### 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 ofCalifornia

Berkeley, CA 94720
May 1994

This work was supported by the Assistant Secretary forEnergy Eficiency and Renewable Energy, Office of 

### Planning and Analysis and Office of Building Technologies of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098. 

### LBr.;33717

. UC-3S0



## BASELINE DATA FOR THE.-RESIDENTIAL SECTOR AND DEVELOPMENT OF A RESIDENTIAL FORECASTING DATABASE

lamesW. 

### 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 EnvironmentDivision



### Lawrence Berkeley 

### J..aboratory 

### University ofCalifomia

Berkeley, CA94720
May 1994

This workWas supported by Ibe Assistant Sei:reW"y forEnergy Emcieuc;yand RenewableEnergy. 

### Office of, Planning and Analysis and Office of Building Tf:Cbnologies of abe U.s. DepartmeDt ofEnergy under ContractNo. DE-AC03-76SF00098.

.

### ABSTRACT This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. It provides a description ofthe 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 ofappliances 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 

### ABSTRACT This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. Itprovides a description ofthe methodology used 

### to develop the database and describes the data usedfor heating and cooling end-uses as well as for typical household appliances. 

### This repon provides information on end-use unit energy consumption (UEC) , values ofappliances and equipment, historical and cUITent appliance andequipmentmarket shares, appliance and equipmentefficiency and sales trends, costvs. efficiency data for appliances and equipment, product lifetime estimates, thermal shell characteristics of buildings, heating and cooling loads, shellmeasure costdata for new and retrofitbuildings, baseline housing stocks, forecasts ofhousing starts, and forecasts ofenergy prices and other economic drivers. Model inputs and outputs, as well as 

### all otherinformation 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 staffto 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 ofresidential 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 ofthe Office ofPlanning and Assessment and DickJones ofthe Office ofBuilding Technologies in the U.S. Department ofEnergy. We are grateful for their support and insights.

i 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 theirfrustration at how difficult it was for LBL staffto consolidate and systemize theirknowledge onresidential 

### 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 ofthe 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 ofresidentialbuildings: 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 ofthe Office ofPlanning and Assessment and 

### Dick Jones ofthe Office ofBuilding Technologies 

### inthe U.S. Department ofEnergy. 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 Datafor HVAC Equipment and Distribution Systems

.......... 17 3.3. Technology Datafor ShellMeasures

............................................ 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. WaterHeating UECs

............................................................ 64 4.2. HotWater Usage

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

................................... 65 4.4. Shares

....... 7..............................70 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

............................................ 79 

### 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

... 

### 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. BuildingComponent**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 Datafor 

### HV AC Equipment

### and Distribution Systems.....•.••.

### 17 3.3. Technology

## Patafor 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. WaterHeating 

### 'UECs ••••••••••••••••••••••' ••••••••••••••••••••••••••••••**~**

### •••••••••

### 64. 4.2. 

### HotWa1J:,tUsa.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. 

### RefrigemtorUsa.ge••••••••.•••••.••••••.•••••••.••••••.••••••••••••.•••.••••••••••.73 

### 5.3. RefrigeratorTechnology 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.erUsage ......••.•....•..•..•............••..•....•••...•.•....•..•..•......•.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 UECs ............................... 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 ofInstalled 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 ofEfficiency 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 

### 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. OodlesWasher

### Technology**Data.~**

### .....••.....•...•.•...••••....**~**

### .......•.••.•.•.•94 

### 8.4. Shires ........•.............................................•.•.............•••.•..•. 95 8.5. Standards••.•..•...•.........••.•.•.........•••.....•.•.•.•.•..•...•...•......•.•.... 

### 99 9. CLOTHES DRYER END-USE DATA ••••••••••••••••••••••••••••••••••• 100 9.1

..Clodles DryerUECS ...•....•...•..••.•..•..••.•...•.••..••...•...... \ ...•.•.•......100 

### 9.2. 9.3. 9.4. 

### 9.S. Clothes Dryer ()'sage

....................................**~**

### .•••.•....•**~**

### ....••••••.•...101 Clothes DryerTechnolC?gy Data .........•..•.......•.....................•...•...101 

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

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

### 10. LIGHTING END-USE DATA••**~** 

### •••••••••••••••••••••••••••••••••••••••• 106 10.1. 10.2. 

### Ba.seliIle Lighting 

### Usage .......••...•......•.......•..•••...•...•.........••.....

### l06 DistJibution ofInstalled 

### 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 ofEfticiency Improvements in

### Ughting.••..•••..•...•...•..••.**~**

### ......110 

### 11. COOKING END-USE DAT A ••••••**~**

### ••••••••••••••••••••••••••••••••••••• 111 

### 11.1. 11.2. 11.3. 

### C:ooking 'UECS.••.•.••.••••.•••••••••••**~**

### ••••••••.•.••.•••••..•..•.....•**~**

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

### Shares•••••••••••••**~**

### •••••••••••••••••••••••••••••••••••••••••**~**

### •••••••••••••••••••••113 11.4. Efficiency Slandards.....•..••.•..........•.....•..••.••..•............•..••..•..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. UECDATABASE DESCRIPTION •••••••••••••••••••••••• 139

,

iv

### 1. INTRODUCTION The residential forecasting database is designed to support improved energy demand forecasting atLawrence Berkeley Laboratory (LBL) and within the U.S. Department of Energy (US DOE). It is a compilation ofthe major data elements necessary for end-use energy demand forecasting in the residential sector. The work represents an attempt to systematically assess and documentthese data, and to incorporate them into acomputerized database system. This report describes the methodology used in collecting and assessing these data, the sources used, and presents the major pieces ofdata in graphical or tabular form. The residential forecasting database includes the following model input data: * Unitenergy consumption (UECs) ofappliances 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 ofbuildings and heating and cooling loads; * Shell measure cost data fornew and retrofit buildings; * Baseline housing stocks; * Forecasts ofhousing starts; and * 'Forecasts ofenergy prices and other economic drivers. In the future, the-database will be designed to allow results from various forecasting scenarios to be stored in areadily accessible form. Forecast data types will include: * Total energy use byfuel; * 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 ofhow they were derived. The database will serve as the source ofinput 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. InChapter 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 ofthe reports, areincluded in Appendix A.

1 1. INTRODUCTION The residentiul forecasting database 

### is designed to support improved energy demand forecasting atLawrence Berkeley Laboratory .(LBL) and**wi~** 

### the U.S. Department of Energy 

### (US DOE). It 

### is a compilation ofthe 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 incollecting and assessing these**da~** 

### the sources 

### used, and presents the major pieces ofdata 

### in graphical or tabular form. The:residentialforecasting databaseincludes the following modelinput

### data: • Unitenergy consumption (UECs) ofappliances and equipment; • . His1tOrical and CWTent appliance andequipmentmarketshares; • Appliance andequipmentefficiencyand

### sales trends; • Costvs. efficiency datafor appliances and equipment;
• Product·lifetime

### estimates; . • Thermal shellcharacteristics ofbuildings 

### and heating and cooling loads;

• SheU 

### measure costdatafornew and retrofitbuildings; • Baselinehousing stocks; • Fon:casts ofhousing staIts; and •**'Fon~ts** ofenergyprices andothereconomic drivers

.



### .In the fntwre, 

### the· database will 

### be designed 

### to allow resultS from various forecasting 

### scenarios to 

### be storedin a readily**accessibl~** fonn. Forecastdata types 

### will include: • Totalenergyuse by

### fuel; 

### • Energy use byend-use; and • Market

### shares.**UE~** andenergy factors. Model inputs and outputs, as well as all other information in the database, are 

### fully documented 

### with the source 

### and an explanation 

### ofhow they

### were derived. The 

### database will serve 2lS tbe source ofinput data for the residential forecasting .models used 

### in the Energy AnalysisProgram atLBL.

. . . . 

### In Chapter 2,.

# 'we describe the major elements of the residential database and the methodology and sources used 

### indeveloping the 

### estimates. In'Chapter 3,we**d~ribe.the** data for the heating and cooling end-uses. 

### InChapters4 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, aswell assamples ofthe reports.**~**included in Appendix 

### A.

1

### 2. METHODOLOGY The residential forecasting database allows for detailed characterizations ofthe residential sector. The database is based on several major data sources as well as a number ofsmaller 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 ofcurrent housing and construction (US Bureau ofthe 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 ofover 1300 records for all major residential end-uses. Because of the large variability in estimates for any particular value, we selectively aggregated the databased 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.

1The method we used was: 1)collect information on the estimate concerning its representation, including region ofthe 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 ofthe quality ofthe estimate, and 3) record the type ofmethodology ("study type") used to calculate the estimate (e.g. measurement, statistical

- "conditional demand", an aggregate of other estimates, etc.), and4) calculate averages oftheUEC estimates based on quality and study type to determine the best estimate fromthe available data.


### 2 

### 2. METHODOLOGY Theresidential forecasting database allows for detailed characterizations ofthe residential sector. The database 

### is based on several major data sources as well as a number ofsmaller 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 ofcurrenthousing and construction (US Bureau ofthe 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; Cohenet

### ale 1991); Building characterization projects (Ritschard et ale 1992a; NAHB 1986; NAHB 1989; MHI 1991); and Buildingbeating and cooling simulation databases (LBL 1987; Huang etale 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 

### inTable 2.2. 

### 2.1. UECs Dataonend-useunit energy consumptions (UECs) were collectedto 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 anyparticularvalue, we selectivelyaggregated the data based on the quality ofthe 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 ofthe 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 orother measure ofthe quality ofthe estimate, and 3)record the type ofmethodology ("study type") used to calculate the estimate (e.g. measurement, statistical

- "conditional demand", an aggregate of other" estimates, etc.), and4) calculate averages ofthe UECestimates basedonquality and study type todetermine the best estimate from the available data.
2Database 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 forheating and cooling equipment Efficiency, capacity, usage, and UEC units foreach end-use Basic building prototype descriptions U-values and shading coefficients ofbuilding shell components Building prototype shell component dimensions and thermal integrity SP53 regression coefficients for building components Solar loadregression 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 ofnew construction annually, 1980-90 Conditioned floor area ofnew 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. 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 Costw. efflciency data forappliances S laYHSIuOS Base Year (1990) HVAC system shares 6 empty 7 

### IIVACIJ.rI)7

. Costvs. efficiency and rostvs. capacity dataforbeating and cooling equipment.

.. 8 Units08 Efficiency. capacity, usage, and UEC units for each end-use 9 BldPrt09
.Basicbuilding prototype desaiptions 10
. tNWkSlO U-values aDd shading coeffiCients ofbuilding shell oomponents

.. 11 BIdCmpll Buildingprototype shellcomponent dimensions and thermaI integrity 12 l4Ib112 SPS3 regression coefficients for building components 13 S1flb113· SolarloadregreSsion coefficients 14 HsSrck:14 Housing stoclc data, 1990 (willbe 1980-90) IS FuellS Fuelprices 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 Shellmeasure costs for building retrofits (SFonly) 22 HSlCmp22 Completions ofnew constructiOIi annually, 1980-90 23 

### HsArea23**~ondilioned** floor area ofnew coDStruCtion, 1980-90 24 HsFest24 Housing starts forecast 2S 

### ARILft25 Appliance lifetime estimates

.3Table 2.2. Definitions for the Residential Database Datbase 

### Field_ jICode Description Nil m_ Vintage S Stockbuildings 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 ormore 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 Table 2.2. Definitions ror the Residential Database Dalabase Flcld Code Desaiption Vmtage S SlOCkbuildings or equipment. i.e. Ihose inexistence during the year specified N New buildings or equipmenl, i.e. Ihose aurentJy being built. manufactured. 

### orpurchased House Type SF Single family bouse types (detacbedandauached) MF Multifamily bouse types (2 ormore units) MH Manufactured home bousetypeS AI.. Averages across allbuilding types Fuel E E1edricity G Natural Gas 0 Fuel Oil(includeskerosene) 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 (Federnlregions 4, 6, and 9) Year



## . Enduse AC AirConditioning 

### CK Cooking CW OotbesWasher DR Dryer 

### OW DishWasher FZ Freezer lIT Space Heating LT Ughting MS Miscellaneous MW Miaowave RF Refrigerata TV Television WH Wau:rHeating



### - Technology Iheseentries are specific to eachend-use



### 4

### Appliance End-Uses UECs for appliance end-uses in the existing housing stock were derived from analyses performed on the UEC database. Fornew 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 datarather than field data,however, and should beused 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 atLBL (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 forthe 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 ofnew 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 ofnew 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 Appliancemagazine were compared for the residential forecasting database. The sources arein agreement for appliance shares in the existing housing stock for themajor end-uses. Appliance shares forexisting 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 last5 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 

### Appliance End-Uses. UECs for appliance end-uses in the existing housing stock were derived from analyses perfonned on the UEC database. Fornew appliances entering'the

### market. we reliedupon engineering estimates developed for the U.S. DOEappliance standards analysis (US DOE 1988. 1989b. 1989c. 1990b. 1993). These engineering estimates represent testdatarather thanfield 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. TheDEC 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 atLBL (Cohen etale 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 loadsfrom prototypebuilcW:tgs defined forthe database to 

### estimate UECs. Detennining UECs for· typical new buildings is even more difficult than for existing buildings since there are few dataon 

### the energy usage ofnewbuildings,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) differentheating and cooling loads between the existing buildings and new buildings entering the stock, and 2) different heating and cooling equipmentefficiencies ofne",: 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), datafrom 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 agreementfor appliance shares 

### in theexisting housing stockfor 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 datato 

### include only buildingsbuilt 

### inthe last5 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 onheating 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 ofCensus Reports Series C25 on new construction characteristics, and are ided for 1980-90 (US Bureau ofthe Census 1990a). Data on the shares ofheating 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 ofappliances and equipment from 1950 to the present were compiled for major end-uses. These data also show the evolution ofappliance 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 ofthe 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 aeerror 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 ofthe major residential end-uses have been mpiled in thedatabase. 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 atLBL in support ofthe 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 , lrtIIlent of Census Reports Series C25 on new construction characteristics. and are

Ided for 1980-90 (US Bureau ofthe Census 1990a). Dataon the shares ofheating 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 datafor new jings. 

### Technology Characteristics 

### torical Sales, Efficiencies, and Sizes 

### of Appliances and JEquipment
l.on shipments ofappliances andequipmentfrom 1950·to the presentwere compiled for majorend-uses. These.data also show the evolution ofappliance efficiencies over time 

### ting from the early 1970s. Furthermore. the shipments (orsales) data allow the user to mate product lifetimes and the average efficiency ofthecurrent appliance stock. These 1 are from industry 

### reports produced by the majortrade 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 waterheater in a commercialestablishment). and thus may introduce 

### leerror intothe analysis.

. 

### uipment Cost vs. Efficiency 

### Data uipment costvs. 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 ofthe majorresidential 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 !fecompiled 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. Theprototypes 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. andeach prototype building is linked to anHV 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 atLBLin support ofthe 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 ofthermal 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 ofthe bibliographic references that supply the source for each piece ofdata, and it is linked to each record in the database. 2.7. The Database Program All ofthe data are stored in a programmed database that allows the user to choose outputs ofspecific pieces ofdata to bewritten 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 ofthe 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 theuser'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 forthe 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 inChapter 15 ofthis report.

7 2.5. Building Component Costs 

### Costs for increasing levels ofthennalintegrity 

### innew 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 costestimates for retrofitting existing multi-family ormanufacturedhome 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 ofthe bibliographic references thatsupply the sourceforeach piece ofdata. and it

### is linkedto each record in the database..'

. 

### 2.7. The 

### Database Program All ofthe data are stored in a programmed database.that allows the user to. choose outputs ofspecific pieces ofdatato bewritten to printed reports. to thecomputerscreen. orto data fues whil::hwill allow the data to be graphically displayed. further manipulated. orinput

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

### Existing: Capabilities Currently the database allows the user to make pre-defmed printed reports. text files. oX' spreadshr.etfiles thatcanbe used for the user's own analysis. The data can 

### also be

### written to the

### screen, This capabilityis described fully inAppendix

### A. Thedatabase calculates the basecase heating and cooling loads for the prototype buildingsusing a procedure which

### is described! in Chapter 3 and Appendix 

### A. The databaseincludes an algorithm to calculate productlifetimes from historical shipmentand stock data and produces average appliance efficiency andcapacity 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 

### inChapter 15 of

### this report.



### 7

### 3. HEATING AND COOUNG END.USE DATA Heating and cooling together account for about 30% ofelectricity consumption, 70% ofgas consumption, and 90% ofoil 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 yearwithin the same region), microclimates, and regional energy prices. For heating and cooling, we use aregional 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 asregional datafrom 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 aspart ofthe UEC database (Appendix B). Generalized UEC equations The generalized equations forcalculating 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 ofboth 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 

### 3. HEATING AND COOUNG END-USE DATA Heating and cooling together accountfor about30%ofelectricityconsmnption. 70% ofgas , consumption. arnd 90% ofoil consumption in the U.S. residential sector. Theseend-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. Energyconsumptionfor 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. Forheating and cooling. we use a regional disaggregation to segmentthehousingpopulation 

### 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 savingsfrom improvements 

### in building thennal shell characteristics as well 

## as equipment We showthese estimates in Tables 3.1 and 3.2. The sources used in developing 

### UECs for the forecasting database include national data sources as wellasregional datafrom utilities andweatherization 

### 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 etal. 

### 1991) and many different regionalutilityestimates compiled as, 

### partoftheUECdatabase (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 ofboth 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/I00) • 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)


### 80. 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) Minesota(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 isdefined as Federal Regions 4,6, and9. 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 ·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 • SoldII 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 (IL) 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. a3le 3.1. Calibrated Database UEC Estimates forHeating 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

'I 3 1 C rb ted D tab VEC Esd tes Ii H ti ) e

.. al ra a 

### ase ma or ea 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.

bl 3.2. CaUb ted D tab VEC Esti : e ra a ase Ii C U mates or '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 ofgas 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 datausing a statistical regression analysis model. The figures also show national gas heat UECsfrom the LBLResidential 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 ofelectric heating UECs for different regions, technologies, house types vintages, etc. (see Appendix B). In general, electric heat UECs show wide.variations across regions andeven 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 ofsingle-family retrofit programs and savings contains several entries with end-use estimates ofelectric space heating UECs (primarily electric resistance) (Cohen et aL 1991). These data are plotted in Figure 3.8. All ofthese data are from the Pacific Northwest region (except for three data points from the Tennessee Valley Authority), and thus may not berepresentative ofthe rest ofthe U.S. For the residential database, we use the BECA-B data to develop electric resistance space heat UECs forthe 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 ofthisreport provides an estimate offurnace 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 theUEC database (Appendix B).. Central air conditioning fan energy use is included in the Seasonal Energy Efficiency Ratio (SEER) and thus in theUECs. __



### Existing Building UECs 

### Fornatural gas space heating, the American Gas Association's (AGA's) .Gas Househeating Survey provide.s estimates ofaverage 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 ofgas utilities, and are shown overthe 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 shownational gas heat

### UECs 110mthe LBL ResidentialEnergy Model (LBLREM 1991). Since all 

### sources

# are in

### fairly close agreementfornational average natural gas space heating UECs, we developed the UECs for natural gas using the RECS data. 

### Atthe 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 almost250 

### estiniates ofelectric 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 ofsingle-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 

### ofthese data are from the Pacific 

### Northwestregion (except for three 

### data 

### points from the Tennessee Valley Authority). and thus may not be 

### representative ofthe rest ofthe U.S.

. . 

### Forthe 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 Jorthe other building types. 

### Furnace fan energy consumption is not included in either the natural gas orelectric space heating data. Table 13.1 in the Miscellaneous End

..Use Data section ofthis 

### report provides an estimate offurnace 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 Aoridalocations).. Forthe residential database. we use values derived during an earlier LBL study (Koomey et al. 1991a). which are in reasonable agreement with thedatain 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
8 0 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 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

# ~. I

....

# =+==~+--\**-4!"---.~** li

# ~i**~;~::l:::: ~**  60.0;--

### .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 .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 4000 l :; 

# ......_.. i-·----..

# -t·-··-···T·--

..

# -·t
..- ....-· i

..--..._. 

# I ._. --II.f-- RECS



# . 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

### Figure3.6. Electric Resistance Space HeatUECs from Utiity Studies

1000 * Single Family

o All Housing Types

2000 3000 4000 5000 6000 7000

Regional HDD base 65F Source: Data inResidential 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 HDDbase 65F Source: DatainResidential 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 . .FIgure3.6. ElectricResistance SpaceReatVECs from UtUltyStudies
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 

# I0 i IJ 

# ! (J. ! 

# r i 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 base65F Source: Dara inResidential UEC Database (Appendix B).

FIgure3.7 •. EledrlcHeat Pump SpaceReat 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 

# I !Cb I I 

### l f 

## o o 1000 2000 3000 4000 5000

. 6000 7000 Regional HDD base65F Source: Data iIl Residential UEC Database (Appendix B).

14 • Single Family IJ All Housing Types

• .SingleFamily 

## o All Housing TypesFigure3.8. 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 Figure 3.8. AverageEJec:trlc Space Heat Use from Retrofit Programs InBECA·BDatabase**~~~--~--~---~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-..... I :**~** 

### iii

## ~oo 

# i I 

# i 1**O+-----+----~---4----~--~~--_+----+_--~** 3000 3500 4000 4500 5000 5500 6000 6500 7000 Beating DegreeDaysBase6SF Source: Cohen etal. 1991.



### 15 • Pre-retrofit 

### o Post-retrofitFigure 3.9. National Average Central Air Conditioner (CAC) UEC



### r~~~~~II ii I

_------ ! i------

i 

# +S -~~ ~~ ~~~~~~~ 

# 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

0I! 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

01

es

3 <! Figure 3.9. NatiollJal Average CentralAir Conditioner(CAC) VEe
4000

## I~ 1-' 3500

# 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

Figure3.10. NatiolDai Average RoomAIr 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 thatthe 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 ofdata 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 androom 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 ofefficiency 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 ofthese measures is a ratio ofthe 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 ofthe equipment (The SEER accounts for seasonally induced part-load operation). The average efficiency ofnew 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 ofabout 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 ofresidential cooling equipment

17 

### 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 thatthe 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 ofheating and cooling equipment have been generally rising since the early 1970s, when data are first available. The sources ofdata on BVAC equipmentefficiency trends include appliance manufacturers trade associations (AHAM 1991; ARI 1991; GAMA 1991). Fuel-fIred furnace and boilerefficiencies 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 ofefficiency 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 ofthe useful cooling or heating provided, in kBtu, 

### to the electrical energy required, in kWh. For room airconditioners, the effIciency measure 

### is the Energy Efficiency Ratio (EER), which is based on full load operation ofthe equipment. (The SEER accounts for seasonally induced part-load operation). The average efficiency ofnew 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 ofequipment include both new construction markets and replacementmarkets. Gas furnaces represent the major portion ofthe residential heating equipment market, with current sales around 2 million units per year. Heat pumps are the major central heating competition forgas furnaces, with CUlTent sales ofabout 0.75 million units peryear. 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 boilerefficiencies are not well known

..Air conditioning equipment efficiency has also risen dramatically over the last 20 years, as have shipments ofresidential cooling equipment

17Figure 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 etaL 1980; LBLREM 1991 forFurnaces; ARI 1991 forHeat Pumps. Shipments not adjusted for imports, exports, ornon-rsidential uses. Flgure 3.12. Shipment-Weighted Efficiencles forResidential Furnaces and Heat Pumps, 1975.1990


### 80

75 

### 70 "
55-= '

_ _ _ _ 

### ' i'

. 

### ' ~ 

### m 160

- Heat Pump i 

### Ii 

# 50~~~ ~ 

# t ------ I ------ i ---- 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 tobe 100% efficient



### 18 Figare3.11. Amlul Residential FurnaceandHeatPump**Shlpm~ts,** 1951-1990
2S**~----~------~----~------~-----r------~,------~----~** 

# I ! 

# I :! 20 ----- Elce 

### I I \ 

## II' i 

## -V 

# -----Qas 

# 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 etal. 1980; LBLREM 1991 forFurnaces; ARI 1991 forHeatPumps•. ShipmentsnotadjlJsted for imports. exports, or**nOD-~dentiaI** uses. 1990

FIgure3.U. Shlp,ment-WelghtedEfOciencles for ResidentialFurnacesandHeat Pumps,.197S-1990


### 80



## i' 

# E70**~---'~~-----+-------+------Tr-----+------+-----~~--~**

### ~ ;

### ~**~+-** 

### ____**~**

### ___**~~~_-+**

### _______**~**

### _____**~~**

### __**~**

### _____ 

## +i

_____**~**

### ~ 

# i

_ 60**+---.----+------r------+-------~----~--- ~**  • Oil 55**+-----~----~-------+-----~----~----** 

# i

------ HeatPump**SOi-~,--~---~------+_----~----~-----+_----~----~** 

### 1975 19TI 1979 

### 1981 1983 1985 1987' Source: US DOE 1982b; GAMA 1991 for Fumaces;ARI 1991 for HeatPumps. ElecuicFurnaces assumed to be 100% efficienL



### 18 

### 1989 1991gure3.13. 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 asup to 300 kBtu perhour. ipments not adjusted for imports, exports, or non-residential uses. yure 3.14. Shipments ofDirect 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 11VntdS-Wal 1 1 E Direc VntW ll I TR Floor Furme I Vntd Room

=I IUnvntd Room -- TOTAL Shipments

arce: GAMA 1991.


### 19 1990

1200

1000

800 k

Soo

9 

### I 600 C
a 

### .3 400

2

200

0 __

gore3.13. Annual BollerShipmentsfor Residential SizeBoilers, 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 etaI. 1980. sidential size defmed as up to 300kBtu perbour. ipmenu not adjustedfor impOMS, exports, or non·residential uses.**~re** 3.14. Shipments ofDIrect HeaUng Equipmentfor Gasand 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 DirectVatwill 1111111111 AoorFunIace

--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 ] VDIdRoomFigure 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 andHP); AHAM 1991; Fechtl et aL 1980 (RAC). Shipments not adjustd for imports, expots, and non-residential uses Data are for CACs of <65kBtuh and IHPs of<65 kBtuh. Figure 3.16. Shipment-Weighted Efficiencies forCooling Equipment, 1972-1990 1990

P 10

9

U = .E

2--% C 

### E 8

7

6

5 -- Iiiii i 2 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 :. 

## -"-' ..• 

## - .

FIgUre 3.15. AlltnDalI Residential Cooling EquipmentShiplDeats, 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 (Citeand HP); AHAM 1991;Fechtc1 etal. 1980(RAC). Shipmems Dotadjusted for impons. expons.and non-residential uses. Dataare for CACs of 

### <6S kBtuh and

### lIPs of<.65 1cBtuh. .Flgure 3.16. Shipment. Weighted EfficiencieslorCooling 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, CACandHP 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 bemajor sources ofinefficiency in overallheating 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 thequality ofthe 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 ofthe heat losses in ducts are attributable to air leakage, and halfare due to conduction. 'Theyalso estimate thathydronic 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 ofthe 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 theresidential forecasting database, we set distribution system efficiency for forced air systems at 80% in the Northregion, 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 dataare 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 ofheating 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 ofenergy conservation potential for new equipment (ADM 1987). The database gives coefficients that are used in the equation with the associated data found inTable 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 ofthe 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). ___ =



### ,Distribution System Efficiency 

### Recently, residentialheating and cooling distribution systems have been shown to 

### be 

### major sources ofin,efficiency in overallheating 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

### ofthe construction in

### addition to the ductlocation. 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-ruMofthe heat losses in 

### ducts are 

### attributable to 

### air

### leakage, and halfare 

### due to 

### conduction. 'They also estimate thathydronic systems are

### typically 90% 

### efficientin single- , family buildiltlgs and appI'Qximately

### 70% efficientinmulti-family buildings. , ' Modera (199:3) estimates that distribution system performance in 

### new construction is ofthe 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 systemshavethe 

### same performance characteristics. For theresidential forecasting database, we setdisaibution system efficiencyfor forced air 

### systems at 80% in

### the North**regio~** 

### where basements are 

### the predominantfoundation 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 costvs., 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 ofheating 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 ofenergy conservation potential for new equipme.nt (ADM 1987). The database gives coefficients that

## are used in 

### the**equatio~** with 

### the associated data found inTable 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 ofthe system. The. cost,lefficiency datais 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. Parametersfor 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 ofEquipment is afunction ofCapacity 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 ofcost with respect to efficiency
(1)Heatpump (HP) cost arebased on data for split systems. Hydroic (H20)costs are based on data for hotwater boilers. Electric oom(ERM) costs are based on data for elecric baseboards, with increasing capacity frmnadding additional baseboards. (2) Base cost capacity, and cost vs.capacityrlationship fromMEANS 1992 residential cost data (MEANS 1992). Converted to 1990 using the producer price index. Costs include installation but notthermal distribution system. (3) Costvs 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 ofcurrent units. and are only used as areference pointforcost purposes. (5) HP base unit HSPFis 7, and HP base unitheating capacity is 36 kBtuh.To first approximation, HSPF and heating capacity scale more or less linearly with their cooling counterparts. Valid Rangs forEquipment Cost Functions . · Heating Output Capacity (kBtnh) Efficicncy End-use System Technology 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 HWBoiler Gas 80 203 68 90 AFUE Heating Hydronic HWBoiler 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 Tahle3.3. Parametersfor NewSiagJe-FamiJyHVAC EquipmeatCostFUIlcUoDS 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 CostofEquipmcut is a fuDCtion ofCapacity and Efficieoc:y

## accarding tothe following equation: Cost: (b**+m·[C~IJ)·(ElE1)Aeff** b = C3StatBase Capacityand Efficicacy (S) E= Equipmcut EfficieDcy m= O:JStSlape (SJkBtuIbr) E1 

## = Base Efficic:ucy C

## = DlUiPu=tCapacity (Outpal.tBtuIbr) ef(

## =ElasticityofcOst with respect toefficiency Cl :**B;~**Capacity(Output,kB*) (1)

### Hc:atp1Dllp (lIP)costs arc basedem datafor splitsystems. (H2O) costs an: based OD datafor botwataboilers. ElCClricroom(ERM) costs an: based CD datafarcJccP: baseboards, with iDaeasiDg c:apacityfran

### addiag additicmaJ. baseboards. (2)Base cost, Q)pacity. BDd. costvs. capacityn:latioDship fromMEANS 1992resideatial costdata(MEANS 1992). Converted to 1990$ DSing the producer.price index. Costs iDcludc iasta1latioo but Dot thermal dislributiOD system. (3) Cost vs c:fficiencyrelaQoaship from ADM 1987. Converted to 1990S using the producerprice iPde1. (4) Base efficiency and capacityan: Dot DecessarilyIbc typical efficiency aDd capacity ofcmrentuuits. and are only used as I n:feRuce pointfor costpurposes. (5) HPbase unit HSPFis 7. and HPbase UDitheating capacityis 36kBtuh. TofU'Sl approximatioD. HSPF lUld heaziDgcapacity scale more orleSs 1ineariy with theircooling**co~tcrparts.**



# - Valid**RaDg'~** forEquipmaatCost 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 HeatPump Electric 18 60 6.8 i4.7 SEER Cooling Room RoomAir' Flectric 6 21 9.3 13.5 EERTable 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 foothouse. 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 andModem 1993, andJansky and Modem 1994. Base case efficiencies for hydronic systems from Andrews and Modera 1991. Savings estimates from Andrews and Modera 1991. Wecalculate efficiency from their energy savings dataas efficiency = base efficiency/(l-savings (%)). Duct leakrepair costs from Proctor 1992b, $300 ($200 labor, $100 materials) for65% tighter duct systemL We assume twice this cost will achieve ducts that are 80% tighter than the base case. Duct insulation costsestimated 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**~able** lA. DistIribatioD System Cost, Size, and EmdencyRelationships Cor SiDg1e-Family Housing (Costs for 1750 Squarefoot 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 lireiIIlStaUedcosts to consumerincluding all contractormarlwps. 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) dataprimarily dcri.vedfrom 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 airsystems from Madera 1993. Treidler andModem 1993, andJansky andMadera 1994. Base case efficiencies forhydronic systems from Andrews andMadera 1991. Savings estimates from Andrews and Madera 1991. We calculateefficiency from their energy savings data asefficiency

# = base efficiency/(1-savings (%».**Du~** leakrepaircosts from Proctor 1992b, $300 ($200 labor, $100 materials) for65%**tigh~**duct**~L** We assume twice this costwillachieve ducts that are 80% tighterthan the base case. DuctiJasulaDon**co~ts**estimated at$798for RS-8 from MEANS 1992for 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 ofthe lifetimes ofheating 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 (firstowner 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 

### Product Lifetimes 

### The database contains estimates from several sources ofthe 

### lifetimes ofheating 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 i Llf i**~quipment** et 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 Datafor Shell Measures The database includes the costs for various levels ofefficiency ofthe major building heat loss and heat gain components. The costs for new buildings arethe 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 forsingle-family buildings. The database includes shell measure costs for new single-family, multi-family, and manufactured home building types on acost per square foot ofcomponent basis for roofs, walls, underfloor insulation, and windows; cost per linear foot offoundation 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 perfloor 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 inTable 3.7. The database also includes retrofit measure costs for single-family building types only. The cost units are the same as fornew buildings. These data are provided byregion and as national averages, and are derived from previous LBL work which relied on a variety of regional studies ofbuilding retrofit costs (Boghosian 1991). The costs for single-family buildings are shown in Table 3.8. 3.4. Fuel and Equipment Shares Market shares ofheating and cooling equipment are included inthe database in two places. First, shares ofheating 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 ofheating 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 ofthe Census 1990a). Stock Shares Stock shares ofmain heating fuels and cooling equipment areincluded 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 offigures that follow. Figure 3.17 shows the heating fuel shares for 1981 through 1990. The data highlight the slowness ofchanges in housing stock for a major element such as fuel shares. Figure 3.18 shows the breakdown offuel and technology shares forthe 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% ofthe heating systems. Air conditioning shares have experienced large changes during the last decade. As shown in Figure 3.19, the share ofcentral air conditioning (not including heatpumps) rose from about 22% in 1981 to about 32% ofthe stock in 1990. Heat pump shares grew from 3% to 7% over this same period. The percentage ofbuildings with room air conditioners orno 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 ofevaporative coolers.
25 

### 3.3. Techllology Data for Shell Measures The database includes the costs for various levels ofefficiency ofthe major building heat loss andhent gaincomponents. Thecostsfor new buildings are the**increm~ntal** 

### costs from a certain base case level. and represent the incremental costs at the time of original COnStruCtiOIl. Forexisting buildings (orrettofit cases) we 

### only have costs forsingle-family buildings.. ' ' , The database includes shell measure costs for new single-family,**multi~fami1y.** 

### and manufactured bome building types 

### on a cost persquarefoot ofcomponent basis forroofs. 

### walls. underfloorinsulati,on. and windows; costper linearfoot offoundation for slab and heated basementt foundations. and a cost per house basis for infiltration measures., Using theforecasting prototypes. these costs can be 

### converted into costperfloor area data. These data are provided by region and as national averages. and are derived from NAHB data (NAHB 1986). Thecosts for single-family buildings are shown 

### inTable 3.7. The database also includes retrofit measure costs for single-familybuilding 'types only., Thecostunits arethe same as fornewbuildings. Thesedata,are provided byregion and as national averages. and are derived from previous LBL work which relied on a variety of regional stlll(Jiesofbuilding retrofit costs (Boghosian 1991). The costs for single-family buildings aJ'e 

### shown inTable 3.8. 

### 3.4. Fuel and Equipment Shares ' '

. . 

# . Market

### shaJ-es ofheating and cooling equipmentare included 

### inthe database 

### intwo places. 

## rll'St, share:; ofheating and cooling equipment byregion and for the national average are included 

### inthe appliance shares database. Second. we have constructed a data. setwhich estimates HV ACsystem type shares (combinations ofheating and cooling equipment) for bothexisting 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 oftheCensus 1990a). Stock Sbllires 

### Stockshares ofmain heating fuels and cooling equipment areincluded 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 datahighlighqhe slowness ofch:mges 

### in housing stockfor a majorelementsuch as fuel shares. Figure 3.18 shows the breakdown offuel 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% ofthe residential sector. Heat pumps comprise only about 7% ofthe heating systems. Airconditioning shares have experienced large changes during the last decade. As 

### shown 

### in Figure 3

..19, the share ofcentral air conditioning (not including heatpumps) rose from about 22% 

### :in 1981 to 

### about 32% ofthe stockin 1990. Heat pump shares 

### grew from 3% to 

### 7% overthis same period. 

### The percentage ofbuildings with room airconditioners orno 'air conditioning has dropped during this period. Figure 3.20 shows that the' 1990 shares for airconditioning are relatively consistent across housing types, except that manufactured homes have: a much larger percentage ofevaporative coolers.


### 2S 

## ...e 3.7. Shell Measure Costs for New Single-Family Buildings Component Unit Cost Cost/sqft ofConditioned 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(persqfi 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 IInsulation(persqftof 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 7rInsulation(persqft 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 (perlin.ftofFoundation) 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(perHouse) 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 (persqftofWindow) 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 toRegional 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 forbase 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% offloor-area. One Story Prototype: 1540 sqft, dimensions 28x55 ft. window area= 12% offloor area.



### 26 :e 3.7. SheD Measure Costs for New Single-Family Buildings Component UnitCost Costlsqft ofConditioned floor Area (S1990/sqft)· (1990$) for different prototypes North South US NonhRegion South Region US Region ;1 Region Region Region 1Story 2 Story 1Story 2 Story 1Story 2 Story**~/nsulalion** (per sqfto/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 sqfto/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 JTInsulalion (persqfto/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 (perlin.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 [valionReduclion (perHouse)

## a~ 

# I0.00 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 (persqft 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 

### wILowE. 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 andinflluation 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 etaI. 1994a. Costs for base windows taken from NAHB 1986. Costs premiaforothertechnologies from Eley Associates 1991. Adjusted to Regional costs using MEANS 1989 data. Adjusled from $198910$1990 using CPI inflatorof 1.054. 3)Two SlOry Prototype: 2240 sqft. dimensions 28x40 ft, window area 

### = 12% offloor-area. One SlOry Prototype: 1540 sqft. dimensions 28x55 flo window area

### = 12% offloor area.



### 26'able 3.8. Shell Measure Costs for Existing Single-Family Buildings (Retrofit Costs) Component Unit Cost Cost/sqft ofConditioned Floor Area ($1990/sqft) (1990$) fordifferent prototypes North South US North Region South Region US Region evel Reion Region Regionon 1 Story 2 Story 2 Story Sto ry 2 Story ?ooflnsulation(persqft 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 iddR19 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 addR38 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 addR60 1.47 1.27 138 1.47 0.74 1.27 0.63 1.38 0.69 WallInsulation(persqfi 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 SlabInsulation(perlin. 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 addR5 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(persqftofFoundation) 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 (persqftofWindow) 1Pane 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 andinfiltration measures from Boghosian 1991. Adjustedto Regional costs using MEANS 1989data 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 forother technologies from Eley Associates 1991. Adjusted toRegional costsusing 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% offloor area. One Story Prototype: 1540 sqft, dimensions 28x55 ft. window area = 12% offloor area.



### 27**~able** 3.8. Shell Meas:ure Costs (or Existing Single-Family Buildings (RetrofitCosts) Component Unit Cost Cost/sqftofConditioned FloorArea ($ 1990/sqfl) (1990$) for differentprotolyJiJCS Nonh South US NonhRegion Soudl Region US Region ..evel Region Region Region 1 SlO!Y_ 2 ,Story 1 Story 2 Story 1Story 2 Story ?oo/lnsulalion (per Sf/fto/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 sqfto/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 SlabInsulation (per lin.ftof 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 addRIO 4ft 21.87 18.85 20.49 2.36 1.33 2.03 1.14 2.21 1.24 FloorInsrdalion (persqft 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 (perHouse) reduce ACHby25% ' 

# f 258 223 242 0.17 0.12 0.14 0.10 0.16 Windows(persqftqfWindow) , 1Pane 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 2Pane 

### wiLowE 19.23 16.75 18.11 2.31 2.31 2.01 2.01 2.17 2 Pane 

### wILowE and Argon fill 19.87 17.31 18.71 2.38 2.38 208 

### 2.08 2.2S 2 Pane 

### wiLowE. 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 HeatMInor 22.80 19.86 21.47 274 274 ,2.38 2.38 2.58 Sources: 1) WUlatiOID andinfiltration measures from BoghOsian 1991. AdjustedtoRegional costs using MEANS '1989 dara. Adjusted from $1989 10 $1990 using CPI inflatorofl.054. 0.52 0.11 1.48 1.94 2.17 

### 2.2S 2.24 2.54 2.58

2)WIndow measurecosts from Koomey eta1. 1994a. Costs for base windows raken from NAHB 1986. Costs premia fl)r odlertechnologies from Eley Associates 1991. Adjusted toRegional costsusIng' MEANS 1989 data. Adjusted from $198910 $1990 using CPI inflatorof 1.054. Costs shown are total window costs. 3)Two Stelry Prototype: 2240 sqft. dImensions 28x40 flo window area

### = 12% offloor area. One Story Prototype: 1540 sqft, dimensions 28XSS ft. window area

### = 12% offloor area. ,



### 27Figure 3.17. Space Heating Fuel Shares inTotal Housing Stock, National, 1981-1990
0.60
- 0.50

- A 0.40
- 0

, 0.30--
" 0.20
- 0.10
0.001980 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 

### DOH20 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 hotwater systems, FRN = furnace, RM = room heating. Values are "primary heating fuel" from US DOE 1992.

28 FIgure 3.17. Space HeatingFuel Shares inTotal 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 

## i --to 

# -- , 

## i I : I j I 

# 1 ! I • i 

# - I 

# J ! 1986 1988 1990 

# ------Gas
-G--LPG

• Oil 

### --<>-- E1ec Res

• ElecHP ----fr-- Other



### on includeskerosene. Elec Res

## = electricresistance heating, BeeHP

## = electricheatpumpbeating. Otberisprimarilywood.. Values are "primary heating fuel" from US.DOE 1982a, 1986,1989aand 1992. FIgure3.18. Space HeatingFuelfl'ecbnology Shares by HouseType,National, 1990
0.50**~GH20** 0.45 0.40**~GFRN** 0.35 I!BI GRM 

## e- 

# rn 01120**~** 0.30 Col 

# g 0.25 E30FRN u 

# .. 0.20 CIII 

### 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 orhotwatersystems, FRN= furnace, RM = room beating. Values are "primary heating fuel" (rom US DOE 1992.Figure 3.19. Air Conditioning Shares inTotal 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 Figure 3.19. Air Conditioning Shares inTotal 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 airconditioning, BC

### = evaporative cooler, Fuel AC 

### =gas driven ,air conditioning. In 1990, RAC homes averaged 1.50units. F"tgUre 3.20. Aill" Conditioning Shares in HousingStock by HouseType,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 airconditioning. 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 ofhydronic heating systems in multi-family buildings, 3) the greater percentage of electrically-heated homes in the south region, and 4) the use ofLPG as aheating fuel in manufactured houses. New Shares Shares ofheating and cooling equipment for new buildings are taken from combinations of data from the Census C25 survey (US Bureau ofthe Census 1990a) and the 1987 RECS data forbuildings built between 1980 and 1987 (US DOE 1989a). We have also developed HVAC system shares using these same data sets. Some ofthese 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. Atthe same time, the percentage of new buildings with central air conditioning has been rising dramatically, so that 80% ofnew single-family homes and 60% ofnew multi-family units have central air conditioning installed at the time ofconstruction. 3.5. Forecasting Prototypes For the analysis ofconservation potential from building envelope measures, we define a set ofbuilding prototypes that represent the major characteristics ofthe residential building population. The important parameters include the component areas ofthe building (roof, wall, floor, etc.) and the thermal characteristics ofthose 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 ofthese 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 ofage, 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 ofthe 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 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 ofhydronic heating systems 

### in multi-family buildings, 3) the greaterpercentage ofelectrically-heated homes in the south region. and 4) the use ofLPG as a heating fuel in manufactured houses.

.. 

### New Shares Shares ofheating and cboling equipmentfor new buildings are taken from combinations of data from the Census C25 survey (US Bureau of the Census 1990a) and the 1987 RECS datafor buildings builtbetween 1980and 1987 (US DOE 1989a). We have also developed 

### HVAC system shares using these same data sets. Some ofthese 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. Atthe same time. the percentage of new buildings with central air conditioning has been rising dramatically, so that 80% ofnew single-family homes and 60% ofnew multi-family units have central

### airconditioning installed atthetime ofconstruction. 

### 3.5. Forecasting Prototypes Forthe analysis ofconservation potentialfrom building envelope measures. we define a set ofbuilding prototypes that represent the major characteristics ofthe residential building popUlation. The important parameters include the component areas ofthe building (roof, wall, floor, etc.) and the thermal characteristics ofthose components. The prototypes are characterized from data taken from surveys of either the building stock or recently constructed buildings. OncederIDed. the heating·and coolingenergyconsumption ofthese buildings can be 

### assessed with improvedbuilding components to estimatepotential energy savings.from improvements to the buildingenvelopes. We define building prototypes that representthe 

### 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 ourpart 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 stockincludes a diverse building population 

### intenus ofage,

building size. and insulation levels. we also segment the existing building stockfor 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 ofthe existing stock in thatheating fuel category. We call this fraction the "shell share." "The population ofany specific building prototype is thus the (total stock) 

### X (heating fuel share) 

### X (shell share).

30Figure 3.21. Existing Stock HVAC System Shares for Single-Family Homes: National and Regional
0.50
0.40 0.30 

## a g 0.20 i. At ·- National

-----

.Hat Eqmt -> 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\\\~~~__ ____%X% I_ _ _~ 

# 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

___·- -~ ----------- . ... 'A

# 4

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 0 -NE0.00 H---20 1

_ - 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 =heatpump. RM = room heating, OTHis allotherheating technologics. US DOE 1992 data converted tonorth and south using census divisions andHDDto 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! .___ r f RA r .r-. -----s- ^ X r r ^ * JII I

· I--------------------.----

d---1.11--^- * .^.-*--·_------ ---..-.. ____-____._...__..___.__.__.__.. nMn 

# --- - E



# - F;;; M 

# 6m0

_ W 6 Z Figure3.21. Existing Stock HVAC SystemSbares (orSiagle-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~~~~--~~~ ~~~~** **~ ~** **~ ~** **~.~ ~** **~ ~ ~** **~** HealFuel-> . - Electric- . - Gas

- - LPG

- - 011

- Other**o~o~----------------------~---------------------~----------------~** 0.40 cooling type**~** CAe 

# mHP**~** 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. Otherheating fuel isprimarilywoOc!.. H2O= steam**~ h~**  water'.**J~RN**

## = furnace. HP

## = heatpump. RM

## = roomheating.

### omis all oth"heating·technologies. US DOE

. 1992data CUlVcrted 10north and sOU1h using census divisions andHDD 10approximate the federal region**~~.** . .



### 31ire3.2. Existing Stock HVAC System Shares for Multi-Family Homes: National and Regional

....... National cooling type CAC HP SRAC O NON

-

FRN HP RM H20 FRN RM FRN H20 FRN RM OTH -Electri-

- Gas- LPG -Oil- Other

0.30 --- North------

- 0.20 o00 0.20

. --- - 0.15
a ' 0.10
0.05 

## o.oo , -- -- at Eqmt-> FRN HP RM t Fuel -> - ElectricH20 FRN-GasRM FRN H20 FRN LPG - Oil
-- RM OTH Other
0.35 0.30

-- South -cooling type

_ 0.25

- ---- [--------- H CAC P 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. Otherheating fuel is primarily wood. H20 = steam orhot water, FRN = furnac HP= heat pump. RM = room heating, OTH = allother heating technologies. US DOE 1992 data converted tonorth 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 -: atFuel ->
035

-------- -cooling type'

-___- CAC E HP O RAC [ NON

---
.o r______

- xxcm ---..........--- .____.....__..__.-- 

# g r

^ s LYYY I----- 

### I 

## i _- .............. _. _ ...---- 

# I 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....._---~** :atEqmt -> FRN HP RM :atFuel -> - 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 !atFUel -> - E1ec:trIc

- H2O FRN RM FRN H2O FRN IUd 

### om LPG

- 011

-- Other -Gas-**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. Otherheating fuel'is priMarily wood. H20

## = steam orhot water. FRN

## = furnace. HP

## = heatpump.RM

## = roomheating.om=all otherheating technologies. US DOE 1992 data converted tonorth and south wing census divisions and HOD 10 approximatethe federal region breakdown.



### 32Figure 3.23. Existing Stock HVAC System Shares forManufactured Homes: National andRegional
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 HeatFuel -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 HeatFuel -> -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: USDOE 1992. Oil heating fuel category includes kerosene. Otherheating fuel is primarily wood. FRN =furnace, HP = heatpump, RM= room heating, OTH isall other heating technologies. RECS data converted tonorth and south using census divisions and HDD toapproximate the federal region breakdown.



### 33 FIg1ll"e 3.23. lExlstlngStockHV AC System Sbaresfor**MaDuf~ctured** Homes:.Nationai andRegional
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--,..----·-----cooliDgtypc-------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 HeatFuel->

- 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**~Oil** 

### om Oaher 

### Source:US DOE 1992. Oil beating fuel categoryincludes kerosene. ()lhcrbeating fuel is primarily wood. FRN 

### =fumace. HP

# = bcaJtpump, RM

# = room hcaUng, 

### om is aD otherbeating technologies. RECS dataconverted to north and IOUthusing 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^^:.: :---;--00 

### -0--' 

# G 0o.5o0 ^-
- -: : - -... - ,e

1980 191 1982 1983 1984 1985 1986 1987 1988 1989 1990 i I ' { { t { {

Source: US Bureau ofthe 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 ofthe Census 1990. HP data is from heating equipment and subtracted from total central AC to get CAC. 1990



### 34 i I i 

## I - J



## - - Figure 3.24. Selected SpaceHeat FueVfechnology Shares InNew Construction, Sfngle-FamUy, National
0.60**~-"""--~-""i----:"---"----:----'--~-""'--'"** j**~ ~**  I O :!. 0.5**-+--~---+~--: -r--.;.-~---;--~r--i·**  'Vi' ! i ! GI : I I E I ! ! oj:

. -: 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-- HtPump

--....-- E1ec Fum**~ElecRoom**

Source: US Bureau ofthe Census 1990 dataon beating fuel shares and beating equipment shares. combined using estimates forbealing/technology combinations in US DOE 1989a FIgure3.25. ".fotal CentralACShares (CAC+HP) InNewConstruction, 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 ofthe Census .1990. lIPdata is from beating equipment and subtracted from total central AC to getCAC.

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 ofthe Census 1990; US DOE 1989a for new construction (1980-87). ~-- GasFurn 

### -0-- Gas H20

--- HtPump

-- ElecFum

~-- 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 ofthe Census 1990. HPdata is from heating equipment and subtracted from total central AC to get CAC. 

### 1988 1989 1990

35 

# i
i .-----c---- 

# i

.. . F"lgUre 3.26.Selt!Cted SpaceHeatFuelfl'echnology Sbaresita 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 ofthe Census 1990; US DOE 1989afornew consttuction (1980-87).
Figure3.27.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: 0.40 til .z:. CAe

.**~** 030 0.20 0.10 0.001980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 Source: US Bureauofthe Census 1990. HP data is.from hcclting equipment and subtracted from total central AC to get CAe.



### 35Figure 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 forbuildings 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



### 1980 US DOE 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990includ kesene.
Source: US Bureau o29. C ent C eond ioning data converted t N orth/South by Housing Sarts byState. 0.70



### .' i



### 0.00 j

36 FIgure 3.28. SpaceReatlngFuel SharesInNew 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 forbuildings built 1980-87. Oil**inclu~** kerosene.. 

## Dus .Nonh 

## 1m South

FIgure3.29. CentralAIr CondiUoning Shares (includesHP) InNew 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 : 

# l' 

# =0.40

. : Col 

# 1 j 

## .6- ,l,

### ~

### ~ ..l.. 

# -**~** 

## 'f T 

# 1 ·W 0.30 

### .. 

### - 

## -



# .. i III 

# y- 

# I 

# I .c: 

# • US VI 

### 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 oflhe 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 setofprototypes 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 forindividual 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 newconstruction 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, foreach heating fuel type and "tight" and "loose" thermal shell package in each region, we calculate the number ofbuildings represented, average building conditioned floor area, typical foundation type and number ofstories, 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 ofthe "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. Thefuel heated buildingstend to be older, and thus, less well insulated. New Single-Family The new single-family prototypes for theNorth 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 ofHome 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 "



### Existing Single-Famil.y Forexisting single-family buildings, we developed a new setofprototypes 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 expectthatbuildings heated byelectricity will. 

### in general, 

### be newer and 

### bettel'inmllatedthan those heated by natural gas oroil. Therefore, weuse theRECS 

### data to defmetll.e prototypes, sU'pplemented by datafrom the GR! and LBLprototypes where the

. RECS dataare eithernotcomplete orhave missing data forindividualhouses. Ultimately. the prototypes defined in this work provide similar results in terms of component specifications and baseline heating arid cooling loads

### to thosefrom the other studies. TheRECS data setis stratiflCd byregion, and for each samplebuilding wecharacterized: 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 windowsinthe house. Loose buildings.are assumed to 

### be easily and cost·effectively insulated, whereas tight buildings are already somewhat 

### insUlated.**Buildin~i** representing newcoilstnlction 

### inthe period 1987-1990 are added to the dataset as "tight" buildings (see the New Single Family prototypes) to fully characterize the housing stockin 1990. Fmally,for each heating fuel type and "tight" and "loose" 

### thermal shell package 

### in.each region,.we calculate the number ofbuildings 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, andare then converted back to R-values to more accurately characterize overall building heat: loss. All buildings are assllmed to be**wood~frame** walls and roofsystems. The{mal ,specifications are given inTable 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, thatfor 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. Thefuel heated buildingstend 

### to be older, andthus, 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 ofHome Builder Annual Builder Survey (NAHB 1989) as described elsewhere (Koomey et ale 1991b). 

### lbese buildings are significantly better insulated than theexisting buildings. with ceilings up to 

### R30, walls above 

### RU, and double-glazed windows with foundation insulation, yetalso have significantly largerconditioned floor areas. 11)e specifications are found in Table 3.9.

.

37Table 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 (%ofstock) Type (sqft) Stories (R) (R) Layers ELF ACH (R) Config. EXISTING BUILDINGS (Po ilation is percent ofexisting 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 SouthRegion 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 ofnew construction) NorthRegion 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 newprototypes from Koomey et a 199la. Populations by heating type from US DOE 1992a. 2) Data fromBoghosian 1991 and Ritschard et aL 1992a for roof,wall, foundation, and window measures are used where data not available inUS DOE 1992a. 3) Breakdown between "Tight" and "Loose" determined approximately asfollows (see writeup): North: "Loose" has roofR-value<10 or wall R-value<4 and average glazing layers<l.7. All others "Tight". South: "Loose"has roofR-value<10 or wall R-value<4 orwall 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 ofthe Census 1990 series heating fuel shares in new construction. Existing and New: 5) Component dimensions are not shown here butare included in the database. 6)Window area assumed as 12% offloor 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. T bl 3.9

a e • BuUding and Thennal Characteristlcs ofSingle-Family BundingPrototypes Cond. Foundation Regional Floor" No. Insulation Heat Sbell Popln. Fodn Area of Roof Wall Glazing Infiltration Floor Perim. Type Group (% ofstock) Type (sqft) Stories (R) (R) Layers ELF ACH (R) Config. EXISTING BUIlDINGS (PO] ulationis percentofexisting stockin 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 ofnew 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 andwindow glazing layers from 1987 RECS data. updatedto 1990populations using newprototypes from Koomey et. al. 1991a. Populationsbybeating type from US DOE 1992a. " 2) Data fromBoghosian 1991 andRitsclwd etal. 1992aforroof. wall. foundation. and window measures are used where data Dot available inUS DOE 1992a. 3)Breakdown between "Tight" and "Loose" determined approximately asfollows (seewriteup):

North: "Loose" basroofR-value<IO orwall R-value<4 and average glazing layers<l.7. All others "1ight". South: "Loose" basroofR-value<IO orwall R-value<4 orwall R-value:<7 and average windowlayers<l.4. New Single Family: 4) Prototype descriptions from Koomeyetal. 1991b. aspresentedin Koomey etaI. 1991a. Original data source is the 1987 NAHB Builders Survey data (NAHB 1989). Populations by heating typefrom US Bureau ofthe Census 1990 series beating fuel shares in newconstruction. Emlingand New: 5) Componentdimensions are not sbown here butare includedin the database. 6) Wmdow area assumed as 12% offloor area 7) Wall beight assumed to be 8 feet per story in alllocatioDS.

_ 8) Infilttation air changes perbour (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 percensus region. The combination produces 60 differentcombinations ofcities 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 theprototypes 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 ofthe 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 foreach 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 ofprototype 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 ofinsulation packages and window configurations in 45 different climates. Changes in building loads from improvements to single building components are reduced to a set ofcomponent 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 ofanother. 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 effectofinternal gains and other non-temperature related effects. 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 percensus'region. Thecombination produces 60 differentcombinations ofcities and building prototypes.' Fortheprototypes 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 ofthe 

### 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 

## apre-1980 and post-1980 vintage in the exiSting stock foreachregion 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 inTable 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 componentloads generated from nOB-2 

### simulations ofprototype buildings done under ASHRAE Special Project S3 ($PS3) 

### (Huang et al. 

### 1987b). The building prototypes considered in this projectinclude a one-story single-family building. a two-story townhouse, and an apartment module. Simulations are performed with a wide variety ofinsulation packages and window configurationsin45**diff~t** 

### climates. Changes in building loads from improvements to single building components are reduced to a setof(:omponentloads 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 componentU-value or some other measure ofthenna! integrity. Each heat gain or loss component-is considered to be independent ofanother. 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 effectofinternal gains and other non-teinperaturerelated effects.


### "'''Table 3.10. Buiding and Thermal Characteristics ofMulti-Family Building Prototypes Cond. Foundation Regional Floor Insulation Heat Shell Popln. Fndn Area Roof Wall Glazing Infiltration Floor Perim. Type Group (% ofstock) Type (sqft) (R) (R) Layers ELF ACH (R) Config. EXISTING BUILDINGS (Population is percent ofexisting 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 SouthRegion 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) NorthRegion 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 SothRegion 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 andHuang 1989. 2)Prototype populations and heating typesupdated using US DOE 1992 data for existing stock and US Bureau ofthe Census 1990 data on heating fuel shares in new construction fornew 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 T bl 3 10 B ild·

a e • 

# . UI lOR aDdTh ermaleba racteristlCS 0

## rMI· F •• B ·Id· Pr u b- amll}' UJ lOR ototypes Cond. Foundation 

### Regional Roor Insulation Heat 

### Shell Popln. Fndn Area Roof Wall Glazing Inflluation Floor Perim. -Type Group_ (% ofstock)**T~** (sqft) (R) (R) Layers ELF ACH (R) Config. EXISTINGBun.DINGS (population ispercentofexisting 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 SouthRtgion 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 BUD..DINGS (Populationispercentofnew 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} Prototypecharacteristics from Ritschardand Huang 1989. New Prototype is 1980sprototype from Ritschard and Huang 1989. 2)Prototype populations and heating typesupdated using US DOE 1992 dara for existing stockand US Bureau oftheCensus 1990dataon heating fuel shares in new construction for new buildings. , 3) Building dimensions arenot shown here. but are included in the database. Building dimensions are averages aaossall units in building lypCS, including bottom/mid/trip floor units and middle/end units (e.g

.. foundation perimeteris exposed perimeterlength). 4)Airchanges per hour (ACH) calculated &om EquivalentLeakage Fraction (ELF) givenin Ritscbard and Huang 1990 using simulated ACHin Huang et al1987b assuming ACHis for heating season.

40Table 3.11. Building and Thermal Characteristics ofManufactured 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 ispercent ofexising stock in 1990) NorthRegion 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 ofnew construcion). North Region All All 1009o Crawl 1195 1 26 18 2.0 0.00028 0.36 14 n/a SouthRegion 

### All All | 

### 100% Crawl 1195 1 20 12 1.3 0.00042 0.45 10 n/a 1) Prototype caracteristcs from Koomey etal. 1991a 2) Prototypepopulations and heating types are updated using US DOE 1992 data for existing building stock. Because oflimited data,new buildings are not segmented by heating type, and we assume there is not a strong correlation between heating fuel and thermal integrity fornew buildings. 3) Building dimensions are not shown here, butare included in the database. Foundation dimensions arebased on average width of20 feet (average between single and double-wide). 4) Equivalent Ieakage Fracion (ELF) calculated from air changes perhour (ACH) givenin Koomey etaL 1991a using simulated ACH in Huang etaL 1987b assuming ACH isfor heating season.

. . .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<



### 41 "

Tb B 8 Ie 3.11.**Uultdln~** andThermalCharacteristicsorManuractured Home undln2Prototypes Cond. Foundaticm Regional Floor No. lDsDladon Heat SbeD Papin. Fneln ,Area of Roof Wall Glazing InfiJtratioo 

### Floor Perim. Type Group (9& ofstock)**T~ (~ft)**  Stories (R) (R) 

### J..avers ELF 

### ACH (R) Config. EXISIlNG BUILDINGS (Population is

### percentofexisting '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 SouthRegion 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 NEWBUILDING!) (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 SouthRegion All All' 100% Crawl 1195 . 1 20 12 1.3 0.00042 0.45 10 

### oJa 1)Prototypecbaracteristics from Koomey etal. 1991a 2) PrototypeP'lpulatiODS and heating types arc updated using US DOE 1992 datafor existingbuilding stock. Because oflimited data, new buildings are notsegmented byheating type, and 

### we assume there is Dota strong c:ouelatioo between healing fuel andthennal integrityfornew buikimgs. 3)BuildingdilneusiODS 

### Ire not sbown here. butare included in the database. Foundationdimensions arebasecII on average widlh of20 feet (averagebetweeD single and double-Wide). 4) Equivalentl.cabgeFraction (ELF)calcula!edfrom aircbangesperhour (ACH) given inKoomeyetaI. 1991ausing simulated ACH in Huang etal. 1987b AASllII1ing ACH is forheatingseason..



## ,.

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 ofthose locations in this project. We useWashington 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) ismultiplied 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 ofuse. 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 ofnewer 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 designenergy use forbuildings, 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 There are two ways 

### this database can be used. FlI'St, 

### the database gives componentloads per unit ofcomponent 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 

### projectincludes simulations for 45 

### different locations. We consideronlythree 

### ofthose locationsin

### this project. WeuseWashington DC to 

### represent the national average climate. Chicago 

## n. to represent the North region. and Charleston. SC to 

### represent the South. The componentloads for these locations are given 

### inTables 3.13 and 3.14. These component loads are additive. Forexample. ceiling area is multiplied by the appropriate ceiling load. the appropriate foundation dimension (square feet orlinearfeet) is multiplied bythe 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. Thecoefficient methodology is used 

### within the database tp 

### calculateheating and cooling loads. The U-value assumptions for the 

### differentcomponentconstructions are given 

### in Table 3.17. Windowshave 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 ofuse. These are shown in Table 3.18. 

### Insome ways. the SPS3 databaseis 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 energyuse for buildings. and werenotmeant to 

### represent actual conditions 

### inreal life. Forexample. 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 ofthe building. To accountfor differences between the designenergy use and actual field usage. the building loads are calibrated to the baseline UEC derived from other data. Thisprocess 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 coefficientmethod described above.

42Table 3.12. Building Heating and Cooling Load Calculation Methodology Building load (MMBtu) = roofload + wallload + fndnload + infilload + windload + solarload + resload, whee: roofioad ,aheating orcooling load from roof walload Is heating orcooling load from walls fndnload =heating orcooling load from foundation infilload heating orcooling load from infilration windload =heating or coolingload from conduction through windows solarload heating or coolingload from solar gain through windows resload = residual heating or cooling load 

### Method1: ComponentloadsgivenaskBtupersquare footorkBtuperlineal foot aremultipliedby the component dimension. These valuesaregivenin Table313and314 Method2: Componentloadsarederived from thecomponentdimension, thethermal parameterparticular to the component,andthe comoonent.coeficientsriven in Table3.15. and3.16as 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 inF-day/yr 

### curvein F-day/yr).,and interept inkBtu/ft 2 (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 slopein F-day/yr curve in (F-day/yr)2 intercept inkBtu/ft Infiltration load (MMBtu) = flooraa*((ELF*1000)*slope + (ELF 000)2*curve)/1000 with: floorarea in ft 2 (total conditioned floor area ofbuilding) 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 fourcardinal directions (N E, S.and W) with: windarea is window area inft2 shadco isthe glazingshading coefficient alphas inkBtu/ft 2 are preliminary solar load estimates assuming a linearrelationship with window solar aperatue (rea * shading coefficient) b. adjusted solar load 

### A * (1 +Beta A) with: A is the sum ofthe preliminary solar load estimates from above (MMBtu) (1+ Beta *A) is a dimensionless term for solar usability to account forits deacreasing effectiveness to offset heating and increasing penalty to increase cooling loads. This usability is a linear function ofthe 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 - 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 beatingorcoolillg load from tool walUoad 1= heating

### orcooling load from walls , fndnload:: beatingor

### coolingload from foundation infiUoad I:he.'Uingorcooling load from infaJlnUion wiDdJoad I:**h~g**

## orcoolingload from conduction through windows soJarload a**h~g** orcooling load from solargain through windows resload c::residualheatinrorc:oolirutload ' Method 1: CompollelllloadlgivDI tUkBtllpeTsqlUiTe/oOiorkBtllperlineal/DOt

### ore 'mIdtiplied by thecomponent dWrasion. TMse valuu megiwn in Table 3.13 and3.14 Method2: Compoll'Dllloads ore derivedfrom lhe componenldimension. 1MIhennDlpaTtllneler paniclllQr10 the

. companen' and lhe cOmDonent,coefficienls fiven in Table 3.15.and3.16 asfollows:

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-ft l slope inp.-day/yr 

### curve

### in (F-day/yr)l,

### and interceptinkBbJ/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** ' 

### slopein F·day/yr 

### curve in(F-day/yr)l interceptilllkBbJ/ft InfiltratioD ' load (MMBtu)

# = Ooorarea*«(ELF*l000)*s1ope+(ELF*IOOO)2*curve)/lOOO with: 

## tloOrarea illI ft2 (tara!conditioned floorareaof**buil~g)** ELFdimensionless (leakage area/lOtal conditioned floormea) slope in**kB~.OOI** ELF, curve inkBtuJ(O.OOl 

### ELF)2 Window Solar a unadjusted

### Solar load;

. A (MMBtu) I:**L<windarea"shad~a1pha)/lOOO** overthefour cardinal directions (N? E. S,and W) with: windareai:; window areainft2

. sbadco islItle glazing shadingcoefficient ' alphas in kBtu/ft 2 are preliminary solar load estimaaes assuming a linearrelationship,with window solar aperature (lues.

### * shading coefficient) b. adjusted solarload: A" (I +Beca

.. A) with: A is the mm ofthepreliminary solarlOad estimates from above (MMBtu) (I + Beta .'A) is a dimensionless tennfor solarusability to account for its deaaeasing effectiveness to offset healing &nd increasing penalty to increasecoolingloads. This usability is a linearfunction ofthe total building solar heat gain (A). Residual Joad (MMBtu)

### =resiid (MMBtu) Source: Huang eta:1. 1987b. Values for Beracan befound 10 rabies 10this repon.

. ,


### 43Table3.13. 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/sqftofceiling) R-7 10.21 2.89 13.73 2.17 5.69 3.05 ceiliginsulation 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

anddepth 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

anddepth 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 offndn) 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 offloor) 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 ofwindow) 2-Pane(U=0.49) 53.20 0.95 73.47 1.08 15.99 .6.47

numberofpane 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/sqftofwindow) 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 supportofASHRAE Special Project53. Component loads are additive. Simulations assume thermostat sctpoints of 70F forheating with no setback and 78F for cooling with nosetup, typical internal gains, and window shading coefficients of0.80 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) forELF=0.0007, 0.0005,0.0003, respectively. 3) Window solar loads given are for windows @ 12% offloor area, equally distributed on four sides of the building.

44 Table3.13. BuDding Compoaeat Loads (orSlagle-FamilyBaJldlnes {alsoused (orManu(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 (lcBtulsqftofceiling) 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 (kBtulsqftofwall) 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 jPerimeterR-vaUle R-5 4ft 12.15 -6.90 22.01 -S.49 19.32 -41051

anddepth 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 ofbsmt) 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 UDbeateclBsmt R-O 8.61 0.89 12.61 0.26 5.69 -2.34 (kBtulsqftoffudn) R-ll fir 1.34 2.53 3.15 1.59 258 -Ul9 IlNierjfoorR-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 (kBtuIsqftoffndn) R-ll fir 1.34 3.73 3.93 2.71 3.10 0.01 IlNierjfoorR-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 (kBtulsqftofftoor) 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 WladowCoadacdoD I-Pane(U=1.l0) 112.34 2.09 158.16 2.43 45.91
. -7.28 (kBtulsqftofwindow) 2-Pane (U=O.49) S3.20 0.9S 73.47 1.08 . 15.99 -6.47

I1II1ftberof 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 WindowSolar 1.00

. -53.09 40.88 -70.68 31.08 -31.s8 64.76 (kBtulsqCtofwindow) 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 ResidualLoad (MMBtulunit) 1.98 -2.06 2.79 -1.96 -0.18 9.38 1)Compoaeutloads arc from OOE-2 simulations done inHuang etale 1987b. in supportofASHRAESpecial Project53. Component 10ads arc additive. Simulations assume thermostat sctpoints of70Ffor heating with no setback and 78Ffor cooling with no setup. typicalinternal gains. and window shading coefficients of0.80 during winter to account for framing effects aad 0.60during summer for shades above the glazing SC given in the table. 2)Forinfdtration, 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% offloor area, equally distributed on four sides of the building.

44Table 3.14. Building Component Loads forMutl-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/sqftofceiling) 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 ofwall) 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 ofslab) 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

anddepth 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 anddepth R-104ft 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 (kBtusqftoffdn) 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 CrawlSpace 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 offloor) 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 numberof 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 Shadingcoefficiet 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 ofASHRAE Special Project 53. Component loads are additivc. Simulations assume thermostat setpoints of70P forheating withno setback and 78Ffor cooling with no setup, typical internal gains, and window shading coefficients of 0.80 during winterto account forframing effects and 0.60during sunmer for shades above the glazing SC given in thetable. 2) Forinfiltation, air danges per hour(ACH) during heating season areWashington (0.83.0.58,0.35), Chicago (0.89.0.660.40), and Charleston (0.740.53,032) forELF=0.0007, 0.0005,0.0003. respectively. 3) Window solar loads given are forwindows @ 12% offloor area, equally distributed on four sides ofthe building.



### 45 T bl 3 14 B Ddl C

a e • 

# . u**nl~** ornponent 1.0 cis fI M III F U B IIdl a or u • amJ, u**n~** Component Component US (Wabington DC) North(ChicagoIL) 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/sqftofceiling) 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/sqftofwall) 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 ofslab) R-' 2ft 29.52. ·15.17 49.66 -11.39 12.74 -79.04 puinraerR-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 ofbsmt) 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 UnheatedBsmt R-o 5.48 0.41 8.08 0.04 2.29 ·2.33 (kBNlsqftof£rlCin) R-U fir 1.87 1.83 3.so 1.02 0.97

. -U1 IIIIIlDfI«nR-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/sqCtof£ruin) R-UOr 3.30 3.20 6.36 2.17 2.34 -O.os. IlniMrfIoorR-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":lftoffloor) 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

nuntberof 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 WlbdowSolar 1.OD -54.82 40.34 -72.79 .30.40 -33.47 64.87 (kBtulsqftofwindow) 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 ResidualLoad (MMBtuiunit) 1.28 4.18 1.25 2.56 3.22 .10.78 1)Component loads m:c from DOE-2 simulationsdonemHuang Clale 1987b, in supportofASHRAESpecial Project 53. Component loads are Itddilivc. Simulations assume IhemostatlClpOints of70PfOr heating withno setbackand78Ffor cooling withno setup, typicalintanal gains. andwindow shading coefficients of0.80 during winta'to accounlforframing cffecu and0.60durinl: summerfor shades abovethe glaziDgSC given in thetable.

. 2)Forinfillralion. air dwlgesperhour(ACH)during heating season areWashington (0.83.0.58,0.35), Chicago**(0.89,O~66,O.40).** andCharleston (0.74,0.53.0.32) forELF=O.OOO7, 0.0005, 0.0003, respc:ctively.

. 3)Window solarloads given are for windows@ 1290 offloor area, equally distributed onfour sides of"the building.

45Table 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 Table3.15. Building ComponentLoadsCoefficients 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 etaI. 1987b. For a description ofhow to use these coefficients. see Table 3.12.



### 46Table 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 ofhow to use these coefficients, see Table 3.12.



### 47 Table3.16. BUliidilllg ComponentLoads 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 ofhow to use these coefficients, see Table 3.12.



### 47Table 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 WoodFrame Window, 80%glass, double clear glass, 1/2" air space 3.0-gla 0.30 0.61 WoodFrame 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 WoodFrame Window, 80%glass, spectrally selective double glass Super 0.20 0.51 WoodFrame Window, 80%glass, superwindow HMirror 0.29 0.39 WoodFrame Window, 80%glass, heat mirror surface Floors ROO 021 Uninsulated 2x10 floor over basement or crawl space (crawlor 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 offoundation concrete an insulation, ifany, 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 forwhole window unit, including the window frame.



### 48 ____

Table 3.17. Construction Type and U·value and ShadingCoeflicientAssumptions Component Construction U-val SC Construction assumptions Roof ROO .0.25 Uninsulated ceiling belowawe 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 Uninsulated 2x4.wood frame wall R07 0.11 R07 insulated 2x4 wood frame wall Rll 0.09 Rilinsulated lx4 wood frame wall R13 0.07 Rl3 insulated 2x4 wood frame wall RI9 0.06 RI9 insulated lx6wood frame wall R27 0.04 RI9 insulated 2x6 wood frame wall with insulated sheathing R34 0.03 Rl9 insulated 2x6 wood frame wallwith 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"airspace 2-gla IoE 0.36 0.59 WoodFrame Window. 80%glass. low emissivity film 2-gla IoEAr 0.30 0.59 WoodFrame 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. heatmirror surface Floors ROO 0.21 Uninsulated 2x10 flooroverbasementorcrawl space (crawl or R11 0.07 RII insulated lxlOfloor overbasement or crawl space unheated RI9 O.OS Rl9 insulated lxlO floor overbasement or crawl space basement) R30 0.03 RJO insulated 2x10 floor**ov~r**basement or crawl space R38 -**-0~03--** RJ8 msulated 2x10 floor overbasement or crawl space R49 0.02 R49 insulated 2x10 floor overbasement or crawl space Slab R-O 0.48 Uninsulated Slab R-S2ft 0.25 Exteriorvertical 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 Exteriorvertical 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 Exteriorvertical basement wall insulation todepth andR-value listed R-I04ft 0.67 Exterior vertical basementwall insulation to depth and R-value listed· R-S 8ft 0.67 Exterior vertical basement wall insulation todepth and R-value listed R·IO Sft 0.45 Exteriorvertical basementwall insulation todepth and R-value listed I) AU U-value assumptions from SPS3 project (Huang etal. 19S7b) for insulated components. Foundation (Slab and Heated Basement) U-values are the U-value offoundation concrete an insulation, ifany, and are not the effective U-value ofthe 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.



### 48Table 3.1L. Window Component Loads for Specific Glazing Types 

### Component Loads (kBtu/square foot ofwindow) Locaion/ Heating Cooling Window Type U-value SC Conduction Solar Total Conduction Solar Total WashingtonDC(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-glaloE 036 0.59 54.5 -44.0 10.4 0.8 17.6 18.4 2-glaloEAr 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 inHuang etal. 1987b. Values calculated for One Story Prototype, 1540 square feeL Window area assumed as 12% offloorarea, equally distributed aroundfour sides ofbuilding. Window U-valies and shading coefficients are from Koomey et a. 1994a.

49 _ _

Table3.Ut. Window ComponentLoads for SpedftcGlazingTypes
; 

# - ComponentLoads (kBtulsquare footofwindow) 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 inHuang etal. 1987b. Values calcula1ledfOr One Story Prototype, 1540 square

### feet: Wmdow area assumed as 12% offloor

### area. equally distributed aroundfoUr 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 forthe 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 ofbuilding 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 ofthese 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 ofthe amount oferror in the model used to calculate UECs from building loads and equipment data. This calibration multiplier is then applied to the UEC calculated fornew buildings to determine the database UEC for new buildings. The calibration ofthe heating and cooling energy use model is shown in Tables 3.20 and 3.21. The magnitude ofthe 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 multiplieris 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 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 thatthe building loads given for the LBUGRI prototypes 

### are alSo calculated using the SP53 methodology 

### as described elsewhere (Hanford and Huang 1992).

. Theheating and cooling loads fortheLBUGRIprototypes 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 ofbuilding 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 ofthese 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 otherutility data) to the

### calculated UEC. This 

### ratio is a measure ofthe amount oferror 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. Thecalihration ofthe heating and cooling energy use model is shown in Tables 3.20 and 3.21. The magnitude ofthe 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 nottypically cooled. Because we have better knowledge of 

### the characteristics of the heating and cooling efficiencies. the distribution system efficiencies. and the 

### UECs. the calibration multiplieris 

### 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 characterizethe 

### sector.

50

### Table 3.19. Residential Forecasting Database (RFD) Building Prototype Populations and Heating and Cooling Loads (compison of RFDprototype Loadsto LBUGRIprototype 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 HeatPump 0.04 Tight 0.972 134 59.4 6.4 RFL 1990prototypes 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 HeatPump 0.10 Tight 0.858 239 24.2 17.6 RFD1990prototypes

- 27.9 million wtdaverage 38.7 26.

LBUGRIprototypes 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 HeatPump 0.13 All 1 0.13 70.3 8.8 RFD 1990 prototypes 70.7 8.7 LLUGRIproiotypes 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 HeatPump 0.31 All 1 031 22.3 16.9 RFD1990prototypes 23.7 17.7

LBUGRIprototypes 19.7 22.2 % difference 17% -25% 

### 1)RFDprototype populations from Appendix B. 2)LBLGRI prototype populations and population heating andcooling 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 ,{I"
- Table3.19. Residential ForecastingDatabase (RFD) Building PrototypePopulations and Heating and Cooling Loads (co-,."ri..nn ofRFDprolorype Loads to LBUGRIprOlOtYpe loads)

. HOllse Heat HeatFuel 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 HeatPump 0.04 Loose 0.028 0.04 HeatPump 0.04 Tight 0.972 1.34 RFI) 1990prototypes 34.4 million wulaverage

LBUGRlprototypes 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 HeatPump 0.10 

### Loose 0.142 0.40 HeatPump 0.10 TIght 0.858 239 RFD 1990prolOtypes 27.9 million wtdaverage

UJUGRIprolorypu 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 HeatPump 0.13 All 1 0.13 RFD1990prototypes

L8.UGRIproiotypes %difference



# - New SF South Electric 0.13 All 1 0.13 New SF South Fuel 0.57 All 1 0.57 New SF South HeatPump 0.31 All 1 0.31 RFD 1990prototypes

LBIJG1Uprototypes %difference l)·RFD prototypepopulations fromAppendix 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) LBUORlprototypepopulations and population heating andcoolingloads from Hanford and Huang 1m. 3) Heating and COOling loads calculated using ASHRAE SP53 loads database methodology (loads are uncalibrated to acllllal field conditions).

-



### 51Table 3.19 (cont). RFD Building Prototype Populations and Heating and Cooling Loads (comparisonof RFDprototypeLoadstoLBUGRIprototypeloads) House Heat HeatFuel 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 RFD1990 prototypes 15.4 million wtd average 48.9 7.5 LBLGRIprototypes 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 RFD1990prototypes 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 Table 3.19 (conL). RFD BuUding PrototypePopulationsand Heating and CoolingLoads (comparison 

### 0/ RFDprototypeLoadsto LBUGRlprototype loads) House Heat HeatFuel 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 1990prototypes 15.4 million wtdaverage 48.9 7.5

UllGRlprototypes 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 HeatPump 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 1990prototypes 10.2 million wtdaverage 14.0 15.4

LBllGRlprototypu 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 1990prototypes 21.7 4.8

UUGRlprototypes 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 1990prototypes 6.3 11.5

UUGRlprototypes 5.0 17.0 % difference 20% -48% 1) RFD prototype populations from Appendix B. 2) LBUGRI prototypepopulations 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). RFDBuilding Prototype Populations and Heating and Cooling Loads (comparionof RFD prototypeLoadsto LBL/GRIprototypeloads) 

### House Heat HeatFuel 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 HeatPump 0.01 All 1 0.03 583 65 RFD 1990prototypes 2.8 million wtdaverage 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 HeatPump 0.02 All 1 0.05 11.2 16.5 RFD1990prototypes 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)RFDprototype populations from Appendix B. 2)Heatingand cooling loads calculated using ASHRAE SP53 loads database methodology (loads are uncalibrated to acual field conditions). "

Table 3.19 (.eont.). RFDBuildingPrototypePopulationsand HeatiDg and CooIiDgLoads (comparison of RFDprototype Loadsto LBl/GRJ prototype Wads) House Heat HeatFuel 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) 1990prototypes 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 HeatPump 0.02 All I 0.05 11.2 RFD 1990prototypes 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 AppendixB. 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 loadscalculatedusing ASHRAESPS3 loadsdatabase 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 . Calibration ofForecastingPrototype 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 ofForecasting 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 T bl 320 ( 

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# - 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
NEWSINGLE·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 ofForecasting 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 3.0 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 __

T bJ 3 20 ( 1.) C rb t'

a e 

# . con 

# . a I ra Ion 0fF f P t t orecas mg ro 0 ype H t' Lo cis 'th D tab UECs ea mg a WI a ase 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 EXISTINGMULTI-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



### 56Table 3.20 (cont.). Calibrationof 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 E i 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 T bl 3.20 (



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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:' NEWMUr: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). Calibrationof 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 MANUFACTUREDHOME 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 1 00 100% 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 T bl 3.20 ( ) C J"b



### a e cont. • a I rabon 0fF orecasting Prototype H ti 

### Lo ds 'th D tab VEe ea ng 

### a WI 

### a 

### ase s Region

. Prototype Prototype Database I

Heal Bldg Heal Average UEC UEC I Heat Heat Heat Share Papin Load Efficiency (%) (MMBtu) (MMBtu) Calibration I Vintage Region**T~** Fuel Tech (%) (mill) (MMBtu) EQmt Dist System (kWh) (kWh) MulitpJier I
EXISTING MANUFAcruRED HOME I I 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 6.5 0 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



### 58Table 3.20 (cont.). Calibration ofForecasting 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 inkBlukWh. 1)Existing HVAC shares ar from US DOE 1992. Data are segmented intoNorth and South regions using census division and heating degree day data. 2) Building populations ar based on US DOE 1994 total nationalbuilding population estimates and HVAC shaesnoted 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 forall building types refrom US DOE 1989a. UECs for existng single-family electric heating inNorth arx estimated from Cohenet aL 1991 forpost-retrofit houses at 6000 heating degree days (see Fig. 3.8). UECs for single-family electric heating in South are estimated from utility surveydata in the UEC database in Southregion (see Fig. 3.6). Single-family heatpump heating UECs are estimated from averages ofregional utility survey datain UEC database in North and South regions (see Fig. 3.7). Electric andheat pump heating UECs formulti-family and manufactured homeprototypes ar estimated from fuelheating calibration multipliers and single-family UECcalibration multiplicrs for electric heat. 5) Database UECs fornew buildings are calculated using theprototype heating load, equipment efficiency, and distribution system cfficiency and the calibration multiplier fromthe existing vintage buildings. 6) Equipment efficiencies for the new vintage are taken from themost recent data in the database as shown in Figures 3.12 and3.15. For equipmentnot shown in these figures. ornotcovered by availabledata, efficiencies fornew 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, ornot covered by available data, efficiencies for stock equipment ae estimates. 8) Distribution system efficiencies are assumed base cases for stock andnew building systems as shown inTable 3.4.



### 59 Tb1320( t)Crb



### a e 

# . con •• ..8 I ration 0

### fF< sf 

### Prot t areca ang o ype H ti Lo· cis ·th D tab VEe ea ng 

### a WI 8 ase. 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
NEWMANUFACTURED 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 Pumpvalues lire inkBlU/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**~** segmentedintoNorth and Southregions using census divisionandheating degree daydaIa. 2)Building populatiOl'lS IRbasedonUS DOE 1994lOtal natipnalbuildingpopulation estimllCS and HVAC sharesDOtedabove. 3) Prototype heating ItJads are c:alc:ulatcd from prototypedescriptions wingASHRAESPS3 loads da1abasemethodology (sec Table3.19). 4) Exisling buildings database VEesources: Fuel heating UECsfor all building typca 1ftfrom US DOE 19B9L 1JECa foremting single-family electrichealing inNorth IlCestimated &om Coheneta1. 1991 forpost-retrofithouses 

### &16000 heating depedays (sec Fig. 3.8). UECs for single-familyelectricheatirlg in South an: estimated from utility surveydata in theUEC database in Southregion (see Fig. 3.6). Single-familyheat pumpheating UECs are estirnarcd from averages ofregional utility survey da1a in

### UEt databasein North and Southregions (see Fig. 3.7). Electric andheat pump heating UECs for multi-family andmanufac:1UrCd home

### pioror.ypes are estimated from fuel heatingc8Jibration multipliers and single-Camily UEC**c~tion**multipliers for electric heaL. S) DatabaseUECsfornew buildings are calculaledusing the prolOtypeheating load. equipmentefficiency. and distribution system efficiency and thecalilbrationmultiplier Crom theexisting vincage buildings.

. . 6) equipment efficicnc:iesfer thenew vintagean: taken from themostrecentditain the dacabase as shown in Figures 3.12and3.1S. Forequipmentnotshawninthese figures. ornotcovered by available da1a.. efficiencies fornew equipment are estimau:s. 7) Stockequipmentefticiencies an: calculated from hislOrica1 shipmentand**efflci~cy** data in the database with an assumed equipment lifetime. Forequipmentnotshown inthesefigmes.ornotcovered by available dala, efficienciesfor stock equipment are estimaleS. 8) Distribution system efficiencies are assumed base cases for srockandnew building systems as shown inTable 3.4.



### 59Table 3.21. Calibrationof 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 Table 3.21. Calibration ofForeastingPrototype 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
NEWSINGLE·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



### 60Table3.21 (cont). Calibration of Forecasting Prototype Cooling Loads with Database UECs Region Prototype Average

Cool Bldg Cool Efficiency Prototype Database Cool Coo l Shar Popin Load Eqmt Dist UEC UEC Calibion Vintge Region Tye Fud Tech () (mil) (MMBnu) (MM B) t(ku) UB/kWh) Multipier
NEWMULTI-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
NEWMANUFACTURED 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 basedon US DOE 1994 total national building population estimates and HVAC shares noted above 3) Database UECs forstock buildings arefrom LBL electricity supply crves (Kooy et aL 1991a), which ae derived from prototype descriptions. 4)Database UECs fornew buildings are calculated using theprototype coding load, equipment efficiency, and distribution system efficiency and the calibration multiplier from theexisting vintage buildings. 5) Equipment efficiencie; forthe new vintage are taken from the most recent data in thedaabase as shown in Figure 3.12 and 3.15. Forequipment 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 inthe database with an assumed equipment lifetime. For equipment not shown in these figures, or notcovered 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 '.

Table3.21 (cont.). Calibration orForecastingPrototype Cooling Loads with DatabaseUECs 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

,NEWMUU'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'INGMANUFAC7'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 NEWMANUFACIVREJ.) 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 HVAC sbares &l:e from US DOE 1992. Data are segmented inlO North and Soulhregions using ceosusdivision and healingdegree 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) Budingpopulabons lin:ba:sed Oft US DOE 1994 total national building population eslimales and HVAC shares noled above. 3)Database UECs for stodc buildings are fromLBL e1ecuicUy supply curves (KoomeyetaL 1991a). which arederived from prolOt)'JIe descriptions.

. . 4) Database UECs for new baildings are calculatedusing theprototypec:ocIing load, equipmenteffICiency, and**dis~bation** system effIciency and the caliimuion multiplier from theexisting Yinlage buildings. S) Equipmentefficiencic:1 forme new vintage are Iaken {remthe mosticccnt data in the daIabase as shown**~** FigUIC 3.12 and 3.1S. Farequipment notshown in these figures. or notcovered by available dala,efficiencies for newequipmentare esDmares. 6) Srock equipment efficiencies uecalculaled from historica1shipmentandefficiencydatainthedatabase With an assumed equipment lifetime. ForeqllipmenlllOllhownin these figures. ornOI covered by available dala, efTu:ienc:ics for stoe.k equipmenl ueestimales. 7} Distribution s),slCm efrlciencicsue assumed base c:asc:a for stocJt and new b,:,i1ding S)'SlCms asshown in Table 3.4. 8) Prototype heatingloads are calculalCd from prolOlype descriptions using ASHRAESPS3 loads dalabase methodology (seeTable 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 ofinitial implementation depends upon the type of equipment. The standards for heating equipment are given in Table 3.22, while those forcooling 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 isJanuary 1 ofyear 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 

### 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 

### ofinitial

### implementation depends upon the type ofequipment. The standards for heating equipment are given in Table 3.22, while those forcooling are given in

### Table 3.23. All standards are based on an efficiency (orenergy factor) derived from a test

### procedure.

Table 3.22. Minimum Efficiency Standards for· Residential H E i t

eahne**~Qu.pmen** 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 DirectHeating

wallhearer w/fan <42000 BtuIhr RM Gz >42000 BtuIhr RM Gz wall hearer (gravity) <10000 BtuIhr 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 ISJanuary 1ofyear 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 AnnualFuel Utilization Efficiency (%). 4} HSPF is Heating Season PerformanceFactor (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 AirConditioner



### 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 andw/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 isJanuary 1ofyearindicated. 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. Table 3.23. MIDlmum Emclency Standards for Residential C II E i t

00**n~** ;qulpmen Dalal&e Year Minimum Type Code • Fuel 

## meet. Efficieacy CauralAirConditioner

Split System 

## CAe Flee 1992 10.0 SEER SinglePaclcage 

## CAe Flee 1993 

### ,9.7 SEER ' HeatPump

Split System 

### HP Flee 1992 10.0 SEER SinglePackage 

### HP Flee 1993 

### 9.7 SEER Room AirConditioner

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 wloreverse cycle andwlo 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~** cycleand wllouvers 

## RAe Elec 1990 8.5 FER

,w/reversecycleandwlo louvers' , 

## RAe Flee , 1990, 8.0 FER 1) Effective dare ISJanuary 1ofyearmdicated. 2) All standards levels from NAECA 1987. 3) SEER is seaonalenergy efficiencyratio. Unirs are kBtulkWb. 4)EERisenergyefficieDcyratio. Uaitsare kBtulkWb.

### 4. WATER HEATING END-USE DATA Water heating accounts for approximately 15% ofelectricity usage and 25% ofnatural gas consumption in residential buildings. Water heating is comparable to space heating in terms of the complexity of the issues surrounding level ofusage, 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 ofthe unknowns involved, including hotwater demand in gallons, incoming cold watertemperatures, and the hot water temperature at the point of use. These parameters are inter-related. For example, ifthe hot water temperature ofthe 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 ofnational average gaswater 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 ofconsumption 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 byhouse type.

64 

### 4. 

### WATER HEATING END-USE DATA Waterheating accounts for approximately 15% ofelectricity usage and 25% ofnatural gas consumption"in residential buildings. Water heating is comparable to space heating in terms of the complexity ofthe issues surrounding level ofusage, 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 waterheating energy use, since colder areas ofthe country also have colderinlet watertemperatures and thus greaterwater heating requirements. Waterheating is a complex end-use because ofthe unknowns involved. including hot water demand in gallons. incoming cold water temperatures. and the hot water temperature atthe point ofuse. These parameters are inter-related. For example. 

### ifthe hot water temperature "ofthe storage water heater 

### is higher. less hot water 

### will be needed to meet a certain need

sinceitis 

### 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 heatenergy use is 

### limited and the RECS 

### conditional demand estimates and a few studies summarized by 

### Usibelli (1984) 

### provide 

### virtually the only estimates ofnational average gas waterheating energy

### use. Waterheating 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 ofincorporating usage and efficiency data in calculating waterheating UECs. Theequations below show a simplified method thatuses the Energy Factor of a water heater detennined from the DOE 

### test procedure. "This 

### equation may not 

# re valid for levels ofconsumption 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 ofother studies and are 37$0kWhlyr (n=96) for electric waterheating 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 stockand 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 percapita 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 ofoccupants (see Figure 4.1). Assuming standby losses (energy use at zero usage) and a 77 F temperature rise, aquadratic fitthrough 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 ofper 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. 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 anddnew water heating UECs. T hi

a e 4.1. 

## w ater Heating UECs 1990 1990 Fuel Type Srock New Electric (kWblyr) 3750 

### 3S45 Gas (MMBtulyr) 23.7 21.5 Oil (MMBruIyr) 23.7 21.5



### .. 1) Stockelectrtc and gas UECs from Appendix B. 2) New eiecuicand gas UECs estimaledbasedon changes in efficiency from 1990 slockand 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 Northwestestimate percapitawateruse between 16.5 and 21.0 gallons perday. Measured 

### data from the BPA REMP prograIIl (Taylor et al. 1991) specifically gives electric water heating**(~nergy**

### use acrossnumber ofoccupants 

### (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 Figure4

..2. The quadratic curve means thatthe 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 of45.3 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 hotwater setpoint temperature. 45.3 gallonsldayhousehold gives UECs that are similar to the estimated UECsshown above. A morereCent 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 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 inmultifamily buildings. Instantaneous water heaters are a -small portion ofthe 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 of0.983. 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 F".gure 4.1. Water Heating Energyvs Household Size, Raw Data and Quadratic: Fit



## l! 2000**~**
... 1000



## o 1 2 3 4 5 6 Number orOc:cupants Source: Tayloret

### at. 1991. Data includes 200 houses in sample (single-family only). Quadratic fit gives Rsquared of0.983. Standby losses (usage at.zeIO occupancy) are estimated from vacation days during monitoring period. Figure 4.2. HotWater Consumption vs HouseholdSize
90 80**~** 70 ell 

# e 60 en 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 ofOccupants Source: Calculated from kWh vs: household size regression results assuming 77 F temperature rise. Atnational average 2.61 persons/household (US DOE 1992), hot waterconsumption is 45.3 galldayJbousehold. or 17.s gaJIday-capil3.

66

### Historical Efficiency Data Shipments ofeach 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 ofcost vs. efficiency fornew 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 notcurrently available on a wide basis. Product Lifetimes 

### Estimates ofstorage water heater lifetimes are included in the database from several sources and are listed inTable 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 T

T Si: .'1'

'.X.

-.

- .. 

### Historic:ll Efficiency 

### Data Shipments ofeach type.

### of storage water heater are shown in Figure 

### 4.3 and the energy factor of new storage waterheaters 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 Thedatabase includes estimates ofcostvs. efficiency fornew waterheaterspurchased 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 notcurrently available on 

### a wide basis. 

### Product Lifetimes 

### Estimates ofstorage waterheaterlifetimes are included 

### in the database from several sources and are listed inTable4.2.

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 ownerlifetime only); 

### ASHRAE 1987: Lewis and Clarke 

### 1990;LBLREM 1991.



### 67Figure 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 7

### 4) 

# 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 Figure4.3. Annual Storage WaterHeaterShipments, 1951·1990

----EJec



### ----- G8s'**----.~---** Oil



### 1955 

## 1965 1970 1975 1980 '1985 1990 Source: 

### GAMA 1991. Gas 

### includes LPG 

### appliances.

Figure 4.4

.. Shlpment.Welghted EnergyFactorforStorageWaterHeaters, 1972·1990


### 100 i'

90 I 

### i 

### i 

# I 80 I' 

## i 70

### ~ 

## ,!. 60



# .. .s 50 u l**~ ~**  

### 40**~.** 

### 2." 

### ., 30' c:**~** 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 WaterHeater

Option Description Baseline Electric waterheater

- 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 FlueBaffle 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
so 80 75 70 65 60 55 50 Figure 4.5. CostVersus Emciency for New Electric Storage WaterHeater**~ ~------~----~------~------~**  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 HeatLeaks 1 +HeatTraps 3 2 +AddOn Heat Pump 4 3 +R-25lnsulation 1990 Standard

## = 889'0 EF

Flgure 4.6. CostVersus Emciency for New Gas StorageWater 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 waterhearer·40 gallon Wlit. 1990 Standard . 0 + HeatTraps

1+ Reduce Heat Leaks 2 + R-16 Insulation 4 3 +Improve fluc Bafflewith 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 theRECS data, electric water heating gained market share from 1981 to 1990 while fuel-fired systems shares have been dropping. Share fornew 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 factoris 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 MinimumEfficiency 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 ofyear indicated. 2) Standards level from NAECA 1987. Volume is rated storage volume in gallons. 3) Average volume is for typical size unit fromLBL Appliance Energy Conservation Database (LBL i990). 4) UEfC based on usage of453 gal/day and at 77F temperature rise.

7n 

### 4.4. Shares The dtltabase includes fuel and tecbnologyshares for water heating ata 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 waterheating gained madcetshare 

### from 1981 

### to 1990

### while fuel-fired systems 

### shares have been dropping. Share for new units 

### in newhomes favor electricity overgas by about**:~** to 1. 

### 4.5. StandariIs ·EfficiencY standards for waterheaters wereenacted 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. DOEtest

### procedure mentioned above. The standard and estimated UECs associated with 

### the standard are shown in 

### Table4.3.

Tabne 4.3. Minimum Efficiency Standards lor Residential Storage Water Heaters

, Qllcuiated Cak:uJared Year MinimumEfficiency 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 ISJanuary 1 ofyear mdlcated• 2) Standards level fromNAECA 1987. Volume is rated storage volume in gallons. 3) Average volume is Cortypical sizeunit fromLBL ApplianceEnergy Conservation Database (LBL i990). 4)

### tme based onusage of45.3 galIday and at 77Ftemperature riSe.

70Figure 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% ofunits with Electric have common systems, 15 to20% ofunits with Gas have common systems, and 40 to 50%ofunits with Oil have common systems (multifamily units only).

Figure 4.. Water-Heating Fuel Shares forNew 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 builtbetween 1985 and 1990. 36% ofMultifamily 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 F"JgUre 4.7. Water Heating Fuel Shares in Total HousingStock, 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{, ofunilS with Electric have common 

### systems. IS to 20% ofunits with Gas have common systems. and 40 ro 50% ofunits with Oil have common systemS (multifamily units only).

F"agure 4.8. Water-HeatingFuelShares for New Construction by HousingType 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 builtbetween 1985 and 1990. 36% ofMultifamilyunilS with Gas have common systems.



### 71 All. 

### ID] Eleea1c

. 

## DOss II Oil**~LPG**

### 5. REFRIGERATOR END-USE DATA 

### Refrigerators are the single largest consumer ofelectricity among the typical household appliances. Refrigerators use approximately 125 TWh or 15% ofresidential electricity consumption. This is due to the fact that refrigerators arepresent in almost all households, a large percentage of households have multiple refrigerators, and each unit uses a significant amount ofelectricity. 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 testprocedure from U.S. DOE. Research has shown that this test provides a reasonably goodestimate ofactual field usage, but it is not exact (Meier and Heinemeier 1990). The UEC database ofmeasured and estimated data on field energy usage ofrefrigerators contains 112 records, and the estimates showlarge variability (Appendix B). This variability may be partly due to large improvements in efficiency ofthe refrigerators entering the appliance stock. In addition, there are several different classes ofrefrigerators (manual defrost, automatic defrost), size differences, and variations in features that affect the energy consumption ofthe 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. DOEtestprocedure. uEWhyr = 365 * Capacity EC: WMh/yr = HEF
where: 365 is days peryear Capacity is adjusted volume (cubic feet) Adjusted volume = 1.63 x freezer volume (cubic feet) + refrigerator volume (cubic feet) EF is the energy factorfrom 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 oftest 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 forrefrigerators in the UEC database (n=50 for studies that are generally representative ofall 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, whichis consistent with earlier findings (Meier and Heinemeier 1990). To maintain consistency with the AHAM historical data, we include the estimate based onthe AHAM data in the database.

72 

# s.**,REF~IGERATOR** 

### END-USE DATA RefrigeratorS are the single largest consumer ofelectricity among the typical household appliances. Refrigerators use approximately 125 1Wb 

### or 15% ofresidential electricity consumption. This 

### is due to the fact thatrefrigerators 

### are present

### in almost all

### households, a larige percentage of households have multiple refrigerators, and each unit uses 

### a signUicant amountofelectricity. 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 

### testprovides areasonably goodestimate ofactual field usage, butit 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 ofthe refrigerators entering the appliance stocL In**adcUtio~** 

### there are 

### several differentclasses ofrefrigerators (manual defrost. automatic defrost), size**diffen:~ces,** 

### and v.ariations in features that affect the energy consumption ofthe 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 testprocedure. '.' ' 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 oftest UECs for refrigerators (AHAM 1991) and a straight line decay function with a minimum lifetime of7 years and a maximum lifetime of29

### years. The amllysis ofavailable data for refrigerators in the UEC database (n=50 for studies that 

### are**gem~rally** 

### representative ofall 

### product classes) suggests that the UECmay be lower, at around 1150kWh/yr (Appendix B). For automatic defrost units only, which represent the majority ofthe stock. the UEC database analysis results are 1350 kWh/yr. Overall. the UEC database results are slightly lowerthan the test values, whichisconsistentwith earlier findings (Meierand Heinemeier 1990). To maintain consistency with 

### the AHAM historical data,**wc:~** 

### include the estimatebased 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 forthe overall average sales ofnewrefrigerators. This is similarto the average for top-freezer automatic defrost units, which comprise approximately 67% ofthe 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 ofthe refrigerator, the internal temperature of the refrigerator, and the level ofmaintenance. 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 ofnew 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 

### New UECs Newunitaverage 

### VEe derived from the laboratory tests and reported by the industry for 1990 is 

### 916 kWhlyr

### for the overall average sales ofnew refrigerators. This is similarto the 

### average for top-freezer automatic defrost units, whicll comprise approximately 67% ofthe 

### 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 ofthe refrigerator. the internal 

### temperature of the refrigerator, and the level ofmaintenance. 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 ofnew 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 forRefrigerators, 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 FIgureS.l. Annual RefrigeratorShipments, 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.

FIgureSol. Shlpment-Wei'ghled Energy Factorand Capacity rorRefrigerators, 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 UFigure53. Cost Versus Efficiency forNew Refrigerator-Freezer

20

S 16

t12..-

........ _..... .... .................... _ .... 

# I l2 

# ""~ 1 0 """'"" ~~^ ^ "

- **..............................._-._._._.

. o1 

### ~= 

# 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 afterthe year 2000. Costs adjusted from $1987 using aCPI multiplier forhousehold 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. doorinsulation. 2 1+ Evacuated Panels 3 2 + Two-Compressor System 4 3 + Recycle Condensor Heat to Replace Anti-Sweat Heaters 4 + Refrierator Compressor EER to53 _r____ _____r_____ Figure 5.3. Cost Versus Efficiency forNew Refrigerator-Freezer**~ ,-------~------~------;-------~------~------~------~**  

### s 18 

# ----1--- I-----T----

# '---T----:-

. : 3 

## j .

2 

# ---r----T---

..

# _--_._-
....

## -................

# _-_
....

# -_ ..

### -**~ ~** 

8 BaseIiDe

6**~------~--------~------~--------4-------~--------~------~**

500 550 600 700 750 800 8SO ConsumerCost ($1990)
Source: US DOE 1989b for baseline andoptions 1-3. Koomey etaI. 1991a for options 4 and S. Models 4 and5 are availableaCte{ the year 2000. Costs adjusted from S1987 using a CPI multiplier forhousehold appliances of 1.024•. Option Ion Baseline 20.8 c:u.ft. adjusted volwne. Top freezer autodcirosL Meers 1990 Standard. No CFCs. 1 1993 Standard. Enhanced heat InInSCc:r+ foam door+ 5.05 EER compressor+2 in. doorinsulation. 2 1+EvacualCd Pmels 3 2+Two-Compn:ssot: System 4 3+ Recycle CondensorHeat toReplace Anti-Sweat Heaters

. 5 4 + Refrigc:raIOrConrpressor EER toS3



### 75

### Product Lifetimes 

### The database contains estimates ofthelifetimes ofrefrigerators, aslisted inTable 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 Itincludes 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 innew buildings from the RECS data forbuildings builtduring the last 5-7 years from the same data sets. Some ofthese data are shown in Figures 5.4 and 5.5. Note that these are total "saturations" ofrefrigerators (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 ofrefrigerators 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 inrefrigerator saturations is mainly due to the acquisition ofsecond 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 onthe type ofrefrigerator 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.

_, 

### Product Lifetimes 

### The database contains estimateS ofthe lifetimes ofrefrigerators. 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

. ownerliCetil:ne only);LBLREM

1991.



### The databaSe includes shares for refrigerators for stock buildings by housing type 

### at a nationalle.veL Itincludes total shares and specific shares 

### for manual defrost and automatic defrostfrom the RECS 

### data (US DOE 1982a, 1986. 1989a, 1992). It

### also includes

## shares

.in

### new buildings from the RECS 

### data for buildings builtduring 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" ofrefrigerators (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 ofrefrigerators 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 awhole. 'TIus suggests that 

### the growth in

### refrigeratorsaturations is 

### mainly due to

### the acquisition ofsecond 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 ofrefrigerator and the size. The energy usage is 

### based on the U.S. DOE test procedure. More stringent standards were implementedin 1993. 

### These are summarized in Table 5.2.

. .Figure5.4. Refrigerator Ownership Shares by Housing Type, 1981-1990
1.25

1.20

1.15

1.10
1.05

1.001980 i



### . j "- ^ ^ ^ _ ^

-- SF

__ __ ----- sF 

## !

_ , I

_ _ _=--- ALL



## = i i I

_ „ j___~~~~~~~~~~~~~~~~~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 

## __

Figure5.4. RefrigeratorOwnership Shares by HousingType, 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 totalsarurations aaoss households [mcludes multiplerefrigerators**~** household).

Figure5.5. RefrigeratorOwnership 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 datafor buildings built between 1985 and 1990. MH AU.

Shares arefor total saturationsacross households (includes multiple refrigerators per household)

.



## ..,..,Table 5.2. Minimum Efficiency Standards for Residential Refrigerators

Type: MND PAD TAD SAD BAD TADI SADI Refrigerators andRfrigerator-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/othrough-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 1ofyearindicated. 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 fordifferent product classes from AHAM 1991 forshipments in year 1990. 4)EF calculaed from UEC as 365*Capacity/UEC. Units are cuft-day/kWh. 5) Salesby product class arefrom 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 aCapacity Calculated Cal. of Type Ef. Maximum UECEquation (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% Table 5.1. Minimum EfficiencyStandardsror 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· 1991 3 nla 20.6 cuft Rcmgcrar.ars andRefrigerator-Freezers withmanual defrost**Rd~crazor-Fn:czc:r**- panial awoma1icde&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 defrostwith: 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 defrostwith: Top-mounted fr=zer wIthrough-thc-dooriceservice RcfJigcnror-Freez=s

- aUIOmatic deltastwith: SidC-mo1Dltcd freezerwIdtrough-the-doorice**~cc** 1)Effectivediate lis January 1oCyearindicaled. 2) 1990Slandard.<; level equation from NAECA 1987. 1993 Srandards level equation from USDOE 1989b. Capacity 

### measure is adjUSled volume (AV), where A V=refrigeratorvolume + 1.63

.. freezer volume. 3)Average volume fordifferentproductclasses from AHAM 1991 for shipments in year 1990. 4)EFcaIculaJted from UEC as 36S"Capacity/UEC. Units are cuft-dayIkWh. S) Sales byproductclass are from US DOE 1989b.and are datafrom 1988. 6)WeightedIlverage across entire productcategory is similar to data for IheTAD productclass.



### 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 ofextensive research and the relatively small effect ofoccupant 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 ofthe UECs ofnew units entering the market. The UEC database ofmeasured and estimated data on energy usage ofthe freezer stock contains 89 records, but theestimates 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 offreezers (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 asfollows:

_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 oftest 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 ofall product classes) also gives results of 1025 kWh/yr for a freezer UEC. The residential database includes this value for stock UECs offreezers. New UECs New unit UECs derived from the U.S. DOE test procedure andreported by theindustry 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 ofthe two manual defrost classes.

'T7 

### 6. FREEZER END-USE DATA Freezers. specifically those that are separate from the freezer compartment of the refrigerator. are a**~latively** large consumer ofelectricity among the typical household appliances. using approximately 33 1Wh. or 5% of sector electricity consumption. 

### Like refrigerators. freezer energy consumption 

### is well understood because ofextensive

### research and 

### the relatively smalleffectofoccupantbehavior on appliance performance. 

### 6.1. Freezer UECs Freezer 

### UECs for new units 

### are measured using a laboratory test procedure such 

### as for refrigerators. These provideestimates ofthe UECs ofnew units entering the market. The 

### UEC database ofmeasured and estimated 

### data on energy usage ofthe freezer stockcontains 89 

### records. buttheestimateS show large variability (AppendixB). 

### 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 differentsizeS and several differentclasses offreezers (upright and chesttypes. manual defrost and automatic defrost types) which vary widely in energyconsumption. 

### 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 peryear Capacity is adjusted volume (cubic feet) Adjusted volume

## =1.73 x fRezer volume (cubic feel) EP is the eDergy factor from the DOE leStprocedure (cubic feet-dayIkWb)

Stock UECs Basedon historical shipment data oftest 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 stocktest 

### 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 ofall product classes) also gives results of 1025kWhlyr for a freezer VEC. The.residential database includes this value for stockUECs offreezers. New UECs New unitUECs derived from the 

### U.S. DOE test procedure andreported by theindustry 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 1030kWhlyr for upright. automatic defrost (9% of sales). The current sales are best described by an average ofthetwo manual defrost classes.

.

"0

### 6.2. Freezer Usage The energy usage offreezers will vary in the field with number ofdoor openings as well as the ambient temperature in thevicinity ofthe freezer and the level ofmaintenance. 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% ofthe 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 ofnew 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 ofthe two types. These are shown in Figure 63. The values are based on estimates inthe 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 ofthe lifetimes offreezers. 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. 

### 6.2. Freezer 

### Usage Theenergy usage offreezers 

### will 

### vary in the field with number ofdooropenings as well 

### as the ambient

### temperature in the vicinity

### ofthe freezer and the level ofmaintenance. 

### 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 theresidential database, we

### include technology data that best represent the entire freezer market. For some**measure~** 

### we include data that 

### are**averaJ~es** 

### across productclasses. For othermeasures, we include data on chest manual and upright manual freezers, which togethercomprise over90% ofthe unitsales and, together, have characteristics that approximate the market average. The automatic defrostunits

## use . signifi.cantly more energy but 

### are only 

### a small portion ofcurrentsales. 

### IDstorical Efficiency Data**Annu~ll** 

### freezer shipments from 1951 to 1990 are shown in Figure 6.1 

### and the overall average efficiency ofnew 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 overtime. 

### Cost vs. Efficiency for New Equipment The database inCludes estimates ofcostvs. efficiency for new chest manual and 

### upright manual freezers 

### as well 

### as an average ofthe 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 Theda.tabase contains estimates from two sources 

### ofthe lifetimes offreezers. 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'Stowner 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 

# Ii 

### 4= 1 U 

# to a 

# w 4 1. a 

## El w 16

- 15 -^ 14 13 12 11

- 10 9 -__ 8 7- 61972 30 29 28 27 26 25 24 23 22 21 .

U 

### a 

# I I *S *S5 Zs a% la ---

Figure 6.1. AnnualFreezerShipments, 1951·1990



### ----+-----i----

__ o __



### 1970 1975 1980 1985 1990

Figure 6.2. Shipment-WeightedEnergyFactorand Capacity for Freezers, 1972-1990



# - .a 

### :t 

# t II i'

..**~**

::I**~** 

# .. 0 

# - Col II**~** ;..,. 

### ff w c**~** 16 15 14 13 12 11 10 9 8 7 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. CostVersus Efficiency for New Freezers

· _ · · _· · 

### ~~~~~~~~~~~ 

# i.

-.* Chest ManualDefrost (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 and2. Koomey et aL 1991a options 3 and4. Models 3 and4 areavailable after the year 2000. Costs adjusted from $1987 using aCPI multiplier for household appliances of 1.024. The midpoint ofthe twomajorclasses approximates the freezer market 50
45' ---
40 --- 

### A 35

t. SI

## t20

6. 

### = 15 M 

### 10
5 0 

## ---

300 550



### Option Descrittton Manual Defrost Freezer Baseline Upright Manual DefrostFreezer. 26.1 cubic feetadjusted volume. 1990 Standard. Chest Manual Defrost Freezer. 225 cubic feetadjusted volume. 1990 Standard. 

### 1 1993 Standard. 5.05 EER compressor. 2.5 indoor and side insulation. 22 +

### Evacuated panels 3 3 + Condenser to EER =5.3 4 4 + Freezer Condenser Gas Heat I i` .i

I._

,, FIgure6':3. Cost VersusEmdencyforNew Freezers

40

,... 

# t I,

• CheatManual Defrost (CUM) ---0-- Midpoint
.a 3S 

# I» I , i 2S 

# -+-------, .-----+---r-----i • UprightMIQual Defrost (UPM)

tJ i 

# i w -t-
_________ 

### +l_'

_____

# ~~-~---~~~_; r:**~ ~**  1S

10 

# -t-----

s 4



### o**~--------_+--------~----------~--------~--------~** 300 350 400 450 

### soo sso ConsumerCost($1990)
Source: US DOE 1989b for baselineand options 1and2. Koomey etal. 1991aoptions 3 and4. Models 3and4 as:e availableaflUthe year 2000. CosIS adjusted from $1987 using a CPI multiplier,for household 8))J)WIDCCS of1.024. Themidpointofthe twomajor classes approximates the**~** marltet. Option Description Baseline U,prightManual DefrostFreezer. 26.1 cubic feetadjustedvolume. 1990Standard.

, OllestManual DefrostFreezer. 22.5 cubic feetadjustedvolume. 1990 StandaId. 1 H,g3 Standard. 5.05 EER**compress~.** 2.5 indoor and side insulation. 2 2

### +Evacuated panels 3 3,+Condenser to EER=S.3 4 4 .+ FreezerCondenserGas Heat '

### 6.4. Shares 

### The database includes shares for freezers for existing buildings by housing type at the national level Itincludes total shares and specific shares formanual 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 5to 7years from each of the above data sets. Some of these data are shown in Figures 6.4 and 6.5. The shares ofmanual 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 ofautomatic defrost units. The RECS data may be less accurate since the type offreezer is determined during a quick survey ofthe 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 offreezers 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 forthe 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 forthis 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 6.4. Shares The**datab~** 

### includes shares for freezers for existing buildings by housing type at the national level Itincludes 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 ofthese data are shown in 

### Figures 6.4 

### and 

### 6.5. The shares ofmanual 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 ofautomatic defrost units. The RECS 

### data may 

### be less accurate since the type offreezer is detennined during a quick survey ofthe 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 offreezers perhousehold 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 inoverall 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 notstatistically 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.

83Figure 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 arefor total saturations across households (includes multiple freezers per household). 1990 data does not include multiple freezers. (Notpart of 1990 survey. Approximately 1%in earlier years).

Figure6.5. Freezer Ownership Shares for New Construction byHousing Type
0.4



### 0.3 T 

# I

! .0 dW 0.2 --


### 0.1 ---

0.0 4-- - SF --- MH ALL MF Source: US DOE 1992 data forbuildings builtbetween 1985 and 1990. 0.6 -- 

### 0.5
0.4 -- 

## e 0.3

.. m 0.2

0.1 -.
0.0

-

'1980

-r-

--- · -· --- ^" "-·-------"-------·

---**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 arefor total saturations across households (includesmultiple freezers per household). 1990data**~;**notincludemultiple freezers. (Notpartof1990 survey.' Approximately 1% inearlieryears).

FIgure6.5. l?reezer Ownership Shares for New CoDStructlon byHousingType 

# 0.4,----------------------------,

0.3

0.1 

# 0.0 +----= 

### SF MF MH ALL
Source: US DOE 1992 datafor buildings builtbetween 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 1ofyear 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) EFcalculated 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 classes

### 85 T bl 62. Em •

a e . IClency_Sta d d Ii R·d

. IF n ar 5 or e51 entia 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 ChestFreezers and aD otherfreez=s 1) Effectivedate is JanUary I ofyear 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 levelequation from NAECA 1987. 1993 Standards level equation from US DOE 1989b. Capacity measureis adjusted volume (AV), where AV = 1.73

... freezer volume. 3) Average volume for differentproductclasses from AHAM 1991 datafor shipmentsin year 1990. 4) EFcalculated from UECas 365*Capacity/UEC. Units are cuft-dayIkWh. 5) Sales by productclass are from AHAM 1991 data for year 1990. 6) Weightedaverageacross entire productcategory 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 atypical 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 forthe motor, dryer, booster heater, ifpresent, and for the hot waterrequired 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 ofa dishwasher in the field will be directly proportional to the amountthe appliance is used. Recent research has shown that average field usage ofdishwashers is approximately 229 cycles per year (US DOE 1990b). Currently, however, the U.S. DOE test procedure is based on a usage estimate of322 cycles per year. Average energy use for stock dishwashers is difficult to estimate without directmetering 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 notitwas included, as shown by some incredibly high values given for the non-water heat portion. The UEC database contains 31 estimates ofthe total dishwasher energy use and 45estimates ofthe non-water heatportion 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 ofthe dishwasher, calculated using electric water heating at 100% efficiency (i.e. standby losses ofthe electric water heater are not included in theaccounting for the dishwashing appliance). However, the question remains whether ornot the efficiency ofthe water heater used to heat incoming hotwater should be included in the hotwater energy ofthe 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 HotWater Energy are components ofthe 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 ofhot water usage. Thus, we base the UECs in theresidential 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 ofthe entire
85 1. 

### .DISHWASHER END"USE DATA Dishwashers use energy primarily by increasing the water heating use for a residence

.

.Thus" they can be

### majorenergy consumers for a typical household.

.

7.1. 

### Dishwasher' UEC, DishwasherUECs for new units 

### are measured using a laboratorytest procedure 

### from 

### U.S. DOE. 

# nus procedure detennines the total 

### energyuse - both forthe motor, dryer, booster heater, ifpresent, and for the' hot waterrequired from the water heater. These UECs are typically calculated assuming electric waterheat, although somehouseholds' 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. Recentresearch has shown that 

### average field usage ofdishwashers 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 peryear. . .

Average energy use

### for stock dishwashers is 

### difficulttoestimate

### withoutdiJ: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~** 

### inthe UEC

### estimate, evenwhere the source may have 

### explicitly stated whetheror notitwas 

### included. as 

### shown by some incredibly high 

### values given for the non-waterheat portiOJjI. The VEe 

### database contains 31 

### estimates ofthe total dishwasher energy use 

### and 45

### estimates ofthe 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 ofthe dishwasher,

. calculated using electric water heating at 

### 100% efficiency (i.e. 

### standby losses ofthe

### electric waterheater are not included in

### the accounting forthe 

### dishwashing appliance).**Howeve~,** 

### the question remains whetherornot

### the efficiency ofthewaterheaterused

### to heatincoming hotwater should be

### included in

### the hotwater energy ofthe dishwasher.

UEC: U"

. kWblyr =

### EF= Use • (Motor + Dryer +Booster Heater +Hot Watt:tEnergy) 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 ofthe UEC for dishwashers'resulting from weighted averaging ofthe UECs in

### the UEC database is 

### 250kWhlyr 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 ofhot water usage. Thus. we base the 

### UECs in the

### residential database onthe baseline "StandardWater 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 ofthe 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 unitare 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 HotWater Demand 11.90 11.90 gal/cycle HotWater Load 2.04 2.04 kWh/cyle HotWater 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 HotWater Load 467 . 467 kW yr 

### HotWaterEnergy 467 549 kWh/yr TotalEnergy 645 728 kWh/yr EnergyFactor 0.35 0.31 load/Wh Sourcec USDOE 1989c, baseline StandardWater Heating Dishwasher Hotwater load calculated at70F 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 unitsold in 1988. Since appliance standards willnot impact sales until 1994, this assumption is reasonable. 7.2. Dishwasher Usage The energy usage ofdishwashers will vary in the field with number ofcycles the appliance is used as well as the temperature settings (hot wash, hotrinse; cold wash, cold rinse; etc.) for each ofthose cycles. The most recent estimate ofnumber ofcycles 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 ofthese 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% ofnew sales, and it is the only appliance considered in this residential database (US DOE 1990).

87 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 baselineunitare summarized 

### inTable7:1.

Table 7.1. 1990 Stock and New Dishwasber UECs WaterHealerEfiicieDcy Desaiption 100% 85% Units Pe, Cycle Usage

.Motor

### +Heater+DryerEnergy 0.78 0.78 kWh/cycle

HotWaterDemand 

### 11.90 11.90 galIcycle HotWaterLoad 2.04 2.04 kWh/cycle HotWau:rEnergy 2.04 2.40 kWh/cycle Total Energy 2.82 3.18 kWh/cycle Annutll Ultlge Motor

### +Heater+DryerEnergy 179 179 kWblyr HotWaterDemand 2725 2725 galIyr HotWaterLoad 467 401 kWblyr HotWau:rEnergy 467 549 kWblyr Total Energy 645 728 kWblyr .cuergyFactor 0.35 0.31 IoadJkWb



### . Source: US DOE 1989<:. baselineStandardWau:rHeaungDishwasher Hotwaterloadc:a1colated at70F temperature rise AmmaI energy 

### use calaUated assuming 229c:ycles/yr



### New UECs The database UEC for newunits (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 

### willnot impactsales until 1994. 

### this assumption

### isreasonable. 

### 7.2. Dishwasher Usage Theenergyusage ofdishwashers will vary 

### in the field with numberofcycles the appliance is 

### Used as well as the temperature settings (hotwash. hot

### rinse; cold wash. cold 

### rinse; etc.) for each ofthosecycles. The mostrecentestimate ofnumber ofcycles isfrom Proctor and Gamble and is 229 cycles per year (US DOE 1990b). Homeowner usage of various temperature and drying options is difficult to ascertain. Estimatesofthese impacts are 

### used 

### inthe standards analysis for dishwashers, butare notincluded 

### 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 itis 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 ofnew dishwashers sold over time are shown in Figures 7.1 and 7.2. Note that the efficiencies are largely determined by hot water demand sincethe hot water use is the greatest portion ofthe 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 ofnew units sold increased between 1972 and 1980, but has remained stable since that time. Cost vs. Efficiency for New Equipment The database includes estimates ofcost 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 thehot water use and thus the efficiency. Product Lifetimes The daitabase contains estimates from two sources ofthe 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 forbuildings 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 Histolrical 

### Efficiency Data Annual dishwasher shipments and the annual sales and overall average efficiency ofnew dishwflshc::rs sold over time 

### are shown in Figures 7.1**~d** 7.2. Note that the efficiencies 

### are largely determined byhotwater

### demand s4icethe hotwater

### use is the greatestportion ofthe ItOtal 

### energy use. However, these historical 

### data do notspecify themotorand water 

### heat portions separately. 

### In addition, the efficiencyis calculated assumingelectric water**heatinl~.** The average efficiency ofnew units soldincreased between 1972 and 1980.but 

### has reD:lair!ed stable 

### since thattime.

. . Cost 

### 'vs. 

### Efficiency Cor New 

### Equipment The da.tabase includes estimates ofcost 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. ·Atthe upperend. improvements only minimally affect thebot wateruse.and thus theefficiency.

. Produtt:t 

### Lifetimes The

### daltabase contains estimates from two sources ofthe lifetimes ofdishwashers. which are summarized 

### inTable7.2. .'



### 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 (firstownerlifetime 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 dataforbuildings builtduring thelast

### 5 to
. 

### 7 

### years. SlOme ofthese 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 MFhousing 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 ofhouseholds in the stock with dishwashers will continue to grow over time.

. .



### RRFigure 7.1. Annual Dishwasher Shipments, 1951-1990

4.5

- ................ ..... 4.0

- 3.5
3.0

- 2.5 

# 2.0-

. ......... --------.- . g 2.0 1.5

o.o0 0.5

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 forDishwashers, 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 rtgUre 7.1. Annual DishwasherShipments, 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 etale 1980 (1951-1956); Appliance Magazine (1976-90); US DOE 1990b (1951-1975).

Figure 7.2. Shipment-Weighted Emcienc:y rorDisbwashers, 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. EFcalculated assuming electric water heating @ 100% efficiency.



### 89Figure 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 andEF calculated assuming electric water heat at 100% efficiency. Database includes hot waterenergy separately tocalculate costs for gas water heat. Converted from S1988 using CPImultiplier formajor household appliances (CPI data for majorhousehold appliances, stoves, ovens, DW. AC) of0.979. 

### Option Description Baseline WaterHeating Dishwasher. 229 Cycles per Year. Water Heater efficiency = 100% (Electric). 1 Reduce Water Use 2 1+Reduce BoosterUse 3 2 + Improved Motor 4 3 + Fill Control



### 90 Yagure 7.3" CostVersus 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. SlalldaidWater Heating Dishwasher (inCludes boosterhearer)

### choSen torepresent all dish1N8SIlers. USC and EF'calculated assuming electric

## walei' heatat 100% efficiency. 

### DatabaSe inclllidcs bOI waterenergy separare1y toCalculate costsfor gas warei' heSt. Convened from $1988 using CPI llllwtipUerfor majorhousehold appliances (CPl datafor majorhousehold appliances. stoves. ovens•

. OW,AC) of0.979. Option Descriotion .'.

. Baseline WaterHealing Dishwasher. 229 Cycles per Year. 

### WaterHearer effICiency

# =100% (Electric).

- 1 . Reduce Water**~se** . 2 1

### +Reduce BoosterUse . 3 2 + Improved Motor

4 3

### +Fill Control

90Figure 7.4. Dishwasher Ownership Shares by Housing Type, 1981-1990
0.6
0

. .

e 0.4

. 03

0.1

........... ... . ..- "_. 1 

### 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
10 U

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

·-- ·- ·------ F"agure 7.4. DishwaSber Ownership Shares by Housing Type, 1981·1990
0.6**.~---"",!",,"-----,------,------,------,**

. " 005 .-.-.--• .;.----.---'---

..-.-...---: --=--II-...-..!_

........==I!I 

### if:**~** : • SF 

# = 0.4 

## +-.----- .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



# - 0.6 c:: .g 

# -. u 0.5 CIII**~** 

# - t! 0.4 CIII .c 0.3 til 0.2" 0.1 0.0 

### SF MF MH ALL Source: US DOE 1992"data for buildings builtbetween 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 HotWater Motor, Booster, Total 

### Database Year Min. Energy & DryerEnergy UEC 

### Type Code Effective EF (kWhbycl e) W(kWhhcycle) Stadard . DW 1994 0.46 1.60 0.58 2.17 

### StandardWaterHeating DW 1994 0.46 1.60 0.58 2.17 Co actr(Water Heating) .DW 1994 0.62 1.11 0.51 1.61

Soure US DOE 1990b. Hot water energy and motor,booster and dryerenergy donotadd to total energy due to roundingemrrs. 1)

Effective date isMay 14 ofyearindicated. 2) Standards specified inNAECA 1987and effective starting 1990fordishwashers required dishwashers to beequipped with an option todry without heat 3)EF units are load/kWh. 4)UEC per cycle calculated as 1/EF. Includes assumption ofelectric waterheating @ 100% efficiency. Hot water use portion from USDOE 1990b. Otherenergy use isfor Motor, BoosterHeater andDryer within themachine itself. Mandatedefficiency level for standard dishwasher essentially makes itawater heating dishwasher. Thestandard specifies only theEF,and in practice manufactuers may notuse dte specific design options rading offmotor,booster heater,dryer, and hot waterenergies shown above.



### 9o 

### 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 becomeeffective 

### in 1994, as shown 

### inTable 7.3.

.. .
Tllblt: 7.3. Minimum Emclency Standards ror Residential Dishwashers

. HotWattr Motor, Booster, TOIal DaIabase Yea' Min. Energy

. 

### IiDr}'erEnergy' 

### 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. Hotwaterenergy and motor, boosterand dryerenergy do notaddro rolal eoergy due toroundingemn. 1)Effective date isMay 14ofyeariDdicared. 2) Standards specifred illNAECA 1987anddrective starting 1990for**dish~** required**di..~wasbers** to beequippedwith an option todry withoutheaL 3).EF

### unics are loadlkWh. 4) 1tJECpercycle calculatedas lIEF. Includes assumptiOD ofelearicwaterbeating @ 100% etrtciency. Hot

### wateruse poniOD from US DOE 1990b. Otherenergy use is for Motor, 

### BooSterHeareraDdDryerwithin 

### abe machineitself. Mandar.edefficiencylevel for standan1 di$hwasberessentiallymakesitawaterbeadng disbwasher. The standard specifies only the**EF~** aDd in pmcIioe manufacturersmay Dotuse die specific design options ttading offmotDt. boosterheater,c:bler, andbotwaterenergiesshown

. 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 washer UECs for new units are measured using a laboratory test procedure from U.S. DOE Thisprocedure determines the total energy use- both for the motor and other items in the washer and for thehot 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 UECofa clothes washer in the field will be directly 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 as the water heater. In collecting data for the UEC database, we found thatit was difficult to determine ifthe water heat portion ofthe clothes washer 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 21 estimates ofthe total cotheswasher energy use and 35 estimates ofthe 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 ofthe clothes washer, calculated using electric waterheating. A major question is whether or notthe efficiency of the water heaterused to heat incoming hot water should beincluded in the hot waterenergy ofthe clothes washer.

UEC: kWb4r-- v Use * Capacity 

### ~UEQCkWb/yr Us-e-F * - = Use * (Motor +Hot WaterEnergy) where: Use is in cycles/year Capacity is volume (cubic feet) EF isthe energy factor from the DOE test procedure (cubic feet/kWh) Motor and Hot Water Energy are the components ofthe UEC (kWh/cycle)


### Stock UECs The best estimate ofthe UEC forclothes washers resulting from weighted averaging ofthe UECs in the UEC database is 100 kWh/yr 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 ofhot water usage. Thus, we base the UECs in the residential database on the baseline "Standard Clothes Washer" unit used in 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 kWh/yr for the total. This assumes that the typical unit sold in 1988 is representative of the entire stock in 1990, which may be a
93 8. CLOTH,ES WASHER END-USE DATA

.Clothes washers use energy primarily by increasing the 

### waterheating use for a residence.

. Thus. they can be 

### majorenergyconsumers 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 itemsin the washerand for thehot water required from the waterheater. These UECs are typically calculated assuming electric water 

### heat. although some households'.hot water will 

### be supplied by agas waterheater. Obviously; the 

### UECofa clothes washer 

### in the field will be 

### direcdyproportional to the amount.the appliance is 

### used. Average energyuse for stockclothes washers

## Is difficult to 

### estimate withoutdirect metering ofthe appliance as well**~** 

### the waterheater. 

### In collecting data 

### for the UEC**databas~** 

### we found thatit was difficult to 

### determine if

### the waterheat portion ofthe clothes washerwas included in the 

### VEC estimate.even wherethe

# sourcemay have explicidy stated whether or notitwas included, as shown by some incrediblyhigh values given for·the non-waterheat portion. The 

### DEC database CQntains 21 estimates ofthe total 

### cotheswasherenergy Use 

### and 35 estimates ofthe 

### non-waterheatportion only (Appendix B). 

## UEe equaltion Theequationbelow shows the relationship between clothes washerefficiency and energy consumptiOIll. Theenergy factor (EF) 

### includes the hotwater usage ofthe clothes washer, calculated 

### using ·electric water

### heating. A.majorquestion is 

### whether ornotthe

### efficiency of the 

### waterheaterused to heatincoming hot watershould be

### included 

### in the hot waterenergy ofthe

### clothes washer.

UEC: where:



### Stock UECs Use • Capacity kWblyr

## = EF 

## = Use • (Motor +HotWater Energy) Use is ill cycleslyear Capacity is volume (cubic feet) EFisthe energy factor from the DOE lestprocedure (cubic fcetJkWb) Motor ud Hot Wa.Energy are the components ofIbe VEe (kWblcyc1e)



### The best estimate ofthe UEC forclothes washers resultingfrom weighted averaging ofthe UECs in 

### the 

## UEe database is 

### 100kWhlyr 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 ofhot 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 ofthe 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 inTable 8.1.

Table 8.1. 1990 Stock and New Clotheswasher UECs 

### Water Hear Efficiency Descaipri 100% 85% Units Per Cycle Usage 

### MotorEnergy 0.27 kWhcye HotWaterDemand 12.80 12.80 ga/cycle HotWaterLoad 2.82 2.82 kWh/cyde HotWater Energy 2.82 332 kWh/cyce TotalEnergy 3.09 359 kWh/cyde Annual Usage Motor Energy 103 103 kWh/yr HotWater Demand 4864 4864 gal/yr HotWater 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 loadcalculated at90F temperatre rise EF calculated forcapacity of2.60 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.) foreach ofthose cycles. The most recent estimate ofnumber ofcycles 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 ashot 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% ofnew sales, and it is the only appliance considered in the database. reasonable assumption since efficiencies have not been changing since 1979 (see Figure 8.2). TIle data for this baseline unit aresummarized 

### inTable 

### 8.1.

Table 8.1. 1990 Stock and New Cloiheswasher UECs

New UEes Wau:rHeartrfJficieucy DescripciOll 1009& 85$ Per Cycle Usage Motex'Energy ,'0:1.7 0.27 HatWaI,U Demand 12.80 12.80 HatWa1J:ZLoad 2.82 2.82 HotWarer&ergy 2.82 332 TOlal Eoergy 3.09 359 IAnnual Usage' MoUX' Energy 103 103 Hat**W~**Demand 4864 4864 HatWau:rLoad 1071 1071 HotWa1J:ZEnergy 1071 1260 TOlalEnergy 1173 1362 Eoergy Factor 0.84 0.73 Soma:: US DOE 1989c, baseline standanlclotheswasher Hot waterloadcaJcu1ate4 at90F temperature rise EFcalcu1ate.dfor capacityof2.60 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 sameas fot the stock. since 

### w,e base the stock value onthe typical unit sold in 

### 1988. Since appliance standards 

### will notaffect 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.hotrinse; cold wash. cold 

### rinse; etc.) foreach ofthosecycles. The

### most recentestimate ofnumberofcycles 

### is from Proctor and Gamble (US DOE 1990b) and 

### is 380 Cycles peryear. Currently, however. the U.S. DOE test procedure is based on a usage estimate of 416 cycles per year. Clothes washers often have manydifferent options that would also affectenergy usage such ashot vs. cold. rinse. These various temperature settings

## are included

### in the appliance standards analysis andthe 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% ofnew 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 ofnew clothes washers sold overtime 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 hotwater useis the greatest portion ofthe total energyuse. In addition, the efficiency is calculated assuming electric waterheating. The average efficiency ofnew units sold increased between 1972 and 1980, but has remained stable since thattime. The average size ofclothes washers has increased slightly overthe last several years. Cost vs. Efficiency for New Equipment The database includes estimates ofcost vs. efficiency fornew 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 atthe lower end come from elimination of hot and warm water rinse cycles atvirtually no cost. Atthe upper end, improvements only minimally effect the hot water use and thus the efficiency. The primary means ofefficiency improvement isto move to a horizontal axis clothes washer, which uses significantly less hot water. Product Lifetimes The database contains estimates from two sources ofthe lifetimes of clothes washers, 

### which arelisted 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% ofnew housing units have clothes washers.

95 "

Historical Emciency' Data Annual clothes washer shipments from 1957 to 1990 

### are shown 

### in 

### Figure 8.1 

### and the overall average efficiency ofnew clothes washers sold overtime

### is shown 

### in Figure 8.2 along 'with the average 

### size (capacity in cubicfeet). Note that theefficiencies

### are largely determinedby hotwaterdemand 

### since the hotwater

### use isthe greatestportion ofthe 

### total energyuse. 

### In addition, the efficiency

### is calculated assuming electric**w~ter**heating. 

### The average efficiency ofnewunits sold increased between 1972 and 1980, buthas 

### remained ' stable

### .sin('..e thattime. Theaverage size ofclothes washers 

### has increased slightlyover

### the lastseveml

### years. ' Cost '\'5. 

### Emclency 

### for New 

### Equipment The da\tabase includes estimates ofcost 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 lowerendcomefrom elimination ofhotand wann water 

### rinse cycles 

### atVirttl8lly no cost. Atthe'upper end, improvements only minimally effect the hotwater use and thus theefficiency. Theprimary means of

### efficiency improvementistomove to a horizontal 

### axis clothes washer, which uses significantlyless hotwater. 

### Product Lifetimes The database contains estimates from two sources of the lifetin;les of clothes washers, 

### which arelisted 

### 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 (firstowner 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 ofwringer washing machines which are slightly different than automatic washers. Italso includes shares in new buildings built during the previous 

### 5 to7 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% ofnew housing

## units have**cloth~** washers. '

95Figure 8.1. Annual Clothes Washer Shipments, 1957-1990



## 7

. _ ___._ .___ . ____ ._ E
__ _.______..

_ 6
5

-........... _ . _._... ___ .... __ ____ ___ _ ..... ............

0 - ._._ --- .__.--_------- I--------- ---- I------------
01950 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 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

CIIICII 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. Efficiencycalculated assuming electric water heatat 100% efficiency.



### 96Figure 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 DOE 1990. UEC and EF calculated assuming electric water heat at 100% efficiency. Database includes hot water energy separately to calculate costs for gas water heat. Converned from $1988 using CPI multiplier for laundry products of 1.02. Level Dsion Baselie Standard clothes wsher

. 2.60 cuftcapacity. 380 Cycles per Year. WH efficiency = 100% (Electric). 1 Eliminate WamV/Warm Set 2 Eliminate Warm Rinse (1994 Standard) 3 2 + Improve MotorEfficiency 4 3 +Thermal Mixing Valve 5 4 + Plastic Tub 6 2 + Horizontal Axis 7 6 +Thermal Mixing Valve 8 7 +Plastic Tub

.



### 97 Figure 8.3. Cost Versus Efficiency (or New Clothes Washer

3.3

2.8



# -**~** 2.3**~** 

## r! .!:!**~** :::I S:!. 1.8


# .. 0**~ ~**  

# .. fI 

# .. 1.3- c: (oj 2

0.8

0.3 400 450 

# I 8 . 6 

# --1-_····

__·-.

• Horizonlal Axis Washer**~** Sl8I'Idard Oothes Washer

SOO ConsumerCost (51990) 

### sso 600 6SO

Source: US DOE 1990. UEC andEFcalculated assuming electric waterheat at 100% efficiency. Database includes hotwaterenezgy separately tocalculate costs for gas waterheaL Converted from $1988 using CPImultipliezfor laundryproducts of 1.02.

Baseline StandardclOlhes WISher. 2.60cuitcapacity. 380Cycles pel' Year. WH efficiency = 100% (Electric). I EliminarcWmn/WarmSet. . 2 EIiminmte Warm Rinse (1994 Standard)

3 2 +lmpruveMotorEffICiency 4 3 +Thcmta! Mixing Valve 5 4 + Plaslic Tub 6 2 + HorizOntal Axis 7 6 +Thema!Mixing Valve 8 7 +Plaslic Tub**------------------------------------------------------------------~**



### 97Figure 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% ofall 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
. 0.01980

-E .2 0 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 Figure8.4. 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% ofall 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) Wh/cycle) c(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&GData 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 isMay 14 of yearindicated. 2) Standards specified inNAECA 1987 andeffective 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. Hotwater use portion fromUS DOE 1990b. Other energy use formotor. The standard specifies only theEF, and in practice manufacturers may notuse the specific design options trading offmotorand hotwater energies shown above. 5) Other (top loading, semiautomatic; front loading; and suds saving) are notregulated under the 1994 standards but musthave unheated waterrinse option.



### 99 

### - 8.5'. 

### Standards

Efficiency standards for clothes washers-were 

### rustenacted under the National Appliance

,Eru:rgy Conservation Act (NAECA). These standards required 

### only that clothes washers

have an unheated 

### wateroption for the rinse 

### cycle. New standards will 

### beCome effectivein 1991 

### 4. 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 isMay 14ofyearimtic:ated. 2) Standalds

### specified inNAECA 1987 andeffective starting 1990for clothes washers required clothes washers tobeequipped With an unbeated waterrinse option. 3) EFunits are capacity (CulL)jkWh. 4)

### VEe petcycle·CaIallaled ascapacitylEF. using 2.60 CUlL for standard size and 1045 cu.fL for compact size. Includes assumption ofelectric waterhealing at 100% efficiency. Hot wateruse ponion fromUS DOE 199Ob. Otherenergy use for motor. The standard specifleSonly the EF. and in.practicemanufacturers may notusethe specific designoptions trading offmotorand hotwater eoergifS shown above. 5) Other (top loading. semiautomatic; frOnt loading; and suds saving) are notregulated underthe1994 siandards butmustbveunheated waterrinse option.



### • 99

### 9. CLOTHES DRYER END-USE DATA Clothes dryers account for about 6% oftotal electricity usage and 2% oftotal natural gas usage in the residential sector. Dryers are a relatively well understood end-use and have been studied as part ofthe 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 ofwet 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 ofthe clothes dryer UECs in the UEC database resulted in estimates of 1000 kWh/yr for electric (n=67) and 3.9 MMBtu/yr forgas (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 ofthe 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 

### 9. CLOTHES DRYER END-USE DATA Clothes dryers account for about 6% oftotal 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 ofthe 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 ofwet clothing. The UEC ofa 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 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-cycleenergyconsumption, or 

### VEC, from which theenergy factor is derived. The relationship between the UEC and theenergy 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 MMBtulyrforgas (n=9) dryers. These values are close to the baseline new unit energy consumption in the U.S. DOEappliance 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 notbeen changing over time. Efficiencies forelectric 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 ofthe appliancestandards 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 ofelectric clothes dryers: the standard clothes dryer and two types of compact clothes dryers. Since the standard dryer accounts for 94% ofnew sales itis the only electric dryer considered in the database. There is only one class ofgas clothes dryers. Historical Efficiency Data Annual clothes dryer shipments from 1957 to 1990 are shown in Figure 9.1 and the average efficiency ofnew 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 at3412 Btu/kWh. The average efficiency ofnew electric units sold has changed only marginally since 1972, while the elimination ofpilot lights has improved the efficiency ofnew gas dryers. Cost vs. Efficiency for New Equipment Estinates ofcost 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 ofthe lifetimes ofclothes 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 

### 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 359cycles peryear. Currently. however. the U.S. DOE test procedure assumes thatusage averages 416 cyclesperyear.**9.3~.** 

### Clothes Dryer Technology Data**Thl~re** 

### are three differentclasses ofelectric clothes dryers: the standard clothes dryer and two types of compact clothes dryers. Since the standard dryer accounts for 94% ofnew sales it 

### is the only electric dryerconsidered in the database. ,There is only oneclass ofgas clorhes dryers. ' 

### IDstorical Efficiency,Data Annual clothes dryer shipments from 

### 1957 to 1990 are shown in Figure 

### 9.1 and the avelrage efficiency ofnew clothes dryers sold overtime 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 at3412 BtulkWh. The average efficiency ofnew electric units sold has changed only marginallysince 

### 1972, while the elimination ofpilotlights has improved the eftic:iency ofnew gas dryers. Cost vs. Efficiency 

### for New Equipment Estimates ofcost vs. efficiency for new standard clothes dryers are shown in Figure 9.3. Thevalues are based on estimates in the U.S. DOEappliance standards analysis (US DOE 1990b). adjusted to 1990$. Efficiency improvements are relatively minor except for the majornew technologies which may become available for electric clothes dryers. 

### Product Lifetimes**Tabk~** 

### 9.2 shows three different estimates of

### the lifetimes ofclothes dryers. Table 9.2. Estimates of Residential D LU t·

ryer eames'
.' 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 (firstowner lifeume only); Lewis and ClaIke 1990; LBLREM 1991.

.

101Figure 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 0.015 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

0.01972 

# I I -I

_ __--IFigure 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~**

102Figure 93. Cost Versus Efficiency forNew 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 factorrepresents gas consumption convened to kWh @ 3413 Btu/kWh. Test procedure uses 71bs/cycle and the testis run until the moisture content ofthe testload is between 25 and 5.0% ofthe bone dry wieght ofthe 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 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 ConsumerCost ($1990) 

### s



## --a- Heat Pwnp (E) ---0-- Microwave (E)

----- Standard Elec**~** Standard Gas

600 700 800

Source: USDOE 1990b. ConveitedfromS1988 using CPI multiplier for laundry prOduclS of 1.02. Gas energy fac:torrepresenlS gasconsumption converted tokWh @ 3413 Btu/kWh. TestProcedure uses 71bs/cycle and theleStis run untilthe moisturecontent ofthe test load is between 2.5 and 5.0% ofthe bone dry wieghtofthe testload. 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+ heatpump 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 isMay 14 ofyear indicated. 

### 2) Standards specified in NAECA 1987 and effective starting 1990 forgas dryers required that gas dryers shall notbe 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 aconversion of 

### 3412 Btu/kWh. 4)UEC per cycle calculated as capacity Obs)/EF, using 7Ibs forstandard dryers and 3Ibs forcompact size.



### 104 /



### 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 isMay 14ofyear indicated. 2) Standards specified in NAECA 1987 and effective starting 1990 for gas dryers required that gas dryers shallnot be equipped with a pilotligbL 1994 slaDdards levels from US DOE 1990b. 3) EFunits are IbslkWb. Gas dryer EFare also IbslkWb at a conversion of 3412 BtulkWb.

. 4) UEC percyclecalculated as capacity (lbs)/EF, using 7 Ibs for standard dryersand 3 Ibs for compactsize.

104Figure 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.

Figure9.5. Clothes Dryer Fuel and Total Ownership Shares forNew 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 datafor buildings builtbetween 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 Figure9.4. Clothes DryerFuel and TotalOwnecsblpShares,by RousingType, 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 I t

# 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 DryerFuel andTotal Ownership SharesforNewConstruction by HousingType 

### 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 ofenergy use in residences. Average energy use for lighting in the building stock is difficult to measure by metering because ofthe 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 ofbulbs). This situation is further complicated by the fact that the usage level affects the service life ofthe 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 itlargely influences the cost-effectiveness ofenergy-efficient lighting technologies. We use the results ofdetailed lighting surveys to create a breakdown ofusage 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 ofcosts and lifetimes for standard incandescent bulbs andtheir more efficientreplacements. 10.1. Baseline Lighting Usage We divide the current stock ofindoor and outdoor light sockets into six usage bins: less than 1hour, 1to 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 inWashington state (Manclark 1991). We assume, in the absence of better data, that this usage distribution is representative ofresidential lighting usage in the U.S. 10.2. Distribution of Installed Wattage Wefocus mainly on incandescent lamps because they comprise the vast majority oflighting in the residential sector. We base the relative frequency ofeach incandescent lamp wattage on data collected in a survey ofhomes inNew York and New Jersey (Robinson 1992), as shown in the top three rows ofTable 10.1. 10.3. Energy Consumptionper Socket Table 10.1 also shows the calculation of average socket UEC, based on the usage and wattage distributions discussed above. Each combination oflamp 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 ofoccurrence inthe 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 "% oftotal" show the percent of total incandescent lighting energy consumption attributable to each usage bin and wattage bin,respectively.

106 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 ofenergy use inresidences.

. . 

### A

vera.ge energy.use for lighting in 

### the building stock is 

### difficult to measure by metering 

### becau')e ofthe spatially diffuse nature oflighting. 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 ofdiversity in usage and equipment size (Le.,**wattal~e** 

### ofbulbs). 'This situation is 

### further complicated by the fact that the usage level affect:; the service life ofthe device. For instance, 

### an incandescent bulb used 

### one hourper 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 itlargely influences the 

### cost-effectiveness ofenergy-efficient lighting technologies. We use 

### the results ofdetailed lighting surveys to create a breakdown ofusage and wattage for inc:andescentbulbs. We

### use this breakdown to

### calculate total electricity use and 

### use per housellloid for residential lighting. We also present a summary ofcosts and lifetimes for standard incandescentbulbs andtheir

### more efficientreplacements.

. 

### 10.1. BaseUne Lighting Usage We divide the current stock ofindoor and outdoor light sockets into six usage bins: less than 

### 1hour. 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 ofbetJter data, that this usage distribution is 

### representative ofresidential lighting usage in the U.S. 

### 10.2. Distribution of Installed Wattage We focus mainly onincandescent lamps because they comprise the vast majority oflighting in the residential sector. We base the relative frequency ofeach incandescentlamp wauage on data collected in a survey ofhomes in New York and New Jersey (Robinson 1992), as ShOWII in

### the.top three rows ofTable 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 oflamp 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 ofoccurrence 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 "% 

### oftotal" show the**p~rcent** 

### oftotal 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 usageof2.1 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 iscloser to the 122 TWh for 1990 calculated by Atkinson et al. (1992). More than 80% ofincandescent lighting energy is found in single-family homes, with most ofthe 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 inBin Fraction Electricityusepersocketby combinedusage total Hrs/day Hrs/day % of total andwattagebin (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% 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 Avg 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 usepersocket bycombined usage Hrs/day Hrs/day 'Ioo/total andwattage bin (kWhlyrJ 0-1 brs 

### O.S 40% 5 7 11 14 18 27 32 4.9 1-2brs 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 3-4brs 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 adoptedfrom Manclark (1991). Waltage distribution from Robinson (1992) (2) Assumes that osage distribution applies in an identicalmanner aaoss all wattage bins

. %0/ total 10% 14% 12% 17% 21% 26%

100%

. (3) Hours in>5 hour/day bin adjusted to resultinManclarlc's average hourly usage of2.1 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 oflighting usage from Table 10.1 to estimate average lighting UECs per household by housetype. The average UEC perhousehold 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 1221Whfor 1990 calculated by Atkinson et al. (1992). More than 

### 80% ofincandescent lighting energy is 

### found in single-family homes, with most ofthe rest found in multifamily buildings.

107(1)Source for % of 1990 households: RECS (US DOE 1992) (2) Results ofPG&ELighting Survey aredocumented in Kelsey & Richardson (1992). (3)Lighting UEC infirstrow includes incandescent and fluorescents together. Incandescent UEC isnet oftube fluorescent lamps. Fluorescent UEC calculated based on Kelsey & Richardson (1992), 32 lamps perhouse@ 41.1 Watts/lamp used 3.8 hrs. day for5 outof6 days a year. (4)PG&.Efloor area from survey. Floor area by house type from US DOE (1992) (5) Installed waaage/sfbased on PG&E survey, 1.25 W/sfforsingle-family andmobile home, 1.18 W/sffor multi-family, reduced by 12% to account for the fact thatincandescent lamps are 88% ofinstalled wattage. Total wattage forUS homes calculated as theproduct ofPG&E wattage/sf and floor area. (6)PG&E average usage value based on customer-reported usage; US value fromTable 10.1. (7)Annual UEC (kWh/yr) equals average usage * installed watts/1000 (8) PG&E incandescent UEC per socketbased 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
HousingType Parmeter PG&E (2) Single-family Multifamily Mobile Homes Total 

### %of1990households 69% 26% 6% 100I
Lighting UEC (kWh/yr) 1,274 Fluorescent UEC (kWh/yr) 152 - - Incandescent UEC (kWh/yr) 1,118

. Existinghome 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 UECper t WsocketWhsoet/yr) 44.7 51.5 51.5 51.5 51.5 Socketsbouse 25 31 14 15 26

i~~~~~ i i. 

# --**Tablt~** 10.l: Calibration of Alinual Consumption to PG&E survey
Housing'Type Parameter PG&E(2) Single-family Multifamily**Mobi~Homes** Total , '--- % 0/1990Iwuselwlds 6990 26% 6% llX1fo**~-** Ugbtililg UEC (kWhlyr) 1,274 

# - 

# - - - FluorescentUEC (kWblyr) 152 

# - 

# - 

# - IncandescentVEC {kWblyr) 1.118 

# - - - EXistilllg home floor area (sq ft) 1,400 1,865 928 921 1569 Installedincandescentwatts 1,552 2,052 964 1,013 1712 

### .Avg. incandescent usage (hrIday) 1.94 .. 2.10 2.10 2.10 2.10 Alinual incandescentUEC {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 ofPG&ELightingSurvey aredocumented inKelsey & Richardson (1992). (3)Uglllling UEC infIrSt row includes incandescentand fluorescents together. IncandescentVEC isnetoftuoo fluorescent lamps.**~uorescent** UECcalculatedbased on Kelsey & Rich;irdson (1992), 32lamj)s perhouse@ 4U W:ausJ1amp used 3.8brs. day for5 outof6 days a· year. '. 

### (4) PG&£Boerarea from: survey. 

### floor areaby bouse typefrom US DOE (1992) (5) Insuilled wattage/sfbased on PG&E survey; 1.2:5 WiStforsingle-family andmobilehome, 1.18 Wlsffor multi-family, reduced by 12% toaccxnmtfor the tact·thatincandescentlampsare 88% ofinstalled wattage. Total**~lttage** for US homescalculated as the.product ofPG&Ewattage/sfand floor area. (6) PG&E average usage valuebasedon**customer~repotted** usage; US value from Table 10.1. (7) Annual UEC (kWh/yr) equals avemge usage • installed waUS/l000 (8) PG&Eincandescent UEC per socketbasedon surveyresults; US value from Table 10.1. (9) PG&E ydlue for sockets/bouse based on survey data; US values 

### =annual UEC+UEC perSockeL



### 108 

# -

-Table 10.3: 1990 residential incandescent lighting electricity use by house type, usage bin, and 'wattage bin (TWh/yr)-

Bulb wattagebin <40 W 40 W 60W 75W 100W 150W >150W SUm %ofo Sing
. family 

### 0-1 ha/day 0.3 0.9 32 2' 17

. . 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 1-2hrs/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 

### 23 1 5 1

(1)Total 1990 households (94 million) from 1990 RECS (US DOE 1992). (2)Total TW calculated usingnumber ofhouseholds 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 / 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 1-2ht: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 calculatedusing number ofhouseholds byhouse 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 reflectorincandescents and halogens: Atkinson et a. 1992. 

### (2) Source forcompact fluorescents: Koomey et al. 1994a. (3) Prices are to the end user, not including utilityrebates.



### 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 QuadTube 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 

### 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 Generalservice 60 60 1000 75 75 750 100 100 750 CompactFlluorescent Capsule

. 15 60 9,000 Capsule 18 75 9,000

Globe 15 60 9,000 TwiDTube 7 40 10,000 TwinTube 11 40+

. 10,000 TwiDTube 15 60 10,000 TwinTube 20 75 10,000 QuadTube 20 75+ 9,000 QuadThbe 1:1 100 9,000
InCande:scentreflector PAR 38 Rood 150 150 2,000 HalOgelllreflector PAR 38 Flood 90 ISO 2,000 (l)Sourc:e for standard andretleaoriocandesceats andhalogens: Atkinson etal. 1992. (2) SOlUce forcompacttluorescents: Koomey etaI. 1994a. (3) Pricc:s aceta dle enduser, notincluding 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 about: 7% ofresidential electricity consumption, 4% ofnatural gas consumption, and 10% ofLPG 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 ofthese structural changes could decrease residential energy use for cooking overtime. 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 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 forcooking 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 electric cooking, 3 for microwaves, and only 1 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 ofthe 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 ofcooktops, ovens, and microwaves. 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

11. COOKING END-USE DATA Cooking, or the combined total for cooktops, ovens. and microwave ovens. accounts for about 7% ofresidential electricity consumption. 4% ofnatural gas consumption, and 10% ofLPG consumption. Theprimary 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 ofthese structural changes could**decre~e** residential energy use for cooking overtime. Theresidential 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 fornewcooktops, ovens. and microwave.s aremeasured 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 forcooking 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/yrfor microwave ovens. These are taken from weighted averages ofthe 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 ofcooktops, 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.



### 111Figure 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 ......... -_ -- 01975 Source: Appliance Magazine. 1980 1985

112 

# 77 \

. ,

- ....... ....... .V.........._„. -i----I--- i ' i

1980 1985 1990

1990 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 ofcost 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 ofcooking equipment, which are shown in Table 11.1.
Table 11.1. Estimates of Residential Cooking Equipment Lifetimes Lifetime inYears

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 forthe standard cooking appliances at a national level Itincludes 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 ofthese 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 Cost vs. Efficiency for New Equipment

. Estimates ofcost 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 ofcooking equipment, which are shown in Table 11.1.

.



### 11.3. Shares Table 11.1. Estimates or Residential C k' E' L'r:

.

00 In!**~gulpment** 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 (firstowner lifetime only); Lewis and Clarke 1990: LBLREM 1991.



### Thedatabase 

### includes shares for main cooking fuel for**~e** 

### standard cooking appliances at

### a national level Itincludes total shares from the RECS data for 1981. 1984,1987 and 1990. Italso 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 orelectronic ignition systems, which will increase electricity usage for gas ranges.

113Figure 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 forNew 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 (S1990) Source: US DOE 1993.

114 100 90 80
70 5-60

so 2 50

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 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 GasCooktops

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 ConsumerCost ($1990)
Source: US DOE 1993.

114 Ootion BasdiDc I 2'

3 4 S . Descriotion E1cclric**Coo~**Coil E.1eaal 

### o

+Improved CoIdaCtCoo"'''..".rr 1+RdleaiveSUrfaces

4 +ThennostmicallFigure 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 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 ConsumerCost ($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** 

# /.,

..................................., .........................., ................ i3 ,. .-.-.......:.--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 ConduaiooLoss

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+ orFigure 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. Theenergy factor is calculated bydividing the microwave power output by the electric power input



### 116 ",

Figure 11.7 Cost Versus Efficiency for New Microwa\'e O,'ens

100 90 80



# - 70**~** 

# - 60 Lo <:I 

# - U 

# : SO >. .,**~** ,

. ,

i· i 1 i· Basel1J)e 

# ...

c.o 40 L. GI 

# I**---------~---------I** C**~** .30 20 1--' 10 0 0 SO 100 

### ISO 200. 250 Consumer Cost (51990) Source; US DOE 1993. Theenergyfactoris 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

116Figure 118. Cooking Fuel Sharesin Total Housing Stock, 1981-1990

---- Electric

--- G-- Gas

-- MLPG 0.6
0.5 5 0.4

. 2 

### ~ 0.3. a us 02

.... 0.1

...

01980 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 forbuildings builtbetween 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.........................

...



## .................. 

# .... ... '

..... '.

........................

....................... F"agure 11.8. Cooking Fuel Sharesin 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 cookingfuel" only. Not all houses will have both rangetops and ovens. .

F"tgure 11.9. Cooking Fuel Sharesfor 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 dataforbuildings builtbetween 1985 and 1990.· ALL 

### [ill EJecbic 

# o Gas**~LPG**

Fu:el.shares are for "main cookingfuel" only. Not aU houses will have both rangetops and ovens.



### 117Figure 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

01980 1982 1984 1986 1988 1990 Source: US DOE 1982a, 1986, 1989a, 1992.



### 118 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 -.-.----...................,..................-...........-....**~**....-.... . ..........-....- ...-:....-....- ......-....-........**~**.........-..-.................- ... 

# . : : i
OIl "-**~** 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%oftotal electricity usage in theresidential sector because ofthe number ofappliances in the stockand the large number ofdaily hours ofusage per set. Televisions have been studied aspan ofthe U.S. DOE appliance standards process. 12.1. Television UECs Television UECs fornew units are measured using a laboratory test procedure from U.S. DOE, The UEC ofa television in the field will be directly proportional tothe amount the appliance is used. Average energy use for televisions in the stock is estimated by utilities and othergroups 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 hourson + P *hours off where: P totlpower (Po + PS) Po operaing power (W) P standbypower (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 perday 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 fornew televisions are assumed to be the same asfor-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 _·__ __·**------------.~"-.---------------**



### 12. TE'LEVISION END·USE DATA

. - TelevAsions accountfor about5%oftoUll electricityusage 

### in

### theresidential

## sectorbecause ofthe numberof

### appliances in the 

### stockand the large

### nombel' ofdailyhoUlS ofusage per set. Televisions 

### have been studied 

### as pan ofebe 

### U.s. DOE

### appliance standards

### process.
. 

### 12.1. 

### T4rieyisioll UECs Television

### UECs for newunits

## ale measured using a laboratory test procedure from**U.S~** DOE: TheUEeofa television 

### inthe field will 

### be directly proportional tothe 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

## = P

T • hours em + PI- hours off where: 

### PF coWpower(Po + PS> operating power (tW) standby power (kW) hours on and hours offare 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

### = 6560hours (18.0 hours perday). 

### Stock UECs The UECs for televisions in theresidential forecasting database are 500kWh/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 TheUECs 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 andblack and white television sets. Clearly, colortelevisions are themost important, since black and white'televisions are becoming much less prevalent. The database includes shipments of color and black and white televisions, technology datafor standard sizes oftelevisions, and shares ofeach type and the average number oftelevisions per household. Changes in the market, such as increasing numbers ofprojection televisions or other large units, may 

### affect the energy use oftelevisions inthe future but are not addressed here. Historical Efficiency Data Ther are nohistorical efficiency data fortelevisions in theresidential 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 ofoperation 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 forcolor and black and white televisions for stock buildings by housing type at a national level. Itincludes 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 5to 7 years from the same data sets. The shares of televisions in the housing stock are shown in Figure 12.4. Clearly, the penetration ofcolor 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 2per household. 12.5. Standards None applicable at this time.



### 120 ·

# __.. 

# ----.----_
.... _..

# _.

# __.:.-.----



### 12.3.. Television Technology Data The

### lllam difference between different televiSion technologies is between color andblack: 

### and white·television 

### setS. 

### Ccarly,color

### televisions are the DlOst

### important. since blackand 

### white'

### televisions arc becoming much 

### lessprevalent. The

### database includes 

### shipments of color 

### and black 

### and white 

### televisions, teChnology data

### for standard sizesof

### televisions, and**share.~** 

### ofeach type 

### and the 

### average Dumber

### oftelevisions

## per household**Ctan~es** 

### inthe**marb~t,** such as 

### increasing 

### Dumbers ofprojection 

### televiSIons or other large units, may 

### affcctthe energy uscoftelevisions in

### the future 

### butare notaddressed here. 

### Historical Efficiency Data There 

### are no historical 

### efficiency data

### for televisions 

### in

### theresidential 

### forecasting database, 

### but shipments are includedand are shown in 

### Figure 12.l.

. 

### Cost vs. Efficiency '(or New Equipment The**d~ltabase** includes estimates 

### ofcOst 

### vs. efficiency fornew color

### and black 

### and white televisions. 

### These arc shown 

### in Figure 

### 122and 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 

### ofoperation

# veryear. 

### ProdutCt 

### Lifetimes The avt:rage lifetime for 

### 19" and 20" colortelevisionsisestimatedbe 

### 11.5

### years (US DoE



### 1993).· , 

### 12.4.

.

### Shares Thedatabase 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 inthe housing stock are shown in .Figure 12A. Clearly. the peneaation ofcolor 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.

. 120Figure 12.1. Annual Television Shipments, 1976 to 1990


### 25
21)

5 

### S. 10

0 -i

1975 1980 1985 Source: Appliance Magazine. Shipments forB&W notreported after 1987.

.% I 1990 _ __i·_____ " 

# -----,---,._----- .•.. 

## _ ... 

## _ ........

Figure 12.1.**A~ual**Television Shipments, 1976to 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 notreported after 1987•



# .....**~**

i990Figure 12.. Cost Versus Energy Use for New Color Televisions COLOR TELEVISION

Option Description Baseline 19 to 20 inchcolor television. I 0+Reduce sndbypower o2W 2 1 + Reduce W/B scram powerby 6W 3 2+ Reduce W/B seenpow ry 73/41W 

# F

365 370 375 380 Consumer Cost (S1990) Source: US DOE 1993. Energy use calculated using 2200hours ofoperation and 8760 hours of stuadby peryear.

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 inchmonochrme 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 ofoperation and 8760 hours ofstandby 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



### ~~~~~~-

- - - Figure U..l. Cost Versus Energy Use for New ColorTelevisions**~ T-----~------~------~----~**  

# 1-·--

so

o**~---~~------~------+-----~**

36(1 365 370 375 380 ConsumerCost (SI99O) COLOR TELEVISION Option**~ticm** Basdillc 19to 20 inch colortelevision. 1 0 +Reduce ItIDdby**pow~** to2W , 2 1 +Reduce W/B screcapowerby6W 3 2 +Reduc:c WIB saa:n"""**~**73141W

Source: US DOE 1993. Enezgy use calculated using 2200 boors ofoperation and 8760 hours of srandby peryear.

Figure 12.3. Cost Versus Energy Use for New Black andWhiteTelevisions



# - .. i C!!-**50~--------~--------~--------~** 

# r

..------**·1;··e&-·-·--·~** 

## -----.-- 49

:: .48**~** 

### 11 

# _.- ! 

# I 47

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



### ..**~** . . 

## ·---······--1-----···----·

2**~+-~------~--------~--~----~** 61 62 63 64 Consumer Cost (SI951O) BLACKAND WIUTETELEVISION**~on** . ·on Baseline 1310 14 inch monochrome TV 1 Reduce screen power5% 2 Reduce screen**~wa**7%**s~:** US DOE 1988. Costs convened from $1988 using CPI multipliec for video and audio products of0.936•.

. Energy use caJculared using 2200 hours ofoperationand 8760 hours ofsrandby per

### year.

122Figure 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 ofhouseholds with at least one television ofthetype indicated.

Figure 12.5. Average Number ofTelevisions 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 forthose with televisions for the type indicated.



### 123 0

&I 

### IU .0 1.0 0.9 OS 0.7 0.6 0.5 0.4 03 0.2 0.1 0.01980 1982



### 1.80 1.70 1.60 I

= 1.40 1.30 j.4 1.20 1.10 1.00 4 1980 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 ofhouseholds with atleanone television ofthe typeindicated. ---.,-- Color --a--:B&W

FIgure**1~~.s.** AverageNumber ofTelevisionsfor Houses withTelevisions, 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

DaLlis average numberoftelevision sets forthose 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 ExhaustFan 54 15 0.8 Whole HouseFan 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 HotWater 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 thedatabase have been removed from the list I 

### 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** ofstocks and UECsfor 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'03SaerlfoasterOven 86 50 AlI;dio System 81 SO HairDryer 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 ExhaustFan S4 15 Whole House Fan 8 80 Sump/Sew8ge Pump 13

. 40 Garbage Disposer 40 10 HeatTape 3 100 BottledWaterDispenser 1 300 \VmdowFan 9 20 Mower S 10 Instant Hot**Wa~r** O.S 160 Total Miscellaneous Electric Source: Adapted from Meiereta1. 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$ perMBtu)

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 A

41

':I

, X 

### i4. DEMOGRAPHIC AND PRICE DATA Table 14.1 provides otherdata 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.



### 1254 '



### 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 ofshell measure savings based on real-life conditions and applicable building technologies. We have building models thathave 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 atLBL 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 inthis 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 forresearchers in evaluating future potential for these types ofmeasures (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 vintagingroutine, and 2) the heating and cooling load calculation routine. The functions ofthe 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 arejust a few ofthe potential functions for the residential forecasting database.



### 126 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 ofshell measure savings based on**real~1ife** conditions
· and**~lPplicable** building technologies. Wehave buildingmodels that have been compared to

measured data showing fairly good agreement (e.g. the LBUGRI prototypes). but the analyticalwork to estimate the impact ofpotential thermal shell improvements on these build!ings has not been done. Building shell measure conservation potential databases devellop(:d at

### LBI,. and otherplaces have made no attempt to calibrate the models to actual residential sector**dat~** and typically have been used in analyzing design energy use innew cons1ruction

.

.The RECS databases, as well as other data collected by utilities, contain a wealth of

.

infonllation onefficiency measures already in place in theresidential sectorwhich are not well-represented in the residential forecastingdatabas.e described in this report·These data include measures such as water heater wraps, stonn windows, shade nees forcooling load
· reduction, occupant behavior such as building zoning, and others; These types ofdata

would be useful for researchers in evaluating future potential for these types ofmeasures (and thus avoiding double-counting ofsavings) and otherrelatedissues.

. 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 greaterregional detail. as well as error-checking, on the RECS.data. and should

### be included in the future.' Finally, ill its CUlTCnt form, theresidential 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 appliancevintagingroutine, and 2) the heating and cooling IQad calculation routine. The functions ofthe 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) calibratethe appliance efficiency

### data with the estimated UECs, and' 4) provideestimates ofsectorenergy consumption basedon the 

### data in the database. ·Thesearejust a few ofthe potential functions for the residential forecasting database.

.

126

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### 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. TopicalReport: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. TopicalReport:Analysis of Energy ConservationCodes inSingle-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 oftheACEEE 1992 SummerStudy onEnergyEfficiency in Buildings. Washington,DC: American Council for anEnergy-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 ofResidentialDuct Systems inBasements.LBL-33962. Lawrence Berkeley Laboratory. U.S. Bureau of the Census. 1988. American Housing Surveyfor the UnitedStates in 1985,U.S. Department ofCommerce. U.S. Bureau ofthe Census. 1990a. CurrentConstruction Reports, Characteristics of New Housing;SeriesC25.U.S. Department ofCommerce. U.S. Bureau of the Census. 1990b. AmericanHousing Surveyfor the UnitedStates in 1985, U.S. Department of Commerce.

130 NAHB. National Association ofHome Builders. NRC. National Research Center. 1986. 

### An Economic Data Base in Support ofSPC 90.2: Costs of Residential Energy, Thermal Envelope and HVAC Equipmenr. ASHRAE Research Project 494-RP. Association ofHeating. Refrigeration, andAir-conditioning

### Engineers (ASHRAE).



### - NMm, National Association ofHome Builders, NRC. National Research Center. 1989. 

### 1987 Builder Practice Survey Data. No. CR#5023, PO #4556710. National Association ofHome Builders. Neilsen. 1987. 

### The 

### 1987Neilsen Report. Neilsen MediaResearch Company. Proctor, 

### J.**1992a~** 

### Los Angeles Department of Water and Power Heat Pump/Air Conditioner System, Efficiency Study: Summary ofBaseline Data. Larkspur,- California.

_ ProcltOr, J. 1992b. Personal communication 

### with JohnProctor, ProctorEngineering.'**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 theACEEE 

### 1992 SummerStudy on Energy 

### Efficie.ncy in Buildings. Washington.DC: American Councilfor anEnergy-EfficientEconomy.

, 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 ofResidential Duct Systems inBasements. LBL-33962. Lawrence Berkeley Laboratory. U.S. lBureau of the Census. 1988. 

### American Housing Surveyfor the United States in 1985, U.S. Department ofCommerce.

- U.S. Bureau ofthe Census. 19903.0 

### Currenr Construction Repons, Characleristics of New Housing; Series 

### C25. U.S. Department ofCommerce. U.S. Bureau,of the Census. 1990b. 

### American -Housing Surveyfor the United Stares in 1985,U.S. Depanment ofCommerce.

130

### U.S. Bureau ofthe Census. 1992. American Housing Surveyfor the UnitedStates in 1985, U.S. Department ofCommerce. US DOE, U.S. Department ofEnergy. 1982a. ResidentialEnergy ConsumptionSurvey: Housing Characteristics 1981 and 1981 Public UseDataTape. DOE/EIA-0314 (81). EIA, Energy Information Administration. US DOE, U.S. Department ofEnergy. 1982b. ConsumerProductsEfficiency Standards Economic AnalysisDocument.DOE/CE-0029. March. US DOE, U.S. Department ofEnergy. 1983. ResidentialEnergy ConsumptionSurvey: Housing Characteristics 1982. DOE/EIA-0314 (82). EIA, Energy Information Administration. US DOE, U.S. Department ofEnergy. 1986. ResidentialEnergy Consumption Survey: Housing Characteristics 1984 and 1984 PublicUseDataTape. DOE/EIA-0314 (84). EIA, Energy Information Administration. US DOE, U.S. Department of Energy. 1988. Technical Support Document: Energy ConservationStandards for Consumer Products:Refrigerators,Furnaces,and TelevisionSets. DOE/CE-0239. U.S. Department of Energy, Assistant Secretary, Conservation and Renewable Energy, Building Equipment Division. US DOE, U.S. Department ofEnergy. 1989a. ResidentialEnergy ConsumptionSurvey: Housing Characteristics 1987and 1987Public UseDataTape. DOE/EIA-0314 (87). EIA, Energy Information Administration. US DOE, U.S. Department of Energy. 1989b. Technical Support Document: Energy 

### ConservationStandards for Consumer Products:RefrigeratorsandFurnaces. DOE/CE-0277. U.S. Department ofEnergy, Assistant Secretary, Conservation and Renewable Energy, Building Equipment Division. US DOE, U.S. Department of Energy. 1989c. TechnicalSupport Document: Energy ConservationStandards for ConsumerProducts: 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 ofEnergy. 1990a. State EnergyDataReport: Consumption Estimates 1960-1988.Energy Information Administration. DOE/EIA-0214(88). April US DOE, U.S. Department of Energy. 1990b. Technical Support Document: Energy ConservationStandards for ConsumerProducts: 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. ResidentialEnergy Consumption Survey: Housing Characteristics1990and 1990Public UseData 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 HeatingEquipment,MobileHomeFurnaces,KitchenRanges and Ovens, PoolHeaters,Fluorescent Lamp Ballasts,andTelevision Sets. DOE/EE131 U.S. Bureau of'the Census. ,1992. 

### American Housing. Surveyfor: the United States in 

### 1985.U.S. Department ofCommerce. US DOE, U.S. Department ofEnergy. 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 ofEnergy. 1982b. 

### Consumer Products Efficiency Standards' EconomicAnalysisDocument. DOE/CE-0029. March. ' US DOE. U.S.'Department ofEnergy. 1983. 

### Residential Energy Consumption Survey:

, Housing Characteristics 

### 1982. DOEJEIA-0314 (82). EIA. Energy Infonnation Administration. US**DOE~** 

### U.S. Department ofEnergy. 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 Standardsfor: Consumer Products: Refrigerators, Furnaces, and Television Sets. DOE/CE-0239. U.S. Depanment of Energy, Assistant Secretary, Conservation and Renewable Energy, BuildingEquipmentDivision. US DOE, U.S. Department ofEnergy. 1989a. 

### Res.idential Energy Consumption Survey: Housing Characteristics 

### 1987 and 

### 1987Public Use Data Tape. DOE/EIA-0314 (87). EIA. Energy Information Administration. ' US DOE. U.S. Department ofEnergy. 1989b. 

### Technical Support Document:' Energy Conservation Standardsfor Consumer Products: Refrigerators and Furnaces. DOFJCE-0277. U.S. Department ofEnergy, Assistant Secretary, Conservation and lRenewable Energy. BuildingEquipmentDivision. US DOE. U.S. Department of Energy. 1989c. 

### Technical Support Document: Energy Conservation Standardsfor ConsumerProducts: 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 ofEnergy. 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 ConsumerProducts:Dishwashers, Clothes Washers, and Clothes Dryers. DOE/CE-029.9P. U.S.**Dep~ent** 

### of Energy, Assistant Secretary, Conservation and Renewable Energy, BuildingEquipment Division.



## . ' 

### US DOE, U.S. Department ofEnergy. 1992. 

### Residential Energy Consumption Survey: Housing Characteristics 1990 

### and 1990 

### Public Use Data Tape.**DOEIEIA-O~** 

### 14 (90). EIA. EnergyInfonnation Administration.

, 

### - US DOE, U.S. Depanment of Energy. 1993. 

### Technical Support Document: Energy

, Efficiency Standardsfor Consumer Products: Room Air Conditioners, Water Heaters, DirectHeating Equipment, Mobile Home Furnaces, Kitchen Ranges and Ovens, Pool Heaters, FluorescentLampBallasis, and Television Sets. DOEIEE131

### 0009. U.S. Department of Energy, Assistant Secretary, Energy Efficiency and Renewable Energy, Office ofCodes and Standards. US DOE, U.S. Department of Energy. 1994. Annual Energy Outlook 1994 With Projectionsto 2010. DOE/EIA-0383(94). Energy Information Administration. January. Usibelli, A. 1984. Monitored Energy Use ofResidential Water Heaters. Lawrence Berkeley Laboratory. LBL- 17873.

132 

## 0009. 

# u.s. Department ofEnergy. Assistant Secretary. Energy Efficiency and Renewable Energy. Office ofCodes 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 offormats. 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 ofthe 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 oftechnologies contained in the database.

. .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



### 133 

### APPENDIX A. RESIDENTIAL FORECASTING DATABASE DESCRIPTION

The 

### Lawrence Berkeley Laboratory residential forecaSting database is programmed 

### in

. Foxbase+lMac and can outputreports in a .variety offonnats. 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 ofthe 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 oftechnologies contained 

### in the**database~**

133Figure A.I. Residential Forecasting Database Report Options Screen

r* File Edit Residential Forecast Report Options 

## Residential Forecasting Database 

# Incs&sbn r'/iyLa Auary ® 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 Figure A.t. Residential Forecasting Database Report Options Screen

,..... file Edit Residential Forecast ReportOpUons 

## Residential ForecastingDatabase uwrenl."e**&1rh/~**

### ulw'a1D/y 

### ® I Nt>. T.chnolow Ef'flct.1\CII Cha,.Actfristic. 

# o 2 Nil.ONAt £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 Tilp.- - 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'
- 1990HYAC 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.wtrFOP'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'ogromTable 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 datafor 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 ofbuilding 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 forbuilding retrofits (SF only) 22 HstCmp22 Completions ofnew construction annually, 1980-90 23 sArea23 Conditioned floorarea ofnew 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 ofnew construction annually, 1980-90 Conditioned floor area ofnew construction, 1980-90 Housing starts forecast Appliance lifetime estimates Baseline heating and cooling loads for prototypes (calculated by database)



### 135 1



# .- TableA.I. Residential Forecasting DatabaseTItles 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 forheating 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 oftJuildiug shell components 11 BldCmpll Building prototype.shell componentdimensions 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 (SFOnly) 22 HstCmp22 Completions ofnew construction annually, 1980-90 23 HsArca23 Conditioned floor area ofnew 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) HVAC system shares 7 HVACEq07 Cost vs. efficncy and cOst vs capacitydatafor 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 ofnew construCtion annually. 1980-90 23 HsArca23 Conditioned floor area ofnew construction, 1980-90 24 HsFcst24 Housing starts forecast .25 AplUt2S
. Appliance lifetime estimates LoadCalc Baseline heating and cooling loads for prototypes (calculated by database)

135Figure A.2. Sample Page from the Residential Forecasting Database

Resldenltal Forcasting Database

LawrenceBerkceleyLaboratory Fud: Technology: HP- Heat Pup STR-Storgae E - Electric B&W -Blk &Wht TV MND
- Manual Defhot TAD

-TopAuto. Defrost Vintae: HouseType: G-G* CAC
-Centrl AirCndt 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-- HeatCool 3%g 3p4 BUgtc= t Fe 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



# - 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

- SinlleFlmlly 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 NON· North E FaN N 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' ForecasttnsOatabate Lawrence BerfceleyuboratoryTKhnololTBlW

- Blit AWIlt TV CAe

- CmlrllAir Cndtn CHM

-ChestMin. Defrost COL-Color FRN·FumKc H20

- Hydronlc

. 1990 Reslden"

... Energy DOIIbl DilItaba:,,; UP·HeltPump

. MND· MlnullDdrosf NON -None OTH-Odln RAC •Room Air Cndtn RM·Room SOL-Sol

..

llEC Ueal CpgI rMMPtu orKwh! Eqmt Dbl HeatCool 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 •UprilhtAuto. 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,17Table A3. Database Technology Categories


### Index Fuel 4. 

### E Electric G Gas L LPG N None O Oil _T Other 

### Heatfuel (same options asFuel) 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 . 

### 44

j 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

137Table 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 SingleFamily MF Multifamily MH Manufactured Home Vintage S Stock N New R Replacement YEAR (actual year value)

138 Table A.3. Datllb.seTechllOlogy Categories (conL) I Applicable

.Technololg:or (entries specific to enduse) Enduse ·B&.W Black &. White TV

CAC Central Air Conditioning AC CHM ChestManual Defrost FZ 

### COL Color TV lFRN Furnace HI' ]H2O Hydronic HI'

HP Heat Pump HT,AC MND ManualDefrost 

### 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 (Subsetof "Tedmology" field) FRN Fwnace H2O Hydronic HP HeatPump 

### NON None OTH Other RM Room 

### SOL Solar Cooltech (Subsetof"Technology" field) 

### I CAe Central AirConditioning HP 

### HeatPump 

### NO

.None RAC Room Air-Conditioning**~egion** 

# ---

0 National [ North Region· 2 South Region Housetype



# - SF Single Family 

### MF Multifamily MH Manufactured Home Vintage S Stock N New R Replacement YEAR (actlltal year value)

138

### UEC 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 effort to develop baseline data for use in residential sector energy demand forecasting models and to document the source ofeach element within a database structure. a UECs are among the most important inputs to forecasting models and thus require careful examination and documentation. Data on UECs have traditionally come from a variety ofsources, 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 ofequipment, 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 existing 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 ofuncertainty in UEC estimates for the' various residential end-uses. The results ofthis analysis will be used to critically assess the UEC inputs in the residential energy demand forecasting models used at Lawrence Berkeley Laboratory (LBL) and to suggest improvements in these UEC inputs. Lastly, the database allows us to compare UEC estimates from the different analysis techniques described above and to make observations about the applicability ofthose techniques for specific end-uses. We present the results of the analysis in this report, along with conclusions about the nature ofthe data and the best UEC estimates based on'the collected data. A bibliography including all data sources in the UEC database isprovided. DATA COLLECTION AND ANALYSIS METHODOLOGY The data collection effort 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 types, or study types. The sources include only those known to researchers at LBL. In total, over 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 ofthe 1300 UEC estimates into a computerized database. Each record contains the UEC estimate along with documentation ofthe source, otherinformation from the study useful in understanding the reliability of the estimate, and an indicator of the quality ofthe 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 ofrecords for a given end-use category.

139 

### APPENDIX B. 

### APPENDIX B. UEe DATABASE DESCRIPTION

INTRODUCTION The purpose ,of this 

### appendix is 

### to review and assimilate all available estimates ofUnit 

### Energy Consumption ,values (UECs) 

### for the majorresidential end-uses. This projectis part of a larger effon to develop baseline data for use in residential sector energy demand forecasting models and to document the source ofeach 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 ofsources, 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 ofequipment, 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 ofuncertainty in UEC estimates for the various residential end-uses. The results ofthis 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 ofthose 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 isprovided. 

### 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 overthe last several years. We did not attempt to obtain a representative distribution ofsources across utilities, regions, house**typ~,** 

### orstudy types. The sources include only those known toresearchers atLBL. In total, ove,r 1300 UEC records were extracted from a list of98 sources. While the data may not be,statistically representative, they include the, majority of the available

,information. ' We entered each ofthe 1300 UEC estimates into a computerized database. Each record contains the UEC estimate along with documentation ofthe source, otherinformationfrom the study useful in understanding the reliability of the estimate,**~d** 

### an indicator of the quality ofthe estimate as well as othernotes. Our goal was to organize the data sothat we could'analyze it at different levels of disaggregation, depending on the number ofrecords 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 tounderstand the methodology and applicability ofthe data for further analysis. We summarize the important fields in the UEC database in Table B.1 below and discuss how we make use ofthese 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 stockUECs that best represent the data in the database. Because wehad 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 thestockvintage. 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 ofthese end-uses into their most importantproduct 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 EndUse: a code forone ofthe seventeen end-uses included in the database (e.g.heating, cooling, waterheating) Class the appliance class or technology under consideration, ifspecified (e.g. auto-defrost vs. manual defrost refrigerators, central vs. room air conditioning) Study Type one ofsix categories, including metered, conditional demand, engineering, model or other previously aggregated value, utility, or industry (defined indetail below) Vintage representative ofeither stockornew appliances, equipment, orbuildings House type single-family, multi-family, manufactured home, or all/not-specified Year theyearin which thedata were collected or the estimate made Region area of theU.S. thatthe data represent Quality a subjective rating ofdata quality assigned toeach record Source thereport authors and title, orother documentation Notes anecdotal information about the piece ofdata 

### For example, data on UECs come from a variety of different sources including submetering ofindividual 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 eitherfailed to account for these differences at

### all or have notexamined their effects systematically. Thus. we retain as much infonnation about each study as 

### is 

### necess;ary to

### understand the methodology and applicability ofthe data 

### for further analysis. We summarize the 

### important fields in the UEC database in Table B.l below and discuss how

## we make use ofthese supponing. data in 

### the following**sections~** Table B.l. Description of UEe Database Fields Field Description End.USC\ acode for Ol)eofthe seventeen end·uses included in the database (e.g. heating. cooliIig, warerbeating) Class the applianceclass or technology underconsideration. ifspecified (e.g. auto-defrost vs. manual defrostrefrigerators, central vs. room airconditioning) StudyType oneofsixcategories, including metered, conditional demand, engineering, model or other previously aggregated value. utility. orindusuy (defmed indetailbelow) Vintage representative ofeitherstockorne.wappliances.·equipment, orbuUdings House type single-family, multi-family, manufactUred home, oralVnot-specified Year the yearinwhich the datawere collected or the estimale made Region area oftheU.S. tballbe datarepresent QiJaJity a subjectiverating ofdataquality assigned toeach record Soun:e dle reportauthors and title. oroaherdocumentation Notes anecdotal infonnation about the piece ofdata



### We developed proceduresfor 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 thatbestrepresent the data 

### in the database. 

### Because. we had little UEC datafor the 

### new vintage (e.g. recently purchased refrigerators orheating 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 

### notbe 

### 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 colorand black-and-white TVs. Additionally, we subdivide several ofthese end-uses into their most importantproductclasses whereverenergy use varies significantly between classes and the data allow for it The end,.uses and appliance classes are summarized in Table B.2.

140Table B.2. UEC Database Contents by End Use and Class Electric UECsin kWhyr, gas UECsinMMBtluyr 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 ColorTV ETV all/not-specified central air heatpump 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_ 1i

tnegative value from poor regression specification

141 Freezer

EL Heating

Lighting Microwave Refrigerator _ . TableB.2. VEe DatabaseContents by End Use and Class Electric UECs inkWhlyr, gas UECs in MMBIu/yr

End Use Code Class Code .AirConditioning EAC aWDot-specified ALL cenuaJ. air CAC beatpwnp HP room air- RAC Black-White 1V EBW alVnot-specified ALL solidslate/electronic 

### SOS rube/manual ruB ElCooking ECK total cooking 

# -

. oven only oven

range only rangetop Clotheswasher ECW total=motor+h20 

# - motoronly motor 

### EL Clothes Dryer 

### EOR all dryers 

# - Dishwasher EDW total=motor+h20 

# - motor only motor Freezer EfZ alUnot-specified AU.. manualdefrost 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 allmiaowaves 

# - Refrigerator ERF alUnot-specif&ed AU.. manual defrost MND top-mount auto def TAD tbrougb-the-door TID side-by-sideno 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 allgas dryers 

# - Gas Heating 

### GHT all gas beating 

# - Gas Water HeaterGWH all**~as** waterheaters 

# - 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 poorregression 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 motoruse and for range and oven energy use in cooking-are tracked independently, similarly toequipment classes. For end-uses where class data arekept, a separate category is also included for datarecords 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 ofALL records, but a separate class category forestimates thatat leastclaim to include all theclasses ofthe 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. Meteredstudies are those in which individual appliances are measured fortheir energy use under actual or simulated domestic usage conditions. These include utility sub-metering and monitoring studies offield energy usage, as well as a few laboratory tests ofappliances that are typically based on a standardized test procedure intended to replicate field usage patterns. Conditionaldemand 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 ofvariation in both statistical methodology and level ofendusedetail among conditional demand studies. Engineeringestimates 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 ofwater heating energy use. The U.S. Department ofEnergy (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 ofundocumented sources. For this reason, we put model data in its own study type, together with other aggregateestimates ofUEC use, such as averages ofconditional demand studies and utility trade association figures. Estimates from individual utilities that do not disclose a source or methodology

--often simply the best guesses ofutility personnel

- arekept in the utilitycategory, and equipment manufacturers' figures, primarily the new product data from standardized appliance tests, areclassified in the industrystudy 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 ofUEC

142 

### ·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 pumpsystems 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 motoruse and for range and oven energy use in cooking-are tracked independently. similarly toequipmentclasses. 

### Forc::nd-uses where class data are kept, a separate categOl), is also 

### included for datarecords 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 ofALL records. but a separate

.class category forestimates thatatleastclaim to 

### include all 

### the classes ofthe 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.

. 

### Meteredstudies are those in which individual appliances are measured for theirenergy use under actual.or simulated domestic usage conditions. These include utility sub-metering and monitoring studies offield energy usage. as well as afew laboratory tests 

### ofappliances 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 greatdeal ofvariation 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 ofspace' conditioning energy use and gallonS x·.1T estimattes ofwaterheating energy use. The U.S. Department ofEnergy (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 overtime, from a variety ofundocumented sources. For this reason, we put model data in its 

### own study type, 

### tolgetber with 

### other aggregate 

### estimates ofUEC use. such as averages ofconditional demand studies and utility trade association figures. Estimates from individual utilities that do not disclose a source ormethodology --often simplY.lthe best guesses ofutility personnel

- are kept 

### in the 

### utilitycategory, and equipment manufacturers' figures. primarily the new product data from standardized appliance tests, 

### are clas:wtedinthe industry study type 

### (see AHAM 1990). In this**~\llalysis.** 

### we investigate the variability ofUEC estimates within and across study types where the data allow. This gives important insight into the relative range ofUEC

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 Whenthe 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 LBLResidential 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 themid1970s. 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 ofusage and efficiency:
UEC = 

### capacity X usage efficiency 

### Any ofthese parameters can change over time. The most significant factor, and the one we account for in this analysis, is the change in efficiency ofthe appliance stock. The process of normalizingthe data to 1990 stock efficiency levels is necessitated by the enormous 

### changes taking place in the market for certain appliances. Forexample, 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 foreach 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) ofnew units for the years 1972-90 are shown in Tables B.3 and B.4. The calculation assumes that the stock ofequipment 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 ofone, 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 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 obviouslyimportant when analyzing spaceconditioning 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 LBLResidential 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 Foreach 

### UEe record, we post the year in which the data were collected or the estimate 

### made. Thedatabase includes stock UEC estimates thatrange 

### 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 ofappliance sizeorcapacity,level ofusage and efficiency:

UEC 

### = capacity

### X usage efficiency Any ofthese parameters can change over time. The mostsignificantfactor, and the one we account for inthis analysis, is the change in efficiency ofthe appliance stock. Theprocess of

### normalizing the data to 1990 stock efficiency levels is necessitated by the enonnous changes taking place in the market for certain appliances. Forexample, new refrigerators and freezers have increased markedly in efficiency since 1972. Without nonnalizing to a common efficiency level, itwouldbe 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) ofnew units for the years 1972-90 are shown in Tables B.3 and 

### BA. The calculation assumes that the stock ofequipment 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 ofone, 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 overtime. The calculated nonnalizing factors are shown graphically in Figure B.l.

143Table B3. Historical Shipments. 

### MillionsofUnitsShipped

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 I _

.**~ 1~**  TableDol. 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

TableB.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 SWEfdala" interpolated for miSSing yearsFigure 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 fromy-lifetime toy of(SWEFx shipments)

sum fromy-lifetime toy ofshipments
. NormalizedSEF(y) = SEF(y) SEF(1990)

Formula for calculating Historically-weighted UEC: UEC (1990) = UEC(y) x SEF(y)

14S FigureB.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 yeary: SEF(y) = sumfrom Y-lifetime 10 Yo/(SWEF x shipmelUS) sum/rom,-lifetime 10Y0/ shipmelUS
NormalizedSEF(y)

## = SEF(y) SEF(1990)

Formula for calculating Historically-weighted UEC:
UEC (1990) = UEC(y) x SEF(y) -----uGWH

# I

### The effect ofthe historical normalization can be seen in Figures B.2 and B.3. Figure B.2 shows the distribution ofrefrigerator 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 ofthe normalization is twofold -- it reduces the average UEC to its approximate 1990 level, and it decreases the standard deviation, as variation due to the age of the different data sources is reduced. Region For the space-conditioning and water heating end-uses, regional climate and price effects are strongly correlated with energy use. Data records forthese 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 subjectivejudgment ofthe largest population-weighted portion ofthe 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 ofinterest in data collection across regions of the U.S. Where thedata 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 highest ranking and a five represents a zero-weighted study thatis includedjust for the sake ofdocumentation. 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 ofUECs and should be weighted more heavily into aggregate averages. The criteria used to determine the ratings are sample size formetered studies, complexity ofmethodology, reasonableness ofouptput, and level ofend-use detail Quality ratings are assigned only on the basis ofa record's value within its study type. Comparisons across study types are made later, atthe aggregate level. During our analysis, we tried several different types ofweighting schemes. However, the results varied little between these different formulations. 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 ofone will be weighted four times as strongly as a record with arating 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 database contains information on the source ofeach record which refers to a separate database of bibliographical entries. A list ofall the sources is included in the bibliography. Additionally, each record is supported 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 ofdishwasher, clothes washer and dryer UECs, floor-space and climate characteristics for some space-conditioning estimates, and capacity figures for refrigerators and water heaters. These data were too limited and incomplete to permit any further analysis.

146

Theeffect of the historical normalization can be seen in Figures B.2 and B.3. Figure B.2 shows the distribution ofrefrigerator 

### UEC estimates. unadjusted for historical efficiency trends. Figure B.3 shows the same data. adjusted to 1990 stock efficiencies using the historical weighting (butnot thequality rating which eliminates outlying data). 

### The effect ofthe nonnalization is twofold

-- itreduces the average VEC to its approximate 1990level, and it decreases the standard deviation. 

### as variation due to the age of the different data sources isreduced. 

### Regi10n For the space-conditioning and waterheating 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 subjectivejudgment ofthe 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 ofinterest in data collection across regions of the U.S. 

### Where the data

### are sufficient, wecompare UEC estimates across regions. 

### Quality Rating Subjective quality ratings 

### are given to all records on a five-:-point scale, where a one is the highesltranking and a five represents a zero-weighted study that is includedjustfor the

### sake ofdocwnentation. We assume that 

### all records with 

### ratings one through four have some value, !but that studies that are betterdesigned ormore detailed yield morereliableestimates of

### UECs and should beweighted more heavily into aggregate averages. The criteria used to detennine the ratings 

### are sample size for metered studies, complexity ofmethodology. reasonableness ofouptput, and level ofend-use detail. 

### Quality

### ratings are assigned only on the basis of

### a record's value within its study type. Comparisons across study types 

### are madeh:Lter, atthe aggregate level.

.During our analysis. we tried 

### several different types ofweighting 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 ofone will beweighted 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 isnothing to weight it against These single record categories 

### are marked byitalics on the tables 

### that follow. 

### Other Documentation The datlbasecontains information on the source ofeach record which refers to a separate database ofbibliographical 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 percycle estimates ofdishwasher. 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.

. .

146Figure B.2. Distributionof Unweighted "ALL" Class Refrigerator UECs 

### Average=1395, Standard dev.=378 10 ,-----_________ 1

- - 

### E 8 O 6

...... . 

# o 8 .*

- - -

### ........... ..- .- ....... 

### 1 1

-

0
. I I s.

- §! § L -_ 0

2 OO- 

### 2-l'--e '7

--------

__ 

### z

n crv . , ,
UEC Rangc (kWh/yr) 

# I'
Figure BJ. Distribution of "ALL" Class Refrigerator UECs -. Historical Weighting

147 Figure Bol. DistributionofUnweighted "ALL" Class Refrigerator UECs 

### Average=139S. Standard dev.=378 . 10 .,..M

__________M

_____ _

4 2
0

## ~ 8 8 

## § 8 

## 8 8 8 8 

## § 8 

## 8 8 

## § 

# § 00 Q\ 

# - M**~ ~**  11"\ 

## ,... 00 Q\ I 

# . . 

# - - 

# - - 

# - - 

# - 

# - - - ("of 

# - - 

# -. . 0 I 0 0 0 0' 0 0' 0 0 ("of 

### $ 0 0 

# - 8 

# - - 

# - - - - 

# - - 

# - ,... 00 0 0 0 0 0 0**~** '0 o· 0 Q\ 

# - ("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 0 

# - - - - - 

# - - - - - - f' 00**~** 

## 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 ofUEC 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 ofwhich provides several hundred records ofnational and regional conditional demand and engineering estimates. National average space heating, cooling andwater-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 ofUEC values in almost every end-use comes from conditional demand studies, where estimates frequently vary by as much as a factor of5 or 10 within the same end-use. The mostextreme ofthese estimates represent outliers and are almost certainly the results; offlawed statistical methodology and hidden variables. For example, the highest estimate of 1132 kWh/yr for microwave oven use would represent about 3 hours ofon time, every day ofthe 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 notbeen 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 ofthe 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 ofregional 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 ofLBL (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 

### OVERVIEW OF DATA SOURCES In 

### all. the UEC database contains more than 1300 

### separate records ofUEC estimateS taken from 98 

### differeni sources (see Table B.2). 

### The attached bibliography lists the data sources. 

### 'The 

### largestcontributors are 

### two UEC comparison studies from the 

### Electric Power**~t.rCh** 

### Institute (EPRI), each ofwhich provides several hundred records ofnationaI 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 EnergyInfonnation Administration (EIA). 

### The ",idest range ofVEC values in almost every end-use comes from conditional demand studies, where estimates frequently vary by as much as a factor of5. 

### or 10within the same end-use. The most extreme ofthese estimates represent outliers and are 

### almost certainlythe results 

### offlawed 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 ofthe 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

### particularregression 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 ofthe 'available data. 

### ,Metered data in 

### the database are predominantly from the**Bon~eville** 

### PowerAdministration (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 ofnew 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 ofactual 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 equipmentefficiency over time, as we have employed them in the nonnalized historical weightings. ' .

,. 

### National forecasting models tend to be 

### very complete, providing 

### a: high level ofregional 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 ofLBL (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 ofutility 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

, ofaverage 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 ofNew 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 ofusage. 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 ofthese records come from various years ofRECS conditional demand analyses. While the data show expected trends

- that water heating energy useis 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 ofthe small climate dependence of water heater energy use, this comparison is repeated later in Table B.8 with only the national-level estimates.

149 

### 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 ofNew Mexico, Pacific Gas & 

### Electric Company. and many otherutilities and related agencies. Engineering studies are often good estimators of UECs. but may suffer from unknown variables used in the calculations, particularly estimates ofusage. Simulations ofbuilding heating and cooling energy consumption are examples ofUEC 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 ofvariability 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 ofthe single-family and all/not-specified categories. With the possible exception ofwater 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 ofmulti-family and

. manufactured home UECs to observe a pattern. However. all ofthese records come from various years ofRECS conditional demand analyses. While the data show expected trends

- that water heating energy use is greater in single-family homes because ofhigher 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.

149Table BS. Non-Space Conditioning UECs by House Type 

### ElectricUECsInkWh/yr. gasUECsin MMBtu/yr All numbersforstock vintagenormalizedto 1990efficienciesandaveraged 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 . 

# - \.A o Table B.5. Non-Space Conditioning UECs by House Type Eleerr;c UEC! Iii kWhryr. gal UECs in!rIMB""yr Allnumbers for stock v;ntagenormalizedto 1990efficienciesandaveragedusing (5.QR) weighting (except italicized)**~** HOUSETYPE ' , !

# ~anufaclured Home

# I Alii Not**Specifie~** Single·Family Muhi.Family 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. Waterbeater


# --- 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 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 ofthe range boxes demonstrates the wide variations that exist in UEC estimates, while the difference between averages shows the biases ofthe different methodologies. Table B.6 shows the results ofthe 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 ofits 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 ofthe 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 ofthe 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 oftelevision 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 andengineering 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 ofthe 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 

### UEC values specific to eachhouse 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 ofthe range boxes demonstrates the wide variations that**e~st** 

### in UEC estimates, while the difference between averages shows the biases ofthe different methodologies. Table B.6 shows the results ofthe 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 ofits 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 ofthe 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 ofthe differing UEC levels ofthe refrigerator sizes and 

### classes. For example. a small metered sample might contain a greater proportion ofside-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 200kWhlyr 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 ofthe 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 oftelevision owners. Electric cooking estimates vary widely, with almost a factor oftwo difference between metering studies at the low end and engineering estimatesat 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 ofthe 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-waterenergy, assuming electric water heaL Clothes dryer UECs are also very consistent at about 1000 kWhlyr across all study types.

151Table 1.6. Non-Space Conditioning UECs by Study Type Electric UECsin kWh/yr, gas UECs inMMBl/yr Allnumbersfor stock vintage normalized to 1990 efficiencies andaveragedusing (5.QR) weighting (except italicized) STQR=Study Type QualityRating (l=highest.5=lowest) Best WeightedAverage= £ 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 R N CSTEC 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 GasCooking - 

### 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

1 ! ' TableB.6. Non·Space Condilloning UECs by StudyType Electric UECs in kWh(yr. gas UECs in MMBtulyr Allnumbers/or srockvintage nOnnlJliled 10

## 1990**effi~iencies**andaveragedusing (5'QR) weighting (excepl italicized) STQR=S,udy Type QualityRa,ing (1 =highesl. 5=lowest) Best Weigh,edAverage

## = 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. Walerbeater 

### ... 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 Waterbeater 

# - 1 31.60 ' 

### 1 9 21.99 3 1 22.50 

### 1 ModellAggregale 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 BestWeighled 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 23.69Figure 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 11 n 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 ftheweightedaverageas less thanthe lowes input value duetothestronghistorical 'eficency weighing ofthe refrigerator end use Metered Conditional Engineering Modcl/ Utility Weighted LIL-REM Demand Aggregate Average

' e, FigureBA. Electric Cooking UECs by Study Type

- Rangeand 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 weigluedaverage is/eUlMlllhe lowest input vallll!dill! 101Mstrong historical 'ejflCiencyweighlilig o/tIre re/rigeralOrmdlISe



# .**~**...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. B.6.. Electric Water Heater UECs by StudyType - 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 StudyType - 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 higherthan 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 asextreme 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 250kWh/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 splitmay be important ifthere is any trend towards oneor the othermodel in the long-term. The few existing lighting UEC estimates are quite consistently around 1000 kWh/yr. Several ofthese figures represent simple guesses ofresidential 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 ofthe study population, showing the deficit left by potentially excessive estimates ofdish- 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 notparticularly well represented in the database due tolimited end-use research for gas appliances. However, agreement is fairly good across study types for the available data. For cooking and clothes drying, most ofthe 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 ofRECS or the nlere is 

### considerable disagreement about dishwasherenergy use. particularly in the partial U1EC for motor energy. Here the disagreement between metered and conditional demand estimates 

### is especially striking (a factor offour). Conditional demand 

### is avery crude tool for separating the motor and hot-water portions of dish- and clothes washer energy use, 

### h(]lwever, and itis 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 

### islikely to be about .1000 kWhlyrassuming electric water heating. with about250kWhlyrgoing to motors. Freezers average 1000 kWh/yr, weighted between upright auto-defrostJreezers at about 1400kWhlyr 

### and manual defrost (both upright and chest) freezers. which 

### uSe about 900 

### kVnvyr. This 

### splitmay be important if

### there is any trend towards one or the othermodel 

### in thc;: 

### long-tenn.

. Th.e few existing lighting UEC estimates are quite consistently around 1000 kWh/yr.



### . Several ofthese figures represent simple guesses ofresidential 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 ofthe study population. showing the deficit left by potentially excessive estimates ofdish- 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

### notpanicularly 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 ofthe 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 waterheating, 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 ofappliance 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 waterheating 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 ofthe 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 ofRECS 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 ofdata 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 notentirely support the expected trends. In general, there are not enough records to create definitive results by region. Estimates ofnational 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 ofconditioned 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 ofthe all/not-specified house type UECs agree with averages ofthe 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 ofstudies. 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 forparticular 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 thebottom ofTable B.8. engineeringestimates 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 ofspace 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. Waterheaterdata 

### 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 ofCalifornia, Arizona and Nevada, is heavily weighted towards Northern California by the preponderance ofdata 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 notentirely support the expected trends. In general, there are notenough records to create defmitive results by region. Estimates ofnational 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 andlB.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 ofthe smaller exterior surface area in apartments and multiplexes and the smalleramount ofconditioned 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 lastcolumn. Aggregations ofthe 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 ofstudies. 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 ofTable B.8.Table B.7. Space Conditioning UECs by Region ElectricUECs inkWh/yr. gasVECs in MMBtiL/yr All numbers forstock vintagenormalized to 1990efficenciesandaveraged using(5-QR) weighting(except italicized) Ailtnot-specjfied housetype 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 TableB.7. SpateConditioning UECs by Region Electric UECs in kWhryr. gal VEel in MMB,,,lyr Allnumbers for stoc/c vinlage normalized 10 1990efficienciesandaveragedulillg (S-QR) weighling (excePI ilalicized) Aiiilloi-I"ec;;iedhoule 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 41.9Figure 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 

### IV

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. - 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**+-------~------_+------~~**

# ____**--~------_+--** 

# __----lAlUNotspecified Single Family Multi-Family Manufactured Population Home Wrd. Avg. LBL-REMTable B.8. National Space Conditioning and Water Heating UECs by House Type Electric UECs in kWh/yr, gas UECsin MMBUttyr Allnumbersfor stock vintage normalized to 1990 efcienciesandaveragedusing (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. waterheat --- 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 '. TableB.8. National SpaceCondllloning and Water Heating UECs by House Type Electric UECs in kWh1'r, gas UECs in MMBt"lyr Allnumbers/orstock vintage normalized101990tfficienciesandaveraged "sing (S-QR) ",eiglltin8 (except italicized) DATABASE RESULTS Single
. Muld· ManuCaclured cL uP" alion-WI UECs: Houst Conbinations 

# , 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. walerbeat 

# --- 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, 

### t· i h d UEe 11 h l Cl Co hi

OPII anon·we'll It s: Ie no ogy ass 

### m 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 ofthe national space conditioning and water heating records by study type, for records ofthe 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 ofnational-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 ofdish- and clothes washer hot water use to motors. CONCLUSIONS The database ofunit energy consumption (UEC) estimates is auseful tool for assessing the reliability ofresidential forecasting model inputs. The results provide the best estimates of UECs from the data collected. In the analysis ofthe data, this work goes beyond previous attempts atestimating 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 ofvariability 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 ofthe 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 ofdisaggregation 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 ofdifferent 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 ofproblems 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 ofthe 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 mustbe due to the difficulty ofseparating electric heat from other household electric data in conditional demand estimation, aproblem which is not as severe in the gas end-uses. Additionally, the small overall population and the localized nature ofelectrically heated homes maycontribute to the confusion. Significant variation in space conditioning UECs may actually be the result ofregional differences in electricity prices. Table B.9 shows a division ofthe national space conditioning and water heating records by study type, for records ofthe alVnot-specified house type. At this level ofdisaggregation, 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 ofnational-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 ofdish- and clothes washerhot water use to motors. 

### CONCLUSIONS The database ofunitenergy consumption"(UEC) estimates is a useful tool for assessing" the reliability ofresidential forecasting model inputs. Theresults provide the bestestimates of UECs from the data collected. 

### In the analysis ofthe data. this work goes beyond previous attempts atestimating UECs because we attempt to disaggregate the data by appliance class, housing type, and climatic regions where appropriate andwe account for historical trends in UECs due to appliance turnover by calculating stock appliance efficiency and nonnalizing the data to the 1990base 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 inenergy use habits. People use energy in very different ways and on widely different schedules, so that no reasonable size sample group can beperfectly representative of a regional or national average UEC. However, there is also a great deal ofvariability due to systematic error in estimation methodologies and study design. With 

### this in mind,.we analyze the databy study type. or VEC estimation methodology, and rate the quality ofthe 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 ofdisaggregation is not well supported by measured data orconditional demand estimates, even though itis intuitive that differences in UECs between house types exist, because ofdifferent occupancy levels and equipmentchoices. 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 ofproblems 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 ofthe water-heating energy to those end-uses orto 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 mustbe due to the difficulty ofseparating 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 ofelectrically heated homes maycontribute to the confusion. Significant variation in space conditioning UECs may actually be the result ofregional differences in electricity prices.Table B.9. National Space Conditioning and Water Heating UECsby Study Type Electric UECs inkWh/yr, gas UECsinMMBtt/yr All numbers forstock vintagenormalizedto 1990efficienciesandaveraged using (5-QR) weighting (except italicized) All/not-xpecfifedhouse 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. waterbeat --- 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_ Table B.9. Natlonal**Sp~ce** Conditioning and Water Heating UEes by StudyType Electric UECs in kWh&r, gas UECs in MMBill/yr Allnumbers for stocle vintage normalizedto i990efficienciesandaveragedusing (5·QRJ weighring (except IralicizedJ A1VlJoHpec!fledho!t!!typ!only

. Melered Conditional Demand Engineering ModellAggregal( 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. waterbeal 

## ... 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 GasHouseheatingSurvey. AHAM, Association of Home Appliance Manufacturers. 1991. Refrigerator,Freezer, Clothes Washer, andRoomAir Conditioner Energy Efficiency and Consumption Trends. Chicago: AHAM. AHAM, Association of Home Appliance Manufacturers. 1982a. Clothes Washers

-- EnergyEfficiency andConsumption Trends,Chicago, IL: AHAM. AHAM, Association ofHome Appliance Manufacturers. 1982b. ClothesDryers -- Energy Efficiency andConsumption Trends,Chicago, IL: AHAM. AHAM, Association of Home Appliance Manufacturers. 1990. Refrigerators-- Energy Efficiency andConsumptionTrends, Chicago, IL: AHAM. Archibald, R.G., D.H. Finifter, and C.E. Moody. 1982. "Seasonal Variation in Residential Electricity Demand: Evidence from Survey Data," AppliedEconomics, 14: 167-81. Bachmeier, Richard and Mike Robinson. 1988. A ConditionalDemandAnalysis of ResidentialElectricAppliance Use in the Southwest U.S., Report to the Public Utility Commission ofTexas. Barnes, Richard S. and Cathy R. Zoi. 1986. Unit Energy Consumption ofResidential Appliances, San Francisco, CA: Pacific Gas & Electric Co. Barnes, R., R. Gillingham and R. Hagemann. 1978. ResidentialDemand for Electricity, Washington, DC: U.S. Bureau ofLabor 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 ofConcerned 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 LoadStudy, 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 ProceedingsoftheAmerican Council for an Energy-Efficient Economy 1986 Summer Study on EnergyEfficiency in Buildings, 9: 9.15-9.24. Cambridge Systematics, Inc. 1984a. End-Use Consumption Models and Unit Energy Consumption(UEC) Value EstimatesForResidentialElectric And GasAppliances, Final Report, prepared for San Diego Gas & Electric Co., Berkeley, CA: Cambridge Systematics. REFERENCES FOR APPENDIX B AGA, American Gas Association. 

### Annual Gas Househeating Survey. AHAM, Association of Home Appliance Manufacturers. 1991. 

### Refrigerator, Freezer, Clothes Washer, andRoomAir 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 ofHome 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," 

### AppliedEconomics, 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 ofTexas. Barnes, Richard S. and Cathy R. Zoi. 1986. 

### Unit Energy Consumption ofResidential 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 ofLabor 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 ofConcerned 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. 

### ResidentialAppliance 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 ofthe American Councilfor an Energy-Efficient Economy 1986 

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