technology under consideration. if specified (e.g. auto-defrost vs. 

manual defrost refrigerators, central vs. room air conditioning) 

Study Type one of six categories, including metered, conditional demand, engineering, model or other 

previously aggregated value. utility. or indusuy (defmed in detail below) 

Vintage representative of either stock or ne.w appliances.· equipment, or buUdings 

House type single-family, multi-family, manufactUred home, or alVnot-specified 

Year the year in which the data were collected or the estimale made 

Region area of the U.S. tballbe data represent 

QiJaJity a subjective rating of data quality assigned to each record 

Soun:e dle report authors and title. or oaher documentation 

Notes anecdotal infonnation about the piece of data 

We developed procedures for selectively aggregating the observations. Where appropriate, 

weighting factors were used in the analysis based on data quality. historical efficiency 

trends ~Lnd the study type. By weighting and disaggregating as much as possible. we 

sought to generate 1990 stock UECs that best represent the data in the database. Because. 

we had little UEC data for the new vintage (e.g. recently purchased refrigerators or heating 

energy u.se in recently constructed buildings). the results presented in this paper include 

only thotSe for the·stock vintage. Data for new vintage equipment, appliances and buildings will not be discussed further. .. . 

End U!i4~ Jlod Appliance ClaSs . 

The 17 end-use and fuel type combinations included in t,he UEC database are gas and 

electric hea.ting. cooking. water heating, and clothes drying; electric air-conditioning; 

refrigenltor-freezers; stand-alone freezers; clothes washers; dishwashers; microwaves; 

lighting; and color and black-and-white TVs. Additionally, we subdivide several of these 

end-uses into their most important product classes wherever energy use varies significantly 

between classes and the data allow for it The end,.uses and appliance classes are 

summarized in Table B.2. 

140 
Table B.2. UEC Database Contents by End Use and Class

Electric UECs in kWhyr, gas UECs in MMBtluyr

Records in Database

Low High Unweighted REM

End Use Code Class Code N UEC UEC Average 1990 i i , f , ,, ,,

AirCondiioning EAC

Black-White TV EBW

EL Cooking ECK

Clotheswasher ECW

EL Clothes Dryer EDR

Dishwasher EDW

EFZ

EHT

ELT

EMW

ERF

EL Water Heater EWH

Color TV ETV

all/not-specified

central air

heat pump

room air

all/not-specified

solid statelectronic

tube/manual

total cooking

oven only

range only

total=motor+h2o

motor only

all dryers

total=motor+h2o

motor only

all/not-specified

manual defrost

upright auto defrost

all/not-specified

central furnace

heat pump

all elec. resistance

room electric

all lighting

all microwaves

all/not-specified

manual defrost

top-mount auto def

through-the-door

side-by-side no TTD

all el. water heaters

all/not-specified

solid state/electronic

tube/manual

ALL

CAC

HP

RAC

ALL

SDS

TUB

oven

rangetop

motor

motor

ALL

MND

UAD

ALL

CTL

HP

RES

RM

ALL

MND

TAD

TTD

SDN

ALL

SDS

TUB

23

99

39

84

25

3

4

78

6

9

21

35

76

31

45

57

17

15

66

16

76

74

13

12

31

58

14

32

4

4

100

40

7

4

551

546

750

160

50

99

50

310

334

299

403

-69t

304

287

62

288

497

1043

765

1460

406

741

326

734

78

385

385

651

1050

1108

1902

214

161

122

2550

7935

4360

5597

1325

100

288

2138

667

820

1258

449

2059

1836

2562

2274

1880

3336

14155

32400

19659

18311

9660

4405

1132

3033

1800

2555

2031

1734

9000

1792

360

540

1452

2393

2219

984

262

100

195

881

413

475

741

111

970

1080

418

1169

1036

1647

6266

8317

6095

6951

4713

1264

255

1363

1028

1647

1607

1339

3882

609

265

430

1611

2405

2470

683

1011

99

904

172

1105

8100

10200

5700

9400

8200

2120

1227

3852

Gas Cooking GCK total gas cooking - 11 2.05 17.80 6.10 7.32

oven only oven 3 1.00 4.00 233

range only rangetop 3 1.00 200 1.67

Gas Dryer GDR all gas dryers - 12 3.31 5.70 4.07 3.72

Gas Heating GHT all gas heating - 52 11.40 136.60 62.42 58.30

Gas Water Heater GWH all gas water heaters - 23 16.20 51.29 25.26 18.69

TOTAL RECORDS 1322_

tnegative value from poor regression specification

141

Freezer

EL Heating

Lighting

Microwave

Refrigerator

_ .

EE-RM-94-230 COMMENT 61C

Table B.2. VEe Database Contents by End Use and Class 

Electric UECs in kWhlyr, gas UECs in MMBIu/yr 

End Use Code Class Code 

.Air Conditioning EAC aWDot-specified ALL 

cenuaJ. air CAC 

beatpwnp HP 

room air- RAC 

Black-White 1V EBW alVnot-specified ALL 

solid slate/electronic SOS 

rube/manual ruB 

ElCooking ECK total cooking - . oven only oven 

range only rangetop 

Clotheswasher ECW total=motor+h20 - motor only motor 

EL Clothes Dryer EOR all dryers - Dishwasher EDW total=motor+h20 - motor only motor 

Freezer EfZ alUnot-specified AU.. 

manual defrost MND 

upright auto defrost UAD 

El. Heating ElIT alVnot-specified ALL 

cenuaJ. furnace en. 

beat pump HP 

all elec. resistance RES 

room electric RM 

Lighting ELT allligbting - Microwave EMW all miaowaves - Refrigerator ERF alUnot-specif&ed AU.. 

manual defrost MND 

top-mount auto def TAD 

tbrougb-the-door TID 

side-by-side no TID SON 

EL Water Heater EWH all el. water healelS - ColorlV E'IV allInol-specifu:d AU.. 

solid slate/electronic SOS 

tube/manual TIm 

Gas Cooking GCK total gas COOking - oven only oven 

rangeonJy rangetop 

Gas Dryer GOR all gas dryers - Gas Heating GHT all gas beating - Gas Water Heater GWH all ~as water heaters -

N 

23 

99 

39 

84 

2S 

3 

4 

78 

6 

9 

21 

35 

76 

31 

45 

57 

17 

15 

66 

16. 

76 

74 

13 

12 

31 

58 

14 

32 

4 

4 

100 

40 

7 

4 

11 

3 

3 

12 

52 

23 

TOTAL RECORDS 1322 

tnegative value from poor regression specification 

141 

Records in Database 

Low High Unweightecl REM 

UEC UEC Averal!e 1990 

551 2550 1452 1611 

S46 7935 2393 2405 

750 4360 2219 2470 

160 5597 984 683 

50 1325 262 

99 100 100 

50 288 195 

310 2138 881 1011 

334 667 413 

299 820 475 

403 1258 741 

-69t 449 111 99 

304 2059 970 904 

287 1836 1080 

62 2562 418 172 

288 2274 1169 1105 

497 1880 1036 

1043 3336 1647 

765 14155 6266 8100 

1460 32400 8317 10200 

406 19659 6095 5700 

741 18311 6951 9400 

326 9660 4713 8200 

734 4405 1264 2120 

78 1132 2SS 

385 3033 1363 1227 

385 1800 1028 

651 2S55 1647 

1050 2031 1607 

1108 1734 1339 

1902 9000 3882 3852 

214 1792 609 

161 360 26S 

122 540 430 

2.05 17.80 6.10 7.32 

1.00 4.00 2.33 

1.00 2.00 1.67 

3.31 5.70 4.07 3.72 

11.40 136.60 62.42 58.30 

16.20 S"1.29 25.26 18.69 
The appliance and equipment classes that we distinguish are central, room and heat pump air-conditioning and electric heating systems, manual and auto-defrost refrigerators and

freezers, and solid-state/electronic and tube/manual color and black-and-white TVs. Autodefrost refrigerators are further sub-divided into top-mounted (TAD), through-the-door

feature equipped (TTD), and other side-by-side (SDN) models. Electric heating records

which distinguish electric resistance heating from heat pump systems but do not separate

room from forced air furnace are grouped together in a resistance heat (RES) category.

Partial UECs for dish- and clothes washer motor use and for range and oven energy use in

cooking-are tracked independently, similarly to equipment classes.

For end-uses where class data are kept, a separate category is also included for data records

that do not specify a particular class or that explicitly combine sub-estimates for the

different classes. This "ALL" class is therefore not a sum of ALL records, but a separate

class category for estimates that at least claim to include all the classes of the given end-use.

Study Type

For purposes of analysis, the UEC studies have been grouped into six study type

classifications: metered, conditional demand, engineering, model/aggregate, utility estimate

and industry estimate.

Metered studies are those in which individual appliances are measured for their energy use

under actual or simulated domestic usage conditions. These include utility sub-metering

and monitoring studies of field energy usage, as well as a few laboratory tests of appliances

that are typically based on a standardized test procedure intended to replicate field usage

patterns.

Conditional demand studies, including national-level regression analyses, represent

attempts by utilities and others to apportion whole-house energy use data to specific enduses, based on statistical correlation with saturation surveys, weather data and other

variables. There is a great deal of variation in both statistical methodology and level of enduse detail among conditional demand studies.

Engineering estimates are studies that base energy consumption estimates on engineering formullas and certain usage and building characteristics assumptions. Examples are

building simulation program estimates of space conditioning energy use and gallons x AT

estimates of water heating energy use. The U.S. Department of Energy (DOE) appliance

standards analysis Technical Support Documents (see US DOE 1989b, for example) fall

into the engineering category because they use computer models to determine energy

consumption for various design options in new equipment.

Forecasting models generally include UEC data collected and corrected over time, from a

variety of undocumented sources. For this reason, we put model data in its own study

type, together with other aggregate estimates of UEC use, such as averages of conditional

demand studies and utility trade association figures.

Estimates from individual utilities that do not disclose a source or methodology --often

simply the best guesses of utility personnel - are kept in the utility category, and equipment

manufacturers' figures, primarily the new product data from standardized appliance tests,

are classified in the industry study type (see AHAM 1990).

In this analysis, we investigate the variability of UEC estimates within and across study

types where the data allow. This gives important insight into the relative range of UEC

142

EE-RM-94-230 COMMENT 61C

·The.appliance and. equipment classes that we distinguish are central. room and heat pump 

air-conditioning and electric heating systems. manual and auto-defrost refrigerators and 

free:r.ers. and solid-state/electronic and tubeJmanual color and black-and-white 'IV s. Autodefr,ost refrigerators are further sub-divided into top-mounted (TAD), through-the-door 

feature equipped (TID), and other. side-by-side (SDN) models. Electric heating records 

whi(:h distinguish electric resistance heating from heat pump systems but do not separate 

roODl from fo~d air furnace are grouped together in a resistance heat '(RES) category. 

Partial UECs for dish- and clothes washer motor use and for range and oven energy use in 

cooking-are tracked independently. similarly to equipment classes. 

For c::nd-uses where class data are kept, a separate categOl), is also included for data records 

that do not specify a particular class or that explicitly combine. sub~stimates for the 

different classes. This "ALL" class is therefore not a sum of ALL records. but a separate . class category for estimates that at least claim to include all the classes of the given end-use. 

Study Type 

For purposes of analysis. the UEC studies have been grouped into six study type 

classifications: metered. conditional demand. engineering, modeVaggregate. 'utility estimate 

and industry estimate. . 

Metered studies are those in which individual appliances are measured for their energy use 

under actual.or simulated domestic usage conditions. These include utility sub-metering 

and monitoring studies of field energy usage. as well as a few laboratory tests of appliances 

that axe typically based on a standardized test procedure intended t~ replicate field usage patte~~ . . 

Conditional' demand studies. including national-level regression analyses, represent 

attempts by utilities and others to apportion whole-house energy use data to specific enduses, based on statistical correlation with saturation surveys, weather data and other 

variables. There is a great deal of variation in both statistical methodology and level of end~ . use deltail among conditional demand studies~ . 

Engi1u!ering estimates are studies that base energy consumption estimates on engineering 

fonnullas and certain usage and building characteristics assumptions. Examples are 

buildullg simulation program estimates of space' conditioning energy use and gallonS x·.1T 

estimattes of water heating energy use. The U.S. Department of Energy (DOE) appliance 

standards analysis Technical Support Documents (see US DOE 1989b, for example) fall 

into th·e engineering category because. they use computer models to determine energy consw:nption for various design options in new equipment. 

Forecasting models generally include UEC data collected and corrected over time, from a 

variety of undocumented sources. For this reason, we put model data in its own study 

type, tolgetber with other aggregate estimates of UEC use. such as averages of conditional 

demand studies and utility trade association figures. 

Estimates from individual utilities that do not disclose a source or methodology --often 

simplY.lthe best guesses of utility personnel - are kept in the utility category, and equipment 

manufacturers' figures. primarily the new product data from standardized appliance tests, 

are clas:wtedin the industry study type (see AHAM 1990). 

In this ~\llalysis. we investigate the variability of UEC estimates within and across study 

types where the data allow. This gives important insight into the relative range of UEC 

142 
estimates derived from different analysis techniques. We use observations gained from

these comparisons to give weights to average UECs by study type when calculating best

estimates for each end-use UEC.

House Type

When the data source specifies the house type from which the data are derived, we record

those data in the database as either single-family, multi-family, or manufactured home.

These distinctions are obviously important when analyzing space conditioning UECs. For

these end-uses, we also collected the conditioned floor area of the sample and heating or

cooling degree days of the climate under consideration. However, there were few entries

for these parameters other than building simulation program estimates of heating and

cooling UECs.

House type may be an important factor for other UECs that are influenced by occupancy levels, usage patterns, and appliance and equipment sizes that are related to the type of

dwelling. Both the LBL Residential Energy Model (REM) and the REEPS 2.0 forecasting

models allow for different end-use UECs for each house type. Thus, we attempt to find

significant distinctions between UECs by house type in the data.

Data Year and Historical Efficiency Normalization

For each UEC record, we post the year in which the data were collected or the estimate

made. The database includes stock UEC estimates that range as far back in time as the mid1970s. Thus, comparing these estimates with more recent stock data does not account for

changes in UEC values over time. As shown in the equation below, UECs are a function

of appliance size or capacity, level of usage and efficiency:

UEC = capacity X usage

efficiency

Any of these parameters can change over time. The most significant factor, and the one we

account for in this analysis, is the change in efficiency of the appliance stock. The process

of normalizing the data to 1990 stock efficiency levels is necessitated by the enormous

changes taking place in the market for certain appliances. For example, new refrigerators

and freezers have increased markedly in efficiency since 1972. Without normalizing to a

common efficiency level, it would be meaningless to compare refrigerator stock UEC data

from, for instance, a 1976 and a 1986 study. The background trend of efficiency

improvement would largely obscure any other differences one attempted to examine.

To calculate average stock efficiency for each year, we take a shipment-weighted sum of

the new unit efficiencies (available from manufacturers' data) in 'the preceding product

lifetime. Shipments and Shipment-Weighted Efficiency Factors (SWEFS) of new units for

the years 1972-90 are shown in Tables B.3 and B.4. The calculation assumes that the

stock of equipment in any given year is made up of all the new units which have been

purchased recently enough to still be in service, on average. The efficiencies are

normalized so that 1990 has a weight of one, with older vintages having lower weighting

factors to compensate for their higher energy usage levels. The end-uses for which

historical factors are used are gas heating, room and central air-conditioning, electric and

gas water-heating, refrigerators, freezers, clothes washers, and dishwashers. Other enduses are assumed to remain constant with respect to efficiency over time. The calculated

normalizing factors are shown graphically in Figure B. 1.

143

EE-RM-94-230 COMMENT 61C

estimates derived from different analysis techniques. We use observations gained from 

these comparisons to give weights to average UECs by study type when calculating best 

estimates for each end-use UEC. 

House Type 

. When the data source specifies the house type from which the data are derived, we record 

those data in the database as either single-family, multi-family, or manufactured home. 

These distinctions are obviously important when analyzing space conditioning UECs. For 

these end-uses, we also collected the conditioned floor area of the sample and heating or 

cooling degree days of the climate under consideration. However, there were few entries 

for these parameters .other than building simulation program estimates of heating and 

cooling UEes. 

House type may be an important factor for other UECs that are influenced by occupancy 

levels, usage patterns, and appliance and equipment sizes that are related to the type of 

dwelling. Both the LBL Residential Energy Model (REM) and the REEPS 2.0 forecasting 

models allow for different end-use UECs for each house type. Thus, we attempt to fmd 

significant distinctions between UECs by house type in the data. 

Data Year and Historical· Efficiency Normalization 

For each UEe record, we post the year in which the data were collected or the estimate 

made. The database includes stock UEC estimates that range as far back in time as the mid1970s. Thus. comparing these estimates with more recent stock data does not account for 

changes in UEC values over time. As shown in the equation below, UECs are a function 

of appliance size or capacity,level of usage and efficiency: 

UEC = 

capacity X usage 

efficiency 

Any of these parameters can change over time. The most significant factor, and the one we 

account for in this analysis, is the change in efficiency of the appliance stock. The process 

of normalizing the data to 1990 stock efficiency levels is necessitated by the enonnous 

changes taking place in the market for certain appliances. For example, new refrigerators 

and freezers have increased markedly in efficiency since 1972. Without nonnalizing to a 

common efficiency level, it would be meaningless to compare refrigerator stock UEC data 

from, for instance. a 1976 and a 1986 study. The background trend of efficiency 

improvement would largely obscure any other differences one attempted to examine. 

To calculate average stock efficiency for each year, we take a shipment-weighted sum of 

the new unit efficiencies (available from manufacturers' data) in "the preceding product 

lifetime. Shipments and Shipment-Weighted Efficiency Factors (SWEFS) of new units for 

the years 1972-90 are shown in Tables B.3 and BA. The calculation assumes that the 

stock of equipment in any given year is made up of all the new units which have been 

purchased recently enough to still be in service, on average. The efficiencies are 

normalized so that 1990 has a weight of one, with older vintages having lower weighting 

factors to compensate for their higher energy usage levels. The end-uses for which 

historical factors are used are gas heating, room and central air-conditioning, electric and 

gas water-heating, refrigerators, freezers, clothes washers, and dishwashers. Other enduses are assumed to remain constant with respect to efficiency over time. The calculated 

nonnalizing factors are shown graphically in Figure B.l. 

143 
Table B3. Historical Shipments.

Millions of Units Shipped

End avg. life

Use (yr) 1972 1973 1974 1975 1976 1977 1978 1979 1980 !98! 1982 1983 1984 1985 1986 1987 1988 1989 1990

GT 23 1.57 1.57 1.85 2.15 1.81 1.33 1.11 1.09 1.32 !.42 .16 1. 66 1.85 1.82 2.11 2.07 2.09 2.16 1.95

RAC 15 4.31 4.65 4.41 3.97 4.11 3.74 2.40 1.95 2.16 3.69 2.76 2.00 3.10 3.02 2.82 3.80 4.64 5.09 4.15

CAC 12 1.15 1.24 1.36 1.83 2.23 2.08 1.46 1.84 2.31 1.86 1.48 2.04 2.56 2.47 2.67 3.04 3.22 3.49 2.92

EWH 13 1.67 1.70 1.85 2.03 2.04 1.94 1.91 2.03 2.30 2.46 2.72 3.13 3.48 3.45 3.39 3.40 3.33 3.37 3.23

GWH 13 2.88 2.93 3.20 3.51 3.52 3.34 3.29 2.50 2.96 2.79 3.04 3.17 3.50 3.53 3.73 3.95 3.96 4.13 3.91

ERF 19 5.13 5.59 6.12 6.66 7.04 6.06 5.06 5.20 5.86 5.48 *4.86 6.05 6.60 6.86 7.32 7.80 8.08 7.97 7.99

EFZ 21 1.05 1.42 1.42 1.57 2.17 2.90 2.77 1.79 1.53 1.61 1.34 1.34 1.28 1.24 1.22 1.26 1.35 1.22 1.30

ECW 14 5.16 5.50 4.95 4.23 4.49 4.93 5.35 5.26 4.82 4.28 3.96 4.55 5.05 5.28 5.77 6.00 6.19 6.25 6.19

EDW 13 3.20 3.70 3.32 2.70 3.14 3.36 3.56 3.49 2.74 2.48 2.17 3.12 3.49 3.58 3.92 4.03 3.91 3.67 3.64

Source: Product lifetimes from LBL-REM; shipments 1951-1980 LBL-REM, 1981-1990 Appliance® Magazine

Table BA. Shipment-Weighted Efmcency Factors (SWEFs) for New Units

End

Use Unit 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

GHT AFUE% 62.7 62.8 63.0 63.1 63.3 63.5 63.6 64.7 65.9 67.1 68.3 69.6 73.0 73.8 74.3 75.1 75.9 76.6 77.4

RAC EER 5.98 6.10 6.22 6.34 6.46 6.59 6.72 6.87 7.02 7.06 7.14 7.29 7.48 7.70 7.80 8.06 8.23 8.48 8.70

CAC SEER 6.66 6.75 6.84 6.94 7.03 7.13 7.34 7.47 7.55 7.78 8.31 8.43 8.66 8.82 8.87 8.97 9.08 9.19 9.30

EWH % 79.8 79.9 80.1 80.3 80.4 80.6 80.7 81.0 81.3 81.9 82.4 83.0 83.6 84.2 84.8 85.4 86.0 86.6 87.2

GWH % 47.4 47.5 47.7 47.8 48.0 48.1 48.2 48.4 48.6 48.8 49.0 49.2 49.4 49.6 49.8 50.0 50.2 50.4 50.6

ERF cu.fL/kwh/day 3.84 4.01 4.18 4.36 4.55 4.75 4.96 5.27 5.59 6.09 6.12 6.39 6.57 6.72 6.83 7.45 7.60 7.78 8.13

EFZ cu.flkwh/day 7.29 7.67 8.08 8.50 8.95 9.42 9.92 10.38 10.85 11.13 11.28 11.36 11.60 11.55 12.07 12.93 12.91 13.89 14.57

ECW cu.ft/kwb 0.64 0.67 0.71 0.74 0.78 0.82 0.87 0.91 0.94 0.97 0.98 0.99 0.99 0.97 0.97 0.96 0.95 0.98 0.98

EDW load/kwh 0.24 0.25 0.26 0.27 0.28 0.29 0.30 0.31 0.33 0.35 0.36 0.37 0.37 0.37 0.38 0.38 0.37 0.37 0.37

Source: AHAM, GAMA, ARI, and DOE SWEF data,,interpolated for missing years

EE-RM-94-230 COMMENT 61C

I _ 

. ~ 

1~ 

Table Dol. Historical Shipments: 

Millions 0/ Unirs Shipped 

End avg.life 

Use (yrs) 1972 1973 1974 1975 1976 1977 1978 1979 1980 lQIU .'u. 1982 "ftC'l"" ':)103 i984 1985 1986 1987 1988 1989 1990 

I ~I 23 1.57 1.57 1.85 2.15 1.81 1.33 1.11 1.09 1.32 1.42 1.16 1.66 1.85 1.82· 2.11 2.07 2.09 2.16 1.95 

15 4.31 4.65 4.41 3.97 4.11 3.74 2.40 1.95 2.16 3.69 2.16 2.00 3.10 3.02 2.82 ·3.80 4.64 5.09 4.15 

CAe 12 1.15 1.24 1.36 1.83 2.23 2.08 1.46 1.84 2.31 1.86 1.48 2.04 2.56 2.47 2.67 3.04 . 3.22 3.49 2.92 

EWH 13 1.67 1.70 1.85 2.03 2.04 1.94 1.91 2.03 2.30 2.46 2.72 3.13 3.48 3.45 3.39 3.40 3.33 3.37 3.23 

OW" 13 2.88 2.93 3.20 3.51 3.52 3.34 3.29 2.50 2.96 ·2.79 3.04 3.17 3;SO 3.S3 3.73 3.9S 3.96 4.13 3.91 

ERF 19 5.13 5.59 6.12 .6.66 7.04 6.06 5.06 5.20 5.86 S.48 '4.86 6.0S 6.60 6.86 7.32 7.80 8.08 7.97 7.99 

EFZ 21 1.05 1.42 1.42 I.S7 2.17 2.90 2.77 1.79 l.53 1.61 1.34 1.34 1.28 1.24 1.22 1.26 1.3S 1.22 1.30 

ECW 14 5.16 5.50 4.95 4.23 4.49 4.93 5.3S 5.26 4.82 4.28 3.96 4.55 S.05 5.28 S.71 6.00 6.19 6.25 6.19 

EDW 13 3.20 3.70 3.32 2.70 3.14 3.36 3.56 3.49 2.74 2.48 2.17 3.12 3.49 3.S8 3~92 4.03 3.91 3.67 .. 3.64 

Source: Product lifetimes from LDL-REM; sbipments 1951-1980 LBL-REM, 1981-1990 Appliance® Magazine 

Table B.4. Shipment-Weighted Emc1enc), Facton (SWEFs) for New Units . 

End 

Use Unil .. 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

GIIT AFUE% 62.7 62.8 63.0 63.1 63.3 ·63.5 63.6 64.7 65.9 67.1 68.3 69.6 73.0 73.8 74.3 75.1 . 75.9 76.6 77.4 

RAC EER 5.98 ·6.10 6.22 6.34 6.46 6.59 . 6.72 6.81 7.02 7.06 7.14 7.29 7.48 7.70 1.80 8.06 .8.23 8.48 8.70 \ 

CAC SEER 6.66 6.75 6.84 6.94 7.03 7.13 7.34 7.47 1.55 1.78 8.31 8.43 8.66 8.82 8.87 8.97 9.08 9.19 9.30 

EWH % 79.8 79.9 80.1 80.3 80.4 80.6 80.7 81.0 81.3 81.9 82.4 83.0 83.6 84.2 84.8 85.4 86.0 86.6 87.2 

OWH % 47.4 47.S 47.7 47.8 48.0 48.1 48.2 48.4 48.6 48.8 49.0 49.2 49.4 49.6 ·49.8 SO.O SO.2 50.4 50.6 

ERF cu.rLlkwblday 3.84 4.01 4.18 4.36 4.55 4.7S 4.96 5.27 5.59 6.09 6.12 6.39 . 6.51 6.72 6.83 7.45 1.60 7;78 8.13 

EFZ cu.n.ncwblday 7.29 7.61 8.08 8.50 8.9S 9.42 9.92 10.38 10.85 li.n 11.28 11.36 It60 11.~5 12.07 12.93 12.91 13.89 14.57 

ECW cu.ft.llcwb 0.64 0.61 0.71 0.74 0.18 0.82 0.81 0.91 0.94 0.97 0.98 0.99 0.99 0.97 0.91 0.96 0.9S 0.98 0.98 

EDW Joadlkwb 0.24 0.2S 0.26 0.27 0.28 0.29 0.30 0.31 0.33 0.35 0.36 0.37 0.37 0.37. 0.38 0.38 0.37 0.37 0.37 

Source: AllAM. GAMA, ARI. and DOE SWEf dala" interpolated for miSSing years 
Figure B.I. Normalized Stock Efficiency Factors

0.85

00 ........ ;.. . ... . ...

0.60 I ItI I It I I I I I I - I i I I

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990

GT ----- RAC CAC EWH ............... - -....... GWH

-ERF -0- EFZ -0 ECW -- EDW

Formulas for calculating the Stock Efficiency Factor (SEF) in year y:

SEF(y) = sum from y-lifetime to y of (SWEFx shipments)

sum from y-lifetime toy of shipments 

Normalized SEF(y) = SEF(y)

SEF(1990)

Formula for calculating Historically-weighted UEC:

UEC (1990) = UEC(y) x SEF(y)

14S

EE-RM-94-230 COMMENT 61C

Figure B.l. Normalized Stock Efficiency Factors 

1.00 

0.95 +----- -_ .. -:-- . ..-. ~-•... -.-

0.90 

0.85 

0.80 

0.75 

0.70 

-.- /+ 

=-=::~~:;;,.::..:.:~~-~-:~ _...... --- -------'"/+~~:.=~~-=--= .",,-

/+. .... _ ............................... -._ ....... + ...................... _ ............. -................... _ .......... _ .. +/ 

./' . __ ..... _-_. __ ._...... . .................. _ ..... _ .. _"/+-:::-:::-:-.± ............ _ ............. _ ... _ ................... _ ................... : ........... _-_ ... _ .. -r __ ~' --+ 

+--+ . . ----.-:;.;:.--+~ ... --.-.... -.--... --........ ~.-... -.-.. --.---.... __ ._ .... -. __ ....................................... _ .... _ ..... _. 

::: r~-, -~.-;--~--~-~---~--~-,-~-~~--~--~-~-~-, 

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 

----GUT - - - - - RAe - - - - - - - - - CAC EWH 

--+-- ERF ~ E.fZ ---<>- ECW -(r-- EDW 

Formulas for calculating the Stock Efficiency Factor (SEF) in year y: 

SEF(y) = sum from Y-lifetime 10 Y o/(SWEF x shipmelUS) 

sum/rom ,-lifetime 10 Y 0/ shipmelUS 

Normalized SEF(y) = SEF(y) 

SEF(1990) 

Formula for calculating Historically-weighted UEC: 

UEC (1990) = UEC(y) x SEF(y) 

-----uGWH I 
shows 

The effect of the historical normalization can be seen in Figures B.2 and B.3. Figure B.2

trends. 

the 

Figure 

distribution 

B.3 

of refrigerator UEC estimates, unadjusted for historical efficiency

historical weighting 

shows 

(but 

the same data, adjusted to 1990 stock efficiencies using the

of the normalization 

not the quality rating which eliminates outlying data). The effect

and it decreases 

is twofold -- it reduces the average UEC to its approximate 1990 level,

sources is reduced.

the standard deviation, as variation due to the age of the different data

Region

For the space-conditioning 

are strongly correlated with 

and water heating end-uses, regional climate and price effects

both federal and census 

energy use. Data records for these end-uses.are coded with

overlap more than one 

region codes. Where records are for multi-state regions that

subjective judgment of 

federal or census area, we make a determination based on a

data are assigned to a 

the largest population-weighted portion of the data group, and the

than others due single region in each coding. Data from some regions are scarcer

Where the data are 

to 

sufficient, 

the vagaries 

we 

of interest in data collection across regions of the U.S. compare UEC estimates across regions.

Quality Rating

highest 

Subjective 

ranking 

quality 

and 

ratings are given to all records on a five-point scale, where a one is the

of documentation. We 

a five represents a zero-weighted study that is included just for the sake

value, but that studies 

assume that all records with ratings one through four have some

of UECs and should 

that are better designed or more detailed yield more reliable estimates

to determine the ratings 

be weighted more heavily into aggregate averages. The criteria used

reasonableness are sample size for metered studies, complexity of methodology,

the basis of a record's 

of ouptput, 

value 

and 

within 

level of end-use detail Quality ratings are assigned only on

made later, at the 

its study type. Comparisons across study types are aggregate level.

results 

During 

varied 

our analysis, we tried several different types of weighting schemes. However, the little between these different formulations. weight the records in In the analysis that follows, we

record with a rating of 

each 

one will 

disaggregated group according to a factor of (5-QR). Thus, a

of four, twice as strongly 

be weighted four times as strongly as a record with a rating

weightings are performed 

as 

within 

a three, and four-thirds as strongly as a two. Since these

record will not be adjusted 

each disaggregated group, a category with only one

record categories are marked 

for quality, as there is nothing to weight it against. These single by italics on the tables that follow.

Other Documentation

The 

database 

database 

of bibliographical 

contains information on the source of each record which refers to a separate

Additionally, entries. A list of all the sources is included in the bibliography.

remarks or other 

each 

data 

record is supported by a "notes" field which holds any additional

database. Entries that are 

from 

included 

the study which did not fit into the standard fields of the

per cycle 

in the database but are not assessed in this study include

characteristics 

estimates of dishwasher, clothes washer and dryer UECs, floor-space and climate for some space-conditioning and water heaters. These estimates, and capacity figures for refrigerators data were too limited and incomplete analysis. to permit any further

146

EE-RM-94-230 COMMENT 61C

The effect of the historical normalization can be seen in Figures B.2 and B.3. Figure B.2 

shows the distribution of refrigerator UEC estimates. unadjusted for historical efficiency 

trends. Figure B.3 shows the same data. adjusted to 1990 stock efficiencies using the 

historical weighting (but not the quality rating which eliminates outlying data). The effect 

of the nonnalization is twofold -- it reduces the average VEC to its approximate 1990 level, 

and it decreases the standard deviation. as variation due to the age of the different data 

sources is reduced. 

Regi10n 

For the space-conditioning and water heating end-uses. regional climate and price effects 

are strongly correlated with energy use. Data records for these end-uses. are coded with 

both federal and census region codes. Where records are for multi-state regions that .. 

overlap more than one federal or census area. we make a determination based on a 

subjective judgment of the largest popUlation-weighted portion of the data group, and the 

data are assigned to a single region in each coding. Data from some regions are scarcer 

than others due to the vagaries of interest in data collection across regions of the U.S. 

Where the data are sufficient, we compare UEC estimates across regions. 

Quality Rating 

Subjective quality ratings are given to all records on a five-:-point scale, where a one is the 

higheslt ranking and a five represents a zero-weighted study that is included just for the sake 

of docwnentation. We assume that all records with ratings one through four have some 

value, !but that studies that are better designed or more detailed yield more reliable estimates 

of UECs and should be weighted more heavily into aggregate averages. The criteria used 

to detennine the ratings are sample size for metered studies, complexity of methodology. 

reasonableness of ouptput, and level of end-use detail. Quality ratings are assigned only on 

the basis of a record's value within its study type. Comparisons across study types are 

made h:Lter, at the aggregate level. 

. During our analysis. we tried several different types of weighting schemes. However, the 

results varied little between these different fonnulations. In the analysis that follows. we 

weight the records in each disaggregated group according to a factor of (5-QR).Thus. a 

record with a rating of one will be weighted four times as strongly as a record with a rating 

of four, twice as strongly as a three. and four-thirds as strongly as a two. Since these 

weightings are performed within each disaggregated group, a category with only one 

record will not be adjusted for quality. as there is nothing to weight it against These single 

record categories are marked by italics on the tables that follow. 

Other Documentation 

The datlbase contains information on the source of each record which refers to a separate 

database of bibliographical entries. A list of all the sources is included.in the bibliography. 

Additionally. each record is supponed by a "notes" field which holds any additional 

remarkS or other data from the study which did not fit into the standard fields of the 

database. Entries that are included in the database but are not assessed in this study inClude 

per cycle estimates of dishwasher. clothes washer and dryer UECs. floor-space and climate 

charactedstics'for some space-conditioning estimates. and capacity figures for refrigerators 

and watt!r heaters. These data were too limited and incomplete to permit any further . analysis. . . 

146 
Figure B.2. Distribution of Unweighted "ALL" Class Refrigerator UECs

Average=1395, Standard dev.=378

10 1 ,-----_________ - -

E 8

O

6 ...... .

o 8 .* - - -........... ..- .- ....... 1 1 -

0 . I s. I §! - § L -_

0

2 OO- 2 -l'--e 

'7 -------- __ z

n crv . , ,

I' UEC Rangc (kWh/yr)

Figure BJ. Distribution of "ALL" Class Refrigerator UECs -. Historical Weighting

147

EE-RM-94-230 COMMENT 61C

Figure Bol. Distribution of Unweighted "ALL" Class Refrigerator UECs 

Average=139S. Standard dev.=378 . 

10 .,..M __________ M _____ _ 

4 

2 

0 

~ 8 00 8 

Q\ § 8 8 8 8 8 § 8 8 8 § § - M ~ ~ 11"\ ,... 00 Q\ I . . - - - - - - - - - - ("of 

- - -. . 0 I 0 0 0 0' 0 0' 0 0 ("of $ 0 0 - 8 ,... 00 Q\ 

0 -

0 -0 0 0 0 - - - ~ 

- '0 - -

o· 0 - - ("of M ~ 11"\ f' 00 Q\ 

- - - - - - - - - - UEC Range (kWhlyr) 

Figure B.3. DlstlrJbutJon of "ALL" Class Refrigerator UECs·· Historical Weighting 

Average=1160. Standard dev.=280 

... 

.8 

~,2 

0 

8 8' 8 § 8 8 8 8 8 ~ 8 8 8 §' r-;- oc Q\ § - N .M 'o:r 11"\ ,... 00 Q\ 0 0 - - - - - - - - .- - N - - - • I I I 0 I' 0 0 0 0 • N $ 0 

f' 00 

0 

~ - 8 - 0 - 0 - 0 - - -

0 0 ,~ - -0 -

0 -

0 

- N M ~ 11"\ ,... 00 Q\ 

- - - - - - - - UEC Range (kWblyr) 

147 
OVERVIEW OF DATA SOURCES

In all the UEC database contains more than 1300 separate records of UEC estimates taken

from 98 different sources (see Table B.2). The attached bibliography lists the data

sources The largest contributors are two UEC comparison studies from the Electric Power

Research Institute (EPRI), each of which provides several hundred records of national and

regional conditional demand and engineering estimates. National average space heating,

cooling and water-heating UECs also include the conditional demand estimates made over

several years for the Residential Energy Consumption Survey (RECS) by the U.S. DOE

Energy Information Administration (EIA).

The widest range of UEC values in almost every end-use comes from conditional demand

studies, where estimates frequently vary by as much as a factor of 5 or 10 within the same

end-use. The most extreme of these estimates represent outliers and are almost certainly the

results; of flawed statistical methodology and hidden variables. For example, the highest

estimate of 1132 kWh/yr for microwave oven use would represent about 3 hours of on

time, every day of the year, for a typical 1000W microwave -- a high usage level for any

household and patently absurd for a regional average. It is likely in this case that

microwave consumption is affected by an income correlation or other hidden variable

which has not been otherwise accounted for in this particular regression analysis.

Appliance sub-metering may be the ideal method for obtaining accurate end-use data for

simple home appliances. Metering studies are expensive undertakings, however, and tend

to be performed only rarely, limiting the quantity and sample size of the available data.

Metered data in the database are predominantly from the Bonneville Power Administration

(Pratt et aL 1989), Pacific Gas and Electric Co. (Brodsky et al. 1986), and Consumers

Power Company (1984) studies.

Industry data in the database come from trade association and manufacturer reports.

Industry data represent the best information we have about the state of new equipment

entering the market, since these data are typically derived from standardized appliance

testing procedures, performed identically on each manufacturer's product line. As

estimates of actual energy use in real households, standardized testing procedures are

probably highly artificial (see Meier and Heinemeier 1990 and Lambert Engineering, Inc.

1990). However, because usage variation is controlled for by the testing procedure,

industry estimates are extremely useful for tracking equipment efficiency over time, as we

have employed them in the normalized historical weightings.

National forecasting models tend to be very complete, providing a high level of regional

and vintage segment detail, but often contain data that are at best only second-hand. We

include database records for some end-uses from existing residential demand forecasting

models and projects, including the work of LBL (LBL-REM), EPRI (REEPS version 2.0),

EIA (PC-AEO), the Gas Research Institute (GRI), EPA (EGUMS), and others. Model

data can often be limited by data manipulations and hidden assumptions. EGUMS, the

EPA emissions forecasting study, for example, uses appliance UECs that are averaged

together from a small arbitrary sample of utility and laboratory studies, uncorrected for

differences in appliance class, data year, and housing vintage.. We include other data of

this type., where several different estimates have been aggregated together to arrive at a

model input, in the model category.

By definition, UEC records from utility estimates are not well documented. The figures

range from simple guesses based on home auditing experience to more explicit calculations

of average equipment wattages and usage levels, but are most often presented for use by

148

EE-RM-94-230 COMMENT 61C

OVERVIEW OF DATA SOURCES 

In all. the UEC database contains more than 1300 separate records of UEC estimateS taken 

from 98 differeni sources (see Table B.2). The attached bibliography lists the data 

sources. 'The largest contributors are two UEC comparison studies from the Electric Power ~t.rCh Institute (EPRI), each of which provides several hundred records of nationaI and 

regiollal conditional demand and engineering estimates. National average space heating, 

COOlhlg and water.;.heating UECs also include the conditional demand estimates made over 

sever.u years for the Residential Energy Consumption Survey (RECS) by the U.S. DOE 

Energy Infonnation Administration (EIA). 

The ",idest range of VEC values in almost every end-use comes from conditional demand 

studies, where estimates frequently vary by as much as a factor of 5. or 10 within the same 

end-use. The most extreme of these estimates represent outliers and are almost certainly the 

results of flawed statistical methodology and hidden variables. For ,example, the' highest 

estimate of 1132 kWhlyr for microwave oven use would represent. about 3 hours of on 

time, t~very day of the year, for a typical lOOOW microwave -- a high usage level for any 

household and patently absu'rd for a regional average. It is likely in this case that 

microwave consumption is affected by an income correlation or other hidden, variable 

which has not been otherwise accounted for in this particular regression analysis. 

Appliance sub-metering may be the ideal method for obtaining accurate end~use data for 

simple home appliances. Metering studies are expensive undertakings, however, and tend 

to be perfonned only rarely, limiting the quantity and sample size of the 'available data. 

,Metered data in the database are predominantly from the Bon~eville Power Administration 

(Pratt c~t aL 1989), Pacific Gas and Electric Co. (Brodsky et al. 1986), and Consumers 

Power Company (1984) studies. . 

Industly data in the database come from trade association and manufacturer reports. 

Industry data represent the best infonnation we have about the state of new equipment 

entering the market. since these data are typically derived from standardized appliance 

testing procedures, performed identically on each manufacturer's product line. As 

estimates of actual energy use in real households, standardized testing procedures are 

probably highly artificial (see Meier and Heinemeier 1990 and Lamben Engineering. Inc. 

1990). However, because usage variation is controlled for by the testing procedure, 

industry estimates are extremely useful for tracking equipment efficiency over time, as we 

have employed them in the nonnalized historical weightings. ' . 

,. 

National forecasting models tend to be very complete, providing a: high level of regional 

and vintage segment detail, but often contain data that are at best only second-hand. We 

include database records for some end-uses from existing residential demand forecasting 

models and projects, including the work of LBL (LBL-REM). EPRI (REEPS version 2.0), 

EIA (pC-ABO), the Gas Research Institute (ORI), EPA (EGUMS), and others. Model 

data can oflten be limited by data manipulations and hidden assumptions. EOUMS, the 

EPA emissiions forecasting study, for example, uses appliance UECs that are averaged 

together ffOim a small arbitrary sample of utility and laboratory studies, uncorrected for 

differences in appliance class,.data year, and housing vintage., We include other data of 

this type:, where several different estimates have been aggregated together to arrive at a 

m'odel input, in the model category. . " 

By defmition, UEC records from utility estimates are not well documented. The figures , range from simple guesses based on home auditing experience to more explicit calculations , of average equipment wattages and usage levels. but are most often presented for use by 

148 
the residential consumer, in as simplistic a form as possible, with little or no reference to

data methodology. Utility estimates come from Edison Electric Institute, Memphis Light, Gas and Water Division, Public Service Company of New Mexico, Pacific Gas & Electric

Company, and many other utilities and related agencies.

Engineering studies are often good estimators of UECs, but may suffer from unknown

variables used in the calculations, particularly estimates of usage. Simulations of building

heating and cooling energy consumption are examples of UEC sources in the engineering

category. Also included are estimates of energy consumption for new product designs

such as those used in the U.S. DOE appliance standards procedure. Engineering models

are perhaps the simplest method for determining UECs for new vintage appliances,

equipment, and buildings. However, as previously noted, UECs for new vintages are not

included in this analysis.

RESULTS

In the analysis, we separate the end-uses into space conditioning and non-space

conditioning. We assume that, based on the degree of variability within the data, variations

in UEC across climates will not be apparent for simple residential appliances. Therefore,

non-space conditioning end-uses are analyzed only by study type and house type. Space conditioning end-uses are analyzed by region, and by house type and study type for

national average heating and cooling estimates. Water heating is analyzed as both a space

conditioning and a non-space conditioning end-use; that is, both with and without regional

disaggregation.

Non-Space Conditioning UECs

Non-space conditioning records were analyzed by study type and by house type. As

shown in Table B.5, information on house type for the non-space conditioning UECs is

scarce outside of the single-family and all/not-specified categories. With the possible

exception of water heaters, there is not enough data to make any meaningful statement

about the relationship of UEC to house type for these end-uses. Differences between

single-family and all/not-specified are small, in general, and mostly reflect underlying

differences in study type and data quality, rather than actual phenomena related to house

type. In general, only the most detailed studies produce separate UECs for single-family

houses. This is readily apparent for the freezer sub-classes (upright auto-defrost and

manual defrost), where only the best conditional demand studies produce estimates for

single-family dwellings, while other, less-detailed studies (including many utility estimates)

generate "all" house type UECs for these classes.

For both gas and electric water heaters, there are enough estimates of multi-family and

manufactured home UECs to observe a pattern. However, all of these records come from

various years of RECS conditional demand analyses. While the data show expected trends - that water heating energy use is greater in single-family homes because of higher number

of occupants, etc. -- the RECS estimates are lower, on average, than other data in the

database, suggesting that the RECS methodology may produce lower UEC estimates for all

house types. Furthermore, water heating estimates for the "all" house type category tend to

run higher than the estimates for specific house types, again probably due to differences in

data quality and study type. Because of the small climate dependence of water heater

energy use, this comparison is repeated later in Table B.8 with only the national-level

estimates.

149

EE-RM-94-230 COMMENT 61C

the residential consumer. in as simplistic a fonn as possible. with little or no reference to 

data methodology. Utility estimates come from Edison ElectricInstitute. Memphis Light. 

Gas and Water Division. Public Service Company of New Mexico, Pacific Gas & Electric 

Company. and many other utilities and related agencies. 

Engineering studies are often good estimators of UECs. but may suffer from unknown 

variables used in the calculations, particularly estimates of usage. Simulations of building 

heating and cooling energy consumption are examples of UEC sources in the engineering 

category. Also included are estimates of energy consumption for new product designs 

such as those used in the U.S. DOE appliance standards procedure. Engineering models 

are perhaps the simplest method for determining UECs for new vintage appliances, 

equipment. and buildings. However, as previously noted. UECs for new vintages are not 

included in this analysis. 

RESULTS 

In the analysis. we separate the end-uses into space conditioning and non-space 

conditioning. We assume that. based on the degree of variability within the data, variations 

in VEC across climates will not be apparent for simple residential appliances. Therefore. 

non-space conditioning end-uses are analyzed only by study type and house type. Space 

conditioning end-uses are analyzed by region. and by house type and study type for 

national average heating and cooling estimates. Water heating is analyzed as both a space 

conditioning and a non-space conditioning end-use; that is. both with and without regional 

disaggregation.· 

Non-Space Conditioning UECs 

Non-space conditioning records were analyzed by study type and by house type. As 

shown in Table B.S. infonnation on house type for the non-space conditioning UECs is 

scarce outside of the single-family and all/not-specified categories. With the possible 

exception of water heaters. there is not enough data to make any meaningful statement 

about the relationship of UEC to house type for these end-uses~ Differences between 

single-family and alUnot-specified are small, in general. and mostly reflect underlying 

differences in study type and data quality. rather· than actual phenomena related to house 

type. In general. only the most detailed studies produce separate UECs for single-family 

houses. This isreadiIy apparent for the.freezer sub-classes (upright auto-defrost and 

manual defrost). where only the best conditional demand studies produce estimates for 

single-family dwellings. while other, less-detailed studies (including many utility estimates) 

generate "all" house type UECs for these classes. 

For both gas and electric water heaters. there are enough estimates of multi-family and 

. manufactured home UECs to observe a pattern. However. all of these records come from 

various years of RECS conditional demand analyses. While the data show expected trends 

- that water heating energy use is greater in single-family homes because of higher number 

of occupants. etc. -- the RECS estimates are lower. on average. than other data in the 

database. suggesting that the RECS methodology may produce lower UEC estimates for all 

house types. Furthermore. water heating estimates for the "all" house type category tend ~ 

run higher than the estimates for specific house types. again probably due to differences 10 

data quality and study type. Because of the small climate depeqdence of water heater 

energy use, this comparison is repeated later in Table B.8 with only the.national-Ievel 

estimates. 

149 
Table BS. Non-Space Conditioning UECs by House Type

Electric UECs In kWh/yr. gas UECs in MMBtu/yr

All numbersfor stock vintage normalized to 1990 efficiencies and averaged using (5-QR) weighting (except italicized)

HOUSETYPE3.

Al/ Not Specified Single-Family Multi-Family Manufactured Home

End Use Class N UEC N UEC N UEC N UEC

Black/Whie TV ALL 22 198 3 164

Electric Cooking -- 54 808 18 919 I 501 1 565

oven 3 482 1 334

rangetop 6 581 1 322

Clotheswaser total 12 678 3 469

motor 24 127 4 93

El. Clothes Dryer - 48 1047 17 906 1 775 1 880

Dishwasher total 17 1111 8 1101

motor 31 445 6 259

Freezer ALL 41 1059 10 952 1 877 1 1000

UAD 11 1596 4 1119

MND 10 1051 5 691

Lighting -- 10 1016 1 4405

Microwave --- 22 202 6 234

Refrigerator ALL 37 1149 13 1195 1 1100 1 1150

TAD 26 1458 2 1218

MND 10 988 2 766

El. Water heater -- 64 3867 27 3835 3 2285 3 3013

Color TV ALL 32 580 7 756

Gas Cooking -- 7 6.22 1 5.00 1 4.24 1 4.70

Gas Dryer --. 6 4.15 1 4.00 1 3.31 1 3.70

Gas Water heater -- 3 28.45 3 24.67 3 17.37 3 21.25

I .

EE-RM-94-230 COMMENT 61C

\.A -

o 

Table B.5. Non-Space Conditioning UECs by House Type 

Eleerr;c UEC! Iii kWhryr. gal UECs in !rIMB""yr 

All numbers for stock v;ntagenormalized to 1990 efficiencies and averaged using (5.QR) weighting (except italicized) 

~ 

HOUSE TYPE ' , ! 

Alii Not Specifie~ Single·Family Muhi.Family ~anufaclured Home I 

End Usc Class N UEC N UEC N UEe N UEC 

BlackIWbitt 1V ALL 22 198 3 164 

Electric Cooking _. 54 808 18 919 1 501 1 565 

,oven 3 482 1 ' 334 

rangelop 6 581 1 322 

Clotheswasber total 12 678 3 469 

motor 24 127 4 93 

EI. Clothes DrYer - 48 1047· 17 906 I 775 1 880 

Disbwasber total 17 1111 8 '1101 

motor 31 445 6 259 

Freezer ALL 4. 1059 10 952 I 877 I 1000 

UAD 11 1596 4 1119 

MND 10 1051 5 691 

Ugbdng .-- 10 1016 I 4405 

Microwave ._- 22 202 6 234 

Refrigerator ALL 37 1149 13 1195 I 1100 I 1150 

TAD 26 .1458 2 1218 

MND 10 988 2 766 

EI. Water beater --- 64 3867 27 3835 3 2285 3 3013 

Color1V ALL 32 580 7 756 

GasCooldng -- 7 6.22 1 5.00 I 4.24 1 4.70 

Gas Dryer _e. 6 4.15 . I 4.00 1 3.31 1 3.70 

Gas Water healer -- , )3 28.45 3 24.67 3 17.37 3 21.25 
UEC values specific to each house type are not readily available from the database for nonspace conditioning end-uses. However, the different study types are well populated and

provide an interesting avenue for comparison. Figures B.4 to B.7 show the range of UEC

estimates for three of the end-uses with large numbers of database records -- cooking,

refrigeration, and water heating -- broken down by study type. Our weighted averages,

which include both historical and quality rating factors, are shown by the mid-box

crossbars and numerical labels. The large size of the range boxes demonstrates the wide

variations that exist in UEC estimates, while the difference between averages shows the

biases of the different methodologies. Table B.6 shows the results of the same analysis in

tabular form for all non-space conditioning UECs. The final column averages together

records of different study types, with an additional "Study Type Quality Rating" factor

assigned to each study type on the basis of its apparent consistency and reliability for the

given end-use. The result is a "Best Weighted Average" UEC for each end-use, which

makes the best use of the available data. In the figures, the estimates are compared with the

appropriate data estimated in the LBL REM. The results for each end-use are discussed

below.

Refrigerator data do not show great variability across study types, although metered data

are generally higher than other sources. Sample size may be an important issue here,

because of the differing UEC levels of the refrigerator sizes and classes. For example, a

small metered sample might contain a greater proportion of side-by-side or through-the- door featured models, which have considerably higher UECs. Utility estimates for the

refrigerator classes are higher than other figures, perhaps because they have not been

keeping pace with the rapid improvements in new unit efficiencies. We calculate a "best"

1990 stock UEC for refrigerators of 1145 kWh/yr.

Both black-and-white and color TV UECs show good consistency across study types,

although conditional demand figures for color TVs may be slightly higher than for other

study types. The weighted-average estimates are about 200 kWh/yr for black-and-white

and 500 kWh/yr for color TVs. These averages are considerably higher than other

estimates for these end-uses (Meier and Heinemeier 1990, US DOE 1989a) that have

previously been used to develop model inputs. Most of the data we consider comes from

conditional demand studies, which may assign too much consumption to the television enduse, or, on the other hand, may be capturing real usage habits of television owners.

Electric cooking estimates vary widely, with almost a factor of two difference between

metering studies at the low end and engineering estimates at the high end. There are wide

discrepancies in the definitions of the end-use that make comparison between studies

difficult For example, metering studies routinely include only cooktops and ovens, with

other kitchen appliances excluded from measurement, while conditional demand studies

and engineering models often base UECs on available figures for the whole kitchen circuit.

There is even disagreement in the literature over the word "range," which can mean either

the rangetop elements alone or the whole oven and cooktop combination, depending on the

study. However, the weighted average for the cooking end-use, about 800 kWh/yr, is in

good agreement with the sum of the oven and cooktop figures.

