# "d± 6

# , 

# /:)

Residential End-Use Energy Consumption: ASurvey ofConditional 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 HillviewAvenue Palo Alto. Californ:a 94304

EPRI Project Manager S.Braithwcar Market Assessmere Program Customer Systems Division t 

### '\



## Residential End-Use EnergyConsumption: ASurvey ofConditional 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 SurveyofConditionalDemand Estimates Todevelopeffective demand-side management plans, utilities needtocontinuallyexpandtheir understanding of customerenergy consumptionpatterns.This report presentsacrosssectionofutility estimatesof residential electricenergyconsumption byend-use category. The estimateswere derived from asurveyofsome30 conditional demand studiesconducted 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, butfew utilities have access to the most costly source, end-use metering. One alternative to metering isconditional 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 studiesfor utility service areas, researchers identified theoretical issues concerning estimation techniques and summarized estimates of end-use consumption levels. The influence of weather differenceson 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 estimatesfor space conditioning and water heating revealadegree ofvariabilityacrossstudies. Air conditioning and space-heating consumption estimates were standardized for differences in weather by means of simple regression equations. Some of the wide variation inwater heater usagewas explained by differences in average weather conditions

_- . : .- ,, .- _,. ._... \.



### TOPICS

AUDIENCE 

# 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 EnergyConsumption:A SurveyofConditional 

# Demand Estimates To developeffectivedemand-side managementplans. utilities needtocontinuallyexpandtheirunderstandingofcustomerenergy consumptionpatterns.This**t~port**

### presentsacrosssectionofutility estimatesof residential electricenergyconsumption byend-use category. The estimateswerederivedfrom asurveyofsome30 conditional demandstudiesconducted 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 disaggregate 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 adegree ofvaria:>ility across studies. Airconditioning 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 heaterusage was explained by differences in average weather conditionsresult of different analytic model specifications designed to capture the interaction ofwater heater usage with washing machines and dishwashers. More research is needed in this area. UECs for refrigerators and freezers exhibited considerablestabilityacross studies, whereas UECs for discretionaryappliances such as ranges and clothes dryers varied widely. This variation is likely attributable to differences in specificationof the estimated UEC equations. Although marked differences exist between some metered and estimated UECs, theoverall 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 necessaryto developeffective 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.



### Forfurther information on EPRI research programs, call EPRI Technical Information Specialists (415) 855-2411. \, 

# '.

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. ' UECsfor refrigerators andfreezers exhibited**c~nsiderable**stabilityacross studies,whereas UECs for discretionaryappliances such as ranges and clothes dryers varied widely. This variation is likely attributable to differences in specification ofthe estimated UEC equations. Although marked differencesexist between some meteredandestimated UECs, theoverall 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 necessaryto developeffective marketingand 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 ofthe 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 copiesof this report should be directedto 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. utilityassoc.ations. U.S. government agencies (federal, state, and local), media, and foreign organizationswith which EPRI has an information exchange agreement. On request, RRC will send a catalog of EPRI reports.

Elect-: -lower Research Ins;::uteand 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 asan account ofwor- sponsored by the Electric PoOwe -search Institute. Inr: E?RI) Neither EPRI. -emoers of EPRI, the organiza.i,:s)named below norany persc- -;Jng on behalf of an' o' tnem: (a)makes a-.,warranty, expressor impliec. ntn respect totne use ofany inforrr-: n.apparatus. meinca. or orocess disclose- inthis report or that such use -.ay not infringe privately ownec '*nts: or (b)assumes any liabilitieswith resc-e tothe use of. or for damac-s resultingfrom the useof. anyir":-'ation. apparatus. r-enod. or process disc.seo inthis report PreDpa:ec by Camr.r :-e Systematics. Inc Berkee California and Regic-a' Economic Researcn im San C 3o. Calilornia 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 preparedc,' :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!;.' :;eSystemalocs. 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 theresults ofa 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 byPotomac 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



# I ·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 ofconstructingconsumption estimates at. theend-uselevel. This report summarizestheresultsofabroadcrosssection of recent residential conditional demand analyses conducted either in-house by utilities. or for**utiliti~s** 

### byconsultants. Aside from the analysis results. the report should provide useful background information for utilities engaged inend-use analysisof 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. includinga recenteffortbyPotomac Electnc PowerCompany, whose results generally fall inthe rangereported inthisdocument. 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 CustomerSystems Dhision

# CONTENTS

Section 1 Introduction .......... A. Background

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

............ C. Overview ofReport

............. D. Summary ofResults

............. 2 Review of Conditional Demand Methodology .... A. Introduction

. ..... .. B. Overview ofthe Basic Conditional Demand Model 3 Summary ofUEC 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 ofComparisons with 5 Price andIncome Elasticities A. Introduction

....... 

### B. Theoretical Issues

...... C. Review ofElasticities

. . . 6 Summary and Conclusions . . A. Overview

........ B. C. 

### D. . . . . . . . . . . .. 

### End-Use Metering Data

. . . . . . . . . . .

. . . . . . . . . . .

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



### Comparisons ofUEC Estimates

........... Validation ofConditional 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 

# CONTI:NTS

Section 

### Page
1 Introcluction....... 

### · 1-1

.A. 

### \3ackground

..... · 1-1

B. 

### Purposeand Objectives : 1-2 C. OverviewofReport

. · 1-2 D. SummaryofResults

.. · 1-3 2 Review ofConditional Demand Methodology 

### · 2-1 A. Introduction

. . . . . . . . .. .' ., . · 2-1 B. OverviewoftheBasicConditional DemandModel

. · 2-1 3 Summaryof UEC Estimates 

### · 3-1 A. 

### Intrc;>duction

. . · 3-1 B. Space Heating

. · 3-1 C. AirConditioning · 3-7 D. WaterHeating .3-10 E. Refrigeration. .3-15 F. Cooking

... .3-18 G. PoolsandSpas · 3-19 H. OtherAppliances .3-22 4 Validi3tion ofCDA Estimates

. 

### · 4-1 A. 

### Introduction

. . . . . . · 4-1

. . 

### B. Review ofComparisonswith**End~Use** 

### MeteringData · 4-1 

### . 5 Priceand Income Elasticities 

### · 5-1 A. 

### Introduction

... . . .·5-1 B. TheoreticalIssues

... · 5-1 C. ReviewofElasticities · 5-3.
6 Sumrnaryand Conclusions 

### · 6-1 A. 

### Overview

. . . . . . · 6-1 B. ComparisonsofUEC Estimates. · 6-1 C. ValidationofConditional Demand Estimates .6-2 D. Price Elasticity Estimates. · 6-2
7 .Bibliclgraphy.

. . . . . . 

### · 7-1

# FIGURES

Figure Page 1-1 Ranges ofSpaceConditioning 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 AirConditioning 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 OtherUEC Estimates, Single-Family Dwellings

................ 1-5 2-1 Basic Overview ofConditional 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 AirConditioning UECs vs. Cooling Degree-Days

..... ....... 3-7 3-4 Heat Pump Central AirConditioning 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 ofRefrigerator UECs, Single-Family Dwellings and All Dwellings

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

........... 3-16 3-9 Distribution ofRange/Oven UECs, Single-Family Dwellings and All Dwellings

...... . 3-20 3-10 Distribution ofMicrowave Oven UECs, Single-Family Dwellings and All Dwellings

...... 3-21 3-11 Distribution of Clothes Dryer UECs, Single-Family Dwellings and AllDwellings

...... . 3-23 3-12 Distribution ofDishwasher Motor UECs, Single-Family Dwellings andAll Dwellings

. .... 3-24 3-13 Distribution of ColorTV UECs, Single-Family Dwellings and AllDwellings

..... ... . 3-24 '.



# FIGURES

Figure Page 

### 1-1**RangE~s** 

### ofSpaceConditioningand Water Heating UEC Estima.tesSingle-Family Dwellings

. 1-3 1-2 TheRelationship Between EstimatedConventional SpaceHeating UECs and Heating Degree-Days, Single-Family Dwellings.

. . . . . . . . . . ....'.

. . . . . . .. . 1-4 1-3 The Relationship Between Estimated ConventionalCentral AirConditioning UECs andCooling Degree-Days, Single-Family Dwellings

. . . . . . . . . . . . . . . . . . . . . . . 1-4 1-4 TheRelationship Between EstimatedConventionalWaterHeatingUECs andAverage**Templ~ratures,** 

### Single-Family DwelHngs

. . . . . . . . '. 1·5 1-5 RangesofOther**UE~**

### Estimates, Single-FamilyDwellings

. . . 1·5 2·1 Basic OverviewofConditional Demand Model

. . . . . . , 2·1 3·1 ConventionalSpace**He~ting** 

### UECs vs. HeatingDegree Days.

. 3-5 3-2 HeatPumpSpaceHeatingUECs vs. HeatingDegree-Days "

. 3-6 3-3 ConventionalCentral AirConditioningUECs vs. Cooling Degree-Days

. . 3-7 3-4 Heat PumpCentral AirConditioningUECs vs. Cooling Degree-Days.

. . 3-11 3-5 ConventionalWaterHeatingUECs (All H2O 

### Heating) AverageTemperature.

. 3-14 3·6 ConventionalWaterHeating (H2O 

### Heatexc. DW/CW) vs. AverageTemperature.

. 3-15 3-7 DistributionofRefrigeratorUECs, Single-Family Dwellings andAll Dwellings.

. 3·16 3-8 Distribution ofFreezerUECs, Single-Family Dwellings andAll Dwellings.

. . . . 3-16 3-9 Distribution ofRange/Oven UECs, Single-Family DwellingsandAll Dwellings

. 3-20 3-10 DistributionofMicrowave Oven UECs, Single-Family DwellingsandAll Dwellings

.**3~21** 

### 3-11 Distribution ofClothesDryer l!ECs, Single-Family Dwellings andAll Dwellings.

. . 3-23 '3-12 DistributionofDishwasherMotor CEes, Single-family DwellingsandAll Dwellings.

. 3·24 3·13 DistributionofColorTV UECs, Single-Family DwellingsandAll Dwellings

. 3-24t 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 ofPGandE Appliance Metering Project

....... ................. 4-2 4-2. Results ofTVA 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 \, "



# 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

. . . . . . . ......... . PrimarySpace Heating UECs: All Dwellings

. . . . . PrimarySpace Heating UECs: Single-FamilyDwellings SpaceCoolingEucs: All Dwellings

...... . SpaceCoolingEucs: Single-Family Dwellings.

. . . . ConventionalWaterHeating UECs: All Dwellings ConventionalWaterHeating UECs: Single-Family Dwellings. Refrigeratorand FreezerUECs: All Dwellings

.... , Refrigeratorand FreezerUECs: Single-FamilyDwellings. Cooking UECs: All Dwellings.

. . . . . . .' Cooking UECs: Single-Family Dwellings

. . . Pool andSpaPumps: Single-FamilyDwellings OtherUEC Estimates: All Dwellings.

. . . . OtherUEC Estimates:**Single-Famil~r**

### Dwellings Results ofPGandEAppliance Metering Project Results ofTVA SubmeteringStudy 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 ofconditional demand methodology is presented to explain the logic of the approach and to introduce the means ofintegrating 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 inestimates 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. 

# 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 theirDSMlmarketplanningactivities. However, few have access to end-use metered data to be used in the development of these forecasts andanalyses. 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 ofconditional demand methodology is presented to explain the logic of"the approach and to introduce the meansofintegrating 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 weatherrelatedvariationinestimatesacrossserviceareas. A summary ofestimated income and price elasticitiesderivedfrom conditionaldemand studiesis also presented. In addition, studies comparing conditional demand estimates to**end-u~e** 

### metered consumptionlevelsarereviewed.

# 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 ofestimates, 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 ofwork in this area.



### A. Background 1. Originof 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 ofutilities and regulatory bodies. Formany users, it has become an integral part ofthe end-use analysis and forecasting process. 2. The LogicofCDA CDA is based on simple and straightforward logic. Even in the absence of end-use metering, utilities generally have a substantial amount ofinformation on residential energy consumption and its determinants, and this information can be used to infer end-use consumption levels. Four kinds ofdata 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. Applicationsof 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. 

# 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 otherservice territories. [n order to foster comparability ofestimates, the focus is on studiescompleted 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 ofth"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 theCDAt!stimates. An earlier survey of Conditional Demand studies was provided by 

### Lawrence and Parti in a 1984 EPRI report. I 

### This study buiids on theLawrence and Parti review, andcan be used in conjunction with it to obtain a comprehensive ov"en:iew ofworkin this area.



### A. Background 1; Origin ofConditional 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 UnitE"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 atseveral otherutilities. Overthe past decade. the technique has been adopted by a large numberofutilities andregulatorybodies. Formany users. it

### has become an integral partoftheend-use analysisand forecasting process. 

### 2. The LogicofCOA CDA is based on simple and straightforward logic. Even in the absence of end-use metering, utilities generally have a substantial amount ofinformation on residential energy consumption and its determinants, and this information can be used to infer end-useconsumption·levels. Four kinds ofdata 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; dataon weather patterns; andinformation on marketconditions (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, thevariationin end-use consumption levels across households and over time is estimated through the use of interaction terms involving household features, weather. and marketconditions. 

### 3. Applications.of 

### CDA Conditional Demand**anal~'sis** 

### was initially designed to infer monthly orannual 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 numberofotherpurposes, including:
• Estimatingend-useincome and priceelasticities;



# ...Introductiotn

* Evaluating the impacts of utility conservation programs (see EPRI EA 3606); * Assessing the impacts ofbuilding 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 intheDevelopment ofCDA Like most analytical techniques, CDA has been refined over time. Some of the basic directions of these refinements have been: * refinement ofthe specification of individual UEC relationships; * alterations in the design ofappliance saturation surveys to better achieve the objective of obtaining information required for CDA; * integration ofengineering (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 ofprior information on end-use consumption (either engineering estinates or prior CDA estimates); * integration ofend-use metered data into the CDA estimation process; and * improvements intreatment ofpartial 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 ofthe analysis are: * to provide a review ofCDA methodology; * to present a comprehensive cross-section ofCDA estimates ofresidential end-use consumption levels; * to assess the consistency ofthese 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 * tosummarize 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 ofthe CDA model and explains itsuse in deriving unit energy consumption (UEC) estimates for key end uses. At this point, .theanalysis iscast 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 I.. "

II/trod /letiLlIl



### • Evaluating**thl~** 

### impactsofutilitycon$Crvation programs (see EPRl EA 3606); , 

### • Assessingtheimpactsofbuilding standardsand appliance efficiency standards;
• Derivingend-useconsumption estimatesfor commercialcustomers (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:' ofanalysis, and has consider.able potential for c.ddressing a variety of dema'nd-side questions ic..:ing utility planners.


### 4. Themes inthe DevelopmentofCDA Like mbst analytical techniques, CDA has been refined over time. Some of the basic directions of**the~e** 

### refinementshavebeen:
• refinementofthespecification ofindi\'idual UEC relationships; ;. 

### • alterationsin thedesignofapplianresaturation sun:eysto betterachievetheobjectiwof obtaininginfqrmation required forCDA;
• integration ofengineering**(thermod~':lamic)** 

### relationships intothe model,(somet:::1escalled the thermodynamicbehavioral integrated, or TEL approach); , '

• linkageoftheCDA model \"ith apF'::.mce choice models.(forinstance, in the ResidentialEnd-Use Energy PlanningSystem, REEPS);
• incorporation ofpriorInformation (1:1 

### end-use consumption (eitherengineeringestiznates or priorCDA estimates);

• integration ofend-use metered data :nto the CDA estimationprocess; and
• improvementsin treatment

### oipartie.: I.'rcomplete non-responsebias 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. Thespecificobjectivesoftheanalysisare:
• to providea reviewofCDA method.ology;
• .to presenta comprehensivecross-section of,CDA estimatesofresidentialend-useconsumption leyels;
• to assess theconsistencyof'theseestimates, across service areas, aJteraccountingfor cross-regional differencesinweatherpatterns andothergeographicconditions; , • to evaluate the transferabilityof weather-adjustedresidential CDAestimates, acr9ss service areas; .'toreviewattemptsfo validateCDAestimates; and
• to summarize information onpriceelasticities derived from CDAstudies,


### C. Overview of Report Section 2 reviews the basic elements of the conditional demand methodology. The rev'iew outlines thestructureoftheCDA model andexplains**it~** 

### use in deriving unit energy consumption (UEq estimates for key end uses. At this point, .the analysis is castin verygeneralterms. Section 3,

### provides more detail on the CDA mod,el specification relating to individual end uses. It 

### also summarizes a wide range of CDA-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 providea ' 

### these alternative estimation techniques. However,· comprehensive summary of post-19S3 CDA studies substantialdiscrepanciesare found in someareas.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 beingrefined, some interesting patterns seem to beemerging fromrecent studies. Finally, Section 6provides a summary ofthe principal findings and offers a series ofrecommendations concerning the use of CDA to analyze end-use consumption patterns.



## D. Summary ofResults 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 PumpHeating I
Conventional Central

AirCondtioning
RoenviWallAir I

Ileat PumpAiri

Water Heating

0 2,000 4,000 6,000 8,000 10,000 RangesofEstimates (Annual kWh) Ranges exclude thehighest andlowest UECestimates. 

### \



### Section 5 examines the use of CDA to estimate income and price elasticities. Alternative means of specifying income and price effects within the modelare reviewed, and empirical results are summarized. While this application of the CDA model is still beingrefined, someinterestingpatternsseem to be emergingfrom recent studies.

. Finally, Section 6

### provides a 

### summaryof the principal findings**~nd** 

### offers a series ofrecommendations concerning the use of CDA to analyze end-use consumptionpatterns.



### D. Summary of Results The primary result of the study is a summary of estimated UEC values for a wide variety ofelectric appliances. Figures 1-1 

### to 1-5

### .illustratetherangesof estimatesfound in thecourseofthereview. 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/enlionalElectric SpaceIleal
IlealPumplleating
ConventionalCentral

AirCondtioning**Ro<~nvWall**Air

Helll PumpAir

WaterlIeating


## 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 weatherdatafrom thestudiessurveyed.Theranges ofvalues 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** 

### confidenceintervalsarefairly wide (90% 

### confidence), the analysis suggests that space conditioningloadscan be estimatedreasonablywell with theconditional demandapproach. 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 estimatedwaterheating·UECs' and average temperatures for the studiesreviewed.. A band of values based on the standard errorof

### a regression of UECs on average temperatures is shown.

4,000 6,000 8,000 10,000 RangesofEstimates(AnnualkWh) 

### •Rangesexcludethehighestandlowest UECestimates. Figuf'e1-1. Ranges 

### ofSpace Conditioning andWater Heating UEC Estimates· Single-Family DwellingsIntroduction

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 (AnnualkWh) 20,000 Estimated Relationship 15,000 /
PredictedValuePlus Two Standard Errors 

### 10,000

-

5,000 ' / 1 Predicted ValueMinus }j, /^^^^

. /*~ _~''^ 

### ~ TwoStandard Errors 00 2,000 4,000 6,000 8,000 HeatingDegree-Days

UECs(Annual kWh) 8.000 Estimated Relationship 6,000

- PredictedValuePlus TwoStandard Errors 4,000 *

000

0 1,000 2,000 3,000 4,000 Cooling Degree-Days

I
. \.,

"



### ["trodlIetiOJl

UECs (AnnualkWh) 20,000

15.000 '

, , 10,000

.

5,000

. EstimatedRclati!,nship,

I'redictedValuePlus**o~~~------------~~----~------~--~----------~** 

## o 2,000 4,000 HeatingDegree-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

- PredictedValuePlus TwoStandardErrors EstimatedRelationship

PredictedValueMinus' Two**Stan~ard**Errors**o~--~~--------------------~------~------------** 

### o 1,000 2,000 CoolingDegree·Days 3,000

Figure 1-3.The Relationship Between Estimated Conventional Central AirConditioning UECs'and Cooling Degree-Days Single-Family**Dwellin~s** 4,000Introduction

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

. ColorTV SpaPump I -.. PoolPump I

. : ...- 0 400 800 1,200 1,600 2,000 2,400 2.800 Ranges ofEstimates (AnnualkWh) Ranges excludethe highest andlowest UECestimates.

Shadedarea represents judgcmentally-preferred range ofestimates. 

### Figure 1-5. Ranges ofOther UEC Estimates* Single-Family Dwellings 1-5 UECs (Annual kWh) 7,000 6,000 - EstimatedRelationship "~~~~~~-~»^^^ \ PredictedValuePlus \,.. -^~ Two Standard Errors ! 5,000 4,000 3,000 2,000 PredictedValue Minus 1,000 TwoStandard Errors 0 40 45 50 55 60 65 70 75 Average Temperature (DegreesFahrenheit) 

### \

UECs(AnnualkWh)**7,~~, --------------------------------------------------~**  6,000 '-, EstimatedRelationship
'5,000
4,000
3,000
2,000,' PredictedValueMinus 1,000:- TwoStandardErrors PredictedValuePlus TwoStandardErrors

# ~,**o~--------------------------------------------------** 40 45 50 5S 60 6S 70 AverageTemperature(DegreesFahrenheit)

Figure 1-4. The Relationship Between**Es~mated** Conventional Water Heating UECsandAverageTemperatures

, Single-Family Dwellings



### End Usc, 75 Illtroduc;tioll



### -,-------**,-.~----------------------------,-------------** Refrigerators "

FreC7.crs Range/oven MicfOW"dve ClothesDryer

Dishwasher

---.... 1

______________ --'-

__________ _

, ClothesWasher ColorTV SpaPump PoolPump 

### o 400 800 1.200 2,000 RangesofEstimