"d±6, /:)

Residential End-Use Energy Consumption: A Survey of Conditional Demand Estimates

CU-6487

Research Project 2547-1

Final Report, Octooer 1989

Prepared y;

REGIONAL ECONOMIC RESEARCH, INC.

12520 High Bluff Drive. Suite 220

San Diego, California 92130

Principal Investgators

K. M.Parrs 6 / - O (

Under subconzract to

CAMBRIDGE SYSTEMATICS, INC.

2855 Telegraph Avenue. Suite 305

Berkeley. Californ.a 94705

Prepared 'r

Electric Power Resea--h Institute

3412 Hillview Avenue

Palo Alto. Californ:a 94304

EPRI Project Manager

S. Braithwcar

Market Assessmere Program

Customer Systems Division

t

EE-RM-94-230 COMMENT 61D

'\ 

Residential End-Use Energy Consumption: A Survey 

of Conditional Demand Estimates 

CU-6487 

Research Project 2547-1 

Final Report, Octooer 1989 

Prepared t:'! 

REGIONAL ECONOMIC R:SEARCH, INC. 

12520 High Bluff Drive. Suite 220 

San Diego, Califor~ia 92130 

Principal Investigators 

. F. D. Sebold ? 6 t9/'(~I- oc-f: ( K. M.· Parris .) 

Under subcon,ract to 

CAMBRIDGE SYSTEMATICS, INC. 

2855 Telegraph Avenue. Suite 305 

Berkeley. Californ.a 94705 

Prepared <:J~ 

Electric Power Resec::h Institute 

3412 Hillview ;".'enue 

Palo Alto. Californ:a 94304 

EPRI Project Ma!lager 

S. Braithwar: 

Market Assessmer:: Program 

Customer Systems Division 
R E P O R T SUM MARY

SUBJECTS Demand-side planning / Market assessment / Forecasting

Residential energy use Marketing

End use Energy conservation

Demand-side planning

Demand-side planners / Forecasting managers and analysts

Residential End-Use Energy Consumption: A

Survey of Conditional Demand Estimates

To develop effective demand-side management plans, utilities

need to continually expand their understanding of customer energy

consumption patterns. This report presents a cross section of utility

estimates of residential electric energy consumption by end-use

category. The estimates were derived from a survey of some 30

conditional demand studies conducted since 1983.

BACKGROUND

OBJECTIVES

APPROACH

Utilities need information on end-use unit energy consumption (UEC) leveis

for end-use energy forecasting and for demand-side management (DSM)

and market planning. A variety of end-use information sources is available,

but few utilities have access to the most costly source, end-use metering.

One alternative to metering is conditional demand analysis, a statistical approach designed to disaggregate whole-house energy consumption data

from billing records into end-use components.

* To compile information from a range of utilities on estimates of residential

UECs based on applications of the conditional demand approach.

* To assess the consistency and transferability of these estimates across

service areas.

* To review attempts to validate the estimates against metered data.

Reviewing nearly 30 residential conditional demand studies for utility service areas, researchers identified theoretical issues concerning estimation

techniques and summarized estimates of end-use consumption levels. The

influence of weather differences on energy consumption for weather sensitive end uses was examined, and the UECs were compared across service

territories and assessed for transferability.

RESULTS The reported UEC estimates for space conditioning and water heating reveal a degree of variability across studies. Air conditioning and space-heating

consumption estimates were standardized for differences in weather by

means of simple regression equations. Some of the wide variation in water

heater usage was explained by differences in average weather conditions _- . : .- ,, .- _,. ._. ..

\.

TOPICS

AUDIENCE

EE-RM-94-230 COMMENT 61D

R E PO R T SUM'MARY 

SUBJECTS Demand-side planning I Market assessment I FQrecasting 

TOPICS Residential energy use 

End 'use 

Demand-side planning 

Marketing 

Energy conservation 

AUDIENCE Demand-side planners I Forecasting managers and analysts 

Residential End-Use Energy Consumption: A 

Survey of Conditional Demand Estimates 

To develop effective demand-side management plans. utilities 

need to continually expand their understanding of customer energy 

consumption patterns. This t~port presents a cross section of utility 

estimates of residential electric energy consumption by end-use 

category. The estimates were derived from a survey of some 30 

conditional demand studies conducted since 1983. 

BACKGROUND Utilities need information on end-use unit energy consumption (UEej leveis 

for end~use energy forecasting and for demand-side management (DSM) 

and market planning. A variety of end-use information sources is available, 

but ,few utilities have access to the most costly source, end-use metering. 

One alternative to metering is conditional demand analysis, a statistical approach designed to d isaggregate whole-ho\Jse energy consumption data 

from billing records into end-use components. 

OBJECTIVES • To compile information from a range of utilities on estimates Qf residential 

UECs based on applications of the conditional demand approach . 

• To assess the consistency and transferability of these estimates across 

service areas. 

• To review attempts to validate the estimates against metered data. 

APPROACH Reviewing nearly 30 residential conditional demand studies for utility service areas, researchers identified theoretical issues concerning estimation 

techniques and summarized estimates of end-use consumption levels. The 

influence of weather differences on energy consumption for weather sensitive end uses was examined, and the UECs were compared across service 

, territories and assessed for transferability. 

RESULTS The reported UEC eSTimates for space conditioning and water heating re-

. veal a degree of varia:>ility across studies. Air conditioning and space-heating 

consumption estimates were standardized for differences in weather by 

means of simple regr-ession equations. Some of the wide variation in water 

heater usage was explained by differences in average weather conditions 
result of different analytic model specifications designed to capture

the interaction of water heater usage with washing machines and dishwashers. More research is needed in this area.

UECs for refrigerators and freezers exhibited considerable stability across

studies, whereas UECs for discretionary appliances such as ranges and

clothes dryers varied widely. This variation is likely attributable to differences in specification of the estimated UEC equations. Although marked

differences exist between some metered and estimated UECs, the overall

validation evidence is encouraging.

EPRI PERSPECTIVE The need for end-use information continues to grow as utilities expand

their efforts to better understand customer energy consumption. This

understanding is necessary to develop effective marketing and integrated.

resource plans, conduct DSM or least-cost planning assessments, or

forecast at an end-use level. The conditional demand approach is a

powerful tool for making cost-effective uses of available utility customer

data, as evidenced by the widespread applications reported in the survey. Application of the conditional demand approach to the commercial

sector is described in EPRI report EM-5578; application to the disaggregation of residential hourly load patterns is described in reports

EA-4310 and EM-4525.

PROJECT RP2547-1

EPRI Project Manager: Steven D. Braithwait

Customer Systems Division

Contractors: Cambridge Systematics, Inc.; Regional Economic

Research, Inc.

For further information on EPRI research programs, call

EPRI Technical Information Specialists (415) 855-2411.

EE-RM-94-230 COMMENT 61D \, '. 

result of different analytic model specifications designed to capture 

the interaction of water heater usage with washing machines and dishwashers. More research is needed in this area. ' 

UECs for refrigerators and freezers exhibited c~nsiderable stability across 

studies, whereas UECs for discretionary appliances such as ranges and 

clothes dryers varied widely. This variation is likely attributable to differences in specification of the estimated UEC equations. Although marked 

differences exist between some metered and estimated UECs, the overall 

validation evidence is encouraging. 

EPI=l1 PERSPECTIVE The need for end-use information continues to grow as utilities expand 

their efforts to better understand customer energy consumption. This 

understanding is necessary to develop effective marketing and integrated, 

resource plans, conduct DSM or least-cost planning assessments, or 

forecast at an end-use level. The conditional demand approach is a 

powerful tool for making cost-effective uses of available utility customer 

data, as evidenced by the wjdespread applications reported in the survey. Application of the conditional demand approach to the commercial 

sector is described in EPRI report EM-5578; application to the disaggregation of residential hourly load patterns is described in reports 

EA-4310 and EM-4525. ' 

PROJECT AP2547~1 

EPAI Project Manager: Steven D. Braithwait 

Customer Systems Division 

Contractors: Cambridge' Systematics, Inc.; Aegional Economic 

Research, Inc . 

.For further information on EPRI research programs, call 

EPAI Technical Informa.tJofl Specialists (415) 855-2411. 
ORDERING INFORMATION

FReuests for copies of this report should be directed to Researcn Reports Center

(RRC). Box 50490. Palo Alto, CA 94303. (415) 965-4081. There is rno charge for reports

requested by EPRI member utilities and affiliates. U.S. utility assoc.ations. U.S. government

agencies (federal, state, and local), media, and foreign organizations with which EPRI has

an information exchange agreement. On request, RRC will send a catalog of EPRI reports.

Elect-: -lower Research Ins;::ute and EPRI are regisered service marks of Electric ,ver Research Institute. Inc.

Co-D" :": D 1989 Electric Power Research Institute. nc. All rights reserved.

NOTICE

This rort was prepared t. me organization(s) narr--d below as an account of wor- sponsored by the Electric

PoOwe -search Institute. Inr: E?RI) Neither EPRI. -emoers of EPRI, the organiza.i,:s) named below nor any

persc- -;Jng on behalf of an' o' tnem: (a) makes a-., warranty, express or impliec. ntn respect to tne use of any inforrr-: n. apparatus. meinca. or orocess disclose- in this report or that such use -.ay not infringe privately

ownec '* nts: or (b) assumes any liabilities with resc-e to the use of. or for damac-s resulting from the use of.

any ir":-'ation. apparatus. r-enod. or process disc.seo in this report

PreDpa:ec by

Camr.r :-e Systematics. Inc

Berke e California

and

Regic-a' Economic Researcn im

San C 3o. Calilornia

EE-RM-94-230 COMMENT 61D

ORDERING INFORMATION 

Fleouests for copies of this report should be directed to Researcr. Reports Center 

(HRC). Box 50490. Palo Alto. CA 94303. (415) 965-4081. There is :".0 charge for reports 

n~quested by EPRI member utilities and affiliates. U.S. utility assoc:ations. U.S. government 

agencies (federal. state. and local). media. and foreign organizatio~s with which EPRI has 

an mtormalion exchange agreement. On request. RRC will send a :::atalog of EPRI reports. 

EIE!C1-': "ower Researcn Ins:::ute and EPRI are regls:ered service markS of Electroc ::':-...er Research Inslltute. Inc. 

Ce'O"r -;.,: g;. 1989 Elect"c Power Research Institute. ·"c. All rignts reserved. 

NOTICE 

This ''''~rt was prepared c,' :ne organlzatlon(s) narr-':d below· as an account of .-.01". soonsored by tne Electric 

PONE':' :;~searCh InStitute. I": tEPRI) Nellher EPR!. -,emoers 01 EPRI, the organizar.,J"'l:S) named belo·,'I. nor any 

pe'SC" ~::Ilng 0.1 'benalf Of a~'f 0: tnem: (a) makes a-, ... ·.varranty, express or Imphec. ",'~n respect to tne use 01 any 

inforrr~: .:In. apparatus, metnca. or arocess diSClOse-: on tn,s report or thai such use --3¥ nOI infringe p"valely 

owne-; .. ;nIS; 'or (b) assumes, any habihhes with resc-s-;;;: to tne use of. or for damage; resulting from tne use of. 

an~1 ,r·:·"1atlon. apparalus. ~e:nOd. or process disc.:osea in Ih,s repOrt. 

PrE,oa'~ Oy 

Cam!;.' :;e Systemalocs. Inc 

Berke "'f California 

an(J 

Regle"a' Economic Researc!'1 I"IC 

San 0 ;-;::0, Calilorn,a ' 
FOREWORD

Utility efforts to better understand their customers,

and in particular their reliance on end-use models

for forecasting and DSM/market planning, creates

the need for information on end-use consumption

levels. Conditional demand analysis provides a useful means of constructing consumption estimates at

the end-use level.

This report summarizes the results of a broad crosssection of recent residential conditional demand

analyses conducted either in-house by utilities, or

for utilities by consultants. Aside from the analysis

results, the report should provide useful background information for utilities engaged in end-use

analysis of their own. The studies reported here

were identified through an informal telephone survey of utilities. While they constitute a representative set of such studies, they do not exhaust all of

the work done over the last few years. Indeed,

since this report was written we have seen a few

additional studies, including a recent effort by Potomac Electric Power Company, whose results generally fall in the range reported in this document.

This survey is not meant to be the last word on

conditional demand analysis. We plan periodic

updates to reflect changes in conditional demand

methodology and specific applications. In order to

ensure the most comprehensive coverage possible

in future reports, we encourage utilities to keep us

aware of recent studies involving the use of the

conditional demand technique, in either the residential or commercial sectors. Reports can be sent

to me or to the contractor on this report, Regional

Economic Research, Inc.

Steve Braithwait Ph.D.

Technical Manager

Customer Systems Division

EE-RM-94-230 COMMENT 61D

· FOREW,ORD 

Utility efforts to better understand their customers. 

and in particular their reliance on end-use models 

for forecasting and DSM/market planning. creates 

the need for infonnation on end-use consumption 

levels. Conditional demand analysis provides a useful means of constructing consumption estimates at. 

the end-use level. 

This report summarizes the results of a broad crosssection of recent residential conditional demand 

analyses conducted either in-house by utilities. or 

for utiliti~s by consultants. Aside from the analysis 

results. the report should provide useful background information for utilities engaged in end-use 

analysis of their .own. The studies reported here 

were identified through an infonnal telephone survey of utilities. While they constitute a representative set of such' studies. they do not exhaust all of 

the work done over the last few years. Indeed, 

since this report. was written we' have seen a few 

additional studies. including a recent effort by Potomac Electnc Power Company, whose results generally fall in the range reported in this document. 

This sUlVey is not meant to be' the last word on 

conditional demand analysis. We plan periodic 

updates to reflect changes in conditional demand 

methodology and specific applications. In order to 

. ensure the most comprehensive coverage possible 

in future reports. we encourage utilities to keep us 

aware of recent studies involving the use of the 

conditional demand technique,' in either the residential or commercial sectors. Reports can be sent 

to me or to the contractor on this report, Regional 

Economic Research. Inc. 

Steve Braithwait Ph.D. 

Technic.al Manager 

Customer Systems Dhision 
CONTENTS

Section

1 Introduction ..........

A. Background ........

B. Purpose and Objectives ............

C. Overview of Report .............

D. Summary of Results .............

2 Review of Conditional Demand Methodology ....

A. Introduction . ..... ..

B. Overview of the Basic Conditional Demand Model

3 Summary of UEC Estimates ...........

A. Introduction ........

B. Space Heating ........

C. Air Conditioning ..............

D. Water Heating ........

E. Refrigeration ......... . . .. . .. .

F. Cooking ..................

G. Pools and Spas ..............

H. ' Other Appliances ...............

4 Validation of CDA Estimates ..

A. Introduction . ..

B. Review of Comparisons with

5 Price and Income Elasticities

A. Introduction .......

B. Theoretical Issues ......

C. Review of Elasticities . . .

6 Summary and Conclusions . .

A. Overview ........

B.

C.

D.

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

End-Use Metering Data

. . . . . . . . . . .

. . . . . . . . . . .

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

Comparisons of UEC Estimates ...........

Validation of Conditional Demand Estimates ....

Price Elasticity Estimates .............

7 Bibliography ............

Page

. 1-1

. 1-1

; 1-2

. 1-2

. 1-3

. 2-1

. 2-1

. 2-1

.3-1

. 3-1

. 3-1

.3-7

. 3-10

.3-15

. 3-18

. 3-19

. 3-22

. 4-1

. 4-1

. 4-1

. 5-1

.-5-1

. 5-1

. 5-3

. 6-1

. 6-1

. 6-1

. 6-2

. 6-2

. . . . . . . . . . . . . . 7 -1

EE-RM-94-230 COMMENT 61D

CONTI:NTS 

Section Page 

1 Introcluction....... · 1-1 

. A. \3ackground ..... · 1-1 

B. Purpose and Objectives : 1-2 

C. Overview of Report . · 1-2 

D. Summary of Results .. · 1-3 

2 Review of Conditional Demand Methodology · 2-1 

A. Introduction . . . . . . . . .. .' ., . · 2-1 

B. Overview of the Basic Conditional Demand Model . · 2-1 

3 Summary of UEC Estimates · 3-1 

A. Intrc;>duction . . · 3-1 

B. Space Heating . · 3-1 

C. Air Conditioning · 3-7 

D. Water Heating .3-10 

E. Refrigeration. .3-15 

F. Cooking ... .3-18 

G. Pools and Spas · 3-19 

H. Other Appliances .3-22 

4 Validi3tion of CDA Estimates . · 4-1 

A. Introduction . . . . . . · 4-1 . . 

B. Review of Comparisons with End~Use Metering Data · 4-1 

. 5 Price and Income Elasticities · 5-1 

A. Introduction ... . . .·5-1 

B. Theoretical Issues ... · 5-1 

C. Review of Elasticities · 5-3. 

6 Sumrnary and Conclusions · 6-1 

A. Overview . . . . . . · 6-1 

B. Comparisons of UEC Estimates. · 6-1 

C. Validation of Conditional Demand Estimates .6-2 

D. Price Elasticity Estimates. · 6-2 

7 . Bibliclgraphy. . . . . . . · 7-1 
FIGURES

Figure Page

1-1 Ranges of Space Conditioning and Water Heating UEC Estimates Single-Family Dwellings . .1-3

1-2 The Relationship Between Estimated Conventional Space Heating UECs and Heating

Degree-Days, Single-Family Dwellings ............... . ........... 1-4

1-3 The Relationship Between Estimated Conventional Central Air Conditioning UECs and Cooling

Degree-Days, Single-Family Dwellings ........................... 14

1-4 The Relationship Between Estimated Conventional Water Heating UECs and Average

Temperatures, Single-Family Dwellings .......... ...... ........ ..... . 1-5

1-5 Ranges of Other UEC Estimates, Single-Family Dwellings ................ 1-5

2-1 Basic Overview of Conditional Demand Model ................... 2-1

3-1 Conventional Space Heating UECs vs. Heating Degree Days . ............. 3-5

3-2 Heat Pump Space Heating UECs vs. Heating Degree-Days . ............... . 3-6

3-3 Conventional Central Air Conditioning UECs vs. Cooling Degree-Days ..... ....... 3-7

3-4 Heat Pump Central Air Conditioning UECs vs. Cooling Degree-Days ... .. ......... 3-11

3-5 Conventional Water Heating UECs (All H20 Heating) Average Temperature . ......... 3-14

3-6 Conventional Water Heating (H20 Heat exc. DW/CW) vs. Average Temperature ...... .. 3-15

3-7 Distribution of Refrigerator UECs, Single-Family Dwellings and All Dwellings . ....... 3-16

3-8 Distribution of Freezer UECs, Single-Family Dwellings and All Dwellings ........... 3-16

3-9 Distribution of Range/Oven UECs, Single-Family Dwellings and All Dwellings ...... . 3-20

3-10 Distribution of Microwave Oven UECs, Single-Family Dwellings and All Dwellings ...... 3-21

3-11 Distribution of Clothes Dryer UECs, Single-Family Dwellings and All Dwellings ...... . 3-23

3-12 Distribution of Dishwasher Motor UECs, Single-Family Dwellings and All Dwellings . .... 3-24

3-13 Distribution of Color TV UECs, Single-Family Dwellings and All Dwellings ..... ... . 3-24

EE-RM-94-230 COMMENT 61D

'. 

FIGURES 

Figure Page 

1-1 RangE~s of Space Conditioning and Water Heating UEC Estima.tes Single-Family Dwellings . 1-3 

1-2 The Relationship Between Estimated Conventional Space Heating UECs and Heating 

Degree-Days, Single-Family Dwellings. . . . . . . . . . . .... '. . . . . . . .. . 1-4 

1-3 The Relationship Between Estimated Conventional Central Air Conditioning UECs and Cooling 

Degree-Days, Single-Family Dwellings . . . . . . . . . . . . . . . . . . . . . . . 1-4 

1-4 The Relationship Between Estimated Conventional Water Heating UECs and Average 

Templ~ratures, Single-Family DwelHngs . . . . . . . . '. 1·5 

1-5 Ranges of Other UE~ Estimates, Single-Family Dwellings . . . 1·5 

2·1 Basic Overview of Conditional Demand Model . . . . . ., 2·1 

3·1 Conventional Space He~ting UECs vs. Heating Degree Days. . 3-5 

3-2 Heat Pump Space Heating UECs vs. Heating Degree-Days " . 3-6 

3-3 Conventional Central Air Conditioning UECs vs. Cooling Degree-Days . . 3-7 

3-4 Heat Pump Central Air Conditioning UECs vs. Cooling Degree-Days. . . 3-11 

3-5 Conventional Water Heating UECs (All H2O Heating) Average Temperature. . 3-14 

3·6 Conventional Water Heating (H2O Heat exc. DW/CW) vs. Average Temperature. . 3-15 

3-7 Distribution of Refrigerator UECs, Single-Family Dwellings and All Dwellings. . 3·16 

3-8 Distribution of Freezer UECs, Single-Family Dwellings and All Dwellings. . . . . 3-16 

3-9 Distribution of Range/Oven UECs, Single-Family Dwellings and All Dwellings . 3-20 

3-10 Distribution of Microwave Oven UECs, Single-Family Dwellings and All Dwellings . 3~21 

3-11 Distribution of Clothes Dryer l!ECs, Single-Family Dwellings and All Dwellings. . . 3-23 

'3-12 Distribution of Dishwasher Motor CEes, Single-family Dwellings and All Dwellings. . 3·24 

3·13 Distribution of Color TV UECs, Single-Family Dwellings and All Dwellings . 3-24 
t

I

TABLES

Table Page

3-1 Studies Reviewed . . . . . . . . . . . .. ... . . . . . . . . . . . . . . . . . . 3-2

3-2 Primary Space Heating UECs: All Dwellings ........................ 3-4

3-3 Primary Space Heating UECs: Single-Family Dwellings .. 3............3-4

3-4 Space Cooling Eucs: All Dwellings ................... .... .3-9

3-5 Space Cooling Eucs: Single-Family Dwellings ............. .... . 3-9

3-6 Conventional Water Heating UECs: All Dwellings ........... ......... 3-13

3-7 Conventional Water Heating UECs: Single-Family Dwellings ............... 3-13

3-8 Refrigerator and Freezer UECs: All Dwellings ...................... .3-17

3-9 Refrigerator and Freezer UECs: Single-Family Dwellings .................. 3-18

3-10 Cooking UECs: All Dwellings ........ ....... ............... 3-19

3-11 Cooking UECs: Single-Family Dwellings ........... ......... . ... 3-20

3-12 Pool and Spa Pumps: Single-Family Dwellings . ...................... 3-21

3-13 Other UEC Estimates: All Dwellings .............. ............... 3-22

3-14 Other UEC Estimates: Single-Family Dwellings . ....................... 3-23

4-1 Results of PGandE Appliance Metering Project ....... ................. 4-2

4-2. Results of TVA Submetering Study . ................... .. . . . 4-3

4-3 Sierra Pacific Validation Study ................. ............ ... .4-4

5-1 Overall Price Elasticities ..... .. ............ ....... 5-4

5-2 Overall Income Elasticities ..................... ... 5-4

5-3 End-Use Price and Income Elasticities: Texas-New Mexico Study .......... ... . 5-5

5-4 End-Use Price and Income Elasticities: San Diego Gas & Electric Stud . ............ 5-5

EE-RM-94-230 COMMENT 61D

\, 

" 

TABLES 

Table 

3-1 

3-2 

3~'3 

3-4 

3-5 

3-6 

3-7 

3-8 

3-9 

3-10 

3-11 

3-12 

3-13 

3-14 

4-1 

4-2. 

4-3 

5-1 

5-2 

5-3 

5-4 

Studies Reviewed . . . . . . . ......... . 

Primary Space Heating UECs: All Dwellings . . . . . 

Primary Space Heating UECs: Single-Family Dwellings 

Space Cooling Eucs: All Dwellings ...... . 

Space Cooling Eucs: Single-Family Dwellings. . . . . 

Conventional Water Heating UECs: All Dwellings 

Conventional Water Heating UECs: Single-Family Dwellings. 

Refrigerator and Freezer UECs: All Dwellings .... , 

Refrigerator and Freezer UECs: Single-Family Dwellings. 

Cooking UECs: All Dwellings. . . . . . . .' 

Cooking UECs: Single-Family Dwellings . . . 

Pool and Spa Pumps: Single-Family Dwellings 

Other UEC Estimates: All Dwellings. . . . . 

Other UEC Estimates: Single-Famil~r Dwellings 

Results of PGandE Appliance Metering Project 

Results of TVA Submetering Study 

Sierra Pacific Validation Study 

Overall Price Elasticities 

Overall Income Elasticities . . 

.. 

End-Use Price and Income Elasticities: Texas-New Mexico Stud\" 

. End-Use Price and Income Elasticities: San Diego Gas it Electri~ Study 

Page 

" .3-2 

.3-4· 

.3-4 

.3-9 

· 3-9 

, 3-13 

, 3-13 

,3-17 

, 3-18 

· 3-19 

.3-20 

· 3-21 

.3-22 

.3-23 

· 4-2 

.4-3 

.4-4 

.5-4 

· 5-4 

· 5-5 

· 5-5 
ABSTRACT

This report presents the results of a survey and

analysis of recent estimates of residential end-use

unit energy; consumption (UEC) levels obtained

through a statistical analysis procedure known as

conditional demand analysis. Methodological issues

are discussed, and UEC estimates reported for a

wide range of utility service areas, along with estimates, in some cases, of price and income elasticities.

Many utilities now use an end-use approach for

forecasting residential energy sales, and as part of

their DSM/market planning activities. However, few

have access to end-use metered data to be used in

the development of these forecasts and analyses.

Conditional demand analysis provides a statistical

means of estimating UECs from available billing

record data on monthly energy consumption, and

customer information derived from appliance saturation surveys. This approach is beginning to see

relatively wide use at electric utilities, as evidenced

by the large number of studies included in this

survey.

A general review of conditional demand methodology is presented to explain the logic of the

approach and to introduce the means of integrating

technical and behavioral factors into the model

specification. Then, individual end uses are considered. For each end use, alternative specifications

are presented, UEC estimates are summarized, and

the transferability of estimates across service areas

is assessed. In reporting weather-sensitive UECs,

an attempt is made to standardize for the weatherrelated variation in estimates across service areas.

A summary of estimated income and price elasticities derived from conditional demand studies is also

presented. In addition, studies comparing conditional demand estimates to end-use metered consumption levels are reviewed.

EE-RM-94-230 COMMENT 61D

ABSTRACT 

This report presents the results of a survey and 

"analysis of r~cent estimates of residential end-use 

unit energy; consumption (UEC) levels obtained 

through a statistical analysis procedure known as 

conditional demand analysis. Methodological issues 

are disCUSSE!d, and UEC estimates reported for a 

wide range of "utility service areas, along with estimates, in some cases, of price and income elasticities. 

Many utilities now use an end-use approach for 

forecasting residential energy sales, and as part of 

their DSMlmarket planning activities. However, few 

have access to end-use metered data to be used in 

the development of these forecasts and analyses. 

Conditional demand analysis provides a statistical 

means of estimating UECs from available billing 

record data on monthly energy consumption, and 

customer information derived from appliance saturation surveys. This approach is beginning to see 

relatively \'\/ide use at electric utilitie~, as evidenced 

by the large number of studies included in this I 

survey. 

A general review of conditional demand methodology is presented to explain the logic of" the 

approach and to introduce the means of integrating 

technical and behavioral factors into the model 

specification. Then, individual end u~es are considered. For each end use, alternative" specifications 

are presented, UEC estimates are summarized, and 

the transfer~bility" of estimates across service areas 

is assessed. In reporting weather-sensitive UECs, 

an attempt is made to standardize for the weatherrelated variation in estimates across service areas. 

A summary of estimated income and price elasticities derived from conditional demand studies is also 

presented. In addition, studies comparing conditional demand estimates to end-u~e metered consumption levels are reviewed. 
1

INTRODUCTION

This report is designed to assist utility analysts and

planners in the evaluation of end-use consumption

patterns in the residential sector of their own service areas. It presents estimates of end-use consumption levels derived in over 30 studies across

the country, and discusses the transferability of

these estimates to other service territories. In order

to foster comparability of estimates, the focus is on

studies completed since 1984.

The report presents the results of a survey of

applications of Conditional Demand Analysis

(CDA), an econometric technique designed to

extract information on end-use consumption patterns from customer billing records and appliance

survey data. Because of the insights it provides

with respect to customer behavior, CDA is an

extremely valuable analytical tool that is gaining

widespread use throughout the utility industry.

End use consumption estimates are also provided

from a small number of utility end-use metering

studies in which attempts have been made to validate the CDA estimates.

An earlier survey of Conditional Demand studies

was provided by Lawrence and Parti in a 1984 EPRI

report.' This study builds on the Lawrence and

Parti review, and can be used in conjunction with it

to obtain a comprehensive overview of work in this

area.

A. Background

1. Origin of Conditional Demand Analysis

The roots of Conditional Demand Analysis can be

traced to a studv done for San Diego Gas & Electric

Company in 1977 (Parti and Parti, .1977). A condensed version of that study was published in the

Bell Journal (Parti and Parti, 1979), and led to widespread interest in the methodology and subsequent

applications at several other utilities. Over the past

decade, the technique has been adopted by a large

number of utilities and regulatory bodies. For many

users, it has become an integral part of the end-use

analysis and forecasting process.

2. The Logic of CDA

CDA is based on simple and straightforward logic.

Even in the absence of end-use metering, utilities

generally have a substantial amount of information

on residential energy consumption and its determinants, and this information can be used to infer

end-use consumption levels. Four kinds of data are

generally used to implement CDA: whole-house

consumption data, generally in the form of billing

records; information on household appliance holdings and economic/demographic features, typically

obtained from appliance saturation surveys; data on

weather patterns; and information on market conditions (e.g., energy prices). As explained in Section

2, CDA involves the use of regression analysis to

decompose whole-house consumption into

appliance-specific levels. The regression essentially

attributes consumption to end uses on the basis of

the association between appliance holdings and

whole-house consumption. Further, the variation in

end-use consumption levels across households and

over time is estimated through the use of interaction terms involving household features, weather,

and market conditions.

3. Applications of CDA

Conditional Demand analysis was initially designed

to infer monthly or annual residential end-use consumption levels (often referred to as unit energy

consumption values, or UECs) from whole-house

billing data. However, it has been applied for a

number of other purposes, including:

* Estimating end-use income and price elasticities;

A. C. Lawrence and M. Parti, Survtev '

Conditional Energy Demand Models for Estimnatin,

Residential Urit Encrryu Consumiption C'etiicizts,

Electric Power Research Institute. EA-34101

February 1984.

EE-RM-94-230 COMMENT 61D

1 

INTRO()UCTION 

This report is designed to assist utility analysts and 

planners in the evaluation of end-use consumption 

patterns in the residential sector of their own service areas. It presents estimates of end-use consumption levc?ls derived in over 30 studies across 

the country, and discusses the transferability of 

these estimates to other service territories. [n order 

to foster comparability of estimates, the focus is on 

studies completed since 1984. 

The report presents the results of a survey of 

. applications of Conditional Demand Analysis 

(CDA), an econometric technique designed to 

extract information on end-use consumption patterns from cu:.tomer billing records and appliance 

survey data. Because of th"e insights it provides 

with respect to customer behavior, CDA is an 

extremely valuable analytical tool that is gaining 

widespread use throughout the utility industry. 

End use consumption estimates are also provided 

froin a small number of utility end-use metering 

studies in which attempts have been made to validate the CDA t!stimates. 

An earlier survey of Conditional Demand studies 

was provided by Lawrence and Parti in a 1984 EPRI 

report. I This study buiids on the Lawrence and 

Parti review, and can be used in conjunction with it 

to obtain a comprehensive ov"en:iew of work in this 

area. 

A. Background 

1; Origin of Conditional Demand Analysis 

The roots of Conditional Demand Analvsis can be 

traced to a study done for San Diego Ga's & Electric 

Company in 1977 (Parti and Parti, .1977). A conA. G. Lawrence and M. Parti, SlIrt""l/ <,f 

Conditiollal Enl'rg.'l D,'mand Models f,,; E~timatillS 

Rt(sidcntial Unit E"erS!! C,IIlsumpti'1II C." .. fficiL'lIts, 

Electric PO\"er Research Institute. EA-:HIOl 

February 1984. ' 

densed version of that study was published in the 

Bell Journnl (Parti and Parti, 1979), and led to widespread interest in the methodology and subsequent 

applications at several other utilities. Over the past 

decade. the technique has been adopted by a large 

number of utilities and regulatory bodies. For many 

users. it has become an integral part of the end-use 

analysis and forecasting process. 

2. The Logic of COA 

CDA is based on simple and straightforward logic. 

Even in the absence of end-use metering, utilities 

generally have a substantial amount of information 

on residential energy consumption and its determinants, and this information can be used to infer 

end-use consumption· levels. Four kinds of data are 

generally used to implement CDA: whole-house 

consumption data, generally in the form of billing 

records; information on household appliance holdings and economiddem6graphic features. typically 

obtained from appliance saturation surveys; data on 

weather patterns; and information on market conditions (e.g., energy prices). As explained in Section 

2, CDA involves the use of regression analysis to 

decompose whole-house consumption into 

appliance-specific levels. The regression essentially 

attributes ·consumption to end uses on the basis of 

the association between appliance "holdings and 

whole-house consumption. Further, the variation in 

end-use consumption levels across households and 

over time is estimated through the use of interaction terms involving household features, weather. 

and market conditions. 

3. Applications .of CDA 

Conditional Demand anal~'sis was initially designed 

to infer monthly or annual residential end-use consumption levels (often referred to as unit energy 

consumption values, or UECs) from whol~-house 

billing data. However, it has been applied for a 

number of other purposes, including: 

• Estimating end-use income and price elasticities; ... 
Introductiotn

* Evaluating the impacts of utility conservation

programs (see EPRI EA 3606);

* Assessing the impacts of building standards and

appliance efficiency standards;

* Deriving end-use consumption estimates for

commercial customers (see EPRI EM 5578).

In addition, modified CDA techniques have been

applied to the problem of disaggregaring metered

whole-building hourly loads into end-use load

shapes (see EPRI EA 4310 and EM 4525>.

The CDA technique is a flexible means of analysis,

and has considerable potential for addressing a

variety of demand-side questions facing utility

planners.

4. Themes in the Development of CDA

Like most analytical techniques, CDA has been

refined over time. Some of the basic directions of

these refinements have been:

* refinement of the specification of individual UEC

relationships;

* alterations in the design of appliance saturation

surveys to better achieve the objective of

obtaining information required for CDA;

* integration of engineering (thermodynamic)

relationships into the model.(sometimes called

the thermodynamic behavioral integrated, or

TBI, approach);

* linkage of the CDA model with app::ance choice

models (for instance, in the Residential End-Use

Energy Planning System, REEPS);

* incorporation of prior information on end-use

consumption (either engineering estinates or

prior CDA estimates);

* integration of end-use metered data into the

CDA estimation process; and

* improvements in treatment of partial or complete

non-response bias in customer surveys.

Clearly, other refinements will be made in the

future, thereby further improving the usefulness of

the CDA technique.

and the associated estimates of household end-use

consumption. It is intended to update the earlier

Lawrence and Parti survey (1984). This summary

provides a frame of reference for the analysis of

residential energy demand, and should contribute

to the understanding of key end-use consumption

patterns.

The specific objectives of the analysis are:

* to provide a review of CDA methodology;

* to present a comprehensive cross-section of CDA

estimates of residential end-use consumption

levels;

* to assess the consistency of these estimates

across service areas, after accounting for

cross-regional differences in weather patterns

and other geographic conditions;

* to evaluate the transferability of

weather-adjusted residential CDA estimates

across service areas;

*' to review attempts to validate CDA estimates;

and

* to summarize information on price elasticities

derived from CDA studies.

C. Overview of Report

Section 2 reviews the basic elements of the conditional demand methodology. The review outlines

the structure of the CDA model and explains its use

in deriving unit energy consumption (UEC) estimates for key end uses. At this point, .the analysis

is cast in very general terms.

Section 3 provides more detail on the CDA model

specification relating to individual end uses. It also

summarizes a wide range of CDA-based UEC estimates from several utility service areas for these

end uses. To the extent possible; weather-related

UECs are adjusted for regional variations in

weather conditions, and the resultant adjusted

UECs are described and examined for consistency.

For each end use, the transferability of estimated

UECs is assessed.

Section 4 reviews a variety of attempts to validate

CDA estimates against the results of end-use

metering experiments. Conditional demand estimates generally fall within the ranges predicted by

these alternative estimation techniques. However,

substantial discrepancies are found in some areas.

B. Purpose and Objectives

The general purpose of this studv is :o provide a

comprehensive summary of post-1983 CDA studies

EE-RM-94-230 COMMENT 61D

I.. " 

II/trod /let iLlIl 

• Evaluating thl~ impacts of utilitycon$Crvation 

programs (see EPRl EA 3606); , 

• Assessing the impacts of building standards and 

appliance efficiency standards; 

• Deriving end-use consumption estimates for 

commercial customers (see EPRJ EM 3378). 

In addition, modified CDA techniques have, been 

applied to the problem of disaggrega:ing metered 

whole-building hourly loads into e:ld-use load 

shapes (see EPRI EA 4310 and E\1 4523'" 

The CDA technique is a flexible mean:' of analysis, 

and has consider.able potential for c.ddressing a 

variety of dema'nd-side questions ic..:ing utility 

planners. 

4. Themes in the Development of CDA 

Like mbst analytical techniques, CDA has been 

refined over time. Some of the basic directions of 

the~e refinements have been: 

• refinement of the specification of indi\'idual UEC 

relationships; ;. 

• alterations in the design of applianre saturation 

sun:eys to better achieve the objectiw of 

obtaining infqrmation required for CDA; 

• integration of engineering (thermod~':lamic) 

relationships into the model,(somet:::1es called 

the thermodynamic behavioral integrated, or 

TEL approach); , ' 

• linkage of the CDA model \"ith apF'::.mce choice 

models .(for instance, in the Residential End-Use 

Energy Planning System, REEPS); 

• incorporation of prior Information (1:1 end-use 

consumption (either engineering estiznates or 

prior CDA estimates); 

• integration of end-use metered data :nto the 

CDA estimation process; and 

• improvements in treatment oi partie.: I.'r complete 

non-response bias in customer SUI"\·';~·s. . ' 

Clearh', other refinements \,'ill be :;lade in the 

future: thereby further improving the :..t:;efulnessof 

the CO,-\ technique., ' 

B. Purpose and Objectives 

,and the associated estimates of household end-use 

consumption. It is intended to update the earlier 

Lawrence and Parti survey (1984). This summary 

provides a frame of reference for the analysis of 

residential energy demand, and should contribute 

to the understanding of key end-use consumption 

patterns. 

The specific objectives of the analysis are: 

• to provide a review of CDA method.ology; 

• . to present a comprehensive cross-section of ,CDA 

estimates of residential end-use consumption 

leyels; 

• to assess the consistency of 'these estimates, 

across service areas, aJter accounting for 

cross-regional differences in weather patterns 

and other geographic conditions; , 

• to evaluate the transferability of 

weather-adjusted residential CDA estimates, 

acr9ss service areas; 

.' to review attempts fo validate CDA estimates; 

and 

• to summarize information on price elasticities 

derived from CDA studies, 

C. Overview of Report 

Section 2 reviews the basic elements of the conditional demand methodology. The rev'iew outlines 

the structure of the CDA model and explains it~ use 

in deriving unit energy consumption (UEq estimates for key end uses. At this point, .the analysis 

is cast in very general terms. 

Section 3, provides more detail on the CDA mod,el 

specification relating to individual end uses. It also 

summarizes a wide range of CD A-based UEC esti-

'mates from several utility service areas for these 

end uses. To the extent possible; weather-related 

UECs are adjusted for regional variations in 

weather conditions, and the resultant adjusted 

UECs are described and examined forconsistencv. 

For each end use. the transferability of, estimat~d 

UECs is assessed. 

Section 4 reviews a variety of attempts to validate 

CDA estimates against the ,results of end-use 

metering experiments. 'Conditional demand esti-

'mates generally fall within the ranges predicted by 

The general purpose of this study i~ :0 provide a ' these alternative estimation techniques. However,· 

comprehensive summary of post-19S3 CDA studies substantial discrepancies are found in some areas. 
Introductio:

Section 5 examines the use of CDA to estimate

income and price elasticities. Alternative means of

specifying income and price effects within the

model are reviewed, and empirical results are summarized. While this application of the CDA model

is still being refined, some interesting patterns seem

to be emerging from recent studies.

Finally, Section 6 provides a summary of the principal findings and offers a series of recommendations

concerning the use of CDA to analyze end-use

consumption patterns.

D. Summary of Results

The primary result of the study is a summary of

estimated UEC values for a wide variety of electric

appliances. Figures 1-1 to 1-5 illustrate the ranges of

estimates found in the course of the review.

As shown in Figure 1-1, estimated space heating

and air conditioning UECs vary widely across the

studies reviewed. Considerable variation in these

estimates is to be expected, given the diverse

weather conditions in the service areas covered by

the survey. Figures 1-2 and 1-3 relate estimated

space conditioning UECs to heating and cooling

degree days. The relationships take the form of

regression lines estimated using CDA results and

weather data from the studies surveyed. The ranges

of values depicted by the shaded areas are defined

by adding and subtracting two standard errors of

the aggregate regressions from the predicted values. While these confidence intervals are fairly wide

(90% confidence), the analysis suggests that space

conditioning loads can be estimated reasonably well

with the conditional demand approach.

Water heating UEC estimates also span a wide

range in absolute terms (see Figure 1-1). To some

extent, differences in these estimates stem from

conceptual distinctions in the UEC specifications.

Some UECs are defined to include water heating

loads associated with dishwashers and clothes

washers, and some exclude these loads. Some of

the variation may also be attributable to differences

in groundwater temperatures. Figure 14 depicts a

relationship between estimated water heating UECs'

and average temperatures for the studies reviewed.

A band of values based on the standard error of a

regression of UECs on average temperatures is

shown.

Figure 1-1. Ranges of Space Conditioning and Water Heating UEC Estimates*

Single-Family Dwellings

End Use

Conventional Electric

Space Heat .

leat Pump Heating I

Conventional Central

Air Condtioning

RoenviWall Air I

Ileat Pump Airi

Water Heating

0 2,000 4,000 6,000 8,000 10,000

Ranges of Estimates (Annual kWh)

Ranges exclude the highest and lowest UEC estimates.

EE-RM-94-230 COMMENT 61D

\ 

Section 5 examines the use of CDA to estimate 

income and price elasticities. Alternative means of 

specifying income and price effects within the 

model are reviewed, and empirical results are summarized. While this application of the CDA model 

is still being refined, some interesting patterns seem 

to be emerging from recent studies. . 

Finally, Section 6 provides a summary of the principal findings ~nd offers a series of recommendations 

concerning the use of CDA to analyze end-use 

consumption patterns. 

D. Summary of Results 

The primary result of the study is a summary of 

estimated UEC values for a wide variety of electric 

appliances. Figures 1-1 to 1-5 .illustrate the ranges of 

estimates found in the course of the review. 

As shown in Figure 1-1, estimated space heating 

and air conditioning UECs vary widely across the 

studies reviewed.. Considerable .variation in these 

estimates is to be expected; given the diverse 

weather conditions in the service areas covered by 

E.nd Use 

Com/enlional Electric 

Space Ileal 

Ileal Pump lleating 

Conventional Central 

Air Condtioning 

Ro<~nvWall Air 

Helll Pump Air 

Water lIeating 

o 2,000 

Illtroductill1: 

the survey. Figures 1-2 andl~3 relate estimated 

space conditioning UEC!:'. to heating and cooling 

degree days. The relationships take the form of 

regression lines estimated using CDA results and 

weather data from the studies surveyed. The ranges 

of values depicted by 'the shaded areas are defined 

by adding and subtracting two standard errors of 

. the aggregate regressions from the predicted values. While th~se confidence intervals are fairly wide 

(90% confidence), the analysis suggests that space 

conditioning loads can be estimated reasonably well 

with the conditional demand approach. 

Water heating UEC estimates also span a wide 

range in' absolute terms (see Figure 1-1). To some 

extent, differences in these estimates stem from 

conceptual distin'ctions in the UEC specifications, 

Some UECs are defined to include water heating _ 

loads associated with dishwashers and clothes 

washers, and some exclude these loads. Some of 

the variation may also be attributable t<;> differences 

in groundwater temperatures. Figure 1-4 depicts.a 

relationship betw¢en estimated water heating·UECs' 

and average temperatures for the studies reviewed.. 

A band of values based on the standard error of a 

regression of UECs on average temperatures is 

shown. 

4,000 6,000 8,000 10,000 

Ranges of Estimates (Annual kWh) 

• Ranges exclude the highest and lowest UEC estimates. 

Figuf'e1-1. Ranges of Space Conditioning and Water Heating UEC Estimates· 

Single-Family Dwellings 
Introduction

Figure 1-2. The Relationship Between Estimated Conventional

Space Heating UECs and Heating Degree-Days

Single-Family Dwellings

Figure 1-3. The Relationship Between Estimated Conventional

Central Air Conditioning UECs and Cooling Degree-Days

Single-Family Dwellings

UECs (Annual kWh)

20,000

Estimated Relationship

15,000 /

Predicted Value Plus

Two Standard Errors

10,000 -

5,000 ' / 1

Predicted Value Minus

}j, /^^^^ . _~''^ /*~ ~ Two Standard Errors

0

0 2,000 4,000 6,000 8,000

Heating Degree-Days

UECs (Annual kWh)

8.000

Estimated Relationship

6,000 -

Predicted Value Plus

Two Standard Errors

4,000 *

000

0 1,000 2,000 3,000 4,000

Cooling Degree-Days

I .

EE-RM-94-230 COMMENT 61D

\., 

" 

["trod lIet iOJl 

UECs (Annual kWh) 

20,000 

15.000 ' 

, , 

10,000 . 

5,000 . 

Estimated Rclati!,nship , 

I'redicted Value Plus 

o~~~------------~~----~------~--~----------~ 

o 2,000 4,000 

Heating Degree-Days 

6,000 

Figure 1-2. The Relationship getween Estimate~ Conventional 

Space Heating UECs and Heating Degr~e-Days 

Single-Family Dwellings 

UECs (Annual kWh) 

8,000 

8.000 -'----------------------------------------------------., 

6,000 -

4,000 . 

2.000 -

Predicted Value Plus 

Two Standard Errors 

Estimated Relationship 

Predicted Value Minus' 

Two Stan~ard Errors 

o~--~~--------------------~------~------------ o 1,000 2,000 

Cooling Degree·Days 

3,000 

Figure 1-3. The Relationship Between Estimated Conventional 

Central Air Conditioning UECs 'and Cooling Degree-Days 

Single-Family Dwellin~s 

4,000 
Introduction

Figure 1-4. The Relationship Between Estimated Conventional

Water Heating UECs and Average Temperatures

Single-Family Dwellings

End Use.

Refrigerators . l

Freezers I

Range/oven I ;

Microwave

Clothes Dryer I I

Dishwasher "_

Clothes Washer .

Color TV

Spa Pump I -..

Pool Pump . I : ...-

0 400 800 1,200 1,600 2,000 2,400 2.800

Ranges of Estimates (Annual kWh)

Ranges exclude the highest and lowest UEC estimates.

Shaded area represents judgcmentally-preferred range of estimates.

Figure 1-5. Ranges of Other UEC Estimates*

Single-Family Dwellings

1-5

UECs (Annual kWh)

7,000

6,000 - Estimated Relationship

"~~~~~~-~»^^^ \ Predicted Value Plus

\,.. -^~ Two Standard Errors !

5,000

4,000

3,000

2,000

Predicted Value Minus

1,000 Two Standard Errors

0

40 45 50 55 60 65 70 75

Average Temperature (Degrees Fahrenheit)

EE-RM-94-230 COMMENT 61D

\ 

UECs (Annual kWh) 

7,~~, --------------------------------------------------~ 

6,000 '-, Estimated Relationship 

'5,000 

4,000 

3,000 

2,000,' 

Predicted Value Minus 

1,000:- Two Standard Errors 

Predicted Value Plus 

Two Standard Errors 

~, 

o~-------------------------------------------------- 40 45 50 5S 60 6S 70 

Average Temperature (Degrees Fahrenheit) 

Figure 1-4. The Relationship Between Es~mated Conventional 

Water Heating UECs and Average Temperatures 

, Single-Family Dwellings 

End Usc, 

75 

Illtroduc;tioll 

-,------- ,-.~----------------------------,-------------

Refrigerators " 

FreC7.crs 

Range/oven 

MicfOW"dve 

Clothes Dryer 

Dishwasher ---.... 1 ______________ --'-__________ _ 

, Clothes Washer 

Color TV 

Spa Pump 

Pool Pump 

o 400 800 1.200 2,000 

Ranges of Estimates (Annual kWh) 

* Ranges exclude the highest and lowest UEC estimates. 

Shaded area represenlS judgementally-preferred range of estimates. 

Figure 1~5. Ranges of Other UEC Estimates* 

Single-Family Dwellings 

2,400 2.800 

1-5 
Introductionl

Figure 1-5 depicts UEC estimates for other appliances. Two sets of estimates are presented. One set

(represented by the full bars) depicts all estimates

except the highest and lowest. The second set,

indicated by the shaded portions of these bars,

describes judgmental estimates of reasonable ranges

for the UECs in question. As shown, estimates of

UECs for some of these other appliances are very

diverse. Some variation is expected, of course,

given the differences in household characteristics

and market conditions in the service areas in question. Furthermore, part of the.variation in estimates

is semantic. For example, dishwasher and clothes

washer UECs are sometimes defined to include

incremental water heating loads and sometimes

confined to motor loads. Even further, some variation mav be attributable to differences in appliance

stocks. For instance, the mix of manual-defrost-and

frost-free refrigerators may differ across service

areas.

This review reveals a growing use of conditional

demand analysis to estimate end-use energy consumption levels. The UEC estimates generated by

the studies surveyed here generally span a wide

range of values. As indicated above, this variation

is partly attributable to differences in weather conditions, specific definitions of end uses, household

characteristics and other factors discussed at length

in this report. However, the variation in these

estimates undoubtedly can be traced to fundamental differences in model specification and data quality. Two important lessons can be learned from

comparing a large number of conditional demand

studies. First, it is important to specify conditional

demand models carefully, recognizing the underlying physical and behavioral relationships. Models

derived explicitly from thermodynamic principles

and behavioral assumptions tend to yield the most

plausible UEC values. Second, attention to data

quality yields high returns. Validating data should

be an integral part of any conditional demand

study. If designed and executed with these principles in mind, the conditional demand framework

can be a very powerful tool of end-use research.

4 -C

EE-RM-94-230 COMMENT 61D

II/trod lief i,ll1 

Figure 1-5 depicts CEC estimates, for other appliances. Two sets of estimates are presented. One set 

(represented by the full bars) depicts all estimates, 

except the hig;hest and lowest. The second set, 

indicated by the shaded portions of these bars, 

describes judgmental estimates of reasonable ranges 

for the UECs in question. As shown, estimates of 

UECs for somE~ of these other appliances are very 

diverse. Some variation is expected, of course, 

given the differences in household characteristics 

and market conditions in the service areas in question. Furthermore, part of the. variation in estimates 

is semantic. For example, dishwasher and clothes 

washer UECs are sometimes defined to include 

incremental water heating loads and sometimes 

confined to motor loads. Even further, some variation may be attributable to differences in appliance 

stocks. For instance, the mix of manual-defrost·and 

frost-free refrigerators may differ across service 

areas. 

This review reveals a growing use of conditional 

d~marid analysis to estimate end-use energy con-

\ 

sumption levels. The UEC estimates generated by 

the studies surveyed here generally span a wide 

range of values. As indicated above, this variation 

is partly attributable to differences in weather conditions; specific definitions of end uses, household 

characteristics and other factors discussed' at length 

in this report. However, the variation in these, 

estimates undoubtedly can be traced to fundamental differences in model specification and data quality. Two important lessons can be learned from 

comparing a large number of conditional demanp. 

studies. First, it is important to specify conqitional 

demand models carefully, recognizing th~ J.mderlyinft physical and behavioral relationships. Models 

derived explicitly from thermodynamic principles 

and behavioral assumptions tend to yield the most 

plausible UEC values. Second, attention to data 

quality yields high returns. Validating data should' 

be an in~egral part of any conditional demand 

study. If designed and executed with these principles in mind, the conditional demand framework 

can be a very powerful tool of end-use research. 
2

REVIEW OF CONDITIONAL DEMAND METHODOLOGY

A. Introduction

This section provides a brief review of Conditional

Demand methodology. The purpose of the review

is to highlight the nature of the approach and the

means of integrating technical and behavioral factors into the specification. Detailed comments on

the structure of individual UEC equations are

deferred until the next section.

B. Overview of the Basic Conditional

Demand Model

1. Major Determinants of Household Energy Consumption

Figure 2-1 provides an overview of a basic conditional demand model. The underlying spirit of the

approach is that a household's energy consumption

is directly related to the stock of appliances present

in the dwelling and the energy consumption levels

associated with these appliances (unit energy consumption levels, or UECs). Unit consumption. in

turn, is related to specific features of these appliances, dwelling characteristics, and the household's

utilization patterns.

Appliance stocks are typically represented in the

CDA approach by a series of binary (0,1) or cardinal

(0,1,2,... ) indicators, generally defined for fairly

specific appliance types. Binary indicators are used

to indicate whether or not a particular system (e.g.,

central air conditioning) is present, whereas cardinal variables are sometimes used to represent appliances (say, TVs or refrigerators) where muliple

units may be present.

Appliance features include general characteristics

like sizing (e.g., water heater capacities, air conditioner tonnage, etc.), as well as efficiencies and

auxiliary equipment (e.g., intermittent ignir.on

devices). Some direct information on these feat.res

Figure 2-1. Basic Overview of Conditional r),n=nr4 r^nroi

_ 

EE-RM-94-230 COMMENT 61D

2 

REVIEW OF CONDITIONAL'DEMAND METHODOLOGY 

A. Introduction' 

This section provides a brief review of Conditional 

Demand methodology. The purpose of the review 

is to highlight the nature of the approach and the 

means of integrating technical and behavioral factors into the specification. Detailed comments on 

the structure' of individual UEC equations are 

deferred until the next section. 

B. Overview of the Basic Conditional 

Demand Model 

1. Major Determinants of Household Energy 

Consumption . 

Figure 2-1 pro\ides an overview of a basic conditional demand model. The underlying spirit of the 

approach is that a household's energy ·consumption 

is directly related to the stock of appliances present • J 

in the dwelling and the energy ccmsumption le,'els 

associated with these appliances (unit energy (unsumption levels, or UECs). Unit consumption. in 

turn, is' related to specific features of these a!'pliances, dwelling characteristics, and the household's 

utilization patterns. 

. Appliance stocks are typically represented in the 

CDA approach by a series of binary (Oil) or carCinal 

(0,1,2, ... ) indicators, generally defined for fairly 

specific appliance types. Binary indicators are l.!~ed 

to indicate whether or not a particular system (e.g., 

central air conditioning) is preSent, whereas cardi-· 

nal variables are sometimes used to represent aFpliances (say, TVs or refrigerators) where mul:iple 

units may be present. 

Appliance features include general characteristics 

like sizing (e.g., water heater capacities, air conditioner tonnage, etc.), as well. as efficiencies and 

auxiliary equipment (e.g., intermittent igni:ion 

devices). Some direct information on these fear..::es 

~I.ARKET WEATHER ECONOMiC &. 

DEMOCRA?EIC CONDITIONS CONDITIONS CHARACTE?.!STICS 

i I I 

1··············· .. · .... ·····~··· ...... ··· .. ···1 

I 

DWELU:\C t.iILlZATION APPUA.-'::E 

CHARACTERISTICS ?ATTER:->S FEA TUF'::S 

I i ! ! 

i 

~ 'Y 

I .... PPLIANCE-sPEclrrc .1 APPUANCE HOL'SE¥.OL!) 

E:->ERGY 1-2:: CONSUMPTIO;-; LEVELS 

CO~SU~(PTIO:-: 1- (UECs) ·1 STOCXS 

Fjgure 2-1. Basic Overview of Conditional I'),,!~,:> .... n '~,...nol 
may be available for the analysis. However, their

roles can be recognized also indirectly by including

variables that are expected to be correlated with the

features (age of the structure, household size, etc.).

Dwelling characteristics, which are most pertinent

to space conditioning uses, might include square

footage, insulation values, roofing materials, window areas, and other characteristics of the thermal

shell. Again, the analyst may have direct information on some of these factors; however, it may be

necessary to include proxies (e.g., the age of the

structure) to represent them if this is not the case.

Utilization patterns could cover thermostat settings

on water heaters, pools/spas and space conditioning

systems, as well as a variety of behavioral patterns

relating to the use of other appliances. These utilization patterns are not generally directly observable, but can be incorporated indirectly into the

model through the inclusion of market, weather,

economic, and demographic variables thought to

affect them.

2. A General Algebraic Specification of

the Model

The basic conditional demand model can be represented in general algebraic form as:

J

(1) HEC,, = , UEC,,:, ;,

j=1

where the following variable definitions are used:

HEC, = energy consumption by household i

in period t

UECij, = energy consumption through

appliance j in period t

S,,. = a binary indicator of household i's

owrership of appliance j.

Both HEC,, and S:.: are observable. Information on

appliance stocks (S. is generally obtained through

a utility's appliance saturation survey. This information is usually collected only once for each sampled

household, so the rme subscript (t) is dropped in

the remainder of :he discussion. Data on wholehouse energy consumption (HEC,,) is typically

extracted from billing records in monthly, bimonthly, seasonal, or annual form. Values of end-use

energy consumption (UECij,), however, are normally not directly observable (unless, of course,

end-use metering is used). Nonetheless, the CDA

methodology allows the analyst to infer values of

UECij, from the other information used to develop

the model. This feature is the most direct benefit

associated with the use of CDA.

The disaggregation of whole-house consumption is

accomplished through the statistical association of

consumption with the presence of appliances. To

illustrate this,. consider a very simple specification

in which the UECs are treated as constants across

households and over time. This type of model can

be written as:

HEC, = a0 + a, S,, + a2 Si2 + ... a, Sin + e,

where e,, is an error term (the nature of which is

discussed more fully below) and the aj terms are

the UECs. Using standard regression analysis, the

analyst can estimate the ais. These estimates are

based essentially on the tendency for household

consumption to vary as appliance holdings vary. If

homes with electric water heating tend to consume

more energy than homes without this appliance, all

other things equal, this tendency will be captured

by the estimate of the coefficient on the water

heating appliance variable. Each coefficient is interpreted as the increment in consumption due to the

presence of the appliance in question, given the

holdings of other appliances.

Of course, the above form of a conditional demand

model is highly stylized, in that it treats UECs as

constants across households.' In fact, end-use consumption depends upon a variety of factors, as

suggested earlier. This relationship can be formalized as:

(2) UEC,,, = f,(AF,,, STRUC,, UPii,, eii,)

where:

AFj = features of household i's appliance j

STRUC, = pertinent structural features

UP i, = utilization patterns relating to

appliance j

e,, = a random error term for the end use.

i Of course, random-coefficient regression could be

used to recognize random variation of UECs

across households. But the point here refers to

the ability of the analyst to model cross- .household variations in terms of known

variables.

Review of' Comnitionai: .'imandi AltltodohlioLy

\

EE-RM-94-230 COMMENT 61D

\ 

may be available ior the analysis. However, their 

roles can be recognized also indirectly by including 

variables that are e:-..pected to be correlated with the 

features (age of the structure, household size, etc.). 

Dwelling characteristics, which are most pertinent 

. to space conditioning uses, might include square 

footage, 'insulation \'alues, roofing materi:als, window ,1rI~as, and oth~r charaCteristics of the thermal 

sheB. Again, the analyst may have direct information on some of th~s~ factors; however, it may be 

necessary to includ~ proxies (e.g., the age of the 

structure) to repres~nt them if this is not the ca?e. 

Utilization patterns (Quid cover thermostat settings 

on ..... ater heaters, pools/spas and space conditioning 

systems, as well as a variety of behavioral patterns 

relating to thE' use or other ·appliances. These utilization patterns are not ·generally 'directly observabl~, but can be incorporated indirectly into the 

model through the inclusion of market, weather, 

economic, and der.h)graphic variables thought to 

affect them. 

2. A Genercll Algebraic Specification of 

the Mode~1 . , 

The basic conditional demand model can be represented in general al;::~braic form as: 

J 

(1) HEC'1 =L UEC", ,5;1 

j=l 

where the foll()win~ \'ariapledefinitions are used: 

HEe, = enerp; con'sumption by household i 

in per!l.)d t 

UECij, = ener:;y consumption through 

appliance j in period t 

5", = a bi.nar:· indicator of household i's 

ownership of appliance j. 

Both HEClI and 5:., dre observable. Information on 

appliance stocks (5, ,J is generally obtained through 

a utility's appliance :,aturation survey. This information is usually coll~ted only once for each sampled 

household, so the ::~e subscript (t) is dropped in 

the remainder of :he discussion. Data on wholehouse energy wr1:,umption (HEC,,) is typically 

extracted from billirlg records in monthly, bimonthly, seasonaL or annual form. Values of end-use 

energy consumptil.)o (UECij ,), however, are normally not directl:-' I.,bservable (unless, of course, 

end-use metering is used). Nonetheless, the CDA 

methodology allows the analyst to infer values of 

VECiil from the other information used to develop 

the model. This feature is the most direct benefit 

associated with the use of CDA. 

The disaggregation of whole-house consumption is 

accomplished through the statistical association of 

consumption ·with the presence of appliances. To 

illustrate this,. consider a very simple specification 

in which the UECs are treated as constants across 

households and over time. This type of model can 

be written as: 

where ei' is an error term (the nature of which is 

discussed more fully below) and the aj terms are 

. the UECs. Using standard regression analysis, the. 

analyst can estimate the ajs. These estimates are 

based essentially on the tendency for household 

consumption to vary as appliance holdings vary. If 

homes with electric water heating tend to consume . 

more energy than homes without this·appiiarice, all 

oth~r things equal, this tendency will be captured 

by the estimate of the coefficient on the water 

heating appliance variable. Each coefficient is interpreted as the increment. in consumption due to the 

presence· of the appliance in question, given the 

holdings of other appliances. 

Of course, the above form of a conditional demand 

model is highly stYlize'd, in that it treats UECs as 

constants across households. 1 In fact,. end-use COI1-

. sumption depends upon a variety of factors, as 

suggested earlier. This relationship can be formalized as:· 

where: 

AFij = features of household i's appliance j 

·STRUCi = pertinent structural features 

UPij, = utilization patterns relating to 

appliance j 

eill = a random error term for the end use. 

1 Of course, r~ndom-cocffjci~nt regression cl.luld be 

used to recognize random variation of IJECs 

across households. But the point here refers to 

the abilitv of the analvst to model cross-

. househoid variations 'in terms of known 

variables. 
Revieu' of Conditional Demnand Methodology

As noted earlier, it seems reasonable to recognize

the effect of weather conditions (call these WC,),

market conditions (MC,(), and the household's economic and demographic characteristics (EDCj) on

utilization patterns. So the model can be written as:

(3) UP,,, = g,(WC,,, MC,, EDC,)

Explicit assumptions concerning the dependence of

appliance features and structural characteristics on

other variables could also be used, but assume for

simplicity that data on these variables are available

and that their values are taken as given for the

purposes of the analysis.

Substituting (3) into (2) yields:

(4) UEC,,, = F,(AFji, STRUC,, WC,,, MC,,, EDC,, e,,,)

And, finally, substituting (4) into (1) provides the

final general specification:

J

(5) HEC,, = _ F(AF,, STRUC,,, W MC,, EDC,. e,,,) S,

1=1

3. Estimating the Model

The CDA model can be estimated statistically by

standard multivariate regression analysis using data

on household energy consumption. appliance saturations, and other variables contained in (5). While

an extensive discussion of estimation procedures

and problems will not be offered here, some general issues are mentioned below.

Sample Sizes. In general, CDA equations (especially those relating to electricity consumption) tend

to have a large number or coefficients to estimate.

Furthermore, some appliances of interest may have

fairly low saturations. As a result, relatively large

sample sizes are normally required for estimation.

While there is no clear minimum for sample size,

analyses based on fewer than 500 or so households

may provide relatively little information for those

appliances with very low saturations.

Data Problems. Survey and billing data used in

the estimation of a CDA model are generally far

from perfect. Billing data may have irregularities

caused by human error, skips in meter-reading

patterns, and vacancies. Problems with survey data

are generally considerably more troublesome.

Respondents often misreport space heating fuels,

insulation levels, square footage, and other critical

variables. Missing values may be frequent for key

questions like income, square footage, insulation

levels, appliance ages, and other important features. Of course, consistency checks may identify

errors in responses. For instance, households

claiming to have natural gas space heating might

be screened to confirm that gas service is available

to them, and seasonal consumption patterns of

households with various stated heating fuels might

be reviewed. Clearly, cleaning the data is one of

the most important steps in conditional demand

analysis.

Level of Aggregation. The conditional demand

equation can be estimated with annual, seasonal,

bimonthly, or monthly consumption data, However, sorting out end-use consumption levels is dearly

easier with the use of monthly data. In a sense, the

monthly variation in weather-sensitive loads (space

heating, cooling, water heating and perhaps others)

helps to distinguish these loads from each other.

Multicollinearity. In most CDA models, especially

those with a large number of interaction terms,

multicollinearity can be a serious problem.

Multicollinearity is a statistical problem, caused by

high correlation across explanatory variables, that

limits the capability of the regression to distinguish

the impacts of these variables. As a result, it may

be difficult to disentangle the influences of individual appliances or household features on total

energy consumption. For instance, refrigerator consumption may be difficult to distinguish from consumption through unspecified (miscellaneous)

appliances because all households are likely to own

at least one refrigerator. This particular problem can

be alleviated if information on refrigerator features

(e.g. manual/frost-free, size, age) is available from

the survey. At times, choices need to be made

between theoretical elegance and statistical convenience. For example, prior information may have to

be used to constrain the relative or absolute values

of some coefficients, or certain terms may simply

have to be dropped from the equation.

Heteroskedasticity. Given the CDA specification

introduced earlier, note that the error term of the

model is actually a function of error terms from

individual UEC functions and appliance variables.

Assuming that the individual error terms are additive within their respective UEC functions, we

EE-RM-94-230 COMMENT 61D

\.. 

" 

As noted e'arlier,' it seems reasonable to recognize 

the effect of weather conditions (call these WCi')' 

market conditions (Mei

,), and the household's economic and demographic characteristics (EDCi) on' 

utilization patterns. So the model can be written as: 

Explicit Jssumptions concerning the dependence of 

appliance features and structural characteristics on 

other vJriables could also be used, but. assume for 

simplicity that data on these variables are available 

and that their values are taken as given for the 

purposes of the analysis. 

Substituting (3) into (2) yields: 

Arid, finally, substituting (4) into (1) provides the 

final general specification: 

J 

(5) HEC,t = :~ Fj(AFij• STRUC,• weit: MC;t. EDCi• e,lt) . Sij 

j=l 

3. Estimating the Model 

The CDA model can be estimated statistically by 

standard multivariate regression analysis using data. 

on household energy consumption. appliance saturations, and other variables contained in (5). While 

an extensiv.e discussion of estimation procedures 

and problems will not be offered here, some general issues are mentioned below. 

Sample Sizes. In general, CDA equations (especially those relating to electricity consumption) tend 

to have a large number or coefficients to estimate. 

Furthermore, some appliances of interest may have 

fairly low saturations. As a result, relatively large 

sample sizes are normally required for estimation. 

While ther~ is no clear minimum for sample size, 

analyses based on fewer than 500 or so households 

may provide relatively little information for those 

appliances with very low saturations. 

Data Problems. Survey and billing dat.a used in 

the estimation of a CDA model are generally far 

from perfect. Billing data may have irregularities 

caused by human error, skips in meter-reading 

patterns, and vacancies. Problems with survey data 

Rt'l'il7(, o(CllllriitiollaI Demand Methodology 

. are generally considerably more troublesome. 

Respondents often misreport space heating fuels, 

insulation levels. square footage, and other critical 

variables. Missing values may' be frequent for key 

questions like income, square footage, insulation 

levels, appliance ages, and other important features. Of course, consistency checks may identify 

errors in responses. For instance, households 

claiming to have natural gas space heating might 

be screened to confirm that gas service is available 

to them. and seasonal consumption patterns of 

households with \'arious stated hea.ting fuels might 

be reviewed. Clearly, cleaning the data is one of 

the most important steps in conditional demand 

analysis. 

Level of Aggregation. The conditional demand 

equation· can be estimated \\;th annual, seasonal. 

. bimonthly, or monthly consumption data~ Howev-

.er, sorting out end-use consumrtion levels is dearly 

easier with the use of monthly data. In a sense, the 

monthly variation in weather-sensitive loads (space 

heating, cooling, water heating and perhaps others) 

helps to distinguish these loads from each oth~r. 

. Multicollinearity. In most COA models, especially 

those with a large number of interaction terms, 

multicollinearity can be a serious problem. 

Multicollinearity is a statistical problem, caused by' 

high correlation across explanatory variables. that 

limits the capability of the regresSion to distinguish 

the impacts of these' variables. As a result, it may 

be difficult to disentangle the influences of individual appliances or household features on .·total 

energy consumption. for instance, refrigerator consumption may be difficult to distinguish from consumption through unspecified (miscellaneous) 

appliances because all households are likely to own 

at least one refrigerator. This particular problem can 

. be alleviated if information on refrigerator features 

(e.g. manuaUfrost-free, size, age) is available from 

. the survev. At times. choices need to be made 

between theoretical elegance and statistical convenience. For example, prior information may have to 

be used to constrain the relative or absolute values 

of some coefficients, or certain terms may simply 

have to be dropped from the equation. 

Heteroskedasticity. Given the CDA specification 

introduced earlier, note that the error term of the 

model is actuallv a function of error terms from 

individual UEC functions and appliance variables . 

. -\ssuming that the individual error tenns are additi\'e within their respective UEC functions, we 
Rtrzw;c of c)f nditionual Dkentand MA~ethodoloqgy,

could write the household energy consumption

equation as:

J

(5a) HEC,, = I F,(AF,, STRUC,, WC,,

j=l

MCi,, EDCi) · S,i + e,,

J

where e,, = e,,, S,

i=l

Clearly, the variance of the error term e', will

depend upon the values of the S i

, terms. So it may

be advisable to use one of the standard corrections

for heteroskedasticitv in order to improve the efficiency of estimated coefficients. 2

Exogeneity of Appliance Stocks. In most CDA

specifications, appliance stocks are taken as exogenous, or given. The problem with this approach, of

course, is that appliance choices are partly dependent upon the household characteristics used as

regressors in the CDA model. That is, many of the

same factors that are assumed to influence appliance utilization also affect households' choices to

acquire the appliances in question. Simultaneous

equation bias can result from ignoring this issue,

but research to date is not conclusive on the poten,

tial size of this bias. In any event, standard

approaches are available for testing for such biases

and for removing them. '

Excluded Variables. The lack of information on

key variables can also be troublesome. Although

most appliance saturation surveys are reasonably

comprehensive, they are unlikely to provide information on the full range of appliances used by

respondents. If the presence of these missing appliances is correlated with one or more of the appliances covered by the survey, the estimated UECs of

these latter appliances will tend to be biased. For

instance, the presence of an automatic dishwasher

may be correlated with ownership of a variety of

unidentified appliances like hair dryers, VCRs, ceiling fans, etc., in which case the estimated UEC for

dishwashers may tend to be overstated. Or the

presence of an appliance may be correlated with

unknown economic, demographic or attitudinal factors, thus leading to potential bias in the estimates

of the associated UEC. In general, this problem can

be minimized with more comprehensive questionnaires. Given a questionnaire and the data set it

generates, however, the analyst should exercise

caution in accepting UEC estimates without considering the possibility of such biases.

Overall Fit. The overall fit of the conditional

demand model depends upon model specification

and data quality. In general, these models tend to

yield R2 values in the range .40 to .60. By some

standards, this range may seem low. However, it

should be kept in mind that explaining the crosssection behavior of individual households is an

inherently difficult process. Energy consumption is

affected by a variety of factors. (tastes, habits,

special circumstances, etc.) that cannot generally be

incorporated into the model.

4. Constructing Estimates of UECs

Once the Conditional Demand Model is estimated

statistically, it can be used to infer unit energy

consumption for individual households as well as

designated subpopulations. Generally, these UEC

values are defined for given reference values of

time-dependent variables like weather and market

conditions (call these WCREF, and MCREF;, respectively). Using the notation developed above, the

UEC equation can be written as:

(6) UEC,, = F,(AF,, STRUC,, WCREF,. MCREF,, EDC,), for Si = 1,

where F, is an estimated form of F,. As an example,

suppose that the arguments of F, include square

footage (SQFT,), heating degree-days (HDD,), and

the. marginal electricity price (MP,:).' Suppose also

that the form of the function is linear. Then the

UEC for this end use would be equal to an estimated form of F, that might lok like:

(7) UECij = a0 + a, SOFT, + a HDD - a3MP,, for Si = 1,

where the ais are estimated coefficients.

The standard practice for evaluating UECs involves

using the average price level over the period of

estimation, although another price could be inserted. It is also common to use normal weather conditions to derive UECs. When this practice is

followed, the resultant estimates are called weathernormalized UECs.

4 The issue of the appropriate specification of price

variables is discussed in Section 5.

- See. for instance, GCicser (1969).

· See Dubin and McFadden (1984).

EE-RM-94-230 COMMENT 61D I.. 

" 

Rl~';fi{1 of COl/{iiliollll/ Dl!molld Mdirodo/v:\y 

could write th,~ household energy consumption 

equation as: 

J 

(Sa) HEC" = ~F,(AF", STRUCj • WCjl' MCj,• EDC;l' S,j + ejl 

i=l 

J 

where e'l = ~ e'll S't 

i=l 

Clearly, the variance of the error term e'jt will 

depend upon the values of the Sjj terms. So it may 

be advisable to use one 9f the standard corrections 

for heteroskedz1sticity in order to improve the efficiency of estimated coefficients.~ 

Exogeneity of Appliance Stocks. In most COA 

specifications, appliance stocks are taken as exogenous, or given. The problem with this approach, of 

course, is that appliance choices are partly dependent upon the household characteristics used .as 

regressors in the COA model. That is, many of the 

same factors that are .assumed to influence appliance utilizatiol1 also affect households' choices to 

acquire the appliances in question. Simultaneous 

equation bias (an result from ignoring this issue, 

but research to date is not conclusive on the poten. 

of the associated UEC. In general, this problem can 

be minimized with more comprehensive questionnc~ires. Given a questionnaire and the data set it 

generates, however, the analyst should exercise 

caution in. accepting UEe estimates \\;thout considering the· possibility of such biases. 

Overall Fit. The overall fit of the conditional 

demand model depends upon model specification 

and data quality. In general, these models tend to 

yield R~ values in the range ..10 to .60. By some 

standards, this range may seem low. However, it 

should be kept in mind that explaining the crosssection behavior of indi\'idual households is an 

inherently difficult process. Energy consumption is 

affected by a variety of factors. (tastes, habits, 

special circumstances, etc.) that cannot generally be 

incorporated into the modeL 

4. Constructing Estimates of UECs 

Once the Conditional Demand Model is estimated 

statistically, it can be used to infer unit energy 

consumpt;ion for individual households as well as 

designated subpopulations. Generally, these UEC 

values are defined 'for given reference values of 

time-dependent variables like weather' and market 

conditions (call these WCREFj and ~1CREFi' respectively). Using the notation developed above, the 

UEC equation can be written as: 

tial size of this bias. In any event, standard 

approaches ·are a\'ailable for testing for such biases· (6) 

and for removing them. J 

UEC " " ji = .F, (AFil' STRUCj• WCREFj, MCREf,• EDCj). for Sj = 1. 

" '. 

Excluded Variables. The la:k of information on 

key variables can also be troublesome. Although 

most appliance saturation surveys are reasonably 

comprehensive, they are unlikely to provide information on thl~ full range of appliances used by 

respondents. If the presence of these missing appliances is correlated with one 0: moce of the appliances covered bv the survev', the estimated UECs of 

these latter appliances \Viii tend to be biased. For 

instance, the presence of an automatic dishwasher 

may be correlated v\,'ith ownership of a variety of 

unidentified appliiinccs like hair dryers, VCRs, ceiling fans, etc., in which case the estimated UEC for 

dishwashers ma\' tend to be overstated, Or the 

presence of an appliance may be correlated with 

unknown economic. demographi,c or attitudinal factors, thus leading t? potential bias in the estimates 

- S~e, for instancc. Glcjscr (1969). 

See Dubin and :-'IcFaddcn (1984). 

where F, is an estimated form Clf F,. As an example, 

suppose that the arguments of F, include square 

footage (SQFT;), heating degree-days (HODjt), and 

the. inarginal electricity price (MPJ.4 Suppose also 

that the form of the function is linear. Then the 

UEC for this end use would be equal to an estimated form of Fj that might i<x ... k like: 

(7) UEC " jj = ao + a1 SOFTj + a2 HOD '" a3 MPj • for Sj = 1. 

where the ajsare 'estimated c~fficients. 

The standard practice for. e\'aluating UECs involves 

using the average price le\'el over the period of 

estimation, although another price could be inserted. It is also COmmon to use normal weather conditions to derive UECs.· When this practice is 

followed, the resultant estimates are called weathernonnaiized UECs . 

. . 4 Th~ issue of th~ appropriate speci.!lcation of price 

\'ariables is discussed in Section 3. 
Review of Conditional Demand Methodology

Deriving UECs on a household-by-household basis

(by substituting each household's value of the variables in the UEC equation) can be a useful intermediate step; however, estimates are generally derived

for households as a whole or for selected classes of

households (single-family residences only, residents

of a particular operating region, low-income households, etc.). The derivation of group UECs (call a

group mean UECG) is straightforward. Perhaps the

most common approach to this calculation is to

average household-level UEC estimates over the

relevant group. Allowing for the use of sampling

weights on individual households (call these wi),

we would have:

(8) UEC =( w UEC,,)/ w,

i i .

where the summation is over the set of households

with the appliance in question (i.e., for whom

si = 1).5

Another approach to calculating average UECs

involves the insertion of mean values of the explanatory variables into the UEC function. These mean

values are defined for owners of the appliance in

question, and are sometimes called conditional

means. Using equation (7) above to illustrate this

computational approach, we could write:

(9) UECi = ao a, SOFT + a2 HDD + a3MP

where the respective means are defined for households for whom S, = 1.' One note of caution is

5 This assumes that binary values of S$, are used. If

fractional predicted values are used. households

with non-zero probabilities could be included in

this calculation and the respective probabilities

could be used in %weights.

6 Again, the calculation oi means could involve the use of

probabilistic weights if predicted saturations are used in the

estimation process.

offered for the case in which the UEC function is

nonlinear in original variables. If two or more

explanatory variables are used interactively in a

specification (e.g., a cross-product of SQFT and

HDD), the calculation of UECs requires the use of

the conditional mean of this interactive term

(SQFT HDD), as opposed to the interaction of the

means of the original variables (SQFT and HDD).

These two approaches for calculating average UECs

are logically equivalent as long as the UEC equations are linear in parameters, and the choice can

be made on the basis of convenience. For instance,

the first procedure is probably more convenient if

the analyst wants to estimate UECs for a number of

subpopulations. Once household-specific UECs are

calculated, these values can be summarized very

easily for household groupings using standard subroutines in various statistical programs.

5. Comparability of UECs Across Service

Areas

Regardless of the approach to extracting estimated

UECs from the Conditional Demand model, one

point is clear: the values of these UECs depend

upon the values of the variables in the UEC equations. This feature is extremely valuable insofar as it

recognizes the sensitivity of unit energy consumption to household characteristics and market conditions. On the other hand, the dependence of UECs

on other variables makes it difficult to compare

estimated UECs across service areas. For instance,

differences in weather conditions can create substantial differences in estimated space heating

UECs. As a result, comparing UEC estimates across

service areas requires that the estimates be adjusted

for major differences in service area characteristics.

To the extent possible, such adjustments are made

in the UEC estimates presented in this report.

Given that, the transferability of the adjusted estimates across service area boundaries is evaluated.

EE-RM-94-230 COMMENT 61D

\" 

" 

Deriving UECs on a household-by-household basis 

(by substituting each household's value of the variables in the UEC equation) can be a useful intermediate step; however, estimates are .generally derived 

. for households as a whole or for selected classes of 

households (single-family residences only, residents 

of a particular operating region, low-income households, etc.). The derivation of group UECs (call a 

group mean UEC;) is straigl)tforward. Perhaps the 

most common approach to this calculation is to 

average. household-level UEC estimates over the 

relevant group. Allowing for the use of sampling 

weights on individual households (call these Wi), 

we would ha,ve: 

-,~ ~ (8) UECj = ( .. ~ w, UEC,Y -'- Wi 

i . 

where the summation is over the set of households 

with the appliance in question (i.e., for whom 

5ij = 1).5 

Another approach to calculating average UECs 

involves the insertion of mean values of the explanatory variables into the UEC function. These mean 

values are dl~fined for owners of the appliance in 

question, and are someti~es called conditional 

means. Using equation (7) above to illustrate this 

computational approach, we could write: 

where the respecti"e m~an5 are defined for households for whom S, = I." One note of caution is 

5 This assumes that binary values of 5ii are used. If 

fractional prediCted \'alues are used. households 

with non-zero probabilities could be included in 

this calculation and the respective probabilities 

could be used in weights. 

6 Again. the calculation oi means could involve the use of 

probabilistic woei~hts ii predicted saturations are used in the 

estimation process. . . 

ReviC!l(' of Conditio/lal Demand Methodology 

offered for the case in which the UEC function is 

nonlinear in original variables. If two or more 

explanatory variables are used interactively in a 

specification (e.g., a cross-product of SQFT and 

HOD), the calculation of UECs requires the use of 

the conditional mean of this interactive term 

(SQFT' HOD), as opposed to the interaction of the 

means of the original variables (5QFf and HOD) .. 

These two approaches ·for calculating average UECs 

are logically equivalent as long as the UEC equations are linear in parameters, and the choice can 

be made on the basis of convenience~ For instance, 

the first' procedure is probably more convenient if 

the analyst wants to estimate LJECs for a number of 

subpopulations. Once household-specific UECs are 

calculated, these values can be summarized verj 

'easily for household groupings using standard subroutines in various statistical programs. 

5.' Comparability of UECs Across Service 

Areas ' 

Regardless of the approach to extracting estimated 

UECs from the Conditional Demand model, one 

point is cl~ar: the 'values of these UECs depend 

upon the values of the variables in the UEC equations. This feature is extremely ,valuable insofar as it 

recognizes the sensitivity of unit energy consumption to household characteristics and market conditions. On the other hand, the dependence of UECs 

on other variables makes it difficult to compare 

estimated UECs across service areas. For instance. 

differences in weather conditions can create sub-

. stantial differences in estimated space heating 

UECs. As a result, comparing UEC estimates across 

service areas requires that the estimates be adjusted 

for major differences in service area characteristics. 

To the extent possible, such adjustments are made 

in thE: UEC estimates presented in this. report. 

Given that, the transferability of the adjusted estimates across service area boundaries is evaluated. 
3

SUMMARY OF UEC ESTIMATES

A. Introduction

The above review of the general form of the CDA

model and the derivation of UEC estimates provides a background for the main focus of this

report, a summary of recent UEC estimates. This

section presents UEC estimates from several analyses conducted at the service area level. For convenience and clarity, the presentation is organized by

major end use. The following general end uses are

covered:

* space heating

* air conditioning

* water heating

* refrigeration

* cooking

* pools and spas

* others.

For each end use, the presentation entails a series

of steps:

* a review of the detailed form of the relevant

component of the conditional demand model

(e.g., the space heating portion);

* a summary of the range of UECs estimated by

various analysts;

* an attempt to standardize for key determinants

of the variation of UECs across service areas; and

* an assessment of the transferability of these

estimates across service areas.

The summaries cover a fairly large number of studies, some of which relate to all end uses, others of

which focus on only one or some. Table 3-1 lists the

studies that will be cited at one point or another in

the course of the presentation (the year of each

study is indicated in parentheses). In general, the

focus is on the most recent studies available for the

service areas covered. Furthermore, studies covered

in the 1984 Lawrence and Parti review are excluded

from this review. As noted earlier, all studies covered by the survey are conducted at the service area

level. This approach was used in order to facilitate

the standardization of estimates for regional

weather conditions. As shown, 27 studies are covered by the survey. The wide range of service areas

encompassed by these studies should provide a

good basis for the comparisons that follow.

B. Space Heating

1. General Form of Space Heating UEC

Equation

This subsection reviews the typical specification of

the space-heating portion of a conditional demand

model. The purpose of this review is to identify

major determinants of space heating consumption

(as recognized by conditional demand models). A

central issue is the dependence of these estimates

on weather conditions and on structural integrity.

The link with weather conditions is important for

comparisons of estimates across service areas.

For the sake of illustration, define a household's

space heating UEC as UECsH. The space heating

UEC equation can be expressed as:

(10) UECsH, = H(STRUC,, EFF, INS,. EDC,. WCi,, MC,)

As noted earlier, STRUC, refers to a set of structural

features of the housing unit. In the context of space

heating, these could include:

* the square footage of the home

* the number of windows

* the materials used for roofing and siding

* the type of foundation

_1-

EE-RM-94-230 COMMENT 61D

SUMMARY OF UEC ESTIMATES 

A. Introduction 

The above review of the general form of the CDA 

model and the derivation ot VEC estimates provides a background for the main focus of this 

report, a summary of recent VEe estimates. This 

section presents UEe estimates from several analyses conducted at the service area level. For convenience and clarity, the presentation is organized by 

major end use. The following general end uses are 

covered: 

• space heating 

• air conditioning 

• water heating 

• refrigeration 

• cooking 

• pools and spas 

• others. 

For each end use, the presentation entails a series 

of steps: 

• a review of the detailed form or the relevant 

component of the conditional demand model 

(e.g., the space heating portion); 

• a summary of the range of VECs estimated by 

various analysts; 

• an ,attempt to standardize for key determinants 

of the variation of UECs across service areas; and 

• an assessment of the transferability of these 

estimates across service areas. 

The summaries cover a fairly large number of studies, some of which relate to all end uses, others of 

which focus on only one or some. Table 3-1 lists the 

studies that will be cited at one point or another in 

the course of the presentation (the year of each 

study is indicated in parentheses). In general, the 

focus is on the most recent studies available for the 

service areas covered. Furthermore, studies covered 

in the 1984 Lawrence and Parti review are excluded 

from this review. As noted earlier, all studies covered by the survey are conducted at the service area 

level. This approach was used in order to facilitate 

the standardization of estimates for regional 

weather conditions. As shown, 27 studies are covered by the sl,lrvey. The wide range of service areas 

encompassed by these studies should provide a 

good basis for the comparisons that follow. 

B. Space Heating 

1. General Form of Space Heating UEC 

Equation 

This subsection reviews the typical specification of 

the space-heating portion of a conditional demand 

model. The purpose of this' review is to identify 

major determinants of space heating consumption 

(as recognized by conditional demand models). A 

central issue is the dependence of these estilnates 

on weather conditions and on structural integrity. 

The link with weather conditions is important "for 

comparisons of estimates across service areas. 

For the sake of illustration, define a household's 

space heating UEC as UECsHi. The space heating 

VEC equation can.be expressed as: 

As noted earlier, STRUCj refers to a set of structural 

f~atures of the housing unit. In the context of space 

heating, these could include: 

• the square footage of the home 

• the number of windows 

• the materials used for roofing and siding 

• the type of foundation 
'.

Summ,,ary of UEC Estimates

Table 3-1

Studies Reviewed

Utility Service Area Analysts Year

Pacific Gas and Electric (PGandE) In-House 1986

Pacific Gas and Electric (PGandE) RER. 1986

Pacific Gas and Electric (PGandE) In-House 1987

San Diego Gas & Electric (SDG&E) CSI 1985

San Diego Gas & Electric (SDG&E) RER 1986

Northwest Power Planning Council (NPPC) CSI 1984

Tennessee Valley Authority (TVA) Jackson 1985

Florida Power & Light (FP&L) QED 1986

New York State Electric & Gas (NYSEG) In-House 1986

Gulf States Utilities (Gulf) In-House 1986

El Paso Electric (El Paso) In-House 1987

Commonwealth Edison (CommEd) In-House 1985

Mississippi Power (MPC) In-House 1984

Jersey Central Power & Light (JCP&L) In-House 1987

Metropolitan Edison (MetEd) In-House 1987

Nevada Power Company (NPC) AEI 1986

Alabama Power Company (APC) In-House 1983

Rochester Gas & Electric (RG&E) AEI 1986

Arkansas Power & Light (AP&L) In-House 1984

Louisiana Power & Light (LP&L) In-House 1984

Mississippi Power & Light (MP&L) In-House 1984

New Orleans Public Service (NOPS) In-House 1984

Virginia Electric and Power (VEPCO) In-House 1987

Sierra Pacific Power (Sierra) R.L. Wright & Assoc. 1987

American Electric Power (AEP) In-House 1988

Texas-New Mexico Power (TNP) Texas PUC 1988

Baltimore Gas & Electric (BG&E) RER 1988

* the number of floors

* the presence of an attic (and whether or not it is

finished).

The generic variable EFF, indicates the efficiency of

the heating unit itself. Some basic efficiency differences are often taken into account by distinguishing

between types of heating systems (e.g., resistance

heating and heat pumps). But finer efficiency differentials could also be considered (perhaps by incorporating.the age of the heating unit).

The variable INSi pertains to insulation and other

weatherization features present in the thermal

shell. Specific variables could be discrete (indicating

whether or not a particular kind of insulation is

present) or continuous (describing R-values). Several kinds of weatherization variables could be

incorporated into the UEC equation, including:

* ceiling insulation

* wall insulation

· floor insulation

* caulking

* weatherstripping

* dual glazing.

The presence of economic and demographic variables (through EDCj) is meant primarily to capture

the determinants of thermostat settings and other

usage patterns. Examples of such variables include:

* income

* age distribution

* household size

* occupational status.

Weather conditions are obviously important determinants of space heating consumption. The difference between outside temperatures and desired

interior temperatures is a key driver of heater operation. While desired temperatures are not generally

observed (thev are commonly modeled as a function of other variables), a reference temperature can

be chosen to describe "typical" household behavior.

The difference between outside temperatures and

this reference temperature over the period being

EE-RM-94-230 COMMENT 61D

SI/II/II/ary of U EC E~till/ntl!$ 

Table 3-1 

Studies Reviewed 

Utility Service Area Analysts Year 

Pacific Gas and Electric (PGandE) In-House 1986 

Pacifi~ Gas and Electric (PGandE) RER. 1986 

Pacific Gas and Electric (PGandE) In-House 1987 

San Diego Gas & Electric (SD~&E) CSI 1985 

San Diego Gas & Electric (SDG&E) RER 1986· 

Northwest Power Planning Council (NPPC) CSI 1984 

Tennessee Valley Authority (TVA) Jackson 1985 

Florida Power & Light (FP&L) OED 1986 

New York State Electric & Gas (NYSEG) In-!"Iouse 1986 

Gulf States Utilities (Gulf) 

EI Paso Electric (EI Paso) 

Commonwealth Edison (CommEd) 

Mississippi Power (MPC) 

Jersey Central Power & Ught (JCP&L) 

Metropolitan Edison (MetEd) 

Nevada Power Company (NPC) 

Alabama Power Company (APC) 

Rochester Gas & Electric (RG&E) 

Arkansas Power & Ught (AP&L) 

Louisiana Power & Ught (LP&L) 

Mississippi Power & Ught (MP&L) 

New Orleans Public Service (NOPS) 

Virginia Electric and Power (VEPCO) 

Sierra Pacific Power (Sierra) 

American Electric Power (AEP) 

Texas-New Mexico PoWer (TNP) 

Baltimore Gas & Electric (BG&E) 

• the number of floors 

• the presence of an attic (and whether or not it is 

finished). 

The generic variable EFF; indicates the efficiency of 

the heating uniit itself_· Some basic efficiency differences are often taken. into account by distinguishing 

between types of heating systems (e.g., resistance 

heating and heat pumps). But finer efficiency differentials could also be considered (perhaps by incorporating.the ag'~ of the heating unit). 

The variable INS; pertains to insulation and other 

weatherization features present in the thermal 

shell. Specific variables could be discrete (indicating 

whether or not a partiCular kind of insulation is 

present) or continuous (describing R-values). Several kinds of weatherization variables could be 

incorporated into the UEC equation, including: 

• ceiling insula.tion 

• wall insulation 

• floor insulation 

• caulking 

In-House 1986 

In-House 1987 

In-House 1985 

In-House ·1984 

In-House 1987 

In-House 1987 

AEI 1986 

In-House 1983 

AEI 1986 

In-House 1984 

In-House 1984 

In-House 1984 

In-House 1984 

In-House 1987 

R.L. Wright & Assoc. 1987 

In-House 1988 

Texas PUC 1988 

RER 1988 

• weatherstripping 

• dual glazing. 

The presence of economic and demographic variabl"es (through· EDCj) is meant primarily to capture 

the determinants of thermostat settings and other 

usage patterns. Examples of such variables incl~de: 

• income 

• age distribution 

• household size 

• occupational status. 

Weather conditions are obviously important determinants of space heating consumption. The difference between outside temperatures an.d desired 

·interior temperatures isa key driver of heater operation. While desired temperatures are not generally 

observed (they are commonly modeled as a function of other variables), a reference temperature can 

be chosen to describe "typical" household behavior. 

The difference between outside te·mperatures and 

this reference temperature over the period being 
Summary of UEC Estimates

analyzed is often quantified in one of two general

ways:

* via the use of a heating degree-day measure

* through the use of a heating-requirements

specification.

Heating degree-day variables are based on a comparison of daily temperatures with reference values.

For a single day, one heating degree-day is

assigned for each degree by which the average

temperature falls short of some reference temperature. This value is cumulated over the period covered by the analysis (say, a billing cycle). The

reference temperature is typically defined as the

temperature at which heating begins to occur, and

can be thought of as a thermostat setting less some

adjustment for internal heat gain. The heating

requirements variable, on the other hand, takes

into account both the low and high temperatures recorded during some period. It essentially

measures the cumulative number of degrees

below the reference temperature experienced across

hours over the course of the day. It reflects, in

essence, a simplified engineering estimate of heating requirements.

Of course, temperatures are not the only weather

condition affecting space heating consumption.

Other pertinent weather variables are rainfall,

humidity, hours of sunlight, wind speed, etc; While

there have been some attempts to include other

weather variables in CDA models (see, for instance,

RER, 1985), the gains from doing so seem to be

modest.

As suggested by the long list of possible determinants, the modeling of space heating UECs can be a

complicated process. It is not surprising that estimates of space heating UECs are fairly sensitive to

the specification of the UEC equation;

2. Summary of Space Heating UEC

Estimates

This subsection provides both tabular and graphical

summaries of estimated space-hea:,ng UECs. The

analysis covers two major appliance categories:

* primary conventional electric space heating

* heat pump space heating.

UECs can also be estimated for a-_xiliarv electric

space heating; however, too few estimates were

found in the survey to warrant an a-.lvsis.

Tables 3-2 and 3-3 list the estimated weather-normalized space heating UECs reported in the studies

covered by the survey. Table 3-2 covers estimates

compiled for all dwelling types combined, whereas

Table 3-3 reports estimates relating to the singlefamily residential class.. All values are annual kWh

consumption levels; where available, weather-normalized estimates are presented.

Estimates of conventional space heating are often

broken into central (forced air) systems and radiant

(ceiling and/or baseboard) systems. While this

breakdown preserves the integrity of the original.

estimates prepared by subject utilities, it makes

direct comparison of UECs difficult. As a result,

estimates of conventional heating UECs were develdped from the more disaggregated values, by using

information on relative saturations of the heating

systems in question. Estimates of conventional and

heat pump heating UECs were aggregated to obtain

overall space heating UECs for all types of electric

systems. In some cases, the process of aggregating

UECs was aided by the presence of saturation data

in the technical documentation of the studies cited.

In other instances, the subject utilities provided

estimated saturations for use in this studv. UEC

estimates derived for the purposes of these comparisons are presented in italics.

First, consider the relative UECs of different types

of heating systems. Radiant systems have lower

UEC values than central systems in every one of

the'studies in which separate UECs were estimated.

One should be cautious in inferring relative efficiencies from this result. It may reflect the fact that

radiant heating systems generally have room controls, and can be used more easily on a localized

basis. It is also possible that homes with central

electric heating systems are larger than those with

radiant systems, and/or that households with the

central systems have higher incomes than those

with radiant systems.

Second, heat pump heating UECs are sometimes

greater than conventional heating values. This

result should also be interpreted cautiously. Heat

pumps are clearly more efficient than conventional

systems; however, there is some evidence that

heat pumps tend to be used in larger homes

and in areas (within service territories) with more

severe winters. Furthermore, it is very possible that

the lower cost of thermal comfort associated with

heat pumps encourages higher winter thermostat

settings.

EE-RM-94-230 COMMENT 61D

analyzed is often quantified in one of two general 

ways: 

• via the use of a heating degree-day measure. 

• through the use of a heating-requirements 

specification. 

Heating degree-day· variables are based on a comparison of daily temperatures with reterence values. 

For a single day, one heating degree-day is 

assigned for each degree by \'I/hich the averag~ 

temperature falls short of some reterence temperature. This value is cumulated over the period cov~ 

ered by the analysis (say. a billing cycle). The 

reference temperature is typically defined as the 

temperature at which heating begins to occur. and 

can be thought of as a thermostat· setting less some 

adjustment for internal heat gain. The heating 

requirements variable. on the other hand. takes 

into account both the 10\'1.' and high temperatures recorded during some period.· It essentially 

measures ,the cumulative number of degrees 

below the reference temperature experienced across 

hours over I:he cour.se of the day. It reflects, in 

ess~nce. a simplified engineering estimate of heating requirements. 

Of course. temperatur~s are not th~ only weather 

condition af:fecti~g space heating consumption. 

Other pertinent weather \·ariable:;. are rainfall. 

humidity, hours of sunlight, wind s~ed. etc While 

there have been some attempts t..."1 include other 

weather variables in CDA models (~~. for instance. 

RER. 1985). the gains from doin~ ;:0 seem to be 

modest. 

As suggested by the 10.ng list of F'~'ssible determinants. the moqeling of space heatinf: UECs can be a 

complicat~d process. It is notsurr:ising that estimates of spac~ heating UECs are fairly sensitive to 

.' the specification of the UECequatior1; 

2.Summal:Y of Space Heating UEC 

Estimat.~s 

This subsection provides both tabu I.:.: and graphical 

summaries of estimated space-hea:l~g UECs. The 

analysis covers two major appliance -:J.tegories: 

• primary conventional electric spa.:.: heating 

• heat pump space heating. 

UECs can also be estimated for a::.xiliary electric 

space heating; however, too few .::.timates were 

found in the survev to warrant an a:-. .llvsis. . . 

Summary of UEC Estimates 

Tables 3-2 and 3~3 list the estimated weather-normalized space heating UECs reported in the studies 

covered by the survey. Table 3-2 covers estimates 

compiled for all dwelling types combined. whereas 

Table 3-3 reports estimates relating to the singlefamily residential class .. All values are annual kWh 

consumption levels; where available. weather-normalized estimates are presented .. 

Estimates of conventional space heating are often 

broken into central (forced air) systems .and radiant 

(ceiling and/or baseboard) systems. While this 

breakdown preserves the integrity of the original. 

estimates prepared by subject utilities. it makes 

direct comparison of UECs difficult. As a result. 

estimates of conventional heating UECs were devel-

"ped from the more disaggregated values. by using 

information on relative saturations of the heating 

systems in question. Estimates of conventional and 

heat pump heating UECs were aggregated to obtain 

overall· space heating UECs for all types of electric 

systems. In some cases. the process of aggregating 

UECs was aided by, the presence of saturation data 

in the techni~a1 documentati.on of the studies cited. 

In other instances, the subject utilities provided 

estimated saturations for use in this study. UEC 

estimates derived for the .purposes of these comparisons .are presented in italics. 

First. consider the relative UECs of different types 

of neating systems. Radiant systems have lower 

UEC values than central systems in every one of 

the' studies in which separate UECs were estimated .. 

One should be cautious in inferring relative emciendes from this result. It may reflect the fact that 

radiant heating systems generally have room controls. and can be used more easily on a localized 

basis. It is also possible that homes with central 

electric heating systems are larger than those with 

radiant systems. and/or that households with the 

central systems have higher incomes than those 

with jadiant systems. 

Second. heat pump he~ting UECs are sometimes 

greater than conventional heating values. This 

result should also be interpreted cautiously. Heat 

pumps are clearly more efficient than conventional 

systems; however, there is some evidence that 

. heat' pumps tend to be used in larger homes 

and in areas (within service territories)' with more 

severe winters. Furthermore, it is very possible that 

the lower cost of thermal comfort associated with 

heat pumps. encourages ~igher winter thermostat 

settings. 
Surmnrrlr of UEC Estimiates

Table 3-2

Primary Space Heating UECS: All Dwellings

Type of Heating System

Utility (yr) Central Radiant Cent. + Rad. Heat Pump All types

PGandE (87) 2,675 1,875a 2.115 3,675 2.318

PGandE (86) - - 2.350 2,625 2,386

SDG&E (85) - - 1,123 3.390 1,363

SDG&E (84) - - 1,133 2,015 1,226

NPPC (84) 9,806 9,660 9,733b 7,493 .

TVA (85) - - 5,429 5,135

FP&L. (86) - - 741 798 765

NYSEG (86) - - - - 8,500

Gulf (86) 4,113 2,673 - 2,746

El Paso (87) - - - - 1,797

ComrnEd (87) - - - - 10,140

MPC (84) 2,750 830 - 3,740 -

JCP&L (87) - - - - 10,932

MetEd (87) - - - - 11,717

NPC (86) 3,393c 2,049c 3,166c 4,919c 3,570 c

APC (83) - - 4,766 5,114

RG&E (86) - - 5,294 . 15.763 6,47e .

TNP (88) 1,460 326 1.092 980 -

BG&E (88) - - 7.106 5.920 6,261

a All non-central electric.

b Estimated on assumption of equal saturations of central and radiant systems.

c Weighted averages of UECs for individual dwelling types.

d Total heat pump usage was broken into heating cooling estimates by identifying monthly usage as either

heating!cooling (or both, in transition months).

e Estimated using saturations from 1984 data base.

Table 3-3

Primary Space Heating UECS: Single-Family Dwellings

Type of Heating System

Utility (yr) Central Radiant Cent. - Rad. Heat Pump All types

PGandE !87) 3.825 2,625a 3,743 3.950 3,348

PGandE (86). - - 3,200 2,775 3,092

PGandE !86a) - - 4.361 3.060 4,030

SDG&E (85) - - 1,732 3.390 2,311

SOG&E (84) - - 1,689 2.015 1,762

TVA (85) . - 6.057 5,657 -

AP&L (84 - - 6,799 5.046 -

LP&L (6.4 - - 3.721 3,963

MP&L (84 - - 5.328 4.982 -

NOPS (84j - - 2.498 3.371 -

FP&L (86 - -- - 811

CommEd (87) - '- - - 13.000

VEPCO (87) 6.342 5,750D 5,987 5,015 5,389

NPC (86) 3.792 2,246 3.422 5,209 3,922

RG&E (86i - - 12.053 15,763 12,450

Sierra (88 - - - - 4.692c

AEP (88) 6.451 6,711 .6.653 7.963 6,947

BG&E (E8i - - 9.052 6,368 6.987

a All electric non-central.

b Baseboa-d only.

c Assumes 1,700 square feet per dwelling.

3-4

EE-RM-94-230 COMMENT 61D

Slll/l/llfl/'Y of U EC Estimate:> 

3-4 

Table 3-2· 

Primary Space Heating UECS: All Dwellings 

Type of Heating System 

Utility (yr) Central Radiant Cent. + Rad. Heat Pump 

PGandE (87) 2.675 1.875a 2.115 

PGandE (86) . 2.350 

SOG&E (65) 1,123 

SOG&E (84) 1.133 

NPPC(84) . 9.806 9,660 9.733b 

TVA (85) 5.429 

FP&l. (86) 741 

NYSEG (86) 

Gulf (86) 4,113 2.673 

EI Paso (87) 

ComrnEd (87) 

MPC(84) 2,750 830 

JCP&L (87) 

MetEd (87) 

NPC (86) 3,39SC 2,049c 3.166" 

APC (63) 4.766 

RG&E: (~6) 5.294 

TNP (88) 1,460 326 1.092 

BG&E (88) 7.106 

a All non-central electric. 

I) Estimated on assumption of equal saturations ·01 central and radiant systems. 

c Weighted averages of UECs for individual dwelling types. 

3.675. 

2.625 

3.390 

2,015 

·7.493· 

5.135 

798 

2.746 

3.740 

4,919c 

5.114d 

15.763 

980 

5.920 

All types 

2.318 

2,386 

1,363 

1,226 

765 

8.500 

1,797 

10,140 

10,932 

11.717 

3,570c 

6,41~ 

6,261 

d . Total heat pump usage was broken into heating cooling estimates by identifying monthly usage as either 

.heating!cooling (or both, in transition months). 

e Estimated using saturations from 1984 data base. 

Table 3-3 

Primary Space Heating UECS: Single-Fami,ly Dwellings 

Type of Heating System 

Utility <yr) Centrat Radiant Cent. + Rad. Heat Pump AU types 

PGandE !87) 3.825 2.625a 3,143 3.950 3,348 

PGandE (86) • 3.200 2.775 3,092 

PGandE f86a) 4.361 3.060 4,030 

SOG&E (85) 1,732 3.390 2,311 

SOG&E (84) 1.689 . 2.015 1,762 

TVA (8S) 6.057 5,657 

AP&L (H4) 6.799 5.046 

LP&L (M) 3.721 3.963 

MP&L (134; 5.328 4,982 

NOPS (;34) 2.498 3.371 

FP&l (S6) 811 

CommE·; (87) 13.000 

VEPCO (87) 6.342 5.750° 5.987 5.015 5.389 

NPC (8E;) 3.792 2.246 3.422 5.209 3.922 

RG&E (a61 12.053 15.763 12.450 

Sierra (88, 4.692c 

AEP (881 6.451 6.711 ·6.653 7.963 6,947 

BG&E (88, 9.052 6.368 6.987 

a All electric non.cent~al. 

b Baseboa~d only. 

c Assumes 1.700 square feet per dwelling. 
Summnan of UEC Estimates

As shown in Table 3-2, estimated space heating

UECs for all dwellings vary considerably across

service areas. UECs for conventional systems (central, radiant, or both) range from almost 10,000

kWh per year for Northwest Power Planning Council (NPPC) to roughly 740 kWh for Florida Power &

Light (FP&L). Heat pump values vary from a low of

roughly 800 kWh for FP&L to almost 7,500 kWh for

NPPC. Aggregated space heating UECs follow a

similar pattern, with three values close to 10,000

kWh and several around or below 2,000 kWh.

Considerable variation is also found within the

single-family dwelling class (see Table 3-3). The

overall value for FP&L (584) is again the lowest

estimate, with Commonwealth Edison's value

(13,000) the highest. In the next subsection, the

sources of this variation are considered.

3. Standardization of Space Heating UECs

for Differences in Weather Conditions

The variation in UEC estimates is unsurprising,

given the sharp differences in weather conditions,

energy prices, and other factors across the service

areas in question. While it would be virtually

impossible to standardize these estimates for all of

the conditions that vary across service areas, some

insights can be gained by controlling (albeit crudely) for weather differences. Of course, this analysis

requires information on weather conditions in each

of the service areas under consideration. Because

most of the studies covered by the analysis used

heating degree-days to represent heating requirements, a degree-day measure was estimated for

each service area. In order to facilitate this portion

of the analysis, National Oceanic and Atmospheric

Administration (NOAA) data on heating degreedays for weather stations within the service areas in

question were used. These data were aggregated

into service area values through the use of either

actual customer geographic distributions (when

available) or rough judgments about the distributions of residential customers. For the purposes of

comparison, a 65°F base was used in the calculation

of all heating degree-day estimates.

The dependence of conventional space heating UECs

on weather conditions is 'illustrated in Figure 3-1.

Single-family residential estimates are distinguished

from those pertaining to all dwelling types. For

both residence classes, as shown, the link between

these estimates and the regional values of heating

2,000 4,000 6,000

Heating Degree-Days

Models:

UEC(AII Dwellings) = -1012 + 1.62(HDD)

UEC(Single Family) = -702 + 1.65(!HDD)

All Dwelling

Types

Single-Family

Dwellings

8,000

R-Square Value:

All Dwellings: 0.6901

Single Family: 0.7311

Figure 3-1. Conventional Space Heating UECs

vs. Heating Degree-Days

3-5

Unit Energy Consumption

14,000

12,000 ·

10.000 -

8,000 -

6,000 -

4,000 "

2,000

0 -

0

EE-RM-94-230 COMMENT 61D

As shown in Table 3-2, estimated space heating 

UECs for all dwellings vary considerably across 

service areas. UECs for conventional systems (ceiltraL radiant, or both) range from almost 10,OOO 

kWh per year for Northwest Power Planning Council (NPPC) to roughly 740 kWh for Florida Power & 

Light (FP&L). Heat pump values vary from ,a low of 

roughly 800 kWh for FP&L to almost 7,500 kWh for 

NPPC. Aggregated space heating UECs follow a 

similar pattern, with three values close to 10,000 

kWh and severa) around or below 2,000 kWh. 

Considerable variation is also found within the 

single-family dwelling class (see Table 3-3). The 

overall value for FP&L (584) is again the lowest 

estimate, with Commonwealth Edison's value 

(13,000) the highest. In the next subsection, the 

sources of this variation are considered. 

3. Standardization of Space Heating UECs 

for Differences in Weather Conditions 

The variation in UEC estimates is unsurprising, 

given the sharp differences in weather conditions, 

energy prices, and other factors across the service 

areas in question. While it would be virtually 

impossible to standardize these estimates for all of 

Unit Energy Consumption 

14,000 

12,000 -

10.000 -

8,000 -

6,000 -

4,OCO .: 

2.000 --

SlIIll1nar:( of UEC EstimatL's 

the conditions that vary across service areas, some 

insights can be gained by controlling (albeit crudely) for weather differences. Of course, this analysis 

requires information on weather conditions in each 

of the service areas under consideration. Because 

most of the _ studies covered by the analysis used 

heating- degree-days to represent heating require- -

ments, a degree-day measure was estimated for 

each service area. In order to facilitate this portionof the analysis, National Oceanic and Atmospheric 

Administration (NOAA) data on heating degreedays for weather stations within the service areas in 

question were used. These data were aggregated 

into service area values _ through the use of either 

actual customer geographic distributions (when 

available) -or rough judgments about the' distributions of residential customers. For _the purposes of 

comparison, a 65°F base was used in the calculation 

of all heating degree-day estimates. -

. The dependence of conventional space heating UECs 

on weather conditions is -illustrated in Figure 3-l. 

Single-family residential estimates are distinguished 

from those pertaining to all dwelling types. For 

both residence classes, as shown, the link between 

these estimates and the regional values of heating 

All Dwelling 

Types 

Single-Family 

Dwellings 

o --- -...:~. ---------- --- -------- - ----------------------' 

o 2,000 

Moclels: 

UEC(AfI Dwellings} = -1012 + 1.62(HOO) 

UEC(Single Family) = -702 + 1.65(1100) 

4,000 6,000 

Heating Degree-Days 

Figure 3-1. Conventional Space Heating UECs 

vs. Heating Degree-Days 

8,000 

R-Square Value: 

Afl Dwellings: 0.6901 

Single Family: 0.7311 

3-5 
Suinrlarlu o(t' UE:C ELtia;:lc

degree-days is strong.' Simple regressions of the

estimates for single-family dwellings on the degreeday measure yielded an R2 value over .70. The R2

for all dwellings combined was roughly .69. To

some extent, this latter result may be explained by

variations in the dwelling-type mix across service.

areas. Although the estimated UECs trace out a

clear pattern, substantial variation around the estimated relationship is found. Clearly, some of this

variation can be attributed to non-weather differences across service areas. Electricity prices vary

substantially, for instance, and could explain a substantial portion of the overall variation. Additional-.

ly, model specifications differ appreciably across

specific studies, leading to differences in estimated

UECs. Furthermore, the simple weather variable

constructed for this analysis may well be a distorted

estimate of the effective conditions faced bv households with electric space heating.

Figure 3-2 illustrates the relation between heat

Four estimates that would have been outliers

were omitted from these and other plots in this

Section. These estimates were derived as part of

a feasibility study, using data not collected

specifically for CDA estimation. Moreover, the

analyst who developed the estimates recognized

that they were unreasonably low.

pump heating UECs and heating degree-days.

Again, the pattern is very plausible, with UEC

values generally ordered according to relative heating requirements. The "fits" of the both relationships are strong, with R2 values around .76. The

unexplained variance should not be particularly

surprising. First, of course, market conditions vary

substantially across these service areas. Second,

model specifications differ across studies. And

third, heat pump saturations are very low in most

service territories; thus, UEC estimates tend to have

high standard errors. Finally, heat pump owners

_may be non-representative of households within a

given service territory. In some service areas, they

may tend to live in extreme subclimates or have

very different economic/demographic features. Evidence suggests, for instance, that heat pump owners tend to have relatively large homes and high

incomes in some areas. The degree of these differences between heat pump owners and other households with electric space heating undoubtedly

varies across service areas, making comparisons of

heat pump UECs very difficult.

4. Transferability of UEC

Service Areas

On the basis of the evidence

Unit Energy Consumption

20.000 -

15.000 -

10.000 -

5.000

0 - -

0

Models:

UEC(AII Dwellings)

UEC(Single Family)

^ '-.

2,000 4,000 6,000

Heating Degree-Days

= -1268 + 1.95(HDD)

= -1809 + 2.07(11DD)

Estimates Across

summarized above, it

All Dwelling

Types

Single-Family

Dwellings

8,000

R-Square Values:

All Dwellings: 0.7634

Single Family: 0.7682

Figure 3-2. Heat Pump Space Heating UECs

vs. Heating Degree-Days

3-6

EE-RM-94-230 COMMENT 61D

degree-days is 'strong. 1 Simple regressions of the pump heating UECs and heating degree":days. 

estimates' for single-family dwellings on the degree- Again, the ·pattern is very plausible, with UEC 

day measure yielded an R2 value over .70. The R2 values generally ordered according to relative heatfor all dwellings combined was roughly .69. To ing requirements. The "fits" of the both relationsome extent, this latter result may be explained by ships are strong, with R2 values around .76. The 

variations in th'e dwelling-type mix across service, unexplained variance should not be particularly 

areas. Although the estimated UECs trace out a surprising. First, of course, market conditions vary 

clear pattern, substantial variation around the esti- substantially across these service areas. Second, 

mated relationship is found. Clearly, some of this model specifications differ across studies. And 

variation can be attributed to non-weather differ- third, heat pump saturations are very low' in most 

ences across .service areas. Electricity prices vary service territories; thus, UEC estimates tend to have 

substantially, tor instance, and could explain a sub- high standard errors. Finally, heat pump owners 

stantial portion of the overall variation. Additional-.. _may be non-representative of households within a 

ly, model specifications differ appreciably across given service territory. In some service' areas, they 

specific studies, leading 'to differences in estimated may tend to live in extreme subclimates or have 

UECs. Furthermore, the simple weather variable very different economiddemographic features. Eviconstructed for this analysis may well be a distorted dence suggests, for instance, that heat pump ownestimate of thl~ effective conditions faced by house- ers tend to have relatively large homes and high 

holds with electric space heating. incomes in some areas. The degree of these differF' 3 2 '11 t t th . 1 t' b t heat ences between heat pump owners and other house- Igure - 1 us ra es e re a IOn e ween '. , . . holds With electnc space heatmg undoubtedly 

1 Four estimates that would have been outliers 

were omitted from these and other plots in this 

Section, These estimates were derived as part of 

a feasibility study, using data not collected 

specifically for CDA estimation. Moreover, the 

analyst who developed the estimates recognized 

that they were unreasonably low, 

Unit Energy Consumption 

20,000 -

IS.000 .. 

10,000 .. 

5.000 . 

o 

o 

:\1odells: 

2,000 

UEC(AII Dwellings) = -1268 + 1.95(HOO) 

UEC(Single Family) = -1809 + 2.07(IIDD) 

varies across service areas, making comparisons of 

heat pump UECs very difficult. 

4. Transferability of UEC Estimates Across 

Service Areas 

On the basis of the evidence summarized above, it 

4,000 

Heating Degree-Days 

6,000 

o 

8,000 

AU Dwelling 

Types 

Single:.Family 

Dwellings 

---8---

R-Square Values: 

AU Dwellings: 0.7634 

Single Family: 0.7682 

Figure 3-2. Heat Pump Space Heating UECs 

vs. Heating Degree-Days ' 

3-6 
Sulmmaru of UEC Estimates

appears that conventional space heating UECs trace

out a consistent pattern when mapped against

weather conditions. Whether or not these estimates

are "transferable" in a rigorous sense is open to

interpretation. One one hand, the degree of correlation between space heating UECs and heating

degree-days seems to be high enough to suggest

that CDA estimates from other service areas provide an important reference point for any analysis

of end-use consumption patterns. The estimated

UEC schedules shown in Figures 3-2 and 3-3 might

be used to obtain direct preliminary estimates of

average space heating UECs in service areas for

which no study has yet been done. However,

variations in market conditions, building standards,

and other factors can be expected to play a substantial role in determining space heating usage. Consequently, these area-specific factors should be taken

into account in developing end-use estimates.

C. Air Conditioning

1. General Form of the Air Conditioning

UEC Equation

This subsection considers the typical specification of

the space cooling portion of a conditional demand

model. As with space heating, both simple specifications and integrated behavioral/thermal

approaches are reviewed. In the course of this

review, several determinants of space cooling consumption (as recognized by conditional demand

models) are identified. The dependence of these

estimates on weather conditions and on structural

integrity is highlighted, and the role of weather

conditions is addressed at some length in the comparison of UEC estimates.

A household's space cooling UEC is referred to as

UECsci. The general space cooling UEC equation

can be expressed as:

(11) UECscj = C(STRUC,, EFF,, INS,, EDCj, WC,,, MC,,)

As noted earlier, STRUC, refers to a set of structural

features of the housing unit. It could include many

of the same variables discussed above for space

heating. The variable EFFI indicates the efficiency

of the cooling unit itself. Some basic efficiency

differences are often taken into account by distinguishing between types of cooling systems (e.g.,

conventional central air conditioning, room/wall air

conditioners, and heat pumps). Theoretically, finer

efficiency differentials could also be considered if

data on these features are available.

Unit Energy Consumption

6,000 -

5,000 -

4,000

3.000 -

2,000 -

1,000- -

0 -------

0 500 1.000 1.500 2,000 2,SC

Cooling Degree-Days

Models:

UEC(AJI Dwellings) = 565 + 1.18(CDD)

UEC(Single Family) = 458 + 134(CDD)

10 3,000 3,500 4,000

R-Square Values:

All Dwellings: 0.6225

Single Family: 0.8726

Figure 3-3. Conventional Central Air Conditioning UECs

vs. Cooling Degree-Days

3-7

. All Dwelling

Types

Single-Family

Dwellings

.---- )---

, _

=

EE-RM-94-230 COMMENT 61D

appears that conventional space heating UECs trace 

out a consistent pattern when mapped against 

weather conditions. Whether or not these estimates 

are "transferable" in a rigorous sense is open to 

interpretation. One one hand, the degree of correlation between space heating UECs" and heating 

degree-days seems to be high enough to suggest 

that CDA eSfimates from other service areas provide an important reference point tor any analysis"" 

of end-use consumption patterns. The estimated 

UEC schedules shown in Figures 3-2 and 3-3 might 

be used to obtain direct preliminary estimates of 

average spacl~ heating UECs in service areas for 

which no study has yet been done. However, 

variations in market conditions, building standards, 

and other factors can be expected to playa substantial role in determining space heating usage. Conse~ 

quently, these area-specific factors should be taken 

into account in developing end-use estimates. 

C. Air Conditioning 

1. General Form of the Air Conditioning 

UEC Equation 

This subsection considers the typical specification of 

the space cooling portion of a conditional demand 

Unit Energy Consumption 

6,000 ,.. 

5,000 :-

4,000 ~ 

3.000 ~ 

"2,000 ~ 

1,000 :-

0----

Summary vi UEC Estimates 

model. As with space heating, both simple specifications and integrated behavioral/thermal 

approaches are reviewed. In the course of this 

review, several determinants of space cooling consumption (as recognized by conditional demand 

models) "are identified. The dependence of these 

estimates on weather conditions and on structural" 

integrity is highlighte"d: and the role of weather 

conditions is addressed at some length in the comparison of UEe estimates. 

A household's space cooling UEC is referred .to as 

UECsCi. The general space cooling UEC equation 

can be expressed as: 

As noted earlier, STRUCi refers to a set of structural 

features of the housing unit. It could "include many 

of the same variables discussed above for space 

heating. The variable EFFi indicates the efficiency 

of the cooling unit itself. Some basic efficiency 

" differences are often taken into account by distinguishing between types of cooling systems (e.g., 

conventional central air conditioning, room/wall air 

. conditioners, and heat pumps). Theoretically, finer 

efficiency differentials could also be considered if 

data on these features are available. 

c 

" All Dwelling 

Types 

Single-Family 

Dwellings 

.... -~)---

o 500 1.000 2,000 2,500 3,000 3,500 4,000 

Cooling Degree-Days 

" Models: 

UEC(AJIOIfellings) = 565 + l.18(CDD) 

UEC(Single Family) = 458 + L'-t(CDD) 

R-Square Values: 

All Dwellings: 0.6225 

Single Family: 0.8726 

Figure 3-3. Conventional Central Air Conditioning UECs 

vs. Cooling Degree-Days' 

3-7 
Slumnnmna of UEC Estimates

As in the case of the heating specification, the

variable INSi pertains to insulation and other

weatherization features present in the thermal

shell. Again, note that specific variables could be

discrete (indicating whether or not a particular kind

of insulation is present) or continuous (describing

R-values). The same weatherization variables could

be incorporated into the space cooling UEC equation as discussed earlier for the heating equation.

Economic and demographic variables (EDCj) are

typically included to capture the determinants of air

conditioner thermostat settings. As for space heating, these variables might include income, age distribution, and other household features.

Weather conditions have obvious impacts on space

cooling consumption. In a standard thermodynamic

specification, the difference between outside temperatures and desired interior temperatures is a

critical component of predicted cooling loads. The

temperature differential for a "typical" household is

derived using a constant reference temperature.

The cumulative difference between outside temperatures and this reference temperature over a distinct time interval (say, a day or a billing period) is

usually captured in the form of:

* a cooling degree-day measure

* a cooling-requirements specification.

These measures are similar to the heating measures

described earlier (See Lawrence and Parti, 1984).

Like space heating, cooling is undoubtedly sensitive

to other weather conditions like humidity and wind

speed. There have been some attempts to include

other weather variables in CDA models (RER, 1985;.

QED, 1986), and these approaches appear to hold

some promise. Still, temperature-based weather

measures are clearly the dominant variables used in

conditional demand analyses.

2. Summary of Air Conditioning UEC

Estimates

This subsection presents estimates of UECs for the

following space cooling appliances:

* conventional central air conditioning

* room air conditioners

· heat pump air conditioners.

The survey did not uncover enough estimates of

other space cooling appliances (evaporative coolers,

attic fans, etc.) to allow meaningful comparisons.

3-8

Table 3-4 provides an overview of cooling UECs for

residential customers in all dwelling types. Corresponding estimates for the single-family dwelling

category are contained in Table 3-5. Unfortunately,

some studies reported UECs for only one or the

other of these two customer categories. These studies are listed in only one of the two tables.

The survey indicated that some studies combine

heat pumps with conventional central systems (perhaps because of low heat pump saturations). To

enhance the comparability of these estimates with

disaggregated estimates of conventional and heat

pump systems, the latter were aggregated using

information on relative saturations of these two

appliances. The aggregated estimates are listed

under "All Central" systems. Room/wall and central systems were not combined, however, given

that these two cooling configurations are not

directly comparable.

As shown in Table 3-4, conventional central cooling

systems UECs take on a wide range of values.

Southern service areas (TVA, Gulf Power, and

MPC) are estimated to have UECs in the vicinity of

3,000 kWh per year, while estimates for San Diego

Gas & Electric (SDG&E) are below 1,000 kWh.

Generally higher values are estimated for singlefamily dwellings (Table 3-5), although comparisons

are obscured by the lack of corresponding estimates

for some individual service areas. For the five studies presenting both sets of estimates, those for

single-family dwellings are an average of 13%

higher than those for all dwelling types combined.

Of course, the relative values for single-family

dwellings and all residence types depend on the

dwelling-type mix of the service area (or the sample

itself). So it is probably unwise to place much

importance on variations in these ratios. Like the

values obtained for all dwelling types, single-family

conventional central cooling UECs take on a wide

range of values. As discussed below, this pattern is

tied strongly to weather conditions.

Considerable variation is also found for heat

pumps, with reported UECs falling in the range of

750 to 4,200 kWh annually. With the exception of

PGandE, all utilities report somewhat higher UECs

for heat pump cooling than for conventional central

systems. Of course, heat pump saturations are

fairly low in virtually all service areas, so estimates

of UECs are likely to have relatively wide confidence intervals. Moreover, differentials between

heat pumps and conventional systems are strongly

affected by differences in square footage, specific

EE-RM-94-230 COMMENT 61D

Summary af UEC .E:;timtltes 

As in the case of the heating specification, the 

variab.le INSj pertains to insu.lation and other 

weatherization features present in the thermal 

shell. Again, note that specific variables could be 

discrete (indicating' whether or not a particular kind 

of insulation is present) or continuous (describing 

R-values). The same weatherization variables could 

be incorporated into the space cooling UEC equa-' 

tion as discussed earlier for the heating equation. 

Economic and demographic variables (EDCi) are 

typically included to capture the determinants of air 

conditioner thermostat settings. As for space heating, these variables might include income, age distribution, and other household features. 

Weather conditions have obvious impacts on space 

cooling consumption. In a standard thermodynamic 

specification, the difference. between outside temperatures and desired interior temperatures is a 

critical component of predicted cooling loads. The 

temperature qifferential fora "typical" household is 

derived using a constant reference temperature. 

The cumulative difference between outside. temperatures and this reference temperature over a distinct time interval (say, a day or a billing period) is 

usually captured in the form of: . 

• ,a cooling degree-day measure 

• a cooling-requin~ments specification. 

These measures are similar to the heati'ng measures 

described earlier (See Lawrence and Parti, 1984). 

, Like space heating. cooling is undoubtedly sensitive 

to other weathE!r conditions like humidity and wind 

speed. There hav,e been some' attempts to include 

other weather variables in CDA models (RER, 1985;. 

QED, 1986), and these approaches appear to hold 

some promise. Still, temperature-based weather 

measures are c1earh' the dominant variables used in 

conditional demand analyses. 

2. Summary of Air Conditioning UEC 

Estimates 

This subsection presents estimates of UECs for the 

following space cooling appliances: 

• conventional central air conditioning 

• room air conditioners 

• heat pump air conditioners. 

The survey did not uncover enough estimate's of 

other space cooling appliances (evaporative coolers, 

attic fans, etc.) to allow meaningful comparisons. 

3-8 

Table 3-4 provides an overview of cooling UECs for 

residential customers in all dwelling types. Corresponding estimates for the single-family. dwelling 

category are contained in Table 3-5. Unfortunately, 

some studies reported UECs for only one or the 

other of.these t~o customer categories. These studies are fisted in only ,one of the two tables .. 

The survey indicated that some studies combine 

heat pumps with conventional central systems (perhaps because of low heat pump saturations). To 

enhance the comparability of these estimates with 

disaggregated estimates of conventional and heat 

pump systems, the latter were aggregated using 

information on relative saturations of these two 

appliances. The aggregated estimates are listed 

under "AII Central" systems. Room/wall and central systems were not combined, however, given 

that these two' cooling configurations are not 

directly co~parable. 

As shown in Table 3-4, conventional central cooling 

systems UECs take on a wide range of values. 

Southern service areas (TV A, Gulf Power, and 

MPq are estimated to have UECs in the vicinity of 

3,000 kWh per year, while estimates for San Diego 

Gas & Electric (SDG&E) are below 1,000 kWh. 

Generally higher values are estimated for singlefamily dwellings (Table 3-5), although comparisons 

are obscured by the lack of corresponding estimates' 

for some individual service areas. For the five studies presenting both sets of estimates! those for 

single-family dwellings are an average of 13% 

higher than those for all dwelling types combined. 

Of course, the' relative values for single-family 

dwellings and all residence types depend on the 

dwelling~type mix of the service area (or the sample 

itself). So it is probably unwise to place much 

importance on variations in these ratios. Like the 

values obtained for all dwelling types, single-family 

conventional central cooling UECs take on a wide 

range of values. As discussed below, this pattern is 

tied strongly to weather conditions. 

Considerable variation is also found for heat 

pumps, with. reported UECs falling in the range of 

750 to 4,200 kWh annually. With the exception of 

PGandE, all utilities report somewhat higher UECs 

for heat pump cooling than for conventional central 

systems. Of course, heat pump saturations are 

fairly low in virtually all service areas, so estimates 

of UECs are likely to have relati\'ely wide confic:ience intervals. Moreover, differentials between 

heat pumps and conventional systems are strongly 

affected by differences in square footage, specific 
Table 3-4

Space Cooling UECS: All Dwellings

Convent'l All Room or

Utility Central Heat Pump Central Wall Units

PGandE (87) 1,375 1.175 1,363 375a

PGandE (86) 1,275 750 1244 400a

SOG&E (85) 651 780 667 232b

SDG&E (84) 857 906 863 249b

TVA (85) 3.260 4,200 - 1,245a

FP&L (86) - - 4,439 2.179 a

NYSEG (86) - - 2.500 300b

Gulf (86) 3,315 3.859 - 1,320"

El Paso (87) - - -

CommEd (87) - - 1,810 490a

MPC (84) 3,000 3.840 - 690a

JCP&L (87) - - 1,253 33C¢

232 d

MetEd (87) -865 27'

240d

NPC (86) - 3,280 e 2,873b

RG&E (86) - - -955'

APC (83) 4,146 3,974 - 1,1739

1,957n

TNP (88) 4,437 3,782 - 817

BG&E. 2,352 1,947 2,228 646b

a All units combined. e Homes without evaporative coolers.

b Per unit. f All air conditioning systems.

c Homes with only one unit. 9 First room unit.

d Homes with more than one unit. h Second room unit.

Table 3-5

Space Cooling UECS: Single-Family Dwellings

Convent'l All Room or Utility Central Heat Pump Central Wall Units

PGandE (87) 1,600 1.275 1,577 400 a

PGandE (86) 1,475 775 1.425 400 a

PGandE (86a) 1,917 1.557 1,891 531a

SDG&E (85) 858 780 847 281 b

SDG&E (84) 992 906 980 249b

TVA (85) 3.419 4,360 - 1,181a

AP&L (84) - - 1,658 459 a

LP&L (84) -- 1,834 339 a

MP&L (84) - - 1,668 518 a

NOPS (84) -- 1,306

FP&L (86) -- 5.074 2,439a

CommEd (87) - - 2,030 510 a

VEPCO (87) 2,124 2,141 2,131 1,057a

709 b

NPC (86) - - 4.451 3.264 a

R6&E (86) - 1,011 -

Sierra (88) - 1,500 346 a

AEP (88) -- 1,487 335°

BG&E (88) 2.660 2,079 2.464 7270

a All units combined. c Homes without evaporative coolers.

b Per unit. a All air conditioning, including room/wall units.

Summary of UEC Estimates

3-9

EE-RM-94-230 COMMENT 61D

Suilllllary of UEC Estimates 

Table 3-4 

Space Cooling UECS: All Dwellings 

Convent'l All Room or 

Utility Central Heat Pump Central Wall Units ' 

PGandE (87) 1,375 1.175 1,363 375 8 

PGa~E (86) 1.275 750 1244 400 a 

SOG&E (85) 651 780 667 232b 

SOG&E (84)' 857 906 863 249b 

TVA (85) 3.260 4.200 - 1,245a 

FP&L (86) - - 4,439 2. 179 a 

NYSEG (86) - - 2.500 '300b 

Gulf (86) 3.315 3.859 - 1.320b 

EI Paso (87) , - - - - CommEd (87) - - 1.810 490a 

MPC (84) 3.000 3.840 - 6908 

JCP&L (87) - - 1.253 33c , 

232d 

MetEd (87) - - 865 27c 

240d , 

NPC(86) . 3.280e 2,873 b - - RG&E (86) - - - 955' 

APC (83) 4.146 3.974 - 1,1739 

1.957n 

TNP (88) 4,437 3.782 - 817 

BG&E, 2,352 1.947 2,228 646b 

'a All units combined. e Homes without evaporative coolers. 

b Per unit. t All air conditioning systems. 

C Homes with only one unit. 9 First room unit. 

d Homes with more than one unit. n Second room unit. 

Table 3-5 

Space Cooling UECS: Single-Family Dwellings 

Convent'l All Room or 

Utility Central Heat Pump Central Wall Units 

PGandE (87) 1,600 1.275 1.577 400a 

PGandE (86) 1.475 n5 1.425 400a 

PGandE (86a) 1.917 1.557 1.891 531 a 

SDG&E (85) 858 780 847 281 b 

SOG&E (84) 992 906 980 249 b 

TVA (85) 3,419 4.360 - 1,181 a 

AP&L (84) - - 1.658 459 8 

LP&L (84) - - 1.834 339 a 

MP&L (84) - - 1.668 518 B 

, NOPS,(84) - - 1.306 - FP&L (86) - - 5.074 2.439a 

CommEd (87) - - 2,030 510a 

VEPCO (87) 2.124 ' 2.141 2.131 1.0578 

709 b 

NPC (86) - - 4,451 c 3.264 a 

R6&E (86) - - 1.011 0 - Sierra (88) - - 1.500 346 a 

AEP (88) - - 1,487 335 b 

BG&E (88) 2.660 2.079 2,464 727b 

a All units combined, c Homes without evaporative coolers. 

b Per unit. d All air conditioning. including room/wall units. 

3-9 
Suliinnary of UEC Estimates

microclimate conditions, and thermostat settings of

the households in question.

Aggregated central cooling UECs (the most broadly

based indicator shown) trace out an interesting

pattern. They span a range from under 700 kWh

(SDG&E) to over 4,400 kWh (FP&L). In general,

these differences seem to be linked to weather

variation; however, other factors like relative energy

prices probably also play a role. The impact of

weather conditions is revisited below.

Room/wall unit UECs are reported in one of two

ways: as UECs per unit, or.as UECs for all units

present. Both kinds of measures are presented in

Tables 3-4 and 3-5. These estimates suggest that

room/wall units (all units present) have UECs under

half the corresponding values associated with central systems. This may be partly because room/wall

systems are more often used for localized cooling.

However, they also tend to be owned by lower

income households, those living in smaller homes,

and (arguably) households facing more moderate

summer conditions.

3. Standardization of Air Conditioning UECs

for Differences in Weather Conditions

This subsection considers the relationship between

estimated air conditioning UECs and weather conditions in the service areas covered by the analysis.

To support the analysis, NOAA data on temperatures were used to form estimates of cooling

degree-days for the service areas covered by survey. A reference temperature of 65°F was used in

all of these calculations in order to preserve comparability across service territories.

Figure 3-3 illustrates the relationship between cooling degree-days and estimated conventional cooling

UECs. Cooling degree-days are estimated from

NOAA data on individual weather stations and

either actual or assumed customer distributions

across these stations.

As depicted in Figure 3-3, the correlation between

weather and conventional air conditioning UECs is

reasonably strong for both residence categories. The

R2 of the regression linking single-family UECs with

CDDs is close to .87. The R: of the regression for all

dwelling types is only .62. primarily due to a

couple of extreme values. Some variation around

the regression line is to be expected, of course, as a

result of all the factors discussed earlier for space

heating. In general, the degree of unexplained variation seems to be onlyvslightly greater than for

3-10

space heating. This is somewhat unexpected. Air

conditioning is typically considered a more discretionary end use than space heating, and should be

more price-sensitive. Furthermore, humidity and

other weather factors should have a more important

influence on cooling loads than on heating loads.

Figure 3-4 depicts heat pump cooling UECs and the

associated CDD estimates. Unfortunately, relatively

few observations are available for this end use.

While the fits between estimated UECs and CDDs

appear fairly tight, this is attributable in part to the

small sample size.

4. Transferability of Air Conditioning UEC

Estimates Across Service Areas

The analysis of the weather sensitivity of cooling

UECs allows us to offer a preliminary assessment of

the transferability of these UEC estimates across

service areas. In general, the conclusions are the

same as for space heating. First, conventional central cooling UECs trace out a sufficiently consistent

pattern against weather conditions to warrant some

confidence in the estimates. Adjusting values for

other service areas for differences in weather conditions (by using a predicted value from the relationships shown in Figure 3-3) should yield a

reasonable starting point for the analysis of central

air conditioning usage within a given service area.

Second, heat pump cooling UECs are also reasonably consistent, given weather conditions. However, the evidence presented in Figure 3-4 is too scant

to support anything but a rough preliminary estimate of the corresponding UEC in any given service area. Very little can be said for window/wall air

conditioning, partly because of the different ways

of modeling consumption through these appliances.

D. Water Heating

1. General Specification

Electric water heating can account for a substantial

portion of a household's overall electricity consumption. The UEC for this end use (UECwHi) can

be related to several factors. The UEC function

could be written as:

(12) UECWH, = W(EFFi

, INSi

, LOC,, EDC i, WC,, MCi, DWi

, CW,)

where EFF, reflects the efficiency of the water heating unit, while INS, indicates the presence of a

EE-RM-94-230 COMMENT 61D

SUlllmnry of UEC E:;;tilllntc:' 

microclimate conditions, and thermostat settings of 

the households in question. 

Aggregated central cooling CECs (the'most broadly 

based indicatoI shown) trace out an interesting 

pattern. They span a range trom under 700 kWh 

(SOG&E) to over 4,400 kWh (FP&L). In general, 

these differenc.::?s seem to be linked to weather 

variation; however, other factors like relative energy 

prices probably also play a role. The impact of 

weather conditions is re\'isited below. 

Room/wall unit UECs are reported in on~ of, two 

ways: as UECs per unit, or ,as UECs for all units 

present. Both kinds of measures ,are presented in 

Tables 3-4 and 3-5. These estimates suggest that 

room/wall units (all units present) have UECs under 

half the corresponding \'alue:sassociated with central systems. This may be partly because room/wall 

systems are more often used for localized cooling. 

However, they also tend to be owned by lower 

income households, those li\;ng in smaller homes, 

and (arguably) households facing more moderate 

summer conditions. 

3. Standardizi3tion of Air Conditioning UECs 

for Differences in Weather Conditions 

This subsection considers the relationship between 

estimated air conditioning CECs and weather conditions in the service areas covered by the analysis. 

, To support the analysis, l\:OAA data on temperatures were used to form estima tes of cooling 

degree-days for the service areas covered by survey. A reference temperature of 65°F was used in 

all of these calculations in on;ier to preserve comparability across servicE~, territories. 

Figure' 3-3 ilIustr,ates the rela~onship' between cooling degree-days and estimated conventional cooling 

UECs. Cooling, degree-days are estimated from 

NOAA data on individual weather stations and 

either actual or assumed customer distributions 

across these stations. 

As depicted in Figure 3-3, the correlation between 

weather' and conventional air conditioning UECs is 

reasonably strong for both residence categories. The 

R2 of the regression linking single-family,UECs with 

COOs is close to .87. The R: (If the regression for all 

d';velling types is only .6~. primarily due to a 

couple of extreme values. Some variation around 

the regression line is to be e:\pected, of ("ourse, as a 

result of all the factors discussed earlier for space 

heating. In general, the degree of unexplained variation seems to be only·slightly, greater than for 

3-10 

space heating. This is somewhat unexpected. Air 

conditioning is typically considered a more discretionary end use than space heating, and should be 

more price-sensitive. Furthermore, humidity and 

other weather factors should have a more important 

influence on cooling loads than on heating ~oads. 

Figure 3-4 depicts heat pump cooling UECs and the 

associated COO estimates. Unfortunately, relatively 

few observations are available for this end use. 

While the fits between estimated UECs and COOs 

appear fairly tight, this is attributable in part to the 

small sample size. 

4. Transferability of Air Conditioning UEC 

Estimates Across, Service Areas 

The analysis of the weather sensitivity of cooling 

UECs allows us to offer a preliminary assessment of 

the transferability of these UEC estimates across 

service areas. In general, the conclusions are the 

same as for space heating. First, conventional central cooling UECs trace out a sufficiently consistent 

pattern against weather conditions to warrant some 

confidence in the estimates. Adjusting values for 

other service areas for differences in weather conditions (by using a predicted value from the relation-

,ships shown in Figure 3-3) should yield' a 

'reasonable starting point for the analysis of central 

,air conditioning usage within a given service area. 

Second, heat pump cooling UECs are also reasonably consistent, given weather conditions. Howev- ' 

er, the evidence presented in Figure 3-4 is too scant 

to support anything bu~ a rough preliminary estimate of the corresponding UEC in any given service area. Very little can be said for window/wall air 

conditioning, partly because of the different ways 

of modeling consumption throu'gh these appliances. 

D. Water Heating 

1. General Specification 

Electric water heating can account for a substantial 

portion of a household's overall electricity consumption. The UEC for this end use (UEC\lHj,) can 

be related to several factors. the UEC function 

could be written as: 

(12) UEC WHj = W(EFFj. INSj. lOCj. EOCj. WC" MCj • OWj. CW;l 

where EFF; retlects the efficiency of the water heating unit, while INSj indicates the presence of a 
Summary of UEC Estimnates

Unit Energy Consumption

.5,000 -

4,000

3,000

2,000

.1.000

O -

0 500 1.000 1,500 2,000 2,500

Cooling Degree-Days

All Dwelling

Types

Single-Family

Dwellings

3,000 3,500 4,000

Models:

UEC(AII Dwellings) = -1359 + 2.4S(CDD)

UEC(Single Family) = -3028 + 4.17(CDD)

R-Square Values:

All Dwellings: 0.8614

Single Family. 0.8680

Figure 3-4. Heat Pump Central Air Conditioning UECs

vs. Cooling Degree-Days

water heater blanket. (Note that a variable representing the presence of a low-flow showerhead

could also be included.)

The variable LOC, signifies whether :he unit is in a

heated or an unheated area. In are.- with extreme

weather, this could be an important variable.

EDCj is a set of economic and demographic variables. For water heating, income and household

size are typically used to represent these features.

Household size is probably a particularly critical

variable in any water heating UEC specification.

The number of people in a household has a direct

bearing on the number of showers tken, the number of loads run through the dishwasher and washing machine, and other uses of heo water. Most

evidence suggests that water hear-ng use is less

than proportional to household size. however, so

specifications involving logarithms or some other

transformation of household size are often used.

One can argue that water heating consumption

depends significantly on weather conditions (WC),

although seasonal rather than. daily conditions are

more important. Two reasons can be offered. First,

households' usage of hot water is affected by

weather patterns (for instance, taking hotter showers in the winter). Second, inlet temperatures vary

over the year as groundwater temperatures fluctuate. In service areas with extreme summer and/or

winter conditions, the seasonal variation in water

heating consumption could be substantial. Ignoring

this phenomenon could bias the estimates of water

heating UECs downward (and space conditioning

UECs upward).

Water heating consumption may be somewhat sensitive to market conditions (as represented, for

instance, by a marginal price variable). While the

degree of sensitivity of utilization patterns is not

expected to be great, given the limited range of

options for adjusting consumption in the short run,

it may still be non-trivial.

Most conditional demand specifications include

binary indicators of the presence of dishwashers

and washing machines. These appliances consume

power directly (through motor loads), but they may

also influence the use of hot water. The presence of

an automatic washing machine may mean that

clothes are washed at home rather than at the

laundromat. The presence of a dishwasher probably

has a more subtle influence on water heating.

3-11

'-----

_ I

/L

v

EE-RM-94-230 COMMENT 61D

Unit EnellO' Consumption 

, 5,000 .. 

4,000" 

3,000' 

2,000 : 

.1,000 . -: 

. ' /~. 

: 

Summary of UEC Estimates 

All Dwelling 

Types. 

Single-Family 

Dwellings 

- '" ...... -:..:;"' .. -

o ~ ..... __ ... -.L.~._ __~ ___________ .....o.-__ -, 

o 500 1.000 1,500 2,000 2,500 3,000 3,500 4,000 

Cooling Degree-Days 

Models: 

UE.C(A11 Dwellings) = -1359 + ~~(CDD) 

UEC(Single Family) = -3028 + ~.17(CDD) 

R-Square Values: 

All Dwellings: 0.8614 

Single Family: 0.8680 

Figure 3-4. Heat Pump Central Air Conditioning UECs 

vs. Cooling Degree-Days 

water heater blanket. (Note that i \'ariable representing the presence of a 10w-flC'~\' showerhead 

could also be included.) 

The variable LOCi signifies whether ~he unit is in a 

heated or an unheated area. !n are~~ with extreme 

weather, this could be an important ·.ariable. 

EDCi is a set of economic and demographic vari- . 

abIes. For wa.ter heating. income ~nd household 

size are typically used to represen! these features. 

Household size is probably a particularly critical 

variable in any water heating UEC specification. 

The number of people in a househC'ld has a direct 

bearing on the number of showers ~~ken, the number of loads run through the dishwa,;.her and washing machine,. and other uses of h('~ water. Most 

evidence suggests that water hea:'_"'1g use is less 

than proportional to household siZe. however, so 

specifications involving logarithms (If some other 

transformation of household size are I..)iten used. 

One can argue that water heatin~ consumption 

depends significantly on weather cO:1ditions (WC,), 

although seasonal rather than· daily conditions are 

more important. Two reasons can be offered. First, 

households' usage of hot water :~ affected by 

weather patterns (for instance, taking hotter showers in the winter). Second, inlet temperatures vary 

over the year as groundwater temperatures fluctuate. In service areas with extreme summer and/or 

winter conditions, the seasonal variation in water 

heating consumption could be substantial. Ignoring 

this phenomenon could bias the estimates of water 

'heating UECs downward (and space conditioning 

UECs upward). 

Water heating consumption may be somewhat sensith;e to market (onditions (a5 represented, for 

instance, by a marginal price variable). While the 

degree of sensitivity of utilization patterns is not 

expected to be great, given the limited range of 

options for adjusting consumption in the short run, 

it may still be non-trivial. 

Most conditional demand specifications include 

binary indicators of 'the presence of dishwashers 

and washing machines. These appliances consume 

power directly (through motor loads), but they may 

also influence the use of hot water. The presence of 

an automatic washing machine may mean that 

clothes are washed at home rather than at the 

laundr9mat. The presence of a dishwasher probably 

has a mO.re subtle influence on water heating. 

3-11 
Sum(luaryl of LIEC Estiilates

Theoretically, it could actually conserve water,

given the practice of storing dishes in the unit until

a full load is accumulated. On the other hand,

higher water temperatures are often deemed necessary for dishwashers than for manual dishwashing.

Most analysts find net positive contributions to

water heating UECs for dishwashers, but the

results are somewhat inconsistent.

The treatment of dishwashers and washing

machines in estimating water heating UECs is sufficiently important to warrant further consideration

here. For the purposes of illustration, consider the

following expression for the sum of usage from

water heaters, dishwashers and washing machines

(grouping these end uses is reasonable because

they are obviously interrelated):

(13) UECW., Sw,, + UECow Sow, + UECcw Scwi

where UECv,, reflects the water heating UEC,

including energy consumption for heating water for

use in washing dishes and clothes; UECDvw and

UECovi represent the motor loads of dishwashers

and washing machines, respectively; and Siv,, Sow0

and Scw, are binary variables reflecting the presence

of electric water heating, a dishwasher, and a washing machine, respectively.

For now, ignore the influence of household characteristics on water heating loads and focus on the

interaction of appliance stocks. To recognize the

influence of dishwashers and washing machines on

water heating loads, the water heating UEC can be

expressed as:

(14) UECwHi = ao - a, Sow, + a2 Sc i

Substituting this expression into (13) yields:

(15) (a. - a, So, -'-a2 SCw,) 3HS, + UECow, Sow, + UECCW, Scw;

Given this specification, the following concepts can

be defined (averages are indicated by dropping the

subscript i):

* The coefficient a,, reflects the water heating UEC

in the absence of both dishwashers and washing

machines; it is sometimes called a base water

heating UEC.

* The coefficients a, and a: represent the

incremental water heating loads associated with

dishwasher and washing machine ownership

(given electric water heating).

3-12

* The total water heating UEC, which depends on

dishwasher and washing machine saturations, is:

ao + a, Sw+ a2 SC

* The overall unit electricity consumption

associated with dishwashers depends

upon electric water heating saturations, and can

be written as:

a, SWH + UECDW

Note that overall dishwasher-related electricity

consumption contains a motor-load UEC as well

as.the incremental water heating load weighted

by the share of homes with electric water

heating.

* The overall unit electricity consumption

associated with washing machines depends upon

electric water heating saturations, and is given

by:

a2 SWH + UECCW

These concepts will be used below in interpreting

the results of various conditional demand studies.

2. Summary of Estimated UECs

Tables 3-6 and 3-7 present a cross-section of estimates of water heating UECs. The. first thing to

note is that the differential treatment of incremental

water heating associated with dishwashers and

washing machines makes comparisons very difficult. As explained above, there are two ways to

present water heating UECs:

* First, the water heating UEC can be derived

using the relevant saturations of dishwashers

and washing machines (along with all the other

arguments of the UEC function). In this case, the

water heating UEC encompasses the incremental

use of water associated with these appliances.

UECs for dishwashers and washing machines

(if explicitly derived) would include only motor

loads.

* Second; consumption relating to the use of water

for dishwashers and washing machines can be

isolated and presented as part of the UECs for

those appliances. (The rest of their UECs would

be motor loads.)

Unfortunately, the studies surveyed were fairly

evenly divided with respect to these practices. And

in some cases, it was unclear which of the two

approaches had been taken.

EE-RM-94-230 COMMENT 61D

S[III["/(/"~1 of LlEe [sliwal,''; 

Theoretically, it could actually conserve water, 

. given the practice of storing dishes in the unit until 

a full load is ac(:umulated. On the other hand, 

higher water temperatures are often deemed necessary for dishwashers than for manual dishwashing. 

Most analysts find net positive contributions to 

water heating UECs for dishwashers, but the 

results are somewhat inconsistent. 

The treatment of dishwashers and washing 

machines in estimat·ing water heating UECs is sufficiently important to warrant further consideration 

here. For the purposes of illustration, consider the 

following expression for the sum of usage from 

water heaters, dishwashers and washing machines 

(grouping these end uses is reasonable because 

they are obviollsly interrelated): 

where UECWlh reflects the water heating UEC, 

including energy consumption for heating water for 

use in washing dishes and clothes; UEColV; and 

UEC,w; represE'nt the motor loads of dishwashers 

. and washing machines, respectively; and SWH;, Sow; 

and Scw; are binary variables reflecting the presence 

of electric water heating, a dishwasher, and a washing machine, respectively. 

For now, ignore the influence of household characteri.stics on water heating loads and focus on the 

interaction of appliance stocks. To recognize the 

influence of dishwashers and washing machines on 

water heating loads, th~ water heating' UEC can be 

expressed as: 

Substituting this. expression into (13) yields: 

Given this specification, the following concepts can 

be defined (averages are indicated by dropping the 

subscript i): 

• The coefficient all retlects the water heating UEC 

in the absenCE! of both dishwashers and washing 

machines; it is sometimes called a base water 

heating VEe. 

• The coefficients al and a: represent the 

incrementa] water heating loads associated with 

dishwasher and washing machine ownership 

(given electric water heating). 

3-12 

• The total water heating VEe, which dependson 

dishwasher and washing machine saturations, is: 

ao + a, Sow + a2 Sew 

• The overall unit electricity consumption 

associated with dishwashers depends. 

upon electric water heating saturations, and can 

be written as: . 

a, SWH + UECow 

Note that overall dishwasher-related electricitv 

consumption contains a motor-load UEC as well 

as. the Incremental water heating load weighted 

. by the share of homes with electric water 

heating. 

• The overall unit electricity consumption 

associated with w~shing machines depends upon 

electric water heating saturations, and is given 

by: 

a2.SWH + UECew 

These concepts will be used below in interpreting 

the results of various conditional demand studie~ . 

2. Summary of Estimated UECs 

Tables 3-6 and 3-7 present across-section of estimates of water heating UECs.· The.· first thing to 

note is that the differential treatment of incremental 

water heating associated with dish\ ... ·ashers. and 

washing machines makes comparisons very difficult. As explained above, there are 1\\'0 ways to 

present water heating UECs: . 

• First, the water heating UEe can be derived 

using the relevant saturations of dishwashers 

and washing machines (along with all the other 

arguments of the UEC function). In this case, the 

water heating UEC encompasses the incremen~al 

use of water associated with these appliances. 

UECs for dishwashers and washing machines 

(if explicitly derived) would include only motor 

loads. 

• Second; consumption relating to the use of \'I.·ater 

for dishwashers and washing machines can bt' 

isolated and presented as part of the CECs for 

those appliances. (The rest of their UECs would 

be motor loads.) 

Unfortunately, the studies surveyed were fairly 

evenly divided with respect to these practices. And 

in some cases, it was unclear which of the two 

approach.es had been taken. 
Table 3-6

Conventional Water Heating UECS: All Dwellings

All H20 H20 Heat Incremental Incremental

Utility Heating exc. DW/CW DW Usagea CW Usage'

PGandE (87) 3.775

PGandE (86) 3,525 -

SDG&E (85) 2,112 - - -

SDG&E (84) 2,534 - - -

TVA - 3,701 -

FP&L (86) - 1,486 - 576

NYSEG (86) 3,500b - -

Gulf (86) 3.400 - - -

CommEd (87) - 3,780 610

MPC (84) 2,920 - -

JCP&L (87) - 3:468 - -

MetEd (87) - 3.806

RG&E (86) - 1,926 130 397

NPC (86) - 3,127 - -

APC (83) - 2,175' 869

TNP (88) 4.894 - -

BG&E (88) 3.141 2,369 410 573

a Incremental water heating loads only. Motor loads are not included.

D Average of UECs derived from several models.

c Water heating usage other than for dishwasher.

Table 3-7

Conventional Water Heating UECS: Single-Family Dwellings

All H20 H20 Heat Incremental Incremental

Utility Heating exc. OW/CW OW Usage a CW Usage"

PGandE (87) 3.850 -

PGandE (86) 3.750 -

PGandE (86a) 3.649 -

SDG&E (85) 2.160 - - -

SDG&E (84) 2.412 - - -

TVA (86) - 3.812 - -

AP&L (84) - 4,451 - -

LP&L (84) - 3,687 - -

MP&L (84) - 5,378 - -

NOPS (84) - 3,774 - -

FP&L (86) - 1.902 - -

CommEd (87) - 4,370 b 580

VEPCO (87) 2.927 - -

NPC (86) - 3.485 - -

RG&E (86) - 3,690 267 970

Sierra (88) 2,834 - 964

AEP (88) 4,397 - - -

BG&E (88) 3.398 2,503 426 587

a Incremental water heating loads only. Motor loads are not included.

° Water heating usage other than for dishwasher.

Summnrv of UEC Estini:;:es

3-13

EE-RM-94-230 COMMENT 61D

I 

Summary of u£c E::.til7:':::?'5 

Table 3·6 

Conventional Water Heating UECS: All Dwellings 

All H20 H20 Heat Incremental 

Utility Heating exc.OW/CW OW Usagea 

PGandE (87) 3,775 

PGandE (86) 3,525 

SDG&E (85) 2,112 

SDG&E (84) 2,534 

TVA 3,701 

FP&L (86) 1,486 

NYSEG (86) 3,500 b 

Gulf (86) 3,400 

CommEd (87) 3,780 610 

MPC (84) 2,920 

JCP&L (87) 3;468 

MetEd (87) 3,806 

RG&E (86) 1,926 130 

NPC (86) 3,127 

APC (83) 2,175 c 869 

TNP (88) 4,894 

BG&E (88) 3.141 2,369 410 

a Incremental water healing loads only. Motor loads are not included. 

D Average of UECs derived from several models. 

C Water heating. usage other than for dishwasher. 

Table 3·7 

Incremental 

CW Usagea 

576 

397 

573 

Conventional Water Heating UECS: Single-Family Dwellings 

All H2O H20 Heat Incremental Incremental 

Utility Heating exc.OW/CW OW Usagea CW UsageS 

PGandE (87) 3,850 - - - PGandE (86) 3,750 - - - PGandE (86a) 3.649 - - - SOG&E (85) 2.160 - - - SDG&E (84) 2,~12 - - - TVA (86) - 3.812 - - AP&L (84) - 4,451 - - LP&L (84) - 3,687 - - MP&L (84) - 5,378 - - NOPS (84) - 3.774 - - FP&L (86) - 1,902 - - CommEd (87) - 4,370 b 580 - VEPCO (87) 2.927 - - - NPC (86) - 3,485 - - RG&E (86) - 3,690 267 97Q 

Sierra (88) 2.834 - 964 - AEP (88) 4.397 - - - BG&E (88) 3,398 2,503 426 587 

a Incremental water heating loads only. Motor loads are not included. 

o Water heating usage other than lor dishwasher. 
Sunmanrn / I' LIEC Estii,:.:c;r

3. Standardization of Water Heating UECs

for Differences in Weather Conditions

Ideally, water heating UECs should be standardized

for variations in groundwater temperatures across

service areas to facilitate their comparison. Unfortunately, reliable data on groundwater temperatures

are extremely difficult to find. Instead, average

annual temperatures, as reported by NOAA, were

used to represent groundwater temperatures for the

service areas covered by the survey.- In general,

there is a close correspondence (on an annual basis)

between ambient air temperatures and water temperatures at the service inlet, so this approach

should be reasonable.

Figures 3-5 and 3-6 depict the relationship between

average temperatures and water heating UECs. 2

Figure 3-5 presents overall water heating usage;

Figure 3-6 shows water heating consumption except

dishwasher and clothes washer increments.

\

Estimates for four service areas were omitted from

the latter figure because they seem implausibly

2 One estimate was ornitte from the scatter

because it was not plausible given the average

temperature in the service territory in question.

high. Simple descriptive regressions relating usage

to temperatures were run, and the results are listed

at the bottom of the two figures. The patterns are

somewhat irregular, partly because of the small

number of estimates defined appropriately for' the

two figures. Figure 3-5 does not exhibit the kind of

pattern one would expect. The variation in UEC

values is considerable, even within a fairly narrow

range of annual temperatures. Figure 3-6 shows

more plausible tendencies for weather-sensitivity,

but some estimates still differ sharply from values

predicted by the regression. Water heating consumption is fairly clearly dependent on weather,

and this should be taken into account in comparing

UEC estimates across service areas. But the evidence gathered thus far does not seem to provide

very useful reference values at different levels of

average temperature.

4. Transferability of Estimates

As shown above, the variation in water heating

UECs is partly attributable to different model specifications as well as differences in weather. Even

after taking into account these differences, estimated UECs are not particularly consistent across

service areas. Clearly, an.analyst from one utility

Figure 3-5. Conventional Water Heating UECs (All H20 Heating)

vs. Average Temperature

3-14

EE-RM-94-230 COMMENT 61D

SU/1/mary o( Ll EC E~ti,,:.;~l·~ 

3. Standardiization of Water Heating UECs 

for Differences in Weather Conditions 

Ideally, water heating UECs should be standardized 

for variations in groundwater temperatures across 

service areas to facilitate their comparison. Unfortunately, reliabl,~ data l10 groundwater temperatures 

are extremely difficult to find. Instead. average 

annual temperatures. as reported by NOAA, were 

used to represent groundwater temperatures for the 

'service areas covered by the survey,· In general, 

there is a close corresFondence (on an annual basis) 

between ambient air ~emperatures and water temperatures at ·the sen'ice inlet, so this. approa'ch 

should be reasona~le. 

Figures 3-5 and 3-6 depict the relationship between 

average temperature:; and water heating UECs. 2 

Figure 3-5 presents \.);:eraII water heating usage; 

. Figure 3-6 shows water heating consumption except 

dishwasher and clothes washer increments. 

, 

Estimates for four service areas were omitted from 

the latter figure because they seem implausibly 

2 One estimate was·ornitte-': from the scatter 

because it was not plau;:!:-le given the average 

temperature in the seni.::e territory in question. 

Unit Energy Consumption 

::,000 .. 

-l,500 '. 

4,000 -

2,500 -

2,000 .. 

high. Simple descriptive regressions relating usage 

to temperatures were run, and the results are listed 

at the bottom of the two figures. The ·patterns are 

somewhat irregular, pa'rtly because of the small 

number of estimates defined appropriately for" the 

two figures. Figure 3-5 does not exhibit the kind of 

pattern one would expect. The variation in UEC 

values is considerable, even within a fairly narrow 

range of annual temperatures. Figure 3-6 shows 

more plausible tendencies for weather-sensitivity, 

but some estimates still differ sharply from values 

predicted by the regression. Water heating consumption is fairly clearly dependent on weather, 

and 'this should be taken into account in comparing 

UEC estimates across service areas. But the evidence gathered thus far does not seem to provide 

very useful ref.erence values at different levels of 

average temperature . 

4. Transferability of Estimates 

As shown above, the variation' in water heating 

UECs is partly attributable to different model specifications as well as differences in weather. Even 

after taking into account these differences, estimated UECs are not particularly consistent across 

service areas. Clearly, an. analyst from one utility 

"'.- .. ----

All Dwelling 

Types 

Single-Family 

Dwel.lings 

1,500 -

40 

_ ... __ ... _-_._._-_ .. _----------'-----' 

3-14 

45 SO 55 60 65 

Average TemPerature (Degrees Fahrenheit) 

Models: 

UEC(AlI Dwelli~s, = 5145 - 34.37(Deg. F) 

UEC(Single Family) = 7293 - 70.85(Deg. F) 

70 75 

R·Square. Value: 

All Dwellings: 0.0623 

Single Family: 0.1860 

Figure 3·5. Conventional Water Heating UECs (All H20 Heating) 

vs. Average Temperature 
Summary of UEC Estimates

Unit Energy Consumption

5,000 -

4,500 .

4,000 -

3,500 -

3,000 -

2,500 -

2,000 -

1,500

All Dwelling

Types

Single-Family Dwellings

--- &

0

--. , z

1,000

40 45 50 55 60 65

Average Temperature (Degrees Fahrenheit)

Models:

UEC(AII Dwellings) = 7053 - 69.75(Deg. F)

UEC(Single Family) = 6881 - 6123(Deg. F)

70 75

R-Square Value:

All Dwellings: 05062

Single Family: 0.4286

Figure 3-6. Conventional Water Heating (H20 heat exc. DW/CW)

vs. Average Temperature

should not simply borrow a water heating UEC

from'another. At best, estimates from comparable

service areas could provide rough reference points

for an analysis of water heating consumption.

where Ro, is the energy usage through a first refrigerator, SSR, indicates the-ownership of a second

refrigerator, and f, is the usage of the second

refrigerator.

Another common specification is:

E. Refrigeration (17) UECR,, = R,i + f2(EDC,)SFF,i

1. General Specification

This subsection focuses on energy consumption

through refrigerators and freezers. Deriving UECs

for refrigerators is very difficult as a consequence of

the widespread ownership of this appliance. Saturations'of primary refrigerators tend to be extremely

close to 100%; thus, it is extremely difficult to

separate refrigerator energy consumption from

other universal end uses like lighting. Typically,

CDA specifications are' designed to sort out incremental energy consumption resulting from a second

or subsequent refrigerator, or a frost-free unit (as

opposed to a manual defrost unit). One standard

specification could be written as:

.(16) UECR,i = Roi + t,(EDC,)SsR,

where R1, is the usage of a manual defrost refrigerator, SFFR, indicates the ownership of a frost-free

unit, and f2 is the incremental usage of a frost-free

unit relative to a manual defrost refrigerator.

Of course, other variations of these specifications

could be used to distinguish among various numbers and types of refrigerators. The important thing

to note is that Ro, and R1l are typically included in

the constant term.of the CDA model, along with

miscellaneous end uses. It should also be recognized that CDA estimation of primary refrigerator

energy use would become much more feasible if

appliance saturation surveys contained questions

about the characteristics of these appliances (e.g.,

size categories, frost-free features, designs, ages,

etc.).

3-15

- --

EE-RM-94-230 COMMENT 61D

Unit Energy Consumption 

5,000 ;-

4,500 =-, ", 

4,000 -

3,500 -

3,000 -

2,500 " 

2,000 -

1,500 :... 

1,000 ----'--. 

40 45 

". 

50 ,55 

'" 

60 65 

o 

o 

". 

70 

'SlIllImary of U£C Estimates 

-. 

75 

All Dwelling 

Types 

----E::rSingle-Family 

Dwellings 

---0---

Average Temperature (Degrees Fahrenheit) 

Models: 

UEC(AII Owellinj!s) = 7053 - 69.75(Deg. F) 

UEC(Single Family) = 6881 - 61.23(Deg. F) 

R-Square Value: 

All Dwellings: 0.5062 

Single Family: 0.4286 

Figure 3-6. Conventional Water Heating (H20 heat exc. DW/CW) 

vs. Average Temperature 

should not simply borrow a water heating UEC 

from' another, At best, estimates from comparabl~ 

service areas could provide rough reference points 

for an analysis of water heating consumption. , 

E. , Refrigeration, ' 

1. General Specification 

This subsection focuses on energy consumption 

through refrigerators and freezers. Deriving UECs 

for refrigerators is very difficult as a consequence of 

the widespread ownership of this appliance. Saturations 'of primary refrigerators tend to be extremely' 

close to 100%; thus, it is extremely difficult to 

separate refrigerator' energy consumption from 

other universal end uses like lighting. Typically, 

CDA specifications are' designed to sort out incremental energy consumption resulting from a second 

,or subsequent refrigerator, or a frost-free unit (as 

opposed to a manual defrost unit). One standard 

specification could be wriften as: 

where R,i is the energy usage through a first refrigerator, SSRj indicates the- ownership of a second 

refrigerator, and f\ is the usage of the 'second 

refrigerator. 

Another common specification is: 

where R\i is the usage of a manual defrost refrigerator, SFFRi indicates the ownership of a frost-free 

unit, and f~ is the incremental L1.sage of a frost-free 

unit relative to a manual defrost refrigerator. 

Of course, other variations of these specifications 

could be used to distinguish among various numbers and types of refrigerators. The important thing 

to note 'is that Ro; and RJi are typically included in 

the constant term of the CDA model, along with 

miscellaneous end uses. It should also be recognized that CDA estimation of primary' refrigerator 

energy use would become much more feasible if 

appliance saturation surveys contained qilestions. 

about the characteristics of these appliances (e.g., 

size categories, frost-free features, designs, ages, 

etc.). 

3-15 
6

I

i !

I :

i

i

i

I

:uflfllarif eiJ UEC Etiiwtes

FREQUENCY

9

4

i 2

900-1099 1100-1299 1300-1499

UECs

(Usage of First Refrigerator or Average per Unit of All Refrigerators)

.. . i

3

I1

I

Figure 3-7. Distribution of Refrigerator UECs

Single-Family Dwellings and All Dwellings

FREQUENCY

11

9

5

3

2 2

< 700 700-899 900-1099 1100-1299 1300-1499 1500 +

UECs

Figure 3-8. Distribution of Freezer UECs

Single-Family Dwellings and All Dwellings

3-16

2

-

-- --- -- ......... __.----

5urnmaryn of UEC EstimatLes

< 900 1500-1699 1700 +

2

i

EE-RM-94-230 COMMENT 61D

::,:wllIll7ry of U EC E~tilllalL':; 

3-16 

FREQUENCY 

3 

; 1 

1 r( ------

i i 

< 900 900·1099 

9 

r----· 

6 

1100-1299 

• I 

; : 

II 

! : 

UECs 

1300-1499, 

4 

1500-1699 

(Usage of First Refrigerator or Average per Unit of All Refrigerators) 

FREQliENCY 

2 

Figure 3-7. Distribution of Refrigerator UECs 

Single-Family Dwellings and AII'Dwellings 

11 

9 

5 

2 --_._--_ ... _ .. , . 

: 1 

-----_. __ .. _._ ....... __ . 

< 700 700-899 900-1099 1100-1299 1300-1499 

UECs 

Figure 3-8_ Distribution of Freezer UECs 

Single-Family Dwellings and All Dwellings 

2 

1700 + 

/ 

3 

1500 + 
Summary of UEC Estimates

UECs for stand-alone freezers can generally be

modeled fairly simply. A traditional CDA specification would be:

(1S) UECFi = m,(EFFi

, EDC,)

where UECF; is the freezer UEC, EFFi reflects the

efficiency of the unit (as determined, for instance,

by defrost features), and EDCj represents salient

household characteristics. Often, upright freezers

are distinguished from chest freezers in CDA models. While multiple freezers are possible, relatively

little attention is paid to this complication in the

literature.

2. Summary of Estimated UECs

Tables 3-8 and 3-9 present summaries of estimated

refrigerator and freezer UECs for all homes and for

single-family homes. Figures 3-7 and 3-8 present

frequency distributions for the UEC estimates. The

range of UEC values found in CDA literature is

broader than one would hope. It is difficult to

compare these estimates as a consequence of the

wide differences in conditional demand model specifications. However, some general patterns are

revealed by the data:

* Manual defrost refrigerators appear to have

usage levels in the 770-1,300 kWh range.

* The increment in usage associated with a frostfree refrigerator is generally estimated to fall in

the 600-850 kWh interval.

* Estimates of UECs for first and subsequent

refrigerators (without regard to defrost features)

vary too much to produce a clear pattern. As

expected, though, second and subsequent units

generally have lower estimated consumption

levels than first units, with a difference of

roughly 300-400 kWh.

* If all of the estimates in Tables 3-8 and 3-9 were

converted to single values for refrigerators in

general (something that cannot be done with any

great precision, of course, without saturation

data), the range of UECs would be something on

the order of 1,200-1,500 kWh. Differences could

reflect weather variation along with differences

in household features.

* Estimated freezer UECs also cover a wide span;

however, a substantial portion of the estimates

fall in the 900-1,100 range.

Table 3-8

Refrigerator and Freezer UECS: All Dwellings

Incr. for

Manual Frost-Free First Subseq. All Ref.

Utility Refrig. Refrig.a Refrig. Refrig. per Unit Freez.

PGandE (87) - - 1.425 1.025

PGandE (86) - - - - 1,425 925

SDG&E (85) 772" 622b 1,227 - -927

SDG&E (84) 836 611 1,282 - - 880

TVA (86) -- -- - 1,595

FP&L (86) - - - 1.540 1,299

NYSEG (86) - - - 660 - 670

Gulf (86) - - 1,500 1,200 - 1,500

CommEd (87) - - - 1,400 1,150

JCP&L (87) - - 1,301 - 1,406

MetEd (87) - - 948 - 1,184b

NPC (86) - - - 1.621 - 1,045

RG&E (86) 1,297 802 1,939 1.500 1,891 1,209

APC (83) 852 - - - 1,576e

.1,809'

TNP (88) - - - 785 804

BG&E (88) - - - - 1256 1,225

a Incremental use over and above that for a manual defrost refrigerator.

b For first unit.

c Estimates total usage of 3.429 if two or more units are present.

d Estimates total usage of 3,111 if two or more units are present.

e Manual unit.

f Frost-free unit.

3-17

EE-RM-94-230 COMMENT 61D

L'ECs for stand-alone freezers can generallY be 

modeled fairly simply. A traditional CDA specification would be: 

,,'here UECFi is the freezer UEC, EFE reflects the 

efficiency of the unit (as determined, for instance, 

by defrost features), and EDCi represents salient 

household charaCteristics. Often, upright freezers 

are distinguished fr~m chest freezers in CDA models. While multiple freezers are possible, relatively 

little attention is paid to this complication in the 

literature. 

2. Summary of Estimated UECs 

Tables 3-8 and 3-9 present summaries of estimated 

refrigerator and freezer UECs for all homes and for 

single-family homes. Figures 3-7 and 3-8 present 

frequency distributions for the UEC estimates. The 

range of UEC values found in CDA literature is 

broader than one would hope. It is difficult to 

compare thes.e estimates as a consequence of the 

wide differences in conditional demand model specifications. However, some general patterns are 

revealed by the data: 

SUllllllary of UEC Estimates 

• Manual defrost refrigerators appear to haveusage levels in the 770-1,300 kWh range. 

• The increment in usage associated with a frostfree refrigerator is generally estimated to fall in 

the 600-850 kWh interval. 

• Estimates of UECs for first and subsequent 

refrigerators (without regard to de(rost features) 

vary too much to produce a clear pattern. As 

expected, though, second and subsequent units 

generally have lower estimated consumption 

levels than first units, with a difference of -

roughly 300-400- kWh. 

• If all of the estimates in Tables 3-8 and 3-9 were 

converted to single values for refrigerators in 

general (something that cannot be done with any 

great precision, of course, without saturation 

data), the range of UECs would be something on 

the order of 1,200-1,500 kWh. Qifferences could 

reflect weather \'ariation along with differences 

in household features. 

• Estimated freezer UECs also cover a wide span; 

howe\'er, a substantial portion of the estimates 

fall in the 900-1,100 range. 

Table 3-8 

, i 

Refrigerator and Freezer UECS: All Dwellings 

Utility 

PGandE (8?) 

PGandE (8!» 

SDG&E (85) 

SDG&E (84) 

TVA (86) 

FP&L (86) 

NYSEG (86) 

Gulf (86) 

CommEd (87) 

JCP&L (87) 

MetEd (87) 

NPC (86) 

RG&E (86) 

APC (83) 

TNP (88) 

BG&E (88) 

Manual 

Refrig. 

772 b 

836 

7,297 

Incr. for 

Frost-Free First 

Refrig.a Refrig. 

622 b 1,227 

611 1,282 

1,500 

1.301 

948 

802 1.939 

852 

a Incremental use over and above that for a manual defrost refrigerator. 

b For lirst unit. 

C Estimates total usage of 3.429 if two or more units are present. 

d Estimates total usage of 3.111 if two or more units are present. 

e Manual unit. 

I Frost.lre.:! unit. 

Subseq, 

Re~rig. 

660 

1,200 

: 

1.621 

1,500 

All Ref. 

per Unit 

1.425 

1.425 

1,540 

1,400 

1,891 

785 

1,256 

FreeL 

1,025 

925 

927 

880 

1,595 

1,299 

670 

1,500 

1,150 

l,406 b 

1,184 b 

1,045 

1,209 

1,5769 

'1,809' 

804 

1,225 

3-17 
Sumtinai of UEC Estimates

Table 3-9

Refrigerator and Freezer UECS: Single-Family Dwellings

Incr. for

Manual Frost-Free First Subseq. All Ref.

Utility Retrig. Refrig.a Refrig. Refrig. per Unit Freez.

PGandE (87) - - - - 1,475 1,026

PGandE (86) - - - 1,425 925

PGandE (86a) - - - -1,327 738

SDG&E (85) 1,007 480 - - 1.385 820d

1,225e

SDG&E (84) 836 611 - -1,317 880

TVA (86) - - - 1420

AP&L (84) - 995 - 933

1.043 e

LP&L (84) - 1.493 - - - 497a

1,441 e

MP&L (84) - 900 - - 787

1,113 e

NOPS (84) - 1.131 877

1,490e

FP&L (86) - - - - 1,561

CommEd (87) - - - - 1,510 1,250

VEPCO (87) - - 1,259 1,259b 1,238

NPC (86) - - 1,621 - 1,045

RG&E (86) 1.297c 802c 1,939C 1,500c -1,209c

Sierra (88) - - - - 810 574

AEP (88) - - - - 748 800

BG&E (88) - - - - 1,295 1.229

a Incremental use over and above that for a manual defrost refrigerator.

" For first unit.

c Estimates for age-specific groupings were the same for all residence types. The figures here were calculated on the

assumption that relative saturations are also the same across residence types.

d Manual defrost. e Frost-free.

3. Transferability of Results

The refrigerator and freezer UECs presented above

provide good reference points for consumption

through these end uses. To that extent and to the

extent that UECs are relatively unaffected by crossarea differences, the estimates are transferable

across service areas.

This does not mean that the UEC values shown

above are highly accurate estimates of true refrigeration consumption. In some respects, the treatment

of refrigerators is one of the weaker elements of

CDA literature. This is due partly to the high

saturation of these appliances and the typical lack

of information on refrigerator characteristics. Still,

the substantial consumption associated with this

appliance makes it an important focus for end-use

analysis, so the CDA technique, as well as appliance saturation survey designs, will have to

improve in this area.

3-18

F. Cooking

1. General Specification

Electric ranges/ovens can also be substantial consumers of electricity. A standard general specification is:

(19) UECCOOKi = m3(EDC i

, SEAS,, SMICRi)

where UECco)Ki: is the relevant UEC value, EDC,

contains household features that affect usage patterns, SEAS, is a seasonal variable that picks up

differences in usage patterns across seasons, and

SMICR; is a binary indicator of the presence of a

microwave oven. The latter term is expected to

have a negative effect on the usage of electric

ranges/ovens; however, it also uses energy itself.

EE-RM-94-230 COMMENT 61D

Summary of UEC Estimaks 

Table 3-9 

Refrigerator and Freezer UECS: Single-Family Dwellings 

Incr. for 

Manual Frost-Free First Subseq. All Ref. 

Utility Refrig. Refrig.B Refrig. Refrig. per Unit Freez. 

PGandE (87) , - - - - 1,475 1,026 

PGandE (86) - - - - 1,425 925 

PGandE (86a) - - - - 1,327 738 

SDG&E (85) l,007 tl '480 b - '- 1,385 820d 

l,225e 

SDG&E (84) 836 611 - - 1,317 880 

TVA (86) - - - - - 1,420 

AP&L (84) , - 995 - - - 933 d 

t,043 e 

LP&L (84) _. 1,493 - - - 497d 

1,441 e 

MP&L (84) - 900 - - - 787d 

l,l13e 

NOPS (84) - 1,131 - - - 877d 

l,490e 

FP&L (86) - - - - 1,561 .- CommEd (87) - - - - 1,510 1,250 

VEPCO (87) - - 1,259 - 1,259 tl 1,238 

NPC (86) - - - 1,621 - 1,045 

RG&E (86) 1,297c 802 c 1,939c 1,500c ,- 1,209c 

Sierra (88) - - - - 8tO 574 

AEP (88) - - - - 748 800 

BG&E (88) - - - - 1,295 1,229 

a Incremental use over and above that for a manual defrost refrigerator. 

tl For first unit. 

c Estimates for age· specific groupings were the same for all residence types. The figures here were calculated on the 

assumption that relative saturations are also the same across residence types. 

d Manual defrost. 

e Frost·free. 

3. Transferabiility of Results 

The refrigerator and freezer UECs presented above 

provide good reference points for consumption 

through these end uses. To that extent and to the 

extent that UECs are relatively unaffected by crossarea differences, the estimates are transferable 

across service areas. 

This does not mean that the UEC values shown' 

above are highly accurate estimates of true refrigeration consumption. In some respects, the treatment 

of refrigerators is one of the weaker elements of 

CDA literature. This is due partly to the high 

saturation of these appliances and the typical lack 

of information on refrigerator characteristics. Still. 

the substantial consumption associated with this 

appliance makes it a,n important focus for end-use 

analysis, so the CDA technique, as well as appliance saturation survey deSigns, will have to 

improve in this aJea. 

3-18 

F. Cooking 

1. General Specification 

Electric ranges/o\"ens can also be substantial consumers of electricity. A standard general specification is: 

where UECCOOKi: is the relevant UEC value, EDCi 

contains household features that affect usage patterns, SEAS! is a seasonal variable that picks up 

differences in usage patterns across seasons, and 

SMICRi is a binary indicator of the, presen'ce of a 

microwave oven. The latter term is expected to 

have a negative effect on the usage of electric 

ranges/ovens; however, it also uses energy itself. 
Sumimnry of UEC Estimates

As a result, an additional UEC equation is necessary for this appliance. A standard form is:

(20) UECM,,,, = m4(EDC,, SEAS,)

2. Summary of Estimated UECs

Estimated cooking and microwave UECs are presented in Tables 3-10 and 3-11. Figures 3-9 and 3-10

illustrate the distribution of UEC values generated

by the studies reviewed. The variation in range/

oven UECs is remarkable, with estimates from 310

to 1,498 kWh for all dwellings and 330 to 2,138

kWh for single-family homes. There are at least

three reasons for this variation. First, there is often

some confusion about the definition of various

cooking appliances and the determination of which

ones are actually present in the home. Electric

cooking appliances can be ranges, ovens, cooktops,

or a combination of cooktops and ovens. Furthermore, multiple ovens may be present. Second, electric ranges tend to be owned by relatively high

income households. If these households tend to be

more likely to own miscellaneous appliances not

explicitly accounted for in the analysis (e.g., VCRs,

home computers, etc.), the range UEC is likely to

be overstated. CDA models reviewed here differed

with respect to the coverage of specified appliances,

and mav have different degrees of bias. Third,

household cooking habits may differ across' service

areas. Households in moderate climates may be

more likely to cook out or eat out than those facing

inclement weather much of the year. For instance,

estimated UECs tend to be fairly low in the California and Florida service areas.

Microwave estimates are much more consistent,

with UECs in the vicinitv of 200 to 300 kWh being

estimated for this appliance.

3. Transferability of Estimates

The microwave UECs presented above are probably

reasonable estimates of the usage of this appliance.

These values sholid provide a fairly good estimate

of usage in other service areas as well. The general

cooking estimates. however, do not appear consistent enough to be transferred across service area

lines.

G. Pools and Spas

1. General Specification

Energy consumption for pool operation, which is

generally tied to pump operation as well as operaTable 3-10

Cooking UECS:'AII.Dwellings

Utility Range/Oven Microwave

PGandE (87) 375 175

PGandE (86) 625 200

SDG&E (85) 534 308

SDG&E (84) 376

TVA (86) 1,498

FP&L (86) 568 203

NYSEG (86) 600

Gulf (86) 1,015

El Paso (87) 866 687

CommEd (87) 310 280

MPC (84) 1.034

JCP&L (87) 926

MetEd (87) 782

NPC (86) 642

RG&E (86) 593a 162"

APC (83) 955

TNP (88) 1,060

BG&E (88) 610 200

a Without microwave. ,. V . b Incremental electricity use due to microwave ownership.

3-19

EE-RM-94-230 COMMENT 61D

As a result, an additional DEC equation is necessary for this appliarice. A standard form is: 

2. Summary of Estimated UECs 

Estimated cooking and microwave UECs are presented in Tables 3-10 and 3-11. Figures 3-9 and 3-10 

illustrate the distribution of DEC \'alues generated 

by the studies reviewed. The variation in range/ 

oven UECs is remarkable, with estimates from 310 

to 1,498 kWh .for all dwellings and· 330 to 2,138 

kWh for single-family homes. There are at least 

three reasons for this variation. First, there is often 

some confusion about the definition of various 

cooking appliances and the determination of which 

ones are 'actually present in the home. Electric 

.cooking appliances can be ranges, ovens, cooktops, 

or a combination of cooktops and ovens. Furthermore, multiple ovens may be present. Second, electric ranges tend to be owned by relatively high 

income households. If these households tend to be 

more likely to own miscellan'eous appliances not 

explicitly accounted for in the analysiS (e.g., VCRs, 

home computers, etc.), the range UEC is likely to 

be overstated. CDA models reviewed here differed 

with respect to the coverage of specified appliances, 

SlImmary of UEC EstimatL'~ 

and may ha\'e diiierentdegrees of bias. Third, 

household cookin~ habits may differ across' service 

areas. Households in moderate climates may be 

more likely to cook out or eat out than those facing 

inclement weather much of the year. For instance, 

estimated UECs tend to' be fairly low in the California and Florida service areas. . 

Microwave estimates are much more consistent, 

with UECs in the \icinity of 200 to 300 kWh being 

estimated for this appliance. 

3. Transferability of Estimates 

The microwa\'e CEes presented above are probably 

reasonable estima:es of the usage of this appliance. 

These values shodd provide a fairly good estimate 

of usage in other service areas as well. The general 

cooking estimates. however, do not appear consistent enough to l:-e transferred across service area 

lines. 

G. Pools and Spas 

1. General Specification 

Energy consumptlon for pool operation, which is 

generally tied to Fump operation as \" .. ell as operaTable 3-10 

Cooking UECS:'AII.Dwellings 

Utility Range/Oven Microwave 

PGandE (87) 375 175 

PGandE (86) 625 200 

SDG&E (85) 534 30.8 

SDG&E (84) 376 - TVA (86) 1,498 - FP&L (86) 568 203 

NYSEG (86) 600 - Gulf (86) 1:015 - EI Paso (87) 866 687 

CommEd (87) 310 280 

MPC (84) 1,034 - JCP&L (87) 926 - MetEd (87) 782 - NPC (86) 642 - , RG&E (86) 593 a 162b 

APC (83) 955 - TNP (88) , 1,060 - BG&E (88) 610 200 

a Without microwave. ;;;n--; , .... V6, 

b Incremental electricity use due to microwave ownership. 

3-19 
Table 3-11

Cooking UECS: Single-Family Dwellings

Utility Range/Oven Microwave

PGandE (87) 400 175

PGandE (86) 625 200

PGandE (86a) 566 212

SDG&E (85) 509 338

SDG&E (84) 376

TVA (86) 1.520

. AP&L (84)' 1.896

LP&L (84) 1,202

MP&L (84) 2,138

NOPS (84) 1,427

FP&L (86) 669

CommEd (87) 330 280

VEPCO (87) 1,243

NPC (86) 698

RG&E (86) 706 a 87b

Sierra (88) 848 284

AEP (88) 1,040

BG&E (88) 606 200

a Without microwave.

b Incremental electricity use due to microwave ownership.

FREQUENCY

13

I,

6

5

i~~~~~~~~~~~~~~~~~~~~~~~~~~~

"

I~~~~~~~~~~~~~~~~~~~~~~~~~~

4

2

300-499 500-699 700-899 900-1099 1100-1299 1300 +

UECs

Figure 3-9. Distribution of Range/Oven UECs

Single-Family Dwellings and All Dwellings

3-20

6

·.i i i i i

i I

f

Summnlry of UEC Estimates

EE-RM-94-230 COMMENT 61D

SUlIZmary U EC Estimates 

3-20 

Table 3-11 

Cooking UECS: Single-Family Dwellings 

Utility Range/Oven Microwave 

PGandE (87) 400 175 

PGandE (86) 625 200 

- PGandE (86a) 566 212 

SDG&E (85) 509 338 

SDG&E (84) 376 - TVA (86) 1.520 - .' AP&L (84)' 1.896 - 'LP&L (84) 1.202 - MP&L (84) 2.138 - NOPS (84) ~ 1.427 - FP&L (86) 669 - CommEd (87) 330 280 

- VEPCO (87) 1.243 - NPC (86) 698 - RG&E (86) 706a 87 b 

Sierra (88) 848 284 

AEP (88) 1.040 - BG&E (88) 606 200 

a Without microwave. C)~~ 

b Incremental electricity use due to microwave ownership. 

FREQUENCY 

13 , 

"I 

, 

6 

! 

6 

I 

i I i , 4 

i I ; 1 i 

! ; 

! 

i 2 

! I 

300-499 500·699 700·899 900·1099 1100·1299 

UECs 

Figure 3:'9. Distributio,n of Range/Oven UECs 

Single-Family Dwellings and All Dwellings 

fN,,\ 

5 

1300 + 
(21)' UECp,, = p, (SEAS,, WCi,,.MCi,, EDC,, CONS,)

w-here SEAS is an indicator of the season (summer,

winter, etc.) and CONS, is a set of pool-related

energy conservation measures.

A similar specification could be used for spas, with

the understanding that some spa usage may take

the form of spa heating:

Summary of UEC Estimates

FREQUENCY

9

3

1 I

r - ii ._. ..~~~~~~~~~~~~~~~~~~~~~

2

i i

. -i I

200-299

UECs

< 100 100-199 300-399 400 +

2. summary or tstimatea UEcs

.Table 3-12 provides estimates of pool and-spa

pumping UECs. Insofar as these appliances are not

particularly important in some service' areas, relatively few estimates of their UECs were uncovered.

Still, some generalizations can be extracted from

these estimates:

*Pool pumping takes roughly 1,700 to 2,700 kWh

in service areas with moderate winters and warm

Table 3-12

Pool and Spa Pumps: Single-Family Dwellings

Utility Spa Pumps Pool Pumps

PGandE (87) 1.350 2;125

PGandE (86) 925 2,225

PGandE (86a) 922 2,144

SDG&E (85) 707 2,125

SDG&E (84) - 1.766

FP&L (86) 1,202 2,759

NPC (86) 1,947a 3,711

RG&E (86) - 672

Sierra (88) 2.352 a

a Could include some electric spa heating.

3-21

Figure 3-10. Distribution of Microwave Oven UECs

Single-Family Dwellings and All Dwellings

tion of cleaning systems, could be specified as a (22) UECsp,, = p2(SEAS,, WC,, MC,, EOC,, CONS,)

function of several variables. A reasonable specification might be:

'---

,

----

I I I:

i .:I

EE-RM-94-230 COMMENT 61D

FREQUENCY 

3 

< 100 100-199 

9 

200-299 

UECs 

I 

',I 

2 

300-399 

Figure 3-10. Distribution of Microwave Oven UECs 

Single-Family Dwellings and All Dwellings 

Summary of UEC Estimates 

400 + 

tion of cleaning systems, could be specified as a 

function of several variables. A reasonable specification might be: 

2. Summary of Estimated UECs 

\"here SEAS is an indicator of the season (summer, 

winter, etc.) and CONS; is a set of pool-related 

energy conservation measures. 

A similar .speci.fication could be used for spas, with 

,Table 3-12 provides estimates of pool and 'spa 

pumping UECs. Insofar as these' appliances are not 

particularly important in some service' areas, relatively few ~stimates of their UECs were uncovered. 

Still, some generalizations can be extracted from 

these estimates: 

the unde'rstanding that some spa usage may take, • Pool pumping takes roughly 1,700 to 2,700 kWh 

the form of spa heating: in service areas with moderate winters and warm 

Table 3-12 

Pool and Spa Pumps: Single-Family Dwellings 

Utility Spa Pumps Pool Pumps 

PGandE (87) 1.350 2;125 

PGandE (86) 925 2.225 

PGandE (86a) 922 2.144 

SDG&E (85) 707 2.125 

SDG&E (84) - 1.766 

FP&L (86) 1,202 2.759 - NPC (86)' 1,947a 3.711 

RG&E (86) - 672 

Sierra (88) 2.352a -

a Could include some electric spa heating, 

3-21 
Suimmaryf oI' UEC E:stimates

summers. The low estimate for RG&E and the

high value for Nevada Power are unsurprising,

given the weather conditions in Rochester and

Las Vegas.

*Spa pumps appear to use approximately

700-1,350 kWh annually.

3. Transferability of Estimates

The spa and pool pump UECs are consistent

enough to be used as reference values in service

areas with similar weather conditions. Unfortunately, there are too few such estimates to provide

much help for most utilities.

H. Other Appliances

1. General Specification

Most conditional demand models also include a

variety of other UEC equations. The list of appliances covered by these equations depends upon

several factors, including the level of saturations,

the design of utility marketing programs, regulatory

directives, and so on. In general, these other UEC

equations include relatively few arguments - typically, household size, income, square footage, and

a few other variables.

2. Summary of Selected UEC Estimates

A full listing of all UEC estimates covered by the

analyses surveyed would be extremely tedious.

However, Tables 3-13 and 3-14 present estimates for

selected appliances. Figures 3-11 - 3-13 depict frequency distributions for dryer, dishwasher, and

color TV UECs. Examination of these estimates

suggests the following conclusions:

* Clothes dryer UECs vary sharply across studies.

This may reflect actual variations in usage levels,

but probably also indicates the impact of

differences in model specifications. Like electric

ranges, dryers may act as proxies for a host of

excluded appliances.

* Due largely to different ways of decomposing

dishwasher consumption (see our earlier

discussion on water heating specifications),

dishwasher UECs differ dramatically across

studies. If attention is restricted to studies

explicitly estimating motor loads, however, the

Table 3-13

Other UEC Estimates: All Dwellings

Clothes D'Wash C'Wash B&W TV Color TV

Utility Dryer Motor Motor all units all units

PGandE (87) 850 275 - - 350

PGandE (86) 975 400 - - 425

SDG&E (85) 527 287 449 118 530

SDG&E (84) 548 232 88 - 214

TVA (86) 1,265 1.566a 664a - -

FP&L (86) 751 965a 576a 154 270

NYSEG (85) 650 - 655a - 400

El Paso (87) 1,080 704 - - -

CommEd (87) 1,170 420 - - -

JCP&L (87) 973 1.218 a - - 380b

MetEd (87) 1,012 1.194a - -432 b

NPC (86) - 423a - - 498

RG&E (86) 828 93 80 160 c 534c

APC (83) 1,780 924 - . 900

TNP (88) - 228a - -

AEP (88) 1,159 823 303 - 690e

BG&E (88) 1,263 ;.023 509 - 853

a According to model specification. this would include some incremental electric water heating. Insofar as

some homes with the appliance have non-electric water heating, however, this estimate should not be

interpreted as the sum of the motor load and the incremental electric water heating load.

b For first unit.

c Derived from relative saturations with and without VCR present. d For households with no children and at least one non-working adult.

e All TVs.

3-22

EE-RM-94-230 COMMENT 61D

summers. Th<:! low estimate for RG&E and the 

high value for Nevada Power are unsurprising, 

given the weather conditions in Rochester and 

Las Vegas. 

• Spa pumps appear to use approximately 

700-1,350 kWh annually. 

3. Transferability of Estimates 

The spa and po~1 pump UECs are consistent 

enough to be used as reference values. in service 

areas with similar weather conditions. Unfortunately, there are too few such estimates to provide 

much help for most utilities. 

H. Other.Appliances 

1. General Specif:ication 

Most conditional demanq models also include a 

variety of other UEC equations. The list of appliances .covered by these equations depends upon 

several factors, including the level of sat!lrations, 

the design of utility marketing programs, regulatory 

directives, and so on. In general, these other UEC 

equations includ~ relatively few arguments - typically, household size, income, square footage, and 

a few other variables. 

2. Summary of Selected UEC Estim.ates 

A full listing of all UEC estimates covered by the 

analyses surveyed would be extremely tedious. 

However, Tables 3~13 and 3-14 present estimates for 

selected appliances. Figures 3-11 - 3-13 depict frequency distributions for drye·r, .dishwasher, and 

color TV UECs. Examination of these estimates 

suggests the following conclusions: 

• Clothes dryer UECs vary sharply across studies. 

This may reflect actual variations in usage levels, 

but probably also indicates the impact of 

differences in model specifications. Like electric 

ranges, dryers may act as proxies for a host of 

excluded appliances. . 

• Due largely to different ways of decomposing 

dishwasher consumption (see our earlier 

discussion on water heating specifications), 

dishwasher UECs differ dramatically across 

studies. If attention is restricted to studies 

explicitly estimating motor loads, however, the 

Table 3-13 

Other UEC Estimates: All Dwellings 

Clothes D'Wash C'Wash B&WTV Color TV 

Utility Dryer Motor Motor all units all units 

PGandE (87) 850 275 :- - 350 

PGandE (86) 975 400 - - 425 

SDG&E (85) 527 287 449 118 530 

SDG&E (84) 548 232 88 - 214 

TVA (86) 1,265 1.5668 6648 - - FP&L (Bll) 751 965a 5768 154 270 

NYSEG (85) 650 - 6558 - 400 

EI Paso (87) 1,080 7048 - - - CommEc! (87) 1,170 ~20 - - - JCP&L (a7) 973 1.2188 - - 380b 

MetEd (87) 1,012 1.1948 - - 432b 

NPC (86) - 423" - - 498 

RG&E (8.5) 828 93 60 160c 534c 

APC (63) 1,780 924 - - 900d 

TNP (88) - 228a - - - AEP (88) 1,159 823 303 - 690e 

8GIloE (BB) 1,263 ;.023 509 - 853 

a According to model specification. this would include some incremental electric water heating. Insofar as 

some homes with the appliance have non-electric water heating, however, this estimate should not be 

interpre·ted as the sum of the motor load and the incremental electric water heating load. 

b For first unit. 

c Derived from relative saturations with and without VCR present. 

. d For households with no children and·at least one non-working adult. 

e All TVs. 

3-22 
Sum:mary of UEC Estimates

Table 3-14

Other UEC Estimates: Single-Family Dwellings

Clothes D'Wash C'Wash B&W TV Color TV

Utility Dryer Motor Motor all units all units

PGandE (87) 900 275 - - 375

PGandE (86) 975 400 - - 425

PGandE (86a) 887 - - - 582

SDG&E (85) 585 286 403 127 575

SDG&E (84) 548 232 88 - 214

TVA (86) 1.619 1,803a 612 a

AP&L (84) 781 8 40a - - -

LP&L (84) 325 1,467 a - - -

MP&L (84) 1,696 948a - -

NOPS (84) 304 1700a - - -

FP.L (86) 783 - - 192 295

CommEd (87) 1,180 1,010 -

VEPCO (87) 654 253 - - 1.792

NPC (86) 906 4618 - - 541

RG&E (86) 833 98 81 174b 570b

Sierra (88) 728 559 -69 -

BG&E (88) 1,301 1,053 513 - 938

a According to model specification, this would include some incremental electric water heating: Insofar as

some homes with the appliance have non-electric water heating, however, this estimate should not be

interpreted as the sum of the motor load and the incremental electric water heating load.

b Derived using relative saturations with and without VCR, from data on all dwellings.

Figure 3-11. Distribution of Clothes Dryer UECs

Single-Family Dwellings and All Dwellings

3-23

FREQUENCY

8

7

6

'i

.j

|

i

I

4

i

2

300-499 500-699 700-899 900-1099 1100-1299 1300 +

UECs

o

ii

EE-RM-94-230 COMMENT 61D

Summary of UEC Estimates 

Table 3-14 

Other UEC Estimates: Single-Family Dwellings 

Clothes O'Wash C'Wash B&WTV Color TV 

Utility Dryer Motor Motor all' units all units 

PGandE (87) 900 275 - - 375 

PGandE (86) 975 400 - - 425 

PGandE (86a) 887 - - . - 582 

SDG&E (85) 585 286 403 127 575 

SDG&E (84) 548 232 88 - 214 

TVJ\ (86) 1.619 l,803a 612a - - AP~~L (84) 781 840a - - - LP&L (84) 325 1,467a - - - MPI~L (84) 1,696 948a - - - NOPS (84) 304 1,700a - - - FP8.L (86) 783 - - 192 295 

CommEd (87) 1,180 1,010 - - - VEPCO (87) 654 253 - - 1.792 

NPC (86) 906 461 8 - - 541 

RG~~E (86) 833 98 81 174D 570b 

Sierra (88) 728 559 -69 - - BG8:E (88) 1,301 . 1,053 513 - 938 

a According to model specification, this would include'some incremental electric water heating. Insofar.as 

sclme homes with the appliance have non-electric water heating, however, this estimate should not be 

interpreted·as the sum of the motor load and the incremental electric water heating load. 

b D«~rived using relative saturations with and without VCR, from data on all dwellings. 

FREQUE~CY 

.. 

8 

7 

6 

! 

5 

,.. 

2 .. 

. , . , 

,I 

, I 

300499 500-699 700-899 900.1099 1100-1299 

UECs 

Figure 3-11. Distribution of Clothes Dryer UECs 

Single-Family Dwellings and All Dwellings 

4 

1300 + 

3-23 
FREQUENCY

7

|

.1

.

i 4

i

3

I :

'.

iI

2

0

200-399 400-599 600-799

UECs

(Excluding those with some incremental water heating)

Figure 3-12. Distribution of Dishwasher Motor UECs

Single-Family Dwellings and All Dwellings

FREQUENCY

6

5 i

4

2

800-999 1000-1199

4

3,

I

200-299 300-399 400499 500-599 600-699

UECs

0

700-799. 800 +

Figure 3-13. Distribution of Color TV UECs

Single-Family Dwellings and All Dwellings

3-24

':,rin/loar/ of UEC Estimnatcs

' ' '

< 200

EE-RM-94-230 COMMENT 61D

S;,II/II/ary of UEC E:::tillll1tCS 

3-24 

FREQUENCY 

7 i-----· 

i I 

i I 

!.. 4 

2 2 ~----,i 1 I! 

II 

1 ! 

: i 

I' o 

< 200 200-399 400-599 600-799 800-999 

FREQUE!'\CY 

UECs 

(ExciudiDg those with some incremental water heating) 

Figure 3-12_ Distribution of Dishwasher Motor UECs 

Single-Family Dwellings and All Dwellings 

6 

5 

3, 

1 ----

. , o ---.-.--_ ..... _ .. __ .. _--_. __ .. --.--.... "- .--_._-_._ ... _-. - .... -... 

200-299 300-399 400-499 500-599 

UECs 

600-699 700-799. 

Figure 3-13. Distribution of Color TV UECs 

Single-Family Dwelltngs and All Dwellings 

3 

1000-1199 

4 

800 + 
Summary of UEC Estimates

results are relatively consistent. Most of these

estimates (those not marked with a footnote in

Tables 3-13 and 3-14) fall in the vicinity of

250 kWh.

* There is insufficient information on clothes

washer motor loads to warrant generalization.

* Black & white TVs (all units present) seem to use

120-200 kWh per year. Color TVs (again, all units

in the home) are generally estimated to use

between 400 and 600 kWh, although a few

estimates fall outside this range.

3. Transferability of Estimates

The estimated "Other" UECs offer some guidance

for the analyst. In the absence of area-specific estimates, the averages of appropriately defined UECs

from Tables 3-13 and 3-14 could act as starting

points for the analysis of end-use consumption

patterns.

3-25

EE-RM-94-230 COMMENT 61D

results are relal:ively consistent. Most of these 

estimates (those not marked with a footnote in 

Tables 3-13 and 3-14) fall in the vicinity of 

250 kWh. . .' 

• There is insufficient information on clothes 

washer motor loads to warrant generalization. 

• Black & white TVs (all units present) seem to use 

120-200 kWh per year. Color TVs (again, all units 

in the home) are generally estimated to use 

SUl1lmaryof UEC ~~/imates 

between 400 and 600 kWh, although a few 

estimates faIl outside this range. 

3. Transferability of Estimates 

The estimated "Other" UECs offer some guidance 

for the analyst. In the absence of area-specific estimates, the averages of appropriately defined UECs 

fromTabl~s 3-13 and 3-14 could act as starting 

points for the analysis of end-use consumption 

patt'erns. . 

3-25 
4

VALIDATION OF CDA ESTIMATES

A. Introduction

Conditional Demand Analysis is an extremely useful technique, insofar as it allows the analyst to

construct end-use consumption estimates relatively

inexpensively from widely available billing record

data. However, like any estimation technique, it is

subject to a range of uncertainty and potential

biases. Obviously, the analyst needs to be concerned about the degree to which these estimates

mirror actual household consumption patterns.

However, it is difficult to test directly :he correspondence between UEC estimates and actual enduse consumption, because metered data on

appliance consumption are not generally available.

Fortunately, some analysts have conducted validation studies in which reference data have been used

to assess the general accuracy of CDA estimates.

The results of some of those studies are reviewed in

this section.

Several approaches have been used to validate CDA

estimates, including:

1. comparison with engineering estimates

2. out-of-sample simulation of whole-house

consumption

3. comparison with appliance metering results.

Perhaps the least satisfying of these alternatives is

the first. Engineering estimates certainly provide useful reference information, and such estimates

should always be considered by the analvst. Thev,

offer a range of feasible values, but they do not

provide reliable point estimates that can be used to

test the accuracy of CDA estimates. Engineering

estimates are based on a variety of assumptions

concerning the efficiencies of appliances. the characteristics of the thermal shell, and the behavior of

households. Within reason, discrepancies between

engineering estimates and CDA estimates may simply reflect violations of these assumptions rather

than problems in the CDA approach.

Any CDA model will correctly predict, .,: the average, the total level of consumption of households in

the original data set used to estimate parameters of

the model. This feature follows from the nature of

regression analysis. Out-of-sample simulations entail

the application of the CDA model to data on households not used to estimate the model. These simulations are useful, in that they indicate the gereral

ability of CDA estimates to describe overall household consumption behavior. However, they do not

directly test UEC estimates associated with individual appliances.

Theoretically, the best test of the accuracy of CDA

estimates is their comparison with actual metered data

on individual appliances. Unfortunately, appliance

metering is very expensive, and metered end-use

information is seldom available. However, some

utilities have conducted experimental end-use

metering programs, and have used the results of

these programs to gain insights about the reliability

of CDA estimates. This section reviews three of

these studies.

B. Review of Comparisons With End-Use

Metering Data

In the course of the review of CDA studies, three

studies in which CDA estimates were explicitly

compared to end-use metering estimates were identified. In one of these, the comparisons were made

for a common group of households. In the others.

different but representative samples of households

were used. These studies are reviewed briefly

below.

1. The Pacific Gas and Electric Appliance Metering Study

In 1985 and 1986, Pacific Gas and Electric (PGandE)

collected both whole-house and end-use load data

on approximately 750 households as part of its

Appliance Metering Project (AMP). The analysis of

these data is described in Brodsky and McNicoli

4-1

EE-RM-94-230 COMMENT 61D

4 

VALIDATION OF CDA ESTIMATES 

A. Introducition 

Conditional Demand Analysis is an extrt'mely useful technique, insofar ·as it allows the ..:.nalyst to 

construct end-use consumption estimate;: relatively 

inexpensi\'ely from widely available bilEng record 

data. However, like any' estimation technique, it is 

subject to a range of uncertainty and potential 

biases. Obviously, the analyst needs !~1 be concerned about the degree to which these t'stimates 

mirror. actual household consumption patterns. 

However; it is difficult to test directly :he correspondence between UEC estimates and actual enduse consumption, because· metered data on 

appliance consumption are not generally available. 

Fortunately, some analysts have conducted validation studies in which reference data ha\re been used 

to assess the general accuracy of CDA estimates. 

The results of some of those studies are re\;ewed in 

this section. 

Several approaches have been used to validate CDA 

estimates, including: 

1. comparison v ... ith engineering estimate;: 

2. out-of~sampl,~ simulation of \\'hole-hou~e 

consumption 

age, the total level of consumption of households in 

the original data set used to estimate parameters of 

the model. This feature follows from the nature of 

regression anaiysis. Out-oj-sample sillllllatiOlIS entail 

the application of the CDA model to data on households not used to estimate the model. These simulations are useful, in that they indicate the general 

ability of CDA estimates to describe overall household consumption behavior. However, they do not 

directly test UEe estimates associated with individual appliances. 

Theoretically, the best test of the accuracy of CDA 

estimates is their comparison with actual metered data 

on i11dividual appliances. Unfortunately,· appliance 

metering is very expensive, and metered end-use 

information is seldom available. However, some 

utilities have conducted experimental end-use 

metering programs, and have used the results of 

these programs to gain insights about the reliability 

of CDA estimates. This section reviews three of 

these studies. 

B. Review of Comparisons With End-Use 

Metering Data 

3. comparison with appliance metering re~ults. In the course of the review of CDA studies, three 

Perhaps the leelst satisfying of these alte:natives is studies in which CDA estimates were explicitly 

the first. Engineerillg estil,zafescertainly pr~1\"ide use- compared to end-use metering estimates were idenful reference information, and such c:;timates tified. In one of these, the comparisons were made 

should always be considered by the ana!~·st. They -' for a common group of households. In the others. 

offer a range of feasible values, but th",y do not . different but representative samples of households 

provide reliable point estimates that can ~ used to were used. These studies are reviewed briefh' 

test the accuracy of CDA estimates. En~ineering below. 

estimates are based on a variet~· of as~umptions 

concerning the efficiencies of appliances. the char~· 

acteristics of the thermal shell, and the ~havior of 

households. Within reason, discrepancie~ between 

engineering estimates and CDA estimate~ may ·simply reflect \'iolations of these assumpti~'ns rather 

than problems in the CDA approach. 

Any CDA model will correctly predict, ,": the aver1. The Pacific Gas and Electric Appliance 

Metering Study 

In 1985 and 1986, Pacific Gas and Electric (PGandEI 

. collected both whole-house and end-use load data 

on approximately 750 households as part of its 

. Appliance Metering Project (AMP). The analvsis or 

these da ta is described in Brodsky and McNicoll 

·4-1 
(1987). While the report deals primarily with enduse load shapes, the implied UECs will be considered here.

As part of their analysis, PGandE compared AMP

UEC estimates to a set of UECs derived for the

same households using CDA. The latter estimates

were derived from a preliminary form of the CDA

model estimated with 1986 Residential Appliance

Saturation Survey (RASS) data. Both AMP and

CDA estimates were calibrated to 1985-1986 weather

conditions. The results of this comparison are presented in Table 4-1.

In reviewing the estimates contained in Table 4-1,

one should focus on the relative, rather than absolute, UEC values. The absolute values may differ

from typical UEC estimates for three reasons: first,

they are not weather-normalized; second, they pertain only to owner-occupied single-family dwellings; and third, the households monitored in AMP

may not be completely representative of the overall

PGandE customer population. Still, given that the

CDA and AMP estimates were developed for the

same group of households, their comparison yields

some extremely useful insights.'

I It is our understanding that CDA UECs were

developed for all households in the AMP

population, whereas not all appliances were

As shown in 'able 4-1, the PGandE CDA model

seems to overstate the UEC for conventional air

conditioning for the households in the sample. The

CDA estimate is roughly 35% higher than metered

consumption. Room AC consumption was understated, but by only 18c. To some extent, this could

be attributable to differences in the AMP air conditioning subsample and the overall AMP sample

(which was used to generate CDA estimates).

The overall heat pump UECs yielded by the two

approaches are strikingly similar, although the

decomposition into heating and cooling usage

yields some sharp differences. It should be noted

that this allocation of metered estimates required

the use of simplifying assumptions, so it is subject

to potential error.'

Water heating UECs are very similar, with differences amounting to only 11-13%. The CDA estimate

for conventional water heating is lower than the

AMP estimate, and the opposite occurs with solarassisted water heating. The two clothes dryer UECs

metered for every AMP household. Thus, there

are some differences between the households

covered by the two sets of estimates.

2 Fifty percent of the compressor usage and 10

percent of the heat strip usage was allocaied to

cooling; the rest was allocated to heating.

Table 4-1

Results of PGandE Appliance Metering Project

Annual UECs (kWh/year)

Metering Conditional

End Use Results Demand Estimate

Air Conditioning

Conventional Central 1,169 . 1,547

Room AC (All Units) 532 437

Heat Pumps

Total 4,221 3,905

Heating 2.406a 2.724

Cooling 1,815a 1,181

Water Heatingb

with Solar 2,561 2,862

without Solar 4,329 3.803

Refrigerator' 1,980 1.486

Clothes Dryer 798 863

Cooking 656 392

a PGandE estimated the split of heat pump usage by allocating compressor

and heat strip usage.

3 Includes incremental water heating loads associated with dishwashers

and clothes washers.

c First unit. Note that the sample in question included a high percentage of

frost-tree units, so the UEC may not be representative of the broader

population.

4-2

'Valitntionw (of CDA Estimates

EE-RM-94-230 COMMENT 61D

Validati'lIl of Estill/at/!::; 

(1987). While the report deals primarily with end· 

use load shapes, the implied UECs will be consid· 

. ered here. 

As part of their analysis, PGandE compared AMP 

UEe estimates to a set of UECs derived for the 

same households using CDA. The latter estimates 

.were derived from a preliminary form of the· CDA 

model estimated with 1986 Residential Appliance 

Saturati9n SurvE~y (RASS) data. Both AMP and 

CDA estimates were calibrated to 1985-1986 weather 

conditions. The results of this comparison are presented. in Table 4-1. 

(n reviewing the estimates contained in Table -l-1, 

one should focus on the relative, rather than absolute, UEC values. The absolute values may differ 

from typical UEC estimates for three reasons: first, 

they are not weather-normalized; second, they pertain only to owner-occupied single-family dwellings; and third, the households monitored in AMP 

may not b-e completely representative of the 'overall 

PGandE customer population. Still, given .that the 

CDA and AMP E!stimates were developed for the 

same group of households, their comparison yields 

some extremely useful i~sights.l 

1 [t is our understanding that CDA UECs were 

developed for all households in the AMP 

population. whereas not all appliances. were 

As shown in Table 4~1, the PGandE CDA model 

seems to overstate the UEC for conventional air 

conditionin:.; for the households in the sampf~. The 

CDA estimate is roughly 35'70 higher than metered 

consumption. Room AC consumption was understated, but by only 18'70. To some extent, this CQuld 

be attributable to differences in the AMP air conditi(lnin~ subsample and t.he overall AMP sample 

(which was used to generate CDA estimates). 

The overall heat pump UECs yielded by the two 

approaches arc strikingly similar, although the 

decomposition into heating and cooling us~ge 

yields somc sharp differences. It should be noted 

that this allocation of metered estimates required 

the use of simplifying assumptions, so it is subject 

to potential error.: . 

Water hcatinf.; UECs are very si1l1ilar, with differences amounting to only 11-13'70. The CDA estimate 

for conventional water heating is lower than the 

AMP estimate. and the opposite· occurs with solarassisted .watcr heating. The two clothes dryer UECs 

md~'r~'d fur ('\ .... ry AMP household. Thlis, there 

art! S(IIllI.' diffl'rl.'nces between the households' 

CQvt!rl.'d by tlw two sets of estimates. 

2 Fifty pt!rn'nt of the compressor usage and 10 

pt!r<'l'nt (If th .. 11<'<1t strip usage was allocatee to 

coolin);; th(' rl'sl was allocated to heating. 

Table 4-1 

4-2 

Results of PGandE Appliance Metering Project 

Annual UECs (kWh/year) 

Metering Conditional 

End Use Results Demand Estimate 

Air Conditioning 

. Conventional Central 1,169 1,547 

Room AC (All Units) 532 437 

Heat Pumps 

Total 4,221 3,905 

Heating 2,406a 2,724 

Cooling l,815 a ·1,181 

Water Healingb 

with Solar 2,561 2,862 

without Solar 4,329 3,803 

Refrigeratof 1,980 1,486 

Clothes Dryer 798 863 

Cooking 656 392 

a PGandE estimated the split of heal pump usage by allocating compressor 

and heat strip usage. 

o 1n<;ludes incremental water heating loads associated with dishwashers 

and clothes washers. 

C First unit. Note that the sample in question included a high percentage of 

frost-free units. so the UEC may not be representative 01 the broader 

population. 
Validation of CDA Estimates

are very similar. The CDA model seems to perform

very well for these end uses.

Refrigerator usage differs appreciably across the

two techniques, with the CDA model yielding an

estimate 25% lower than the average metered load.

Two factors may explain at least part of this difference. First, households with metered refrigerators

in the AMP population had a higher saturation of

frost-free units than the overall AMP population

covered by the CDA estimates. Second, there may

be a definitional difference here. The AMP UEC is

for the primary refrigerator, whereas the CDA

value is apparently defined for all refrigerators.

Second and subsequent refrigerators probably use

less energy. They are opened less frequently and

arguably are less likely to be frost-free.

The cooking UECs differ fairly sharply across the

two analyses. This is a bit puzzling, given

that PGandE's cooking UECs cited earlier (see

Table 3-10) are very close to the AMP estimate. The

discrepancy indicated by Table 4-1 may stem from

the use of a preliminary CDA specification for the

derivation of the CDA UEC. There could also be a

difference in the specific definition of the cooking

appliance covered by the UEC estimates.

The implications of the PGandE comparison are not

completely clear. Accuracy, like beauty, is in the

eves of the beholder. Most of the CDA UECs are

very similar to the metered values, and most of the

large discrepancies are probably attributable to differences in definition or sample composition. Furthermore, while PGandE's CDA models are

generally among the most sophisticated available,

the model used for the comparison was a preliminary specification. On the other hand, a few large

disparities were found, and this may suggest some

problems inherent in the CDA approach. Certainly,

it indicates that further improvements in the CDA

specification are needed. The feedback provided

by metering experiments should aid this process

considerably.

2. The TVA Submetering Study

TVA conducted a submetering study in 1979-1980.

While some of the appliance-specific sample sizes

were small, the estimated UECs provide a frame of

reference for a set of TVA CDA estimates developed for the same time period. A comparison of

estimates is presented in Table 4-2.

The central air conditioning UECs are very similar

across the two studies, with the conditional

demand approach yielding only slightly higher values. However, note that the metered cooling load

(which represents 80 households) could be an

underestimate, insofar as it is based on recorded

loads during only the June-September period.

Window air conditioning UECs are somewhat different between the two studies, with metered loads

being considerably higher. According to Jackson

(1985), though, only 20 window units were

included in the metered sample, and.the sample

mav have been unrepresentative of households

with window units.

The UECs for electric furnaces are very close. The

CDA estimate is less than 7% higher than the

Table 4-2

Results of TVA Submetering Study

Annual UECs (kWh/year)

Metering Conditional

End Use -Results Demand Estimate

3,431 b

Central Air Conditioning 3.240 a

3.320c

Window Air Conditioning 1.830a 1,478

Electric Furnaces 5,675 a 6.068

Electric Range '895 1,013

Dryer 991 1,650

a Cooling consumption was measured during the period June through

September.

= Conventional central air conditioning.

c Heat pump air conditioning.

= Heating usage covers the period December through February.

4-3

EE-RM-94-230 COMMENT 61D

are very similar. The CDA model seems to perform 

very well for these end uses. 

" 

Refrigerator usage differs appreciably across the 

two techniques, with the CDA model yielding an . 

estimate 25% lower than the average metered load. 

Two factors may explain at least part of this differ-

~nce. First, households with metered refrigerators 

In the AMP population had a higher saturation of 

frost-free units than the overall AMP population 

covered .b~ .. the CDA estimates. Second, there may 

be a defImtIonal difference here. The AMP UEC is 

for the primary' refrigerator, whereas the CDA 

\'alue is apparently defined for all refrigerators. 

Second and subsequent refrigerators probably use 

less energy. They are opened less frequently and 

arguably are l,ess likely to be frost-free. 

The cooking UECs differ fairly sharply across the 

two analyses. This is a bit puzzling, given 

that PGandE's cooking UECs cited earlier (see 

Table 3-10) are very close to the AMP estimate. The 

discrepancy indicated by Table 4-1 may stem from 

the use. of a preliminary CDA specification f~r the 

derivation of the CDA UEe. There could also be a 

diffe:ence in the specific definition of the cooking 

applIance cove'red by ·the UEC estimates. 

The implications' of the PGandE comparison ar~ not 

comple.tely clear. Accuracy, like beauty, is in the 

eyes ot the beholder. ~10st of the CDA UECs are 

very similar to the metered values, and most of the 

I.arge discrepancies are probably attributable to difterences in definition or sample composition. Furthermore, while PGandE's CDA models are 

generally among the most sophisticated available, 

Validation of CDA Estimates 

J 

the model used for the comparison was a preliminary specification. On the other hand, a few large 

disparities were found, and this may suggest some 

problems inherent- in the CDA approach. Certainly, 

it indicates. that further improvements in the CDA 

specification are needed. The feedback prodded 

by metering experiments should aid this process 

considerably. 

2. The TV A Submetering Study 

TVA conducted a submetering study in 1979-1980. 

While some of the appliance-specific sample 5izes 

were small, the estimated UECs provide a frame of 

reference for a set' of TV A CDA estimates develop~d for .the same time period. A comparison of 

eshma.tes IS presented in Table 4-2. 

The central ~ir conditioning UECs are very similar 

across the two studies, with the conditional 

demand approach yielding only slightly higher \'alues. However, note that the metered cooling load 

(which. represents 80 households) could be an 

underestimate, insofar as it is based on recorded 

loa'ds during only the June-September period. 

Window air conditioning UECs are somewhat different between the two studies, with metered loads 

being considerably higher. According to Jackson 

. (1985), though, only 20 window u;'its ,,'ere 

included in the metered sample, and .the sample 

may have been unrepresentative of households 

with window units. 

The UECs tor electric furnaces are verY close. The 

CDA estima te is less than 7% high~r than the 

Table 4-2 

Results of TV A Submetering Study 

Annual UECs (kWh/year) 

Metering Conditional 

End Use . Results Demand Estimate 

3,431 b 

Central Air Conditioning 3.240a 

3.320c 

Window Air Condilioning 1,830 a 1.478 

Electric Furnaces 5.675° 6.068 

Electric Range '895 1.013 

Dryer 991 1,650 

a 

. Cooling consumptiOn was measured during lhe period June through 

September. 

::: Conventional central air conditioning. 

c ~eat pump air conditioning. 

c Heating usage covers the period December through February. 

4-3 
Validation of CDA Estimates

metered load. Since only 12 households were

metered, though, this correspondence is not

conclusive.

The electric range estimates are reasonably close,

although the conditional demand estimate is somewhat higher. The CDA dryer estimate seems to be

overstated substantially. It is much higher than the

metered estimate (which is based on a large sample

consisting of 90 households). Electric ranges and

clothes dryers tend to be luxury appliances, so their

high CDA UEC estimates suggest that they act as

proxies for other appliances omitted from the CDA

model.

In general, the TVA comparison indicates that CDA

provides UEC estimates very close to those yielded

by end-use metering. Given that the samples

underlying the two sets of estimates were different

and given that the metering samples were fairly

small, this is not conclusive evidence of the accuracy of CDA. However, it is fairly reassuring for

analysts who do not have the option to access enduse metered data.

3. The Sierra Pacific Energy Information

Project

Sierra Pacific Power Company (SPPC) has also conducted an end-use metering study, and has compared end-use metered loads *to CDA UEC

estimates. The.comparison is presented in a report

by Wright and Puckett (19SS).

The SPPC conditional der-and analysis was conducted using 1986 Home Energy Survey (HES) data

on roughly 2,800 households. End-use metering

estimates were compiled in the course of SPPC's

Energy Information Project (EIP.Appliances in !

roeugtly--6homnes- were metered as part of that

istudy. - -- _ v

A comparison of the UECs yielded by these two

studies is presented in Table 4-3. As shown, some

of the differences are substantial. Appliance

metering yields a space heating UEC less than half

the size of the conditional demand estimate. The

metering estimate seems quite low though, given

the low winter temperatures in the service area.

The central air conditioning UEC yielded by enduse meters differs from the CDA estimate by 25%.

Given the small metered sample, this difference

does not appear problematic. Based on the estimates reviewed earlier in Section 3, the conditional

demand and metering estimates seem reasonable

for the weather conditions found in the Sierra

Pacific service area. The room air conditioning

UECs are sharply different, but the metered load

may be the result of a small sample size..

Water heating UECs are reasonably close, with the

metered estimate being slightly higher. Dishwasher

loads (presumably motor and booster loads,

although the documentation on the specification is

too general to be sure) differ dramatically. Given

the wide variation in the CDA estimates presented

earlier in Section 3, it is difficult to evaluate the

relative merits of these two estimates.

Table 4-3

Sierra Pacific Validation Study

Annual UECs (kWh/year)

Metering Conditional

End Use Resultsa Demand Estimateb

Space Heat 1.21/sq.ft. 2.76;sq.ft.

Cent'=. Air Cond. 1,123 1.500

Roor Air Cond. 23 346

Wale- Heat 1.150/person 918/person

Disha sherc 81 559

Refrigerators 1.160/unit 810/unit

Freezes 1,022/unit 574/unit

Cook-C 484 848

Micrcoave 77 284

a De-.ed from Energy Information Project.

b Se.eral sets of estimates were derived in the course of the project. The

ores used here are the results of the random-coefficient regression

(RCR) analysis.

C Or me basis of the specification. this should represent motor loads only.

4-4

EE-RM-94-230 COMMENT 61D

, Validatioll of CDA Estimatt'S 

metered load. Since onl~' 12 households were 

metered, though, this correspondence is not 

conclusive. 

The electric range estimates are reasonably close, 

although the (onditional demand estimate is some-

"'hat higher. The CDA dryer estimate seems to be 

overstated substantially. It is much higher than the 

metered estimate (which is based on a large sample 

consisting of 90 household:.). Electric ranges and 

clothes dryers tend to be luxury appliances, so their 

high CDA UEC estimates suggest that they act as 

proxies for other 'appliances omitted from the CDA 

model. 

In general, the TVA comparison indicates that CDA 

provides UEC estimates very close to those yielded 

'by end-use metering. Gi\'en that the samples 

underlying the t\,·o sets of estimates were different 

and given that the metering samples were fairly 

small, this is not conclusiye evidence of the ~ccuracy of CDA. HLw;·ev.er, it is fairly reassurIng for 

analysts who do not have the option to access enduse metered d,ata. 

3. The Sierra Pacific Energy Information 

Project 

Sierra Pacific Power Compa:1Y (SPPC) has also conducted an end-use meterins study, and has compared end-use metered loads to CDA UEC 

estimates. The ,comparison is presented in a report 

by Wright and Pu.:kett (198:;'). 

The SPPC conditional der:~.md analvsis \Vas conducted using 1986 Home Energy Survey (HES) data 

on roughly 2,800 households. End-use metering 

estimates were compiled in the course of SPPC's 

Energy Information Project J!.!.EWAppliances in: 

\ r6tlghl) .e.g homes-'werernetered as part of that \ . 

i.study. :-------.- / ~' 

A comparison of the UECs yielded by these two 

studies is presented in Table 4-3. As shown, some 

of the differences are substantial. Appliance 

metering yields a space heating UEC less than half 

the size of the conditional demand estimate. The 

metering estimate seems quite low though, given 

the low winter temperatures in the service area. 

The central air conditioning UEC yielded by enduse meters differs from the CDAestimate by 25%. 

Given the small metered sample, this difference 

does not appear problematic. Based on the estimates reviewed earlier in Section 3, the conditional 

demand and metering estimates seem reasonable 

for the weather conditions found in the Sierra 

Pacific service area. The room air conditioning 

UECs are sharply different, but the metered load 

may be the result of a small sample size. ' 

Water heating UECs are reasonably close, with ~he 

metered estimate being slightly higher. Dishwasher 

loads (presumably motor and booster loads, 

although the documentation on the specification is 

too general to be sure) differ dramatically. Given 

the wide variation in the CDA estimates presented 

earlier in Section 3, it is difficult to evaluate the 

relative merits of these two estimates. 

Table 4-3 

Sierra Pacific Validation Study 

Annual UECs (kWh/year) 

Metering Conditional 

End Use Resultsa Demand Estimateb 

Spac; Heat 1.21/sq.tt. 2.76iSq.tt. 

Cent·a. Air ,Condo 1.123 1.500 

Roor- ';ir Condo 23 346 

Wate' Heat 1.1 SO/person ' 918/person 

OishlOo:sher 81 559 

Refri;;,;rators 1.160/unit 8iO/unit 

FreeHrs 1.0221unit S74/unit 

Coo<'-C;; 484 848 

MicrC' ... ave 77 284 

a Oe--':ed from Energy Information Project. 

0 . Se.eral sets of estimates were derived in the course of the project. The 

or,* used here are the results 01 the random·coefficient regression 

(R:::~) analysis. 

c Or :ne basis of the specification. this should represent motor loads only. 
Validation of CDA Estimates

The refrigerator UECs are fairly consistent,

although both seem a bit low compared to values

derived for other service areas. Freezer UECs differ

substantially, with the CDA estimate being quite a

bit lower than those presented earlier for other

service areas. Finally, cooking and microwave estimates differ significantly across the two analyses.

The lessons to be taken from the SPPC Study are

unclear. The large discrepancies in UECs are disconcerting, but some aspects of the study may

explain these results. First, the sample size for the

metering experiment is small, and one must exercise caution generalizing from it. Second, the UEC

estimates were derived for different sets of households, and may not be directly comparable. Third,

the CDA model seems to have a fairly spartan

specification., with few interaction terms. In general,

UEC estimates (especially, it seems, for cooking and

dishwashers) are fairly sensitive to model specifications. A more fully specified model might yield

CDA estimates more in line with appliance

metering results.

4. Summary

The three studies cited above offer interesting information, but do not allow a final judgment on the

accuracy of Conditional Demand Analysis. They do

suggest that considerably more work needs to be

done to validate CDA estimates with end-use

metering experiments. Ideally, future studies may

generate comparable estimates for identical groups

of households (something PGandE very nearly did

in its analysis). Further, it would be desirable for

such studies to attempt to trace the source of

differences between metered and predicted UECs to

specific aspects of CDA analysis, like model specification, data quality, and estimation techniques. If

the source of these differences can be understood,

it may facilitate improvement of Conditional

Demand methodology.

4-5

EE-RM-94-230 COMMENT 61D

The r.efrigerator UECs 'are fairly consistent, 

although both seem a bit low compared to values 

derived for other service areas. Freezer UECs differ 

substantially, with the CDA estimate being quite a 

bit 'Iower than those presented earlier for other 

service areas. Finally, cooking and microwave estimates differ significantly across the two a~alyses. 

The lessons to be taken from the SPPCstudy are 

unclear. The large discrepancies in UECs are disconcerting, but some aspects of the study may. 

explain thes,e results. First, the sample size for the 

metering experiment is small, ~nd one must exercise caution generalizing from it. Second, the UEC 

estimates were derived for different sets of households, and may not be directly comparable. Third, 

the CDA model seems to have a fairly spartan 

specification .. with few interaction terms. In general, 

UEC estimates (especially, it seems, for cooking and 

dishwashers} are fairly sensitive to model specifications. A m<ire fully specified model might yield 

Validation of CDA Estimates .. 

CDA estimates more in line with appliance 

metering results. 

4. Summary 

The three studies cited above offer interesting information, but do not allow a final judgment on the 

accuracy of Conditional Demand Analysis. They do 

suggest that considerably more work needs to be 

done to validate CDA estimates with end-use 

metering experiments. Ideally, future studies may 

generate comparable estimates for identical groups 

of households (something PGandE very nearly did 

in its analysis). Further, it would be desirable for 

such studies to attempt to trace the source of 

differences between metered and predicted UECs to 

specific aspects of CDA analysis, like model specifi" 

cation, data quality, and estimation techniques. If 

the source of these differences can be understood, 

it may facilitate improvement. of Conditional 

Demand methodology. 

4-5 
5

PRICE AND INCOME ELASTICITIES

A. Introduction

This section reviews estimates of price and income

elasticities developed through the use of Conditional Demand Analysis. Both income and price are

key variables in any theoretical model of household

energy consumption. Ideally, end-use price and

income elasticities, representing the responsiveness

of appliance consumption to changes in prices and

income, could be estimated as part of the CDA

modeling process. These elasticities could be very

valuable in the application of CDA model results to

long-term forecasting and the analysis of appliance

efficiency changes. This review of CDA studies

indicates that this application of the methodology

has not been developed very fully.

Most CDA models include an income term in one

or more of the UEC specifications, often with constraints on the relative impacts of income across

end uses. In the CDA studies reviewed, however,

income effects were generally not translated into

elasticities, and insufficient information was available to develop such estimates. Thus, this section

does not present a particularly broad, range of

estimates. Further, separate end-use income elasticities were virtually never presented, even when the

specification allowed these elasticities to vary across

end uses.

Price variables are less common in CDA specifications, for the simple reason that most data bases

used to estimate these models do not exhibit

enough price variation to support the estimation of

price impacts. As a result, this section reports only

a handful of price elasticity estimates. When current

energy prices are included in CDA models, they are

used typically to estimate a single overall price

elasticity, rather than.end-use-specific price elasticities. Thus, evidence on end-use sensitivities is

extremely scant.

B. Theoretical Issues

1. Issues

Before presenting estimated income and price elasticities, it might be useful to review a few theoretical issues relating to these measures. Three issues

seem particularly important in this context:

* the dimension of consumer behavior

encompassed by the elasticity measure;

* the specification of the price term;

* the estimation technique.

These issues are considered briefly below.

2. Dimension of Behavior

In a full-blown model of household behavior,

income and energy prices would be hypothesized

to affect appliance purchases, investments in conservation devices, and appliance utilization. A comprehensive model like REEPS takes all three aspects

of behavior into account. Stand-alone CDA analysis

focuses on a limited range of household responses,

the specific coverage depending upon the specification of the model and the kinds of data used in its

estimation.

In general, there are three kinds of energy consumption elasticities: utilization, efficiency, and

ownership elasticities. Utilization elasticities reflect

changes in appliance utilization, given appliance

ownership, appliance features, and the characteristics of the dwelling. Efficiency elasticities capture

induced changes in efficiencies and conservation

measures (e.g., the effect of increased energy prices

on appliance efficiency levels, installation of insulation, set-back thermostats, etc.). Ownership elasticities refer to induced changes in appliance holdings.

To the extent that appliance stocks are taken as

given in CDA, the impact of income and prices on

appliance purchases is obviously ignored. The impacts

of prices and income on conservation decisions may or

may not be reflected in CDA-based elasticities. If

conservation measures are included explicitly in the

model and taken as given, price elasticities will not

reflect these influences. If conservation measures

are excluded from the model, price and income

impacts will probably capture some of the effect of

conservation activities on household consumption.

In most studies, these price impacts may be mod5-1

EE-RM-94-230 COMMENT 61D

5 

PRICE j~ND INCOME ELASTICITIES 

A. Introduction 

This section reviews estimates of price and income 

elasticities developed through the use· of Conditional Demand Analysis. Both income and price are 

key variables in any theoretical model of household 

energy consumption. Ideally, end-use price and 

income elasticities, representing the responsiveness 

of appliance consumption to changes in prices and 

income, could be estimated as part of the CDA 

modeling process. These elasticities could be very 

valuable in the application of CDA model results to 

long-term forecasting and the analysis of appliance 

effiCiency changes. This review of CDA studies 

indicates that this application of the methodology 

has not been developed very fully. 

Most CDA models include an income term in one 

or more of the UEC specifications, often with constraints on the relative impacts of income across 

end· uses. In the CDA studies reviewed, however, 

income effects were generally not translated into 

elasticities, and insufficient information was available to develop sud; estimates. Thus, this section 

does not present a particularly broad. range of 

estimates. Further, separate end-use income elasticities were virtu.ally never presented, even when the 

speCification allowed these elasticities to vary across . end uses. 

Price variables are less common in CDA specifications, for the simple reason that most data bases 

used to estimate these models do not exhibit 

enough price variation to support the estimation of 

price impacts. As a result, this section reports only 

a handful of price elasticity estimates. When current 

energy prices are included in CDA models, they are 

used typically to estimate a single overall .price 

elasticity, 'rather than· end-use-specific price elasticities .. Thus, evidence on end-use sensitivities is 

extremely scant. 

B. Theoretical Issues 

1. Issues 

Before presenting estimated income and p.rice elasticities, it might be useful to review a few theoretical issues relating to these measures. Three issues 

seem particularly important in this context: 

• the dimension of consum.er behavior 

encQmpassed by the elasticity measure; 

• the specification of the price term; 

• the estimation technique. 

These issues are considered briefly below. 

2. Dimension of Behavior 

In a full-blown model of household beha\'ior, 

income and energy prices would be hypothesized 

to affect appliance purchases, im-estments in conservation devices, and appliance utilization: A comprehensive model like REEPS takes all three. aspects 

of behavior into account. Stand-alone CDA anah-sis 

focuses on a limited range of household responses, 

the specific coverage depending upon the specification of the model and the kinds of data used in its 

estimation. 

In general, there are three kinds of energy (onsumption elasticities: utilization, efficiency, and 

ownership elasticities. Utilization elasticities retlect 

changes in appliance utilization. given appliance 

ownership, appliance features, and the characteristics of the dwelling. Efficiency elasticities capture 

induced changes in efficiencies and conservation 

measures (e.g., the effect of increased energy prices 

on appliance efficiency levels, installation of insulation, set-back thermostats, etc.). Ownership elasticities refer to induced changes in appliance holdings. 

To the extent that appliunce stocks are taken as 

given in CDA, the impact of income and prices on 

npplinllce purchases is obviously ignored. The imracts 

of prices and income on consen.'atioll decisiolls mayor 

may not be reflected in CDA-based elasticities. If 

conservation measures are included explicitly in the 

model and taken as glven, price elasticities will not 

reflect these influences. If conservation measures 

are excluded from the model, price and income 

impacts will probably capture some of the effe~ of 

conservation activities on household consumption. 

In most studies, these price impacts may be mod5-1 
ri.- ,: ;d In.come Elasticities

est, given the likely lags in the response of conservation behavior; however, elasticities estimated

partly from cross-sectional variation in prices could

incorporate a substantial conservation response. If

the analysis controls for appliance efficiencies and

thermal integrities, the resultant CDA price and

income elasticities can be considered utilization

elasticities. If efficiencies and shell characteristics

are ,wt incorporated into the model, however, the

estimated elasticities will be a blend of utilization

and efficiency elasticities.

It should be noted that estimated income and price

elasticities derived for unspecified appliance consumption could capture the impacts of these variables on the ownership of unspecified appliances

(ownership elasticities). The estimated impact of

household income, for instance, could derive partly

from its influence on ownership of VCRs and other

small appliances. Price elasticities are not likely to

be strongly affected by this phenomenon when

price varies only over time, but they may be

affected in CDA studies with cross-sectional variation in prices (e.g., the TVA study to be discussed

later).

In general, CDA-based elasticities include impacts

on appliance utilization. Even here, however, individual specifications determine the interpretation of

estimated elasticities. If thermostat settings are

included explicitly as exogenous regressors, the

influence of price and income on these settings is

not captured by price and income terms. Still,

elasticities derived from most conditional demand

specifications reflect utilization (and possibly efficiency) impacts.

Whether the elasticities should be interpreted as

long- or short-run measures is also open to interpretation. Income elasticities, which are derived

from cross-household differences in income and

consumption, reflect long-run (utilization and perhaps efficiency) responses. The interpretation of

price elasticities depends on the source of price

variation in the data base. If a CDA study uses a

data set in which price varies across households

(say, where several service areas are included or

where several rate zones are covered), the estimated price elasticities will reflect long-run utilization responses. If a study makes use of data in

which price varies only over time and where a.

relatively short time frame is covered by the data,

the resultant elasticities should probably be interpreted as short-run measures.

3. Specifications of the Price term

a. Measurement of Price

The literature on energy demand deals at some

'length with the appropriate specification of price

variables. (See, for example, Taylor, 1975.) Electricity rates generally involve a variety of charges and

rate blocks. Including all features of a statutory rate

schedule (fixed charges, block rates, block widths,

etc.) as price variables is not generally feasible, so

analysts typically are forced to summarize these

rate schedules in one or two variables. Several

options are available in this regard. The most popular price variants are discussed below.

Household-Specific Average Price. One option is

to use the household-level average price, defined as

the household's electricity bill divided by its total

consumption. The fact that this variable is a comprehensive indicator of the features of the rate

schedule is both a blessing and a curse. On one

hand, households may focus on a simple measure

like average price (or even just the total bill) when

looking at rate information. However, average price

may not recognize the different influences of specific features of the rate schedule. Fixed customer

charges, for instance, should have income effects

but no substitution effects, whereas marginal rates

should have both effects. Since substitution effects

are likely to be much stronger than income effects,

fixed charges may not be particularly relevant to

consumption decisions.

Furthermore, a household's average price is determined partly by its own consumption as a result of

the use of fixed charges and varying marginal rates.

Thus, any association between average price and

quantith (the stuff of which estimated price elasticities are made)'would reflect the structure of rates as

well as true demand responses. From a statistical

standpoint, observed average price is endogenous

and is correlated with the error term in the CDA

model; as a result, elasticities based on its use may

be biased and inconsistent. As indicated in subsection 4 below, though, there are means of alleviating

this problem.

Household-Specific Marginal Price. The

household's marginal rate may convey more information directly relevant to energy choices. Even

though its exclusive use ignores the income effects

of fixed charges and intramarginal rates (rates on

consumption up to the marginal block), these

income effects are probably small since energy

expenditures are a small portion of income for most

5-2

EE-RM-94-230 COMMENT 61D

est, given the likely lags in the response of conservation behavior; however, elasticities estimated 

partly from cross-sectional variation in prices could 

incorporate a substantial conservation response. If 

the analysis controls for appliance efficiencies and 

thermal integrities, the resultant CDA price and 

income elasticities can be' considered utilization 

elasticities. If efficiencies and shell characteristics 

are 'wt incorporated into .the model, however, the 

estimated elasticities will be a blend of utilization 

and efficiency eblsticities. 

It should be noted that estimated income and price 

ela:sticities derived for unspecified appliance consumption could capture the impacts of these variable:s on· the ownership of unspecified appliances 

(ownership elasticities). The estimated impact of 

hou:sehold income, for instance, could derive partly 

from its influence on ownership of VCRs and other 

small appliances. Price elasticities are not likely to 

be strongly affected by this phenomenon when 

price varies only over time, but they may be 

affected in CDA studies with cross-sectional variation in prices (e.g., the TVA study to be discussed 

later). 

In general, CDA~based elasticities include impacts 

on appliance utilization. Even here, however, individual specifications determine the interpretatio'n of 

estimated elasticities. If thermostat settings are 

included explicitly as exogenous regressors, the 

intluence of price and income on these settings is 

n('lt captured by price and income terms. Still, 

elasticities derived from most conditional demand 

specifications reflect utilization (and possibly efficiency) impacts. . 

Whether the elasticities should be interpreted as 

10ng- or short-run measure~ is. also open to interpretation. Income elasticities, whkh are derived 

from cross-housE:hold differences in income and 

consumption, reflect lon'g-run (utilization and perhaps efficiency) responses. The interpretation of 

price elasticities depends on the source of price 

variation in the data base. If a CDA study uses a 

data set iri which price varies across households 

(say, where several service areas are included or 

where several relte zones are covered), the estimated price ~lasticities will reflect long-run utilization responses. If a' 'study makes use of data in 

which price varies only over time and where a. 

relatively short time frame is covered by the data, 

the resultant elasticities should probably be inter-

. preted as short-run measures. 

5-2 

3. SpeCifications of the Price term 

a. Measurement of Price 

The literature on energy demand deals at some 

. length with the appropriate specification of price 

vari.ables. (See, for example, Taylor, 1975.) Electricity rates generally involve a variety of charges and 

rate blocks. Including all features of a statutory rate 

schedule (fixed charges, block rates, block widths, 

etc.) as price variables is not generally feasible, so 

analysts typically are forced to summarize these 

rate schedules in one or two variables. Several 

options are available in this regard. The most popular price variants are discussed below. . 

Household-Specific Average Price. One option is 

to use the household-level average price, defined as 

the household's electricity. bill divided by its total' 

consumption. The fact that this variable is a comprehensive indicator of the features of the rate 

schedule is both a blessing and a curse. On one 

hand, households may focus on a simple measure 

like average price (or even just the total bill) when 

looking at rate information. However, average price 

may not recognize the different influences of specific features of the rate schedule. Fixed customer 

charges, for instance, should have income effects 

but no substitution effects, whereas marginal rates 

should have both effects. Since substitution effects 

are likely to be much stronger than income effects, 

fixed charges may not be particularly relevant to 

consumption decisions. . 

Furthermore, a household's average price is determined partly by its own consumption 'as a result of 

the 'use of fixed charges and varying marginal rates. 

Thus, any association between average price and 

quantity (the stuff of which estimated price elasticities are made)"would reflect the structure of rates as 

well as true demand responses. From a statistical 

standpoint, observed average price 'is endogenous 

and is correlated with the error term in the CDA 

model; as a result, elasticities based on its use may 

be biased and inconsistent. As indicated in subsection 4 below, though, there are means of alleviating 

this problem. 

Household-Specific Marginal Price. The 

household's marginal rate may convey more information directly relevant to energy choices. E \'en 

though its exclusive use ignores the income effects 

of fixed charges and intramarginalrates (rates on 

consumption up to the marginal block), these 

·income effects' are probably small since energy 

expenditures are a small portion of income for most 
Price and Income Elasticities

households. Additionally, if the income effect is

thought to be substantial, an additional term can be

entered to reflect fixed charges and the relevant

portion of intramarginal expenditures.

The use of household-specific marginal price could

entail the same statistical problems as average price.

If the household's marginal price depends upon the

level of consumption (that is, there is more than

one marginal rate), the use of that specific marginal

rate will cause simultaneity bias. In subsection 4, a

standard approach to mitigating this problem is

considered.

Effective Price. Another alternative found in CDA

literature is a variable that can be called an effective

price. It is defined as the average price at a reference

level of consumption. Because this variable is defined

without regard to the individual household's own

level of consumption, it does not cause the statistical bias discussed above. It does have weaknesses,

of course, one of which is that it does not indicate

the structure of marginal rates facing households.

But it is probably a reasonable choice on practical

grounds.

Tail-Block Rates. Tail-block rates, defined as the

marginal rate at the highest consumption block, are

also used in CDA models. Like the effective price,

tail-block rates are truly exogenous to the

household's consumption. However, they do not

provide information on the remaining characteristics of the rate schedule.

entails no special statistical problems. As noted

above, however, the use of household-specific price

variables will cause simultaneity bias. Consequently, an estimation technique designed to mitigate

this problem should be used.

In the case of the household-specific price variable,

a two-stage least-squares approach is generaliv

used. In the first stage, the average price is

regressed on a series of characteristics of the rate

structure, household appliance stocks, and other

variables considered exogenous to the model. The

estimated first-stage equation is used to construct

predicted values for average price, and these predicted values are used to represent price in the

CDA model.

If a household-specific marginal price is used, a

somewhat different technique is often recommended. Since marginal price takes on only a limited

number of values (i.e., the statutory marginal

rates), an estimation technique designed for limited

dependent variables should be used. A logit model

could be constructed, for instance, to predict the

probabilities that a given household will fall into

the different marginal rate classes, based on its

appliance stock and other characteristics. These estimated probabilities could be used to construct an

expected marginal price (the probability-weighted

average of individual marginal rates). This expected

marginal price is then used in the CDA model.

b. Timing of Price and Income Impact

The timing of price impacts can be an important

factor in the construction of CDA models. To the

extent that price and income variables are meant to

pick up impacts on appliance utilization, current

values (or a value lagged a month or two) could be

appropriate. However, if the impacts of income and

price on decisions concerning the integrity of the

thermal shell and/or major appliance efficiencies is

being considered, the levels experienced at the time

of construction may be more relevant. Designing a

construction-period price variable requires extensive

historical price data as well as information on structural vintage. Developing a construction-period

income variable does not seem feasible at all.

4. Estimation Techniques

As long as price and income are truly exogenous to

the CDA model, estimating the relevant elasticities

C. Review of Elasticities

1. Overview

In this subsection, price and income elasticities

found in the review of recent conditional demand

studies are summarized. Onlv elasticities associated

with current (as opposed to construction-period)

income and prices are covered, primarily because

they are far more plentiful in the literature.

2. Overall Elasticities

First, consider whole-house price and income elasticities - i.e., the elasticities of whole-house consumption with respect to these variables. Table 5-1

provides a summary of the whole-house elasticities

presented in the studies reviewed. (Note again that

these are mixes of utilization and efficiency elasticities.) As shown, the variation in estimates is considerable. To some extent, though, this is explained

5-3

EE-RM-94-230 COMMENT 61D

households. Additionally, if the income effect is 

thought to be substantial. an additional term .can be 

entered to rE~flect fixed charges and the relevant 

portion of intra marginal expenditures. 

The use of household-specific marginal price could 

enta'il the same statistical problems as average price. 

If the household'!5 marginal price depends upon the 

level of consumption (t.hat is, there is more than 

one marginal rate), the use of that specific marginal 

rate will cause simultaneity bias. In subsection 4, a 

standard approach to mitigating this problem is 

considered. 

Effective Pric,e. Another alternative found in CDA 

literature is a variable that can be tailed an effective 

price. It is defined as the average price at a reference 

level of cOllsllmption. Because this variable is defined 

without regard to the individual household's own 

level of consumption, it does not cause the statistical bias discussed above. It does have weaknesses, 

of course, one of which is that it does not indicate 

the structure of marginal rates facing households. 

But it is prob.ably a reasonable choice on practical 

grounds. 

Tail-Block Rates. Tail-block rates, defined as the 

margi.nal rate at the highest consumption block, are 

also' used in CDA models. Like the effective price, 

tail-block rates are truly exogenous to the 

household's consumption. However, they do not 

provide information on the remaining characteristics of the rate schedule. . 

b. Timing of Price and Income Impact 

The timing of price impacts can be an important 

factor in the construction of CDA models. To the 

extent that price and income variables are mea'nt to 

pick up impacts on appliance utilization, current 

values (or a value lagged a month or two) could be 

. appropriate. However, if the .impacts of income and 

price on decisions concerning the integrity of the 

thermal shell and/or major appliance efficiencies is 

. 'being considered. the levels experienced at the time 

of construction may be more relevant. [)esigning a 

cOllsfruction-period price variable requires extensive 

historical price data as well as information on structural vintage. Developing a construction-period 

income variable does not seem feasible at all. 

4. Estimation Techniques 

As long as price and income are truly exogenous to 

the CDA model, estimating the relevant elasticities 

Price alld Income Elasticifi{'::; 

entails no special statistical problems. As noted 

above. however. the use of household-specific price 

variables wiU cause simultaneity bias. Consequently, aA estimation technique designed to mitigate 

this problem should be .used. 

In the case of the household-specific price variable. 

a two-stage least-squares a pproach is generaliy 

used. In the first stage, the average price is 

regressed on a series of characteristics of the rate 

structure, household appliance stocks, and other 

variables considered exogenous to' the model. The 

estimated first-stage equ'ation is used to construct 

predicted values for average price, and these predicted values are used to represent price in the 

CDA model. 

If a household-specific marginal price is used, a 

somewhat different technique is often recommended. Since marginal price takes on only a limited 

number of values (Le., the statutory marginal 

rates), an estimation techniql!.e designed for limited 

. dependent variables should be used. A logit model 

could be constructed, for instance, to predict the 

probabilities that a given household will fall into 

the different marginal rate classes, based on its 

appliance stock and other characteristics. These estimated probabilities could be used to construct an 

expected marginal price (the probability-weighted 

average of individual marginal rates). This expected 

marginal price is then used in the. CDA model. 

C. Review of Elasticities 

1. Overview 

In this subsection, price and income elastici.ties 

found in the review of recent· conditional demand 

studie's are summarized, 'Onlv elasticities associated 

with ClIl"rclit (as opposed t~ construction-period) 

income and prices are covered, primarily because 

they are far more plentiful in the literature • 

2. Overall Elasticities 

First, consider whole-house price and income elasticities - i.e., the elasticities of whole-house consumption with respect to these variables. Table 5-1 

provides a summary of the whole-house elasticities 

presented in the studies revie\,·ed. (Note again that 

these are mixes of utilization and efficiency elasticities.) As shown, the variation in estimates is considerable. To some extent, though, this is explained 

5-3 
Price and .lcorim Elasticities

by the time dimension underlying the estimates.

The TVA and TNP estimates are based partly on

cross-section variation in prices and consumption

across the respective service areas. As a result, they

tend to encompass fuller price responses than those

based strictly on temporal variations in prices. The

other elasticities presented in Table 5-1 fall in the

range -0.14 to -0.32.

Estimated income elasticities are summarized in

Table 5-2. While the listing is hardly comprehensive

in that it covers only five studies, the results are

fairly consistent. The evidence suggests that the

overall income elasticity of electricity consumption

is small.- something on the order of 0.1.

3. End-Use Elasticities

Relatively few conditional demand studies derive

end-use price elasticities. The primary reason for

the paucity of such estimates is that few conditional

demand data sets incorporate enough price variation to support this level of detail in estimation. On

the other hand, most studies estimate end-use

impacts of variations in income; however, these

estimates are seldom published in the form of

elasticities. As a consequence, only scant evidence

is available on end-use income and price elasticities.

Two studies in which end-use price and income

elasticities were estimated are reviewed below.

The first study to be considered is an analysis

conducted by the Texas Public Utility Commission

using data on residential customers of the TexasNew Mexico Power Company (TNP). Apparently,

there was enough variation in prices across the

service area and over time (a period of only 12

months) to allow the estimation of end-use price

elasticities as part of a conditional demand analysis.

Price and income elasticities derived from the TNP

study are presented in Table 5-3. As shown, there

is a substantial amount of variation in price elasticities across the end uses considered. Evaporative

coolers and non-central electric resistance heating

have the highest price elasticities, with estimates on

the order of -0.7 to -0.8. Water heating and conventional central air conditioning have moderate elasticities, with values close to -0.5. Other covered end

uses have comparatively low price elasticities. In

general, the estimated end-use income elasticities

from the TNP study do not satisfy theoretical

expectations.

The second study of end-use price and' income

elasticities was conducted for San Diego Gas &

Electric, and made use of a series of saturation

surveys and an associated 58 months of consumption. The length of the consumption history and the

associated variation in market conditions allowed

Table 5-1

Overall Price Elasticities

(Current Prices)

Service Area Price Actual or Estimated

(Study Year) Variable Predicted Elasticity

TVA (1985) marginala actual tail-block -.28 to -.89

Nevada Power (1986b) marginal actual -.14

Rochester Gas and Electric (1986) average actual -.21

San Diego Gas and Electric (1986) average predicted -.32

effective actual -.29

a Tail-block rate.

Defined as the average price at a fixed reference level of consumption.

Table 5-2

Overall Income Elasticities

Service Area

(Study Year) Income Elasticity

Nevada Power (1986) .11

Rochester Gas & Electric (1986) .10

TVA (1985) .01-.18

San Diego Gas & Electric (1986) .10

5-4

EE-RM-94-230 COMMENT 61D

Price a lid. Il/colI/e flast icit ies 

by. the time dimension underlying the estimates. 

The TVA and TNP estimates' are based partly on 

cross-section variation in prices and consumption 

across the' respective service areas. As a result, they 

tend to encompass fuller price responses than those 

based strictly (In temporal variations in prices. The 

other elasticities presented in Table 5-1 fall in the 

range -0.14 to -0.32. 

Estimated income elasticities are summarized in 

Table 5-2. While the listing is hardly comprehensive 

in that it covers only five~tudies, the results are 

fairly consistent. The evidence suggests that the 

overall income elasticity of electricity consumption 

is small.~ sOniething dn the order of 0.1. 

3. End-Use Elasticities 

Relatively few conditional demand studies derive 

end~use price elasticities.IThe primary reason for 

the paucity of such estimates is that few conditional 

demand data sets incorporate enough price variation to support this level of detail in estimation. On 

the other hand, most studies estimate end-use 

impacts of variations in income; however, these 

estimates are seldom published in the form of 

elasticities. As a consequence, only scant evidence 

is available on end-use income and price elasticities. 

Two studies in which end-use price and income 

elasticities were estimated are reviewed below. 

The first study to be considered is an analysis 

conducted by the Texas Public Utility Commission 

using data on residential customers of the TexasNew Mexico. Power Company (TNP). Apparently, 

there was enough variation in prices across the 

service area and overtime (a period of only 12 

months) to allow the estimation of end-use price 

elasticities as part of a conditional demand analysis. 

Price and income elasticities derived from the TNP 

study are presented in Table 5-3. As shown, there 

is a 'substantial amount of variation in price elasticities across the end uses ·considered. Evaporative 

coolers and non-central electric resistance heating 

have the highest price elasticities, with estimates on 

the order of -0.7 to -0.8. Water heating and conventional central air conditioning have moderate elasticities, with values close to -0.5. Other covered end 

uses have comparatively low price elasticities. In 

genera\, the estimated. end-use income elasticities 

from the TNP study do not satisfy theoretical 

expectations. 

The second study of end-use price and'in.come 

elasticities was conducted for San Diego Gas & 

Electric, and made use of a series' of saturation 

surveys and an associated 58 months of consumption. The length of the consumption history and the 

associated variation in market conqitions allowed 

Table 5-1 

5-4 

Overall Price Elasticities 

(Current Prices) 

Service Area Price Actual or 

(Study Year) Variable Predicted 

TVA (1985) marginal" actual tail-block 

Nevada Power (1986b) marginal aClual 

Rochester Gas and Electric (1986) average actual 

San Diego Gas and Electric (1986) average predicted 

effective b actual 

" Tail-block rate. 

o Defined as the average price at a fixed reference level of consumption. 

Estimated' 

Elasticity 

-.28 to -.89 

-.14 

-.21 

-.32 

-.29 

~.----------------------------------------------------------~ 

Table 5-2 

. Overall Income Elasticities 

Service Area 

(Study Year) 

Nevada Power (1986) 

Rochester Gas & Electric (1986) 

TVA (1985) 

San Diego Gas & Electric (1986) 

Income Elasticity 

.11 

.10 

.01:- .18 

.10 
Price and Income Elasticitics

Table 5-3

End-Use Price and Income Elasticities

Texas-New Mexico Study

Price Income

End Use Elasticity Elasticity

Air Conditioning

Central

Conventional -0.52 -0.09

Heat Pump -0. 7 -0.02

Evaporative Cooler -0.6' -0.11

Room

Conventional 0.02 -0.02

Space Heating

Central

Resistance -0.! 0.02

Heat Pump -0.;. 0.02

Non-Central

Resistance -0.72 0.05

Water Heater . -0.53 -0.03

the estimation of end-use price elasticities. The

results of the analysis are presented in Table 5-4.

Refer first to the end-use price elasticity estimates.

As shown, all of the specified end-use price elasticities are fairly high, ranging from -0.35 for unspecified uses, to -1.2 for room air conditioning. The

reason that the overall elasticity is relatively low

(-0.29) is that no significant price elasticity was

found for many end uses (refrigeration, clothes

dryers, cooking, etc.). To some extent, the variation

in end-use price elasticities can be explained bv

income effects.' When present, air conditioning,

' A price change is sometimes said to have two

types of impacts. The income effect operates

through the effect of the price change on the

household's real (price-adjusted) income level;

the substitution effect is transmitted through the

impact of a price change on relative prices. For

normal goods, these effects reinforce each other.

In general,. the income effect is likely to be

largest when expenditures on a good are

substantial.

electricity space heating, and pool pumping are

heav\ users of electricity; thus, one would expect

the income effect associated with price changes to

be relatively strong for these end uses. Furthermore, the end-use elasticities may indicate something about the extent to which certain end uses are

considered discretionary by households. Water

heating and lighting (the latter a major element of

unspecified usage) are not generally considered discretionary. However, pool pumping and space conditioning may be treated as more flexible uses of

power. Pool pumping schedules can be altered;

thermostat settings can be changed.

The end-use income elasticities follow an irregular

pattern. Space heating and pool pumping elastici-.

ties are high, which makes sense from a theoretical

perspective. The water heating elasticity is zero,

something that also makes sense intuitively. The

elasticity on unspecified end uses is higher than

average, perhaps because of the impact of income

Table 5-4

End-Use Price and Income Elasticities

San Diego Gas & Electric Study

Price Income

End Use Elasticity Elasticity

Space Heating -.82 .30

Central AC -1.0C . .07

Room AC -1.20 -.22

Pool Pump -1.09 .45

Conventional Water Heat -.45 .00

Unspecified -.35. .18

All Other End Uses -.OC .00

All End Uses . -.2- _ .10

5-5

EE-RM-94-230 COMMENT 61D

Price a 1/d 11/come Elast lei lics 

Table 5-3 

End-Use Price and Income Elasticities 

Texas-New Mexico Study 

End Use 

Air Conditioning 

Central 

Conventional 

Heat Pump 

Evaporative Cooler 

Room 

Conventional 

Space Heating 

Central 

Resistance 

Heat Pump 

Non-Central 

Resistance 

Water Heater 

the esttmation of end-use price elasticities. The 

results of the analysis are presented in Table 5-4. 

Refer first to the end-use price elasticity estimates. 

As shown, all of the specified end-use price elasticit~es are fairly high, ranging from -0.35 for unspecifled uses, to -1.2 for room air conditioning. The 

reason that the overall elasticity is relatively low 

(-0.29) is that no significant price elasticity was 

found for many end uses (refrigeration, clothes 

dryers, cooking, etc.). To some extent, the variation 

in end-use price elasticities can be explained bv 

income effects. I When present, air conditioning, 

I . A price chang~ is sometimes said to hdve two 

t~'pes of imp''';ts. The income effect operates 

through the etfect of the price change on the 

household's real (price-adjusted) income le\'el; 

~he substituti(~n effect is transmitted through the 

Impact of a price change on relative prices. For 

normal goods, these effects reinforce each other. 

In general,. the income effeCt is likelv to be 

largest when l~xpenditures on a good are 

substantial. 

Price Income 

Elasticity Elasticity 

·0.52 -0.09 

-0.;; -0.02 

·O.S· -0.11 

0.02 ·0.02 

-0.1:: 0.02 

-0. i:: 0.02 

·0.72 0.05 

-0.52 -0.03 

ele.:tricity space heating, and pool pumping are 

hea\'Y users of electricity; thus, one wo.uld expect 

the income effect associated with price changes to 

be relatively strong for these end uses. Furthermore, ~he end-use .elasticities may indicate something about the extent to which certain end uses are 

considered discretionary by households. Water 

heating and lighting (the latter a major element of 

un~pecified usage) are not generally considered discretionary. However, pool pumping and space conditioning may be treated as more flexible uses of 

pov\er. Pool pumping 'schedules can be altered; 

the!TIlostat settings can be changed. 

The end-use income elasticities foHow an irregular 

pat::ern. Space heating and pool pumping elastici-. 

ties are high, which makes sense from a theoretical . 

perspective. The water heating elasticity is zero, 

something that also makes sense intuitively. The 

elasticity on unspecified end uses is higher than: 

a\'e:-age, perhaps because of the impact of income 

Table 5-4 

. End-Use Price and Income Elasticities 

San Diego Gas & Electric Study 

Price 

End Use Elasticity 

Space Heating ·.S2 

Central AC -1.00 

Room AC ·1.20 

Pool Pump -1.~ 

Conventional Water Heat ·'.45 

Unspecified -.3.5 

All Other End Uses -.OC 

All End Uses -.2': 

Income 

Elasticity 

.30 

.07 

-.22 

.45 

.00 

.18 

.00 

.10 

5-5 
Price and Income Elasticities

on the ownership of unspecified appliances (VCRs,

personal computers, etc.). Air conditioning income

elasticities are somewhat of a puzzle. The elasticity

for central air conditioning is unexpectedly low,

and the elasticity for room air conditioning is negative. The former may suggest that income affects

cooling consumption through the appliance choice

decision but not appreciably through utilization.

Given that room air conditioner saturations are

highest in multifamily dwellings, the negative

income elasticity for this end use could indicate that

higher income households living in these dwellings

tend to be home less often than their lower income

counterparts.

4. Summary

Conditional demand studies can yield useful evidence on income and price elasticities. Income elasticities (both overall and end-use specific) can be

derived in a fairly straightforward fashion on the 

basis of cross-sectional variation in household

income and consumption levels. Price elasticities

can be obtained if sufficient variation in prices is

found in the data base.

In general, the evidence from conditional demand

studies suggests that overall income elasticity of

appliance utilization is low - something on the

order of 0.1. Estimates of overall utilization price

elasticity vary from -0.14 to -0.89, with values at the

upper end of this range derived from studies with

cross-sectional price variation.

Evidence on end-use income and price elasticities is

scant. Based on the two studies reviewed here,

there appears to be considerable variation in elasticities across end uses. It seems safe to say that much

more work needs to be done on this issue before

other more useful generalizations can be made.

5-6

EE-RM-94-230 COMMENT 61D

Price alld Illcoml! Elasticities 

on the ownership of unspecified appliances (VCRs, 

personal computers, etc.). Air conditioning income 

elasticities are somewhat of ~ puzzle. The elasticity 

for centra! air conditioning is unexpectedly low, 

and the elasticity for room air conditioning is negative. The former may suggest that income affects 

cooling consumption through. the appliance choice 

decision but not appreciably through utilization. 

Given that room air conditioner saturations are 

highest in multifamily dwellings, the negative 

income elasticit:y for this end use could indicate that 

higher income households living in these dwellings 

tend to be home less often than their lower income 

counterparts. 

4. Summary 

Conditional d€'mand studies can yield useful evidence on income and price elasticities. Income elasticities (both overall and end-use specific) can be 

5-6 

derived in a fairly straightforward fashion on the 

basis of cross-seCtional variation in household 

income and consumption levels. Price elasticities 

can be obtained if sufficient variation in prices is 

found in the data base. 

l.n general, ~e evidence from conditional demand 

studies suggests that overall income elasticity-of 

appliance utilization is low - something on the 

order of 0.1. Estimates of overall utilization price 

elasticitv varY from -0.14 to -0.89, with values at the 

upper ~nd (;t this range derived from studies with 

cross-sectional price variation. 

E\~idence on end-use income and price elasticities is 

scant. Based on the two studies reviewed here~ . 

there appear~ to be considerable variation in elasticities across end uses. It seems safe to say that much 

more work needs to be done on this issue before 

other more useful generalizations can be made. 
6

SUMMARY AND CONCLUSIONS

A. Overview

This report presents a summary of residential conditiona'l demand studies conducted over the last

five years. The report reviews the specification of a

conditional demand model, summarizes estimates

of end-use consumption levels, discusses price elasticitv estimates, and considers attempts to validate

conditional demand estimates through comparisons

with metered data.

B. Comparisons of UEC Estimates

Section 2 describes a general conditional demand

model, explains the use of the model to infer UECs,

and discusses a number of theoretical and empirical

issues. Section 3 outlines the specification of individual UEC functions and summarizes estimates of

UECs developed by several utilities. As shown

there, the interpretation of UEC estimates depends

critically on the specification of the UEC equations.

In general, careful design requires consistency with

thermodynamic principles and recognition of the

interaction of various factors affecting household

consumption.

The UEC estimates presented in Section 3 were

drawn from a large.number of analyses conducted

at the utility service territory level. Taken together,

they provide a comprehensive picture of residential

end-use consumption behavior in a variety of settings. Of course, these estimates vary across studies

because of variations in weather, market conditions,

and customer characteristics, as w'ell as fundamental differences in model specification. A crude

attempt was made to account for the influence of

weather in comparisons of weather-sensitive UECs.

Space heating, air conditioning, and water heating

UECs were mapped against degree-day and temperature measures to account for the role of weather.

Several conclusions and recommendations arise

from the comparison of UEC estimates:

* Most of the variation in space heating and air

conditioning UECs across studies is attributable

to differences in weather. Once the influence of

weather is considered, estimated UECs seem

reasonably consistent across service areas.

Clearly, CDA space conditioning estimates from

other service areas, adjusted for differences in

weather conditions, provide useful reference

points for CDA analysts.

* Estimated water heating UECs are not

particularly consistent across service areas, even

after differences in weather conditions are

considered. This may be due to variations in

market conditions, customer characteristics, or

water temperatures (if the latter are not captured

very well by average air temperatures). But it is

also likely that differences in model specifications

are largely responsible for these unexplained

variations across service areas. Few studies

account for the influence of inlet temperatures on

water heating usage; some ignore the

interdependence of water heating usage with

ownership of dishwashers and washing

machines. Because of the importance of water

heating energy consumption, particular care

should be taken in specifying this portion of the

model.

* Estimated refrigeration UECs tend to be fairly

similar across studies, in spite of the inherent

difficulties of distinguishing refrigerator loads

from other miscellaneous loads like lighting. To

improve the ability of the CDA model to discern

refrigeration consumption, more information

concerning the characteristics of these appliances

should be gathered in the saturation survey.

* Cooking and clothes dryer UECs are fairly

volatile, the probable result of differences in

6-1

EE-RM-94-230 COMMENT 61D

'. 

6 

SUMMA~RY AND CONCLUSION'S 

A. Overview 

This report pr,esents a summary of residential conditiona1 demand studies conducted over the last 

five years. ThE~ report reviews the specification of a 

conditional demand model, summarizes estimates 

of end-use consumption levels. discusses price elasticity estimates, and considers attempts to validate 

conditional demand estimates through comparisons 

with metered e1ata. . 

B. Comparisons of UEC Estimates 

Section 2 describes a general conditional demand 

model. explains the use of the model to infer UECs, 

and discusses 21 number of theoretical and empirical 

issues. Section 3 outlines the specification of individual UEC functions and summarizes estimates of 

UECs developed by several utilities. As shown 

there. the interpretation of UEC estimates depends 

critically on the' specification of the VEC equations. 

In general. car€'ful design requires consistency with 

thermodynamic principles and recognition of the 

interaction of various factors affecting household 

consumption. 

The UEC estimates presented in Section 3 'were 

drawn from a large, number 'of analyses conducted 

at the utility service territory le\'el. Taken together, 

they provide a .:omprehensive picture of residential 

end-use consumption behavior in a variety of settings. Of course, these estimates vary across studies 

because of variations in weather. market conditions, 

and customer characteristics. as \~'ell as fundamental differences in model specification. A crude 

attempt was made to account for the intluence of 

weather in comparisons of weather-sensitive UECs. 

Space heating, air conditioning. and water heating 

UECs were mapped against degree-day and temperature measures to account for the role of weather. 

Several conclusions and recommendations arise 

from the comparison of VEC estimates: 

• Most of the variation in space heating and air 

conditioning UECs across studies is attributable 

to differences in weather. Once the influence of 

weather is considered. estimated UECs seem 

reasonably consistent across service areas. 

Clearly, CDA space conditioning estimates from 

other service areas. adjusted for differences in 

weather conditions. provide useful reference 

points for CDA analysts. 

• Estimated water heating UECs are not 

particul¥lrly consistent across 'service areas, even 

after differences in weather conditions are 

, considered. This may be due to variations in 

market conditions, customer characteristics, or . 

water temperatures (if the latter are not captured 

very well by average air temperatures). But it is 

also likely that differences in model specifications 

are largely responsible for these unexplained . 

variatio~s across service areas. Few studies 

account for the influence of inlet temperatures on 

water heating usage; some ighore the 

interdependence of water heating usage with, 

ownersl1ip of dishwashers and washing 

mClchines. Because of the importance of water 

heating energy consumption, particular care 

should be taken in specifying this portion of the 

model. 

• Estimated refrigeration UECs tend to be fairly 

similar across studies. in spite of the inherent 

difficulties of distinguishing refrigerator loads 

from other miscellaneous loads like lighting. To 

improve the ability of the CDA model to discern' 

refrigeration consumption. more information 

concernirg the characteristics of these appliances 

should be gathered in the saturation survey. 

• Cooking and clothes dryer UECs are fairly 

volatile. the probable result of differences in 

6-1 
Sutnlttnary/ anti Conclusions

model specification. These appliances

undoubtedly act as proxies for a host of other

excluded appliances, thus leading to biased

estimates of their UECs. The way to minimize

this problem is to incorporate information on a

wide variety of appliances into the model.

*Other estimated UECs from studies surveyed

here vary considerably; still, they should provide

useful general points of departure for the

decomposition of household consumption into

end-use levels.

and CDA samples diminish the direct comparability

of the two sets of estimates. Furthermore, attempts

should be made to ensure representativeness of the

sample used in validation studies. Conditional

demand analysis should be capable of yielding reasonable UEC estimates for "typical" households,

but it is probably unreasonable to expect the technique to generate good estimates for households

with atypical characteristics.

C. Validation of Conditional Demand

Estimates

Section 4 discusses means of validating conditional

demand estimates and summarizes the results of

three studies in which such estimates were compared against metered data. In general, those comparisons were encouraging, in that they confirmed

the ability of the conditional demand technique to

discern major end-use patterns. However, they also

revealed some marked differences between conditional demand estimates and metered loads for

some end uses.

It seems safe to say that considerably more work on

validation needs to be done. Ideally, validation

should involve the development of conditional

demand estimates for precisely the same households as covered by end-use metering of specific

appliances. Differences between metered samples

D. Price Elasticity Estimates

Conditional demand analysis has the potential to

yield end-use price elasticity estimates. In general,

however, utility-specific CDA studies have not been

suited well to this purpose. Most studies use relatively short consumption histories, so little temporal

variation in prices is captured by the data. Furthermore, with few exceptions, there are no significant

cross-sectional variations in statutory rate levels.

Still, a few attempts have been made to incorporate

sufficient price variation to accommodate the estimation of elasticities.

Because of the importance of end-use price elasticities to end-use forecasting and general planning,

much more work needs to be done in this area.

One possibility involves the linkage of several

appliance surveys and the associated consumption

histories for individual service areas. Another

entails the use of multiple service areas (as in

earlier national studies).

6-2

EE-RM-94-230 COMMENT 61D

model specification. These appliances· 

undoubtedly act as proxies for a host of other 

excluded appliances, thus leading to biased 

estimates of their UECs. The way to minimize 

this problem is to incorporate information on it 

wide variety of appliances into the model. 

• Other estimzlted UECs from studies surveyed 

here vary considerably; still, they should provide 

useful general points of departure ior the 

dccompo!!ition of household consumption into 

end-use levels. 

C. Validation of Conditional Demand 

Estimates 

Section 4 discusses means of validating conditional 

demand estimates and summarizes the results of 

three studies in ",'hich such estimates were compared against metered data. In general, those comparisons were encouraging, in that they confirmed 

the ability of the conditional demand technique to· 

discern major end-use patterns. However, they also 

revealed some marked differences between conditional demand estimates and metered loads for 

some end uses. 

It seems safe to say that considerably more work on 

validation needs to be done. Ideally, validation 

should involve the development of conditional 

demand estimate~ for precisely the same households <15 covered by end-use metering of specific 

appliances. Differences between metered samples 

6-2 

and CDA samples diminish the direct comparability 

of the ·two sets of estimates. Furthermore, attempts 

should be made to ensure representativeness of the 

sample used in validation studies. Conditional 

demand analysis should be capable of yielding reasonable UEC estimates for "typical" households, 

but it is probably unreasonable to expect the technique to generate good estimates for households 

with atypical ch~racteristics. 

D. Price Elasticity Estimates 

Conditional demand analysis has the potential to . 

yield end-use price elastiCity estimates. In general, 

however, utility-specific CDA studies have not been, 

suited well to this purpose. Most studies use relatively short consumption histories, so little temporal 

variation in prices is captured by the data. Further--

more, with few exceptions, there are no Significant 

cross-sectional variations in statutory rate levels. 

Still, a few a.ttempts have been made to incorporate 

sufficient price variation to accommodate the estimation of elasticities. 

Because of the importance of end-use price elasticities to end-use forecasting and general plannirig, 

much more wor~ needs to be done in this area. 

One possibility involves the linkage of several 

appliance surveys and the associated consumption 

histories for individual service areas. Another 

entails the use of multiple service areas (as in 

earlier national studies). 
7

BIBLIOGRAPHY

Bachmeier, Richard and Mike Robinson. A Conditional Demand Analysis of Residential Electric Apliance

Use in the Southwest U.S. Report to the Public Ltility

Commission of Texas, February 1988.

Barnes, Richard S. and Cathy R. Zoi. Unit Energy

Consumption.of Residential Appliances. Economics and

Forecasting Department, Pacific Gas and Electric

Company, April 1986.

Barrar, Jack, E. "Estimation of Residential IMonthlv

kWh Usage by End-Use from Whole House Billing

Data." Presented to the 3rd Annual Southeast

Regional AEIC Load Research Conference, Atlanta,

Georgia, August 1983.

Brodsky, Joel B. and Susan E. McNicoll. Pacific Gas

and Electric Company Residential Appliance Load Study,

1985-1986. Prepared for the Regulatory Cost of Service Department, Research Section, Pacific Gas and

Electric Company, September 1987.

Cambridge Systematics, Inc. End-Use Consumption

Models And Unit Energy Consumption (UEC) Value

Estimates For Residential Electric And Gas Appliances,

Final Report. Prepared for San Diego Gas and Electric Company, February 1984.

Cambridge Systematics, Inc. Residential Conservation

Analysis. Prepared for the Northwest Power Planning Council, 1984.

Commonwealth Edison, Strategic Analysis Department. Residential Unit Energy Consumption: A Conditional Energy Denmand Approach. August 1985.

Consumers Power Company, Market Research &

Pricing Department. Appliance Sul7m!etering Studies

1977-1984. September 1984.

Cowing, Thomas G. and William A. Vinhage.

NYSEG-SUNY Energy Demand Model.ng! and Analysis

Project. Technical Report 111-3, November 1986.

Dubin, J. and D. McFadden. "An Econometric

Analysis of Residential Electric Appliance Holdings

and Consumption." Econometrica, March 1984.

Fineman, Elizabeth. Estimating Residential Fuel Consumption by End-Use: Results From Phase I, Final

Report: Submitted to Energy Information Administration, U.S. Department of Energy, Washington, D.C.,

contract no. DE-AC01-79E1-10509, September 1982.

Glejser, H. "A New Test for Heteroskedasticity."

Journal of the American Statistical Association, 64, 1969.

Graham, John, R. Using Conditional Demand Analysis

to Estimate Appliance Electricity Usage. Prepared for

Virginia Power, 1987.

Greenberg, J., B. Reeder and S. Silberstein. A

Review of Energy Use Factors.for Selected Household

Appliances. Prepared for the U.S. Department of Energy, NBSIR 85-3220, Washington, D.C, August 1985.

Hirst, E., R. Goeltz and M. Hubbard. Determinants

of Electricity Use for Residential Water Heating: The

Hood River Conservation Project. Draft paper, research

sponsored by. the Office of Conservation,

Bonneville Power Administration, U.S. Department

of Energy, contract no. DE-AC05-840R21400,

Washington, D.C., April 1986.

Jackson, Gary, L. Residential Appliance Electricity

Demands in the TVA Power Service Area: A Conditional

Demand Analysis Based on TVA'S Residential Surveys

Conducted in .1979, 1981, 1982 and 1983. Prepared for

Tennessee Valley Authority, Systems Forecasting

Group, July 1985.

Kendall, David, L. "A Survey of End-Use Load

Shape Estimation Techniques." Research Triangle

Institute, presented to the Southeast Regional AEIC

Load Research Conference, August 1987.

Kitting, Roberta, R. Regression Analysis Using Appliance Saturation Survey Data. El Paso Electric Company, April 1987.

Klien, Yehuda. "Residential Demand for Space

Heat." University of California at Berkeley, Department of Economics, draft paper, August 1985.

7-1

EE-RM-94-230 COMMENT 61D

" 

7 

BIBLIOGRAPHY 

Bachmeier, Richard and Mike Robinson. A CLlllditiollal Demand Al1alysis of Residential Electric Ar;'iiatlcc 

Use in the SOlltlll~'est U.S. Report to the Public Uility 

Commission of Texas, February 1988. 

Barnes," Richard S. and Cathy R. Zoi. Ullit EI/ergy 

Consumptioll. of Residential Appliances. Economics and 

Forecasting Department, Pacific Gas and Electric 

Company, April 1986. 

Barrar, Jack, E. "Estimation of R~sidential Monthlv 

kWh Usage by End-Use from Whole House Billing 

Data." Pres·ented to the 3rd Annual Southeast 

Regional AElC Load"Research Conference, Atlant~, 

Georgia, August 1983. " 

Brodsky, Joel B. and Susan E. McKicoll. Pacific Gas 

and Electric Company Residential Appl~mce Load Study, 

1985-1986. Prepared for the Regulatory Cost of Service Department. Research Section, Pacific Gas and 

Electric Compan)r; September 1987. " 

Cambridge Systematics, Inc. End-U:;e Consumptiol1 

Models And Ullit E'lergy COlls11mptil'" (UEO Value 

Estimates For Reside,ttial Electric And" Gas App/i.mces, 

Final Report. Prepared for San Diego Gas and Electric Company, February 1984. 

Cambridge Systematics, Inc~ Residentidl Conser..'ation 

Analysis. Prepared for the Northwest Power Planning Council, 1984. 

Commonwealth Edison, Strategic Analysis Department. Residential Unit Energy COlls!ln;~Jtio1!:" A Conditional Energy Demalld Appr.oach. August 1985. 

Consumers Power Company, Market Research & 

Pricing Department. Appliance Submt?terillg Studies 

1977-1984. September 1984. 

COWing, Thomas G. and William A. Vinhage. 

NYSEG-SUNY Energy Demand Modt?6rg mid A,!a/ysis 

Project. Technical Report III-3, November 1986. 

Dubin, J. and D. McFadden. "An Econometric 

Analysis of Residential Electric Appliance Holdings 

and Consumption." Econometrica, March 1984. 

Fineman, Elizabeth. Estimating Residential Fuel COIIsll1nption by End-Use:' Results From Phase l, Final 

Report: Submitted to Energy Information Administration, U.s. Department of Energy, Washington, p.c., 

contract no. DE-ACOl-79EI-I0S09, September 1982. 

Glejser, H. "A New Test for Heteroskedasticity." 

10UT/1iI1 of the American Statistical Association, 64, 1969. 

Graham, John, R. Using Conditional Demand Analysis 

to Estimate Appliance Electricity Usage. Prepared for 

, Virginia Power, 1987. " 

Greenberg, J., B.' Reeder and S. Silberstein. A 

Review of Energy Use Factors, for Selected House/tOld 

Appliances. Prepared for the U.5. Department of Energy, NBSIR 85-3220, Washington, D.C, August 1985. 

Hirst, E., R.Goeltz and M. Hubbard. Determinants 

of Electricity Use for Residential Water Heating: Tlte 

Hood River Conseroatiori Project. Draft paper, research 

sponsored by. the Office of Conservation, 

Bonneville Power Administration, U.s. Department 

of Energy, contract no. DE-AC05-840R21400, 

Washington, D.C., April 1986. 

Jackson, Gary, L. Residential Appliance £Ie~tricity 

Demands ill tile TV A Power Seroice Area: A CO'lditiOlwl 

, Demand Analysis Based on TV A'S Residential Suroeys 

Conducted ill J979, 1981, 1982 and 1983. Prepared' for 

Tennessee Valley Authority, Systems Forecasting 

Group, July 1985. 

Kendall, David, L. "A Survey of End-Use Load, 

Shape Estimation Techriiques." Research Triangle 

Institute, presented to the Southeast Regional AEIC 

Load Research Conference, August 1987. 

,Kitting, Roberta, R. Regression Analysis Usillg App/imice Saturation Suroey Data. El Paso Electric Company, A~riI 1987. ' 

Klien, Yehuda. "Residential Demand for Space 

Heat." University of California at Berkeley, Department of Economics, draft paper, August 1985. 

7-1 
Bibliio rillIhyI .

Lawrence, A.G. and M. Parti. Survey of Conditional

Energy Demand Models for Estimating Residential Unit

Energy Consumption Coefficients. Prepared for Electric

Power Research Institute under EA-3410 Research

Project 576-3, February 1984.

Middle South Utilities Load Research Subcommittee. End Use Estimation: An Alternative to Direct

Metering. 1984.

Parti, Michael, Iftekharul B. Chaudhury and

Anthony Sebald. A Conditional Demand Study of the

Unit Energy Consumption per Appliance. Presented to

Nevada Power Company, February 1986.

Parti, Michael, Iftekharul B. Chaudhury and

Anthony Sebald. A Conditional Demand Study of

Appliance Load Shapes and Annual Energy Usage in the

Nevada Power Company Service Territory. Presented to

Nevada Power Company, May 1986.

Parti, Michael, Anthony V. Sebald, Chester Pabniak

and Iftekharul Bar Chaudhury. Residential End-Use

Consumption and Conservation in the Rochester Gas and

Electric Service Territory. Prepared for Rochester Gas

and Electric Corporation, June 1986.

Pacific Gas And Electric Company, Economics and

Statistics Department. Unit Energy Consumption of

Residential Appliances. August 1982.

Pacific Gas And Electric Company, Economics and

Statistics Department. Residential Conservation Services Audit Program Evaluation Energy Savings Analysis. July 1983.

-Pacific Gas And Electric Company, Economics and

Statistics Department. Unit Energy Consumption Of

Residential Appliances. April 1986.

QED Research, Inc. A Model of Residential Energy

Consumption and Appliance Ownership, Final Report,

Volume II: Project Description and Findings. Prepared

for Florida Power & Light, June 1986.

Regional Economic Research, Inc. Energy Usage

Analysis of Residential Appliances. Prepared for San

Diego Gas & Electric Company, December 1985.

Regional Economic Research, Inc. Analysis of the

Energy-Related Characteristics of New Homes in the

Pacific Gas and Electric Company Service Territory: Task 4:

Conditional Demand Analysis. Prepared for Pacific

Gas and Electric Company, April 1986.

Regional Economic Research, Inc. Residential Conditional Demand Analysis: Final Report. Prepared for

Baltimore Gas & Electric Company, September 1988.

Research Triangle Institute. Residential Conservation

Strategies: An Experimental Evaluation of Consumer

Responses. Final Report, Volume 2: Analysis Results.

Prepared for Florida Power & Light Company,

Mav 1985.

Roddy, Michael, G. An Application of Conditional

Demand Analysis. Prepared for GPU Service Corporation, 1987.

Russ, Michael, John Fritz, Bill Diehl, Dean Stathis

and Gordy Meyer (Middle South Utilities). "End

Use Estimation: An Alternative to Direct Metering."

Presented to Southeastern AEIC Meeting, Atlanta,

Georgia, August 1984.

United Power Association. Residential Appliance Consumrtion: Report And Recommendations. June 1983.

United Power Association. Appliance Report. June

1986.

Villads, Edgar, Matthew Norman and Kevin Gainer. "AEP System's Residential End-Use Forecasting

Model - An Econometric Approach." Presented to

the American Power Conference, Chicago, Illinois,

April 1988.

Waite, Steve. "Modeling Residential Energy Consumption Using Customer Survey and Billing

Data." Draft paper, Northeast Utilities Service

Company.

Wright, Roger L. and Curt D. Puckett. Integrating

EIP .nd HES5 Information for Estimating End-Use

Ener ies. Prepared for Sierra Pacific Power Companv, March 1988.

7-2

/. 

I

EE-RM-94-230 COMMENT 61D

Lawrence, A.G. ~nd M. P~lrti. Survey of Conditional. 

Energy Demand Models for Estimating Residential Unit 

Ellergy Consumption Coefficiellts. Prepared for Electric 

Power Research· Institute under EA-3410 Research 

Project 576-3, February 1984. 

Middle South Utilities Load ~esearch Subcommittee. End Use Estimatioll: An Alternative to Direct 

. Metering. 1984. 

Parti, Michael, Iftekharul B. Chaudhury and 

Anthony Seballd. A Conditional Demand Study of the 

Unit Energy Consumptioll per Appliance. Presented to 

Ne:-,ada Power Company, February 1986. 

Parti, Michael, Iftekharul B. Chaudhury and 

Anthony Sebald. A Conditional Demand Study of 

Appliance Load Shapes and Annual Energy USage in the 

Nevada Power Company Service Territory. Presented to 

Nevada Power Company, May 1986. 

'Parti, MichaeIr Anthony V. Sebald, Chester Pabniak 

and Iftekharul Bar Chaudhury. Residential End-Use 

Consumption and Conservation in tile Rochester Gas and 

Electric Service Territory. Prepared for Rochester Gas 

and Electric Corporation, June 1986. 

Pacific Gas And Electric Company, Economics and 

Statistics Department. Unit Energy Consumption of 

Residential Appliances. August 1982. 

Pacific Gas And Electric Company, Economics and 

Statistics Department. Residential Conservation Services Audit Program Evaluation Energy Savings Analysis. July 1983. 

Pacific Gas And Electric Company, Economics and 

Statistics Department. Ullit Energy Consumption Of 

Residential Applia/lCes. April 1986. 

QED Research, Inc. A Model of Residential Energy 

Consumption and Appliallce Ownership, Final Report, 

Volume 1I: Project Description and Findings. Prepared 

for Florida Power & Light, June 1986. 

Regional Economic Research, Inc. E~ergy Usage 

Analysis of Reside11tial Appliances. Prepared for San 

Diego Gas & Electric Company, December 1985. 

7-2 

Regional Economic Research, Inc. Analysis of the. 

Energy-Related Characteristics of New Homes in the 

Pacific Gas and Electric Company Service Territory: Task 4: 

Condi:ional Demand Analysis. Prepared for Pacific' 

i 

. Gas arid Electric Company, April 1986. 

I 

Regional' Economic Research, Inc. Residential Conditionai Demand Analysis: Final Report. Prepared for 

Baltimore Gas & Electric Company, September 1988. 

Research Triangle Institute. Residential Conservation 

Stratt?.;:ies: An Experimental Evaluation of Consumer 

Respll;lses. Final Report, Volume 2: Analysis Results. 

Prepared for Florida Power & Light Company,' 

Mav 1985. 

Roddy, Michael, G. An Application of Conditional 

Demand Analysis. Prepared for GPU Service Corporation, 1987. 

Russ, Michael, John Fritz; Bill Diehl, Dean Stathis 

and Gordy Meyer (Middle South' Utilities). "End 

Use Estimation: An Alternative to Direct Metering." 

Presented to Southeastern AEIC Meeting, Atlanta, 

Georgia, August 1984. 

United Power AssoCiation. Residential Appliance Consum~ion: Report And Recommendations. June 1983. 

United Power Association. Appliance Report. June 

1986. . 

Villacis, Edgar, Matthew Norman and Kevin Gainer. "AEP System's Residential End-Use F~recasting 

Model - An Econometric Approach." Presented to 

the .-\merican Power Conference, Chicago, Illinois, 

April 1?88. 

Waite, Steve. "Modeling Residential Energy Consumption Using Customer Survey and Billing 

Data." Draft paper, Northeast Utilities .Service 

Company. 

Wright, Roger L. and Curt D. Puckett. Integrating 

EIP :,md HESS Information for Estimating End-Use 

Energies. Prepared for Sierra Pacific Power Company, ~1arch 1988.