Clothes washer estimates are in fairly close agreement across study types. About 100

kWh/yr goes to motor energy and another 500 to hot-water energy, assuming electric water

heat Clothes dryer UECs are also very consistent at about 1000 kWh/yr across all study

types.

151

EE-RM-94-230 COMMENT 61C

UEC values specific to each house type are not readily available from the database for nonspace conditioning end-USes. However, the different study types are well populated and 

provide an interesting avenue for comparison. Figures B.4 to B.7 show the range ofUEC 

estimates for three of the end-uses with large numbers of database records -- cooking, 

refrigeration, and water heating -- broken down by study type. Our weighted averages, 

which include both historical and quality rating factors, are shown by the mid-box 

crossbars and numerical labels. The large size of the range boxes demonstrates the wide 

variations that e~st in UEC estimates, while the difference between averages shows the 

biases of the different methodologies. Table B.6 shows the results of the same analysis in 

tabular fonn for all non-space conditioning UECs. The final column averages together 

records of different study types, with an additional "Study Type Quality Rating" factor 

assigned to each study type on the basis of its apparent consistency and reliability for the 

given end-use. The result is a "Best Weighted Average" UEC for each end-use. which 

makes the best use of the available data. In the figures, the estimates are compared with the 

appropriate data estimated in the LBL REM. The results for each end-use are discussed· 

below. 

Refrigerator data do not show great variability across study types, although metered data 

are generally higher than other sources. Sample size may be an important issue here, 

because of the differing UEC levels of the refrigerator sizes and classes. For example. a 

small metered sample might contain a greater proportion of side-by-side or through-thedoor featured models, which have considerably higher UECs. Utility estimates for the 

refrigerator classes are higher than other figures, perhaps because they have not been 

keeping pace with the rapid improvements in new unit efficiencies. We calculate a "best" 

1990 stock UEC for refrigerators of 1145 kWh/yr. 

Both bbck-and-white and color TV UECs show good consistency across study types, 

although conditional demand figures for color TVs may be slightly higher than for other 

study types. The weighted-average estimates are about 200 kWhlyr for black-and-white 

and 500 kWhlyr for color TVs. These averages are considerably higher than other 

estimates for these end-uses (Meier and Heinemeier 1990, US DOE 1989a) that have 

previously been used to develop model inputs. Most of the data we consider comes from 

conditional demand studies, which may assign too much consumption to.the television enduse, or, on the other-hand, may be capturing real usage habits of television owners. 

Electric cooking estimates vary widely, with almost a factor of two difference between 

metering studies at the low end and engineering estimates at the high end. There are wide 

discrepancies in the definitions of the end-use that make comparison between studies 

difficult For example, metering studies routinely include only cooktops and ovens, with 

other kitchen appliances excluded from measurement, while conditional demand studieS 

and engineering models often base UECs on available figures for the whole kitchen circuit 

There is even disagreement in the literature over the word "range," which can mean either 

the rangetop elements alone or the whole oven and cooktop combination. depending on the 

study. However. the weighted average for the cooking end-use. about 800 kWh/yr, is in 

good agreement with the sum of the oven and cooktop figures. 

Clothes washer estimates are in fairly close agreement across study types. About 100 

kWh/yr goes to motor energy and another 500 to hot-water energy, assuming electric water 

heaL Clothes dryer UECs are also very consistent at about 1000 kWhlyr across all study 

types. 

151 
Table 1.6. Non-Space Conditioning UECs by Study Type

Electric UECs in kWh/yr, gas UECs in MMBl/yr

All numbersfor stock vintage normalized to 1990 efficiencies and averaged using (5.QR) weighting (except italicized)

STQR=Study Type Quality Rating (l=highest. 5=lowest)

Best Weighted Average= £ N*UEC*(5-STQR)

£ N*(5-STQR)

STUDY TYPE Best Weighted

Metered/Monitored Conditional Demand Engineering Model/Aggregate Utility Estimate Average

End Use Class N UEC SQR N UEC STR N UEC STQR N UEC STQRC N CSTEC R 

Black/White TV ALL 15 194 -2 2 218 1 7 181 1 I 182 2 192

Electric Cooking --- 6 631 1 49 850 2 4 1185 3 10 716 1 4 1056 3 816

oven 1 334 1 1 346 1 2 572 4 386

rangetop 3 516 2 1 299 3 1 399 1 2 705 4 485

Clotheswasber total 12 601 3 1 631 1 1 575 1 1 944 4 612

motor 6 94 1 8 163 5 4 94 1 9 106 1 3 105 1 100

El. Clothes Dryer --- 4 927 1 44 1030 1 2 977 1 13 930 .1 4 981 1 1000

Dishwasher total .18 997 1 2 1182 1 5 1343 3 1052

motor 2 128 1 22 522 5 2 242 2 9 284 2 3 361 5 247

Freezer ALL 2 1227 1 37 1008 1 3 1112 1 10 1021 1 1025

UAD 1 1512 1 8 1413 1 2 1433 1 4 1591 3 1451

MND 2 886 1 .8 882 1 1 1050 2 4 1043 2 927

Lighting --- 1 4405 5 2 908 1 1 1124 1 5 998 1 2 1068 1 1007

Microwave --- 3 96 1 18 249 5 4 144 2 3 179 3 132

Refrigerator ALL 5 1333 3 30 1155 1 4 1127 1 11 1062 1 1145

TAD 10 1248 4 9 1333 1 2 1386 1 7 1706 4 1352

MND 6 891 1 6 1023 4 917

El. Water heater --- 11 4437 1 59 3363 3 6 3828 2 12 4062 i 8 5222 5 3754

Color TALL ___33 557 3 2 431 2 6 40 2 6 440 3 509

Gas Cooking - 1 .7:1 6 5.53. 2 3 5.73 2. 5.61

asDer -- 1 4.04 7 3.83 2 1 4.45 3 3.91

Gas Water beater 1 31.60 1 9 21.99 3 1 22.50 1 8 23.80 1 3 38.53 5 23.69

EE-RM-94-230 COMMENT 61C

1 

! ' 

Table B.6. Non·Space Condilloning UECs by Study Type 

Electric UECs in kWh(yr. gas UECs in MMBtulyr 

All numbers/or srock vintage nOnnlJliled 10 1990 effi~iencies and averaged using (5'QR) weighting (excepl italicized) 

STQR=S,udy Type Quality Ra,ing (1 =highesl. 5=lowest) 

Best Weigh,ed Average = I N·UEC~(5·STQR) 

I N·CS·STQR) 

STUDY TYPE 

Metered/MonilOred Condilional Demand Engineering 

End Usc Class N UEC STQR N UEC STQR N UEC STQR 

BlackIWbile 1V ALL 15 194 ,2 2 218 1 ' 

Eleclric Cooking ... '6 631 1 49 850 2 4 1185 3 

oven 1 ' 334 1 

rangetop 3 516 2 I ,299 3 

Clotbeswasber tolal 12 601 3 1 631 I 

motor 6 94 I 8 163 5 4 94 1 

E. Clothes Dryer ... 4 927 I 44' 1030 1 2 971 1 

Dishwasher lOW ' ,18 997 I 

motor 2 128 1 22 522 5 2 242 2 

Freezer ALL 2 1227 I 37 1008 I 3 1112 1 , , UAD 1 1512 1 8 1413 1 

MND ,2 886 1 ,8 882 1 

Ugbting .-. 1 4405 5 2 908 1 I lJU I' 

Mlaowave ... 3 96 1 18 249 S 

Refrigerator ALL 5 1333 3 30 1155 I 4 1127 I 

TAD 10 1248 4 9 1333 I 

MND 6 891 I 

EI. Waler beater ... 11 4437 1 5,9 3363 3 6 3828 '2 ' 

Color1V ALL 33 551 3 2 431 2 

OasCooJdng - 1 5.71 1 • 

OasDtyer ... J 4.04 I 

Gas Water beater - 1 31.60 ' 1 9 21.99 3 1 22.50 1 

Modell Aggregale 

N VEC STQR 

7 181 1 

10 716 1 

1 346 1 

I 399 I 

I 575 1 

9 106 I ' 

13 930 ,I 

2 1182 1 

9 284 2 

10 1021 1 

2 1433 1 

I 1050 2 

5 998 1 

4 144 2 

11 '1062 1 

,2 1386 1 

12 4062 j 

6 407 2 

6 5.53 ,2 

7 3.83 2 

8 23.80 1 

Best Weighled 

UtililY Estimate' Average 

N UEC STJm 'UEC 

1 182, 2 192 

4 1056 3 816 

2 572 4 386 

2 70S 4 485 

I 944 4 612 

3 lOS 1- 100 

4 981 1 1000 

5 1343 3 1052 

3 361 5 247 

1025 

4 1591 3 1451 

4 1043 2 927 

2 1068 I 1007 

3 179 3 132 

1145 

7 1706 4 1352 

6 1023 4 917 

8 5222 5 3754 

6 440 3 I 509 

3 5.73 2, 5.61 

1 4.45 3 3.91 

3 38.53 5 23.69 
Figure BA. Electric Cooking UECs by Study Type - Range and Weighted Average

N=6 N-49 N-=4. N=10 N=4

2500

2000-

1500 850 11n 85 - -

1056

1000 ------- - - 716

500

0 - i I t

Figure B.S. Refrigerator UECs by Study Type - Range and Weighted Average

.N=5 N=30 N=4 N=ll

3500

3000 000....... ________ ... __ .. _. _.___-

2500

1.155 . 2.000 1

l 55 ----------- - I

3 1333

-v 1500 1127 - ^ 11 S00 A ~1062" 

-1145- 1227 1000 __

500

0I I I I

Metered Conditional Engineering Model/ Weighted LBL-REM

Demand Aggregate Average

fthe weighted average as less than the lowes input value due to the strong historical

'eficency weighing of the refrigerator end use

Metered Conditional Engineering Modcl/ Utility Weighted LIL-REM

Demand Aggregate Average

' e,

EE-RM-94-230 COMMENT 61C

FigureBA. Electric Cooking UECs by Study Type - Range and Weighted Average 

-------------------------------------------------------, 

N=6 N=49 N::i:l0 N=4 

2500 .~ .. ~~--~ .. -.-..... -... -... -.--.--------.---.-.--------.-... -..... -.-~.---.. -

2000 i------ --;::==::;-...;..--'-------_ .... _._----

1500 ,----. 850 .. _- 1185 ... -.-,-----.,:--...,...-.--'-----.-'-........... -

10S6 

1000 '-'---"'-"-' ,"-- 716 .. - ·-:·-_··-·········-1011--

-816-

SOO"~ .. _. ___ ._-.L.. __ ~. __________ •••. _.~_. __ • __ ._ 

o +-----~~-----r------+_------r------+------~--~~ 

Metered Conwtio~ Engmcering 

Demand 

ModcU' 

Aggregate 

Utility Weigbted LBL.REM 

Average 

figure E'.5. Refrigerator UECs by Study Type - Range and Welgbted Average 

. N=S N=30 

3500 ~-

N=4 ,N=11 

,3000 

2'.500 ---------------.. ~.--.--

!:. 2,000 nss ~ 

~ .loC 

1333 

1500 -

1000 -_.-----_. 

soo ---~-------.----.--.----- -------_. _._-_.---' 

o +-------~-------+--------~------~--~--~------~ 

,Metered Conditional ' Eogineering ModeU 

~d Aggrega~ 

Wdgbled 

Average 

LBL-REM 

trhe weiglued average is /eUlMlllhe lowest input vallll! dill! 10 1M strong historical 

'ejflCiencyweighlilig o/tIre re/rigeralOrmd lISe 

. ~ ... 
Electric Water Heater UECs by Study Type - Range and Weighted Average

Figure B.7. Gas Water Heater UECs by Study Type - Range and Weighted Average

N=1 N=9 N=I

60

50 ___

40

--31.6-- 30

--22.5--

0 I I I

20 .........

2

Metered Conditional Engineering

Demand

N=11 N=59 N-6 N=12 N=8

N= 11. N=59 N=6 N=12 N=8

9000 . ................................. ...............

000 -__ ._ .............. . 4062

3363 3000 .... . ... ....

7300C0 _ _ „ . ................ ___. . ......... ._ 5222

2000 - ----

2 00D _.... . ..- ...... ...

1000 _.__ _.... .. .. . ............. ................ . ..... ...... . ............

0 t I - I

Metered Conditional Engineering Model/ Utility Weighted LBL-REM

Demand Aggregate Average

N=8 N=3

Model/ Utility Weighted LBL-REM

Aggregate Average

__

Figure B.6.

EE-RM-94-230 COMMENT 61C

B.6 .. Electric Water Heater UECs by Study Type - Range and Weighted Average 

N=11 N=59 N=6 N=12 N=8 9000 ~--.-......... -.................. -.. , .................................................... -... -......... .----, .. _ .... -.................................... -.... _ .. -

8000 ._.-.. -.. -..... -.... - .... -... -.............................................................. _ .. _. 

7000 5222 

6000 - _ .. __ ._.-.... _----_. __ .--_ .. .,.-----,. ._---_ .. _ ... _.-._-

4062 

3363 .... -~ ....... .r----i 

3000 f--... _-_ .... -.... -. 

2000 .--.-.--.. -.-.~_...,l .................................... -.... _ .... -.. - ... -..... - .. --..... - .. --.. _ .. --.................. -.... - .. --.-

1000 ._._-.... _ .... -.. --.---.. _ ....... _ ............. _ ... -......... , .. _ ................ -.... -... -............... -._ ... _ ............................. -.... -.-.-_ .. -

Figure B.1. 

o +-------~------~------~------~------_4----~--~----~ 

Metered Conditional Engineering ModcY 

Dcm:md Aggregate 

Utility Weighted LDL-REM 

Average 

Gas Water Heater UECs by Study Type - Range and Weighted Average 

N=l N=9 N=l N=8 N=3 

60 ~-------.---------.-.-.---.. - ... ------.. - .. --.. -----

50 +---'-.. -----..,...---.-.---.... --.---.-.--

38.5 

40 -t-.---.----.------.. -.-.----.- .. ---........... -... ----- ...--~ 

i 30 r-=Jl:.~:':_ ... --.--·--... -... -... -... ~ ...... -... ---.... -........ -_._.-_.-..... -...... -_.-.-

~ 20 ~-d--~~~--·6----------::~--.-;:jii:r::-- 10 +---------.--,------------ -------~-.-.----.. 

o +-----+------~------~------r_------~----~~----~ 

Metered Conditional Engineering ModeU Utility Weighted LBL-REM 

De~d Ag~gare Average 
There is considerable disagreement about dishwasher energy use, particularly in the partial

UEC for motor energy. Here the disagreement between metered and conditional demand

estimates is especially striking (a factor of four). Conditional demand is a very crude tool

for separating the motor and hot-water portions of dish- and clothes washer energy use,

however, and it is reasonable to assume that the motor energy here is higher than for other

study types. In fact, many conditional demand studies do not distinguish water heating

from mechanical energy at all, in which case the estimates often appear as extreme outliers

to the motor energy range including, quite obviously, the estimate of 2562 kWh/yr for

dishwasher motors. Actual average energy use by dishwashers is likely to be about 1000

kWh/yr assuming electric water heating, with about 250 kWh/yr going to motors.

Freezers average 1000 kWh/yr, weighted between upright auto-defrost freezers at about

1400 kWh/yr and manual defrost (both upright and chest) freezers, which use about 900

kWh/yr. This split may be important if there is any trend towards one or the other model in

the long-term.

The few existing lighting UEC estimates are quite consistently around 1000 kWh/yr.

Several of these figures represent simple guesses of residential lighting use, such as "ten

100 Watt bulbs x 3 hours a day per bulb x 365 days a year = -1000 kWh/yr". Microwave

figures vary widely, with conditional demand coming in artificially high. Other estimates

all average between 100 and 200 kWh/yr. Both lighting and microwave UECs could be

improved with simple household log surveys, tracking domestic usage patterns over time,

to provide better information on typical lighting and microwave cooking practices in

homes.

Electric water-heating data are well populated for all study types and show some interesting

variation. Conditional demand estimates are lower than the rest of the study population,

showing the deficit left by potentially excessive estimates of dish- and clothes washer

motor use. Neglecting the utility estimates, the remaining study types fall in the 3400 to

4500 kWh/yr range, with some limited variation perhaps due to regional climate. Our

weighted average figure is 3750 kWh/yr.

The gas end-uses are not particularly well represented in the database due to limited end-use

research for gas appliances. However, agreement is fairly good across study types for the

available data. For cooking and clothes drying, most of the estimates are from existing

forecasting models, yet these values are similar to those from other study types. Weighted

average UEC estimates are 5.6 MMBtu/yr for gas cooking and 3.9 MMBtu/yr for gas

clothes dryers.

For gas water heating, the agreement between the conditional demand estimates and model

estimates is good, suggesting UECs used in models are reasonable compared to other

estimates. The slightly lower estimate for conditional demand may reflect the accounting

problems of appliance hot water energy, although the weaker conditional demand studies

(which tend to make this mistake) tend not to study the gas end-uses. The best weighted

average for gas water heating is about 24 MMBtu/yr.

Space Conditioning UECs

For space conditioning UECs, we account for differences in climate and house size by

analyzing the data both by region of the country and house type, as well as by study type.

Ideally, the comparison would be made based on degree days and conditioned floor area of

the building or buildings under analysis. However, few studies outside of RECS or the

EE-RM-94-230 COMMENT 61C

nlere is considerable disagreement about dishwasher energy use. particularly in the partial 

U1EC for motor energy. Here the disagreement between metered and conditional demand 

estimates is especially striking (a factor of four). Conditional demand is a very crude tool 

for separating the motor and hot-water portions of dish- and clothes washer energy use, 

h(]lwever, and it is reasonable to assume that the motor energy here is higher than for other 

sbldy types. In fact, .many conditional demand studies do not distinguish water heating 

fn)m mechanical energy at all, in which case the estimates often appear as extreme outliers 

to the motor energy range including. quite obviously. the eStimate of 2562 kWhlyr for 

dishwasher motors. Actual average energy use by dishwashers is likely to be about .1000 

kWhlyr assuming electric water heating. with about 250 kWhlyr going to motors. 

Freezers average 1000 kWh/yr, weighted between upright auto-defrost Jreezers at about 

1400 kWhlyr and manual defrost (both upright and chest) freezers. which uSe about 900 

kVnvyr. This split may be important if there is any trend towards one or the other model in 

thc;: long-tenn. . 

Th.e few existing lighting UEC estimates are quite consistently around 1000 kWh/yr. 

. Several of these figures represent simple guesses of residential lighting use, such as "ten 

100 Watt bulbs x 3 hours a day per bulb x 365 days a year = -1000 kWh/yr". Microwave 

figures vary widely • with conditional demand coming in artificially high. Other estimates 

all average between 1 ()() and 200 kWh/yr. Both lighting and microwave UECs could be 

imlproved with simple household log surveys. tracking domestic usage patterns over time. 

to provide better informa~on on typical lighting and microwave cooking practices in 

hOlDes. . 

Electric water-heating data are well populated for all study types and show some interesting 

variation. Conditional demand ·estimates are lower than the rest of the study population. 

showing the deficit left by potentially excessive estimates of dish- and clothes washer 

motor use. Neglecting the utility estimates. the remaining study types fall in the 3400 to 

4500 kWhlyr range, with some limited variation perhaps due to regional climate. Our 

weiighted average· figure is 3750 kWh/yr. . 

The gas end-uses are not panicularly well represented in the database due to limited end-use resc~arc:h for gas appliances. However, agreement is fairly good across study types for the 

available data. For cooking arid clothes drying, most of the estimates are from existirlg fon~ting models, yet these values are similar to those from other study types. Weighted 

·.ave:rageUEC estimates are 5.6 MMBtulyr for gas cooking and 3.9 MMBtulyr fo~ gas 

clolthes dryers.. . ... 

For gas water heating, the agreement betWeen the conditional demand estimates and model 

.estimates is good, suggesting UECs used in models are reasonable compared to .other 

estimates.· The slightly lower estimate for conditional demand may reflect the accounting 

prohlems of appliance hot water energy. although the weaker conditional demand studies 

(which tend to make this mistake) tend not to study the gas end-uses. The best weighted ave)['ag~~ for gas water heating is about 24 MMBtu/yr. 

Sp~lce Conditi.oning UECs 

For space conditioning UECs. we account for differences in climate and house size by 

anallyzing the data both by region of the country and house type, as well as by study type. 

IdeaLlly. the comparison would be made based on degree days and conditioned floor area of 

the building or buildings under analysis. However. few studies outside of RECS or the 
engineering estimates include data on house size and local climate. Thus, we compare

studies by federal region and house type to account for these differences.

Table B.7 shows the break-down of space conditioning UEC estimates by federal (DOE)

region for houses of the all/not-specified house type. Data are primarily from utility conditional demand estimates and are concentrated in a few federal regions due to the

geographic distribution of the utilities which have pursued UEC studies. The South

Atlantic (region 4), Great Lakes states (region 5), Southwest (region 6), and Far West

(region 9) are the best represented in the data. Water heater data are not included here. The

differences between regions in the water heating end-use UEC data are obscured by differences in data quality and study type.

Between regions, a few intuitive, climate-related trends are readily discernible. The

Southern regions (4 and 6) have the highest air-conditioning use for all classes of

equipment, while the Northern regions (1, 2, 3, 5, 7, 8 and 10) are much lower. Region

9, comprised of California, Arizona and Nevada, is heavily weighted towards Northern

California by the preponderance of data from Pacific Gas and Electric, and thus falls in line

with the milder, Pacific climate. Heating figures, conversely, are highest in the North and

lowest in the South and Northern California. Gas heating data at this level of

disaggregation are scarce and do not entirely support the expected trends. In general, there

are not enough records to create definitive results by region.

Estimates of national average household space conditioning and water heating energy use

are tabulated by house type in Table B.8. The results for central air conditioning and gas

space heating are presented in Figures B.8 andB.9. These estimates are dominated by

national conditional demand estimates (e.g. RECS), survey results (e.g. American Gas

Association), model inputs, and engineering estimates. For all heating and cooling

systems, multi-family consumption levels are roughly half those in single-family dwellings. This is a result of the smaller exterior surface area in apartments and multiplexes and the smaller amount of conditioned space in each unit Manufactured home space

conditioning energy use is generally between single- and multi-family levels. For

comparison with the "all" house type category, we have created average UECs from the

house type data, based on heating and cooling type shares in the last column. Aggregations

of the all/not-specified house type UECs agree with averages of the house type-specific

records except for the electric heating end-use categories. National average water heater

UECs by house type come solely from the RECS regression studies and are quite low

compared to the national all/not-specified house type figures, which come from a wider

variety of studies.

The gas end-uses, space heating and water heating, give consistent results across house

types. The national average for gas space heating in the "all" house category is 67.2

MMBtu/yr, which is almost identical to the population-weighted average across house

types of 68.5 MMBtu/yr. The comparison for water heating is similar. As with electric

water heating, the national average gas water heating UECs for particular house types are

from the RECS conditional demand estimates for various years.

We also aggregate national average UECs across technology types for air conditioning and

electric space heating to calculate average UECs by fuel. There is agreement between these

summations and the data collected under the "all" technology class for air conditioning. The results for electric heating are not as consistent, however, and further highlight the

overall inconsistencies among UEC estimates for electric space heating in the database.

These are summed across house types at the bottom of Table B.8.

EE-RM-94-230 COMMENT 61C

engineering estimates include data on house size and local climate. Thus, we compare 

studies by federal region and house type to account for these differences. 

Table B.7 shows the break-down of space conditioning UEC estimates by federal (DOE) 

region for houses of the allInot-specified house type. Data are primarily from utility 

conditional demand estimates and are concentrated in a few federal regions due to the 

geographic distribution of the utilities which have pursued UEC studies. The South 

Atlantic (region 4), Great Lakes states (region 5), Southwest (region 6), and Far West 

(region 9) are the best represented in the data. Water heater data are not included here. The 

differences between regions in the water heating end-use VEC data are obscured by 

differences in data quality and study type. 

Between regions, a few intuitive, climate-related trends are readily discernible. The 

Southern regions (4 and 6) have the highest air-conditioning use for all classes of 

equipment, while the Northern regions (1, 2, 3, 5, 7, 8 and 10) are much lower. Region 

9, comprised of California, Arizona and Nevada, is heavily weighted towards Northern 

California by the preponderance of data from Pacific Gas and Electric, and thus falls in line 

with the milder, Pacific climate. Heating figures, conversely, are highest in the North and 

lowest in the South and Northern California. Gas heating data at this level of 

disaggregation are scarce and do not entirely support the expected trends. In general, there 

are not enough records to create defmitive results by region. 

Estimates of national average household space conditioning and water heating energy use' 

are tabulated by house type in Table B.8. The results for central air conditioning and gas 

space heating arepresemed in Figures B.8 andl B.9. These estimates are dominated by 

national conditional demand estimates (e.g. RECS), survey results (e.g. American Gas 

Association). model inputs, and engineering estimates. For all heating and cooling 

systems, multi-family consumption levels are roughly half those in single-family 

dwellings. This is a result of the smaller exterior surface area in apartments and multiplexes and the smaller amount of conditioned space in each unit. Manufactured home space 

conditioning energy use is generally between single- and multi-family levels. For 

comparison with the "aU" house type category, we have created average UECs from the' 

house type data, based on heating and cooling type shares in the last column. Aggregations 

of the allInot-specified house type UECs agree with averages of the house type-specific 

records except for the electric heating end-use categories. National average water heater 

UECs by house type come solely from the RECS regression studies and are quite low 

compared to the national alVnot-specified house type figures, which come from a wider 

variety of studies. 

The gas end-uses, space heating and water heating, give consistent results across house 

types. The national average for gas space heating in the "all" house category is 67.2 

MMBtulyr, which is almost identical to the popUlation-weighted average across house 

types of 68.5 MMBtulyr. The comparison for water heating is similar. As with electric 

water heating, the national average gas water heating UECs for particular house types are 

from the RECS conditional demand estimates for various years. 

We also aggregate national average UECs across technology types for air conditioning and 

electric space heating to calculate average UECs by fuel. There is agreement between these 

summations and the data collected under the "all" technology class for air conditioning. 

The results for electric heating are not as consistent, however. and further highlight the 

overall inconsistencies among VEC estimates for electric space heating in the database. 

These are summed across house types at the bottom of Table B.8. 
Table B.7. Space Conditioning UECs by Region

Electric UECs in kWh/yr. gas VECs in MMBtiL/yr

All numbers for stock vintage normalized to 1990 efficencies and averaged using (5-QR) weighting (except italicized)

Ailtnot-specjfied house type only

FEDERAL REGION

1 2 3 4 5 6 7 8 9 10

End Use ClassN UEC N UEC N UEC N UEC N UEC N UEC N UEC N UEC N UEC N UEC

AC ALL 1 955 3 1007

CAC 2 1338 2 1770 3 1937 5 3235 4 1837 5 4046 1 1684 1 2615 12 1525 3 1623

HP 1 1947 3 4005 2 3821 5 1036

RAC 1 380 1 232 2 451 7 1990 4 546 4 1339 1 690 1 1009 9 505 2 413

el. heating ALL 1 5851 4 9011 2 8989 1 765 1 10140 1 1797 8 3481 2 12250

CTL 1 2750 2 2787 3 2643 1 9806

HP 1 11192 2 7605 5 4264 2 14633 4 3329 1 19659 1 14816 7 3268 2 7253

RES 1 10012 1 5294 2 7893 4 4235 2 13086 3 3321 1 17575 1 13260 7 2609 2 7375

RM___ _ 1 830 ___ 2 1500__ 2 3214 1 9660

gas heating --- 1 81 I1 89.9 3 86.72 2 82.64 2 41.18 1 54.1 1 43.2 1 76.5 1 41.9

EE-RM-94-230 COMMENT 61C

Table B.7. Spate Conditioning UECs by Region 

Electric UECs in kWhryr. gal VEel in MMB,,,lyr 

All numbers for stoc/c vinlage normalized 10 1990 efficiencies and averaged ulillg (S-QR) weighling (excePI ilalicized) 

Aiiilloi-I"ec;;ied houle type only 

FEDERAL REGION 

1 2 3 4 .5 6 7 

End Use Class N VEC N VEe N UEC N. VEC N UEC N VEC N VEC 

AC ALL I 955 

CAC i 1338 2 1770 3 1937 5 3235 4 1837 5 4046 I 1684 

UP I 1947 ·3 4005 2 3821 

RAC I 380 1 232 2 451 7 1990 4 546 4 1339 I 690 

el. beating ALL I 5851 4 9011 2 8989 J 765 1 10140 I 1797 

CTL J 2750 2 2787 

HP I IJl92 2 7605 S 4264 2 14633 4 3329 I 19659 

RES 1 10012 I 5294 2 ·7893 4 4235 2 H086 3 3321 I 17575 

. RM 1 830 2 1500 

; gas heating --- I 81 I 89.9 3 86.72 2 82.64 2. 41.18 I 54.1 

8 9 .10 

N UEC N UEC N UEC 

3 1007 

I 2615 12 1525 3 1623 

S 1036 

1 1009 9 50S 2 413 

8 3481 . 2 12250 

3 2643 1 9806 

1 14816 7 3268 2 7253 

1 13260 7 2609 2 7375 

2 3214 I 9660 

1 43.2 I 76.5 I 41.9 
Figure B.& National Average Central Air Conditioner UECs by House Type

Range and Weighted Average

Figure B.9. National Average Gas Space Heating UECs by House Type

Range and Weighted Average

N=16 N=7 N=5 N=4

8000 -----. -................... .......

8000 .. ... ...................... ............ ......................

6000 .. ......................

000 - 2446 ...... ........... ...............

2000 .. ..... ......... ... ..

1000 .--..--- ----_---- -------- ..----..............

O ~ ~ l ~ ~ l ~ ~~~ 

AIl/Not- Single Family Multi-Family Manufactured Population LBL-REM

specified Home Wtd. Avg.

N=10 N=9 N=9 N=6 100 100 . ............................................................................................

90 90 _ ............................................. .......................... I . ......

76.2

80 ........................ 67.2 --

70 *.- .......................... 

60 _ . ................

50 50 -- _----- I-----.------ _.__. - __ __ ..... __.._......____- ...... . .. ................

0 ..................... .. .. . . ................

30 _ ....... ...... ...................... . ......................................... 20

10

0 I i I I I

All/Not- Single Family Multi-Family Manufactured Population LBL-REM

specified Home Wtd. Avg.

-

EE-RM-94-230 COMMENT 61C

Figure B.8. National A ~erage Central Air Conditioner UECs by House Type 

Range and Weighted Average 

N=16 N=7 N=5 8000 .-.. -....................... -..................................... -.. -... -.... -.; .......................................................................................................... -

7000 -' 

6000 ... 

5000 2446 

o +-------~---------r--------+-------~---------+------~ 

Figure B.9. 

MUNot- Single Family Multi-Family Manufaaurcd Population 

specified Home Wtd. A vg. 

National Average Gas Space Heating UECs by House Type 

Range and Weighted Average 

N=lO N::9 

LDL-REM 

100 .,..-------.---.-.. -.--...... - ........... -..... -.. -.-... -.... -.... - ... -.... --... -----..... - .. -.--.... -.-....... "---"-'-

: =·---~--·.~.=~6'2 ... :~:.:::~:=~.~=:==::= .. :~.==:~~~.==:=:~~~~.~~ .. :::~:~.'.~'.::~~::==~~ 67.2 

70 ~ '-' --... - ... --.. -.. -.--.-----.-.---~:s:: ............ -.--

'-----1 ..... -------.... -.-.-... -..... 47 4 "-"- 50 0 ···-·-·_·--··_···············-:·::58·): ... --- . . f-----j .. --......... - .. ---..... --.. -.-............. ----. 

40 -.----.----.----... -.. -.... --..... -... -....... ---.. -...... -~ .............. -.... ----

30 ._ .. -.-... -.. -..... -..... _ ... -.... -.. -._-.. -.............................. -..... -.. - ................... -.. - .. --.......... -....... -....... -........ -.-.--.. .. 

20 -.--.------.-.. - .. -'-0.--... ----.--.. --.. --.--.--.--

10 --... ---.--.. ------.-.---.-...... -.. --.--.-.. - ... -.-......... --.-.-... -.--.-.. -----

o +-------~------_+------~~ ____ --~------_+--__ ----l 

AlUNotspecified Single Family Multi-Family Manufactured Population 

Home Wrd. Avg. 

LBL-REM 
Table B.8. National Space Conditioning and Water Heating UECs by House Type

Electric UECs in kWh/yr, gas UECs in MMBUttyr

All numbersfor stock vintage normalized to 1990 efciencies and averaged using (S-QR) weighting (except italicized)

DATABASE RESULTS

Single Multi- Manufacturcd

All/Not Spec. Family Family Home

End Use Class N UEC N UEC N UEC N UEC

AC ALL 5 1770 3 2134 3 972 3 1434

CAC 16 2446 7 2723 5 1451 4 1799

HP 3 3099 4 2908 3 1228 3 1970

RAC 19 826 4 948 5 649 4 825

el. beating ALL 17 7315 6 8114 6 4276 6 5442

HEP 6 8446 6 7423 4 3184 3 3533

RES 4 9615 4 11524 3 6271 3 5700

el. water heat --- 28 3788 3 3292 3 2285 3 3013

gas healing - 9 66.6 10 75.6 9 47.4 6 50.0

gas water heat - 9 24.5 3 24.7 3 17.4 3 21.3

Population-weighted UECs:Technology Class Combinations

Single Multi Manufactured

End Use Class Famil Family Home

AC ALL 1903 1050 1419

el. heating ALL. 9825 5513 5123

Population-wtid UECs: House Combinations

End Use Saturations (mill. units) ALL .

SF MF MH UEC

40.07 15.19 3.04 1795

16.58 5.99 1.58 2347

4.49 2.23 0.24 2338

18.99 6.97 1.23 866

10.70 9.07 0.85 6316

4.43 2.23 0.23 5924

6.27 6.84 0.63 8642

25.51 10.58 1.91 2998

40.13 11.45 2.49 68.5

32.66 13.97 2.47 22.4

EE-RM-94-230 COMMENT 61C

'. 

Table B.8. National Space Condllloning and Water Heating UECs by House Type 

Electric UECs in kWh1'r, gas UECs in MMBt"lyr 

All numbers/or stock vintage normalized 101990 tfficiencies and averaged "sing (S-QR) ",eiglltin8 (except italicized) 

DATABASE RESULTS 

Single . Muld· ManuCaclured uP" alion-WI cL UECs: Houst Conb inations , 

AIVNol Spec. family family Home End Use Salurations (mUl. units) ALL. 

End Use. Class N UEC N UEC N UEC N UEC SF MF MH UEC 

AC ALL S 1170 3 2134 3 972 3 1434 40.07 15.19 3.04 1795 

CAC 16 2446 7 2723 S 1451 4 1799 16.58 5.99 1.58 2347 

HP 3 3099 4 2908 3 1228 . 3 1970 4.49 2.23 0.24 2338 

RAC 19 826 4 948 S 649 4 825 18.99 6.97 1.23 866 

el. beating. ALL 17 7315 6 8114 6 4276 6 5442 10.70 9.07 0.85 6316 

UP 6 8446 6 7423 4 3184 3 3533. 4.43 . 2.23 0.23 5924 

RES 4 9615 4 11524 3 6271 3 5700 6.27 6.84 0.63 8642 

el. waler beat --- 28 3188 3 3292 3 '2285 3 3013 25.51 .10.58 . 1.91 2998 

gas bealing -- 9 66.6 10 75.~ 9 47.4 6 50.0 40.13 . 11.45 2.49 68.5 

igas water beat .- 9 24.5 3 24.1 3 17.4 3 .21.3 32.66 13.91 2.47 22.4 

P, OPII t· anon·we'll i h d It UEe s: 11 Ie h l no ogy Cl ass Co m hi nallons . 

Single Muld Manufactured 

End Use Class Family Family Home 

AC ALL 1903 1050 1419 

e!. heating ALL 9825 5513 5123 
Table B.9 shows a division of the national space conditioning and water heating records by

study type, for records of the all/not-specified house type. At this level of disaggregation, there are not enough records to make any general conclusions about differences in study

type for most space-conditioning end-uses. Figures for gas heating are consistent across

study types, averaging 60 to 70 MMBtu/yr. Central and room air conditioning show

consistency across study types, but estimates of national-average heating use show greater variation. Electric resistance and heat pump heating estimates vary the most. Conditional

demand water heating UECs are lower than other estimates, potentially due to the

misallocation of dish- and clothes washer hot water use to motors.

CONCLUSIONS

The database of unit energy consumption (UEC) estimates is a useful tool for assessing the

reliability of residential forecasting model inputs. The results provide the best estimates of

UECs from the data collected. In the analysis of the data, this work goes beyond previous

attempts at estimating UECs because we attempt to disaggregate the data by appliance class,

housing type, and climatic regions where appropriate and we account for historical trends

in UECs due to appliance turnover by calculating stock appliance efficiency and

normalizing the data to the 1990 base year.

The analysis shows that there is significant variability in UEC estimates, both within and

across study types. Some of this variability is due to random sampling error, resulting

from the large underlying population variability in energy use habits. People use energy in

very different ways and on widely different schedules, so that no reasonable size sample

group can be perfectly representative of a regional or national average UEC. However,

there is also a great deal of variability due to systematic error in estimation methodologies

and study design. With this in mind, we analyze the data by study type, or UEC estimation

methodology, and rate the quality of the differing methodologies for each end-use.

The analysis suggests two primary areas for future work in developing UECs for model

inputs. First, most models allow for separate UECs for all end-uses by housing type.

This sort of disaggregation is not well supported by measured data or conditional demand

estimates, even though it is intuitive that differences in UECs between house types exist,

because of different occupancy levels and equipment choices. Thus, model UEC inputs for

appliances and water heating will need to be differentiated across housing types using

assumptions about appliance usage and appliance size rather than any real measured data.

The second set of problems highlighted by this analysis is in the UECs for certain specific

end-uses. The most problematic areas include appliance hot water usage and electric

heating UECs. The hot water usage associated with clothes and dishwashers is difficult to

estimate using the standard methodologies, and the accounting of the water-heating energy

to those end-uses or to water heating appears to vary between studies. These differences in

accounting will need to be assessed.

A more important area for future work, however, is in estimating UECs for electric heating

technologies, including resistance furnace, room (or zonal) heating and heat pumps. The

inconsistency in UEC data across study types and housing types for these end-uses is

much greater than for gas heating or air-conditioning. Part of this problem must be due to

the difficulty of separating electric heat from other household electric data in conditional

demand estimation, a problem which is not as severe in the gas end-uses. Additionally, the

small overall population and the localized nature of electrically heated homes may contribute

to the confusion. Significant variation in space conditioning UECs may actually be the

result of regional differences in electricity prices.

EE-RM-94-230 COMMENT 61C

Table B.9 shows a division of the national space conditioning and water heating records by 

study type, for records of the alVnot-specified house type. At this level of disaggregation, 

there are not enough records to make any general conclusions about differences in study 

type for most space-conditioning end-uses. Figures for gas heating are consistent across 

study types, averaging 60 to 70 MMBtuJyr. Central and room air conditioning show 

consistency across soody types, but estimates of national-average heating use show greater 

variation. Electric resistance and heat pump heating estimates vary the most Conditional 

demand water heating UECs are lower than other estimates, potentially due to the 

misallocation of dish- and clothes washer hot water use to motors. 

CONCLUSIONS 

The database of unit energy consumption" (UEC) estimates is a useful tool for assessing" the 

reliability of residential forecasting model inputs. The results provide the best estimates of 

UECs from the data collected. In the analysis of the data. this work goes beyond previous 

attempts at estimating UECs because we attempt to disaggregate the data by appliance class, 

housing type, and climatic regions where appropriate and we account for historical trends 

in UECs due to appliance turnover by calculating stock appliance efficiency and 

nonnalizing the data to the 1990 base year. 

The analysis shows that there is significant variability in VEC estimates, both within and 

across study types. Some of this variability is due .to random sampling error, resulting 

from the large underlying population variability in energy use habits. People use energy in 

very different ways and on widely different schedules, so that no reasonable size sample 

group can be perfectly representative of a regional or national average UEC. However, 

there is also a great deal of variability due to systematic error in estimation methodologies 

and study design. With this in mind,.we analyze the data by study type. or VEC estimation 

methodology, and rate the quality of the differing methodologies for each end-use. 

The analysis suggests two primary areas for future work in developing UECs for model 

inputs. First, most models allow for separate UECs for all end-uses by housing type. 

This sort of disaggregation is not well supported by measured data or conditional demand 

estimates, even though it is intuitive that differences in UECs between house types exist, 

because of different occupancy levels and equipment choices. Thus, model UEC inputs for 

appliances and water heating will need to be differentiated across housing types using 

assumptions about appliance usage and appliance size rather than any real measured data. 

The second set of problems highlighted by this analysis is in the UECs for certain specific 

end-uses. The most problematic areas include appliance hot water usage and electric 

heating UECs. The hot water usage associated with clothes and dishwashers is difficult to 

estimate using the standard methodologies, and the accounting of the water-heating energy 

to those end-uses or to water heating appears to vary between studies. These differences in 

accounting will need to be assessed. 

A more important area for future work, however, is in estimating UECs for electric heating 

technologies, including resistance furnace, room (or zonal) heating and heat pumps. The 

inconsistency in UEC data across study types and housing types for these end-uses is 

much greater than for gas heating or air-conditioning. Part of this ·problem must be due to 

the difficulty of separating electric heat from other household electric data in conditional 

demand estimation, a problem which is not as severe in the gas end-uses. Additionally, the 

small overall population and the localized nature of electrically heated homes may contribute 

to the confusion. Significant variation in space conditioning UECs may actually be the 

result of regional differences in electricity prices. 
Table B.9. National Space Conditioning and Water Heating UECs by Study Type

Electric UECs in kWh/yr, gas UECs in MMBtt/yr

All numbers for stock vintage normalized to 1990 efficiencies and averaged using (5-QR) weighting (except italicized)

All/not-xpecfifed house type only

Metered Conditional Demand Engineering Model/Aggregal Utility Industry

End Use Class N UEC UEC N UEC UEC N UEC N UEC

AC ALL 2 2040 1 1392 2 1661

CAC 9 2240 2 2631 10 2078 1 3906

HP .4161 1 2666 1 2470

RAC 1 978 10 810 2 823 4 843 1 860

el. beating ALL 1 2559 12 6543 1 14155 3 9150

CTL 1 6541

HP 1 12901 1 7661 4 7112

RES 4 9615

RM 1 8329

el. water beat --- 1 4044 1 11 3155 5 4048 7 4600 3 4500 1 4515

gas heating -- 4 70.06 4 63.24 2 66.88

gas waler beat ._--- 3 23.80 1 22.50 5 25.79_

EE-RM-94-230 COMMENT 61C

Table B.9. Natlonal Sp~ce Conditioning and Water Heating UEes by Study Type 

Electric UECs in kWh&r, gas UECs in MMBill/yr 

All numbers for stocle vintage normalized to i990 efficiencies and averaged using (5·QRJ weighring (except IralicizedJ 

A1VlJoHpec!fled ho!t!!typ! only . 

Melered Conditional Demand Engineering Modell Aggregal( ULilily 

End Use Class N VEe N UEC N UEC N VEC N UEC 

AC ALL 2 2040 1 1392 2 1661 

CAe 9 2240 2 2631 ' 10 2018 1· 3906 

HP 1 .4161 1 2666 1 2470 

RAC 1 978 10 810 2 823 4. 843 1 860 

el. beaLing ALL 1 2559 12 6543 1 14155 3 9150 

en. 1 6541 

HP 1 1290J 1 7661 4 1112 

RES 4 9615 

RM 1 8329 

eJ. water beal ... J 4044· 11 3155 5 4048 7 4600 3 4500 

gas bealing ... 4 70.06 4 63.24 

gas waler beal ... 3 23.80 1 22.50 5 25.79 

Industry 

N UEe 

1 45}5 

2 ·66.88 
REFERENCES FOR APPENDIX B

AGA, American Gas Association. Annual Gas Househeating Survey.

AHAM, Association of Home Appliance Manufacturers. 1991. Refrigerator, Freezer,

Clothes Washer, and Room Air Conditioner Energy Efficiency and Consumption

Trends. Chicago: AHAM.

AHAM, Association of Home Appliance Manufacturers. 1982a. Clothes Washers -- Energy Efficiency and Consumption Trends, Chicago, IL: AHAM.

AHAM, Association of Home Appliance Manufacturers. 1982b. Clothes Dryers -- Energy

Efficiency and Consumption Trends, Chicago, IL: AHAM.

AHAM, Association of Home Appliance Manufacturers. 1990. Refrigerators -- Energy

Efficiency and Consumption Trends, Chicago, IL: AHAM.

Archibald, R.G., D.H. Finifter, and C.E. Moody. 1982. "Seasonal Variation in

Residential Electricity Demand: Evidence from Survey Data," Applied Economics,

14: 167-81.

Bachmeier, Richard and Mike Robinson. 1988. A Conditional Demand Analysis of

Residential Electric Appliance Use in the Southwest U.S., Report to the Public

Utility Commission of Texas.

Barnes, Richard S. and Cathy R. Zoi. 1986. Unit Energy Consumption of Residential

Appliances, San Francisco, CA: Pacific Gas & Electric Co.

Barnes, R., R. Gillingham and R. Hagemann. 1978. Residential Demand for Electricity,

Washington, DC: U.S. Bureau of Labor Statistics.

Bernow, S, M. Brower, J. DeCicco, D. Goldstein, J. Jordan, D. Lashof, A. Meyer, P.

Miller, R. Mowris, M. Murray, H. Sachs, M. Zimmerman. 1991. America's

Energy Choices: Investing in a Strong Economy and a Clean Environment,

Washington DC: Union of Concerned Scientists (also published by Alliance to Save

Energy, American Council for an Energy-Efficient Economy, and Natural

Resources Defense Council).

Brodsky, Joel B. and Susan E. McNicoll. 1987. Residential Appliance Load Study, 1985-

1986. San Francisco, CA: Pacific Gas & Electric Co.

Brodsky, Joel B., Brad M. Gray, Les Guliasi, Marek Kanter, and Melvin J. Reuss, Pacific

Gas & Electric Company. 1986. "Geographic and Seasonal Effects on Residential

End-Use Loads" in Proceedings of the American Council for an Energy-Efficient

Economy 1986 Summer Study on Energy Efficiency in Buildings, 9: 9.15-9.24.

Cambridge Systematics, Inc. 1984a. End-Use Consumption Models and Unit Energy

Consumption (UEC) Value Estimates For Residential Electric And Gas Appliances,

Final Report, prepared for San Diego Gas & Electric Co., Berkeley, CA:

Cambridge Systematics.

EE-RM-94-230 COMMENT 61C

REFERENCES FOR APPENDIX B 

AGA, American Gas Association. Annual Gas Househeating Survey. 

AHAM, Association of Home Appliance Manufacturers. 1991. Refrigerator, Freezer, 

Clothes Washer, and Room Air Conditioner Energy Efficiency and Consumption 

Trends. Chicago: AHAM. 

AHAM, Association of Home Appliance Manufacturers. 1982a. Clothes Washers -- Energy Efficiency and Consumption Trends, Chicago, IL: AHAM. 

AHAM, Association of Home Appliance Manufacturers. 1982b. Clothes Dryers -- Energy 

Efficiency and Consumption Trends, Chicago, IL: AHAM. . 

AHAM, Association of Home Appliance Manufacturers. 1990. Refrigerators -- Energy 

Efficiency and Consumption Trends, Chicago, IL: AHAM. 

Archibald, R.G., D.H. Finifter, and C.E. Moody. 1982. "Seasonal Variation in 

Residential Electricity Demand: Evidence from Survey Data," Applied Economics, 

14: 167-81. 

Bachmeier, Richard and Mike Robinson. 1988. A Conditional Demand Analysis of 

Residential Electric Appliance Use in the Southwest U.S., Report to the Public 

Utility Commission of Texas. 

Barnes, Richard S. and Cathy R. Zoi. 1986. Unit Energy Consumption of Residential 

Appliances, San Francisco, CA: Pacitic Gas & Electric Co. 

Bames, R., R. Gillingham and R. Hagemann. 1978. Residential Demandfor Elecn-icity, 

Washington, DC: U.S. Bureau of Labor Statistics. 

Bemow, S, M. Brower, J. DeCicco, D. Goldstein, J. Jordan, D. Lashof, A. Meyer, P. 

Miller, R. Mowris, M. Murray, H. Sachs, M. Zimmerman. 1991. America's 

Energy Choices: Investing in a Strong Economy and a Clean Environment, 

Washington DC: Union of Concerned SCientists (also published by Alliance to Save 

Energy, American Council for an Energy-Efficient. Economy, and Natural 

Resources Defense Council). 

Brodsky, Joel B. and Susan E. McNicoll. 1987. Residential Appliance Load Study, 1985-

1986. San Francisco, CA: Pacitic Gas & Electric Co. 

Brodsky, Joel B., Brad M. Gray, Les Guliasi, Marek Kanter, and Melvin J. Reuss, Pacific 

Gas & Electric Company. 1986. "Geographic and Seasonal Effects on Residential 

End-Use Loads" in Proceedings of the American Council for an Energy-Efficient 

Economy 1986 Summer Study on Energy Efficiency in Buildings, 9: 9.15-9.24. 

Cambridge Systematics, Inc. 1984a. End-Use. Consumption Models and Unit Energy 

Consumption (VEC) Value Estimates For Residential Electric And Gas Appliances, 

Final Report. prepared for San Diego Gas & Electric Co., Berkeley, CA: 

Cambridge Systematics. 
Cambridge Systematics, Inc. 1984b. Residential Conservation Analysis, prepared for

Northwest Power Planning Council, Berkeley, CA: Cambridge Systematics, Inc.

Berkeley, CA.

Commonwealth Edison. 1985. Residential Unit Energy Consumption: A Conditional

Energy Demand Approach, Chicago, IL: Commonwealth Edison.

Consumers Power Co. 1984. Appliance Submetering Studies 1977-84, Jackson, MI:

Consumers Power Co. Project 84-273.

Cowing, Thomas G. and William A. Vinhage. 1986. NYSEG-SUNY Energy Demand

Modeling and Analysis Project, New York State Electric & Gas Co. Technical

Report m-3

DeLima Associates. 1990. Memorandum, DeLima Associates to Ron Ritschard, LBL, San

Rafael, CA: DeLima Associates.

Dole, Stephen H. 1975. Energy Use and Conservation in the Residential Sector: A

Regional Analysis, Santa Monica, CA: Rand Corporation. R-1641-NSF

Dubin, J. and D. McFadden. 1984. "An Econometric Analysis of Residential Electric

Appliance Holdings and Consumption," Econometrica, March.

Economic Sciences Corporation. 1979. Residential Appliance Specific Demand for

Electricity and Natural Gas: A Combined Econometric and Engineering Approach,

prepared for Pacific Gas & Electric Company.

Edison Electric Institute. 1983. Annual Energy Requirements of Electric Household

Appliances, Washington, DC: Edison Electric Institute. EEI-Pub #75-61 Rev.

EPRI, Electric Power Research Institute. 1979. Patterns of Energy Use by Electrical

Appliances, Palo Alto, CA: EPRL EA-682.

EPRI Electric Power Research Institute. 1984. Survey of Conditional Energy Demand

Models for Estimating Residential Unit Energy Consumption Coefficients, Palo

Alto, CA: EPRI EA-3410.

EPRI, Electric Power Research Institute. 1989; Residential End-Use Energy Consumption:

A Survey of Conditional Demand Estimates. Palo Alto, CA: EPRI. CU-6487. This

report reviews studies from numerous utilities, including:

Alabama Power Co. 1983.

American Electric Power. 1988.

Arkansas Power and Light. 1984.

Gulf States Utilities. 1986.

Jersey Central Power and Light. 1987.

Louisiana Power and Light 1984.

Metropolitan Edison. 1987.

Mississippi Power and Light. 1984.

Mississippi Power. 1984.

New Orleans Public Service. 1984.

EE-RM-94-230 COMMENT 61C

Canlbridge Systematics, Inc. '1984b. Residential Conservation Analysis, prepared for 

Northwest Power Planning Counc~l, Berkeley, CA: Cambridge Systematics. Inc. 

JBerkeley. CA . 

Commonwealth Edison. 1985. Resi4ential Unit Energy Consumption: A Conditional 

Energy Demand Approach. Chicago, IL: Commonwealth Edison. 

Con.'lumers Power Co. 1984. Appliance Submetering Studies 1977-84. Jackson. MI: 

Consumers Power Co. Project 84-273. 

Cowing, ThomasG. and William A. Vinhage. 1986. NYSEG-SUNY Energy Demand 

Modeling and Analysis Project, New York State Electric & Gas Co. Technical 

ReportID-3 ' 

DeUma Associates. 1990. Memorandum, DeLima Associates to Ron Ritschard, LBL, San 

Rafael, Ck DeLima Associates. 

'Dole, Stephen H. 1975. Energy Use and Conservation in the'Residential Sector: A 

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Dubin, J. and D. McFadden. 1984. "An Econometric Analysis of Residential Electric App~ce Holdings and COll$umption," Econometrica, March. 

Economic Sciences Corporation. 1979. Residential Appliance ,Specific Demand for 

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EdisoJl Electric Institute. 1983. Annual Energy Requirements of Electric Household 

Appliances. Washington~ DC: Edison Electric Institute. EEl-Pub #75-61 Rev. 

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, Alabama Power Co. 1983. 

American Electric Power. 1988. 

Arkansas Power and Light. 1984. 

Gulf States Utilities. 1986. 

Jersey Central Power and -Light 1987. 

Louisiana Power and Light 1984. 

Metropolitan Edison. 1987. 

Mississippi PO',Yer and Light 1984. 

Mississippi Power. 1984. 

New Orleans Public Service. 1984. 
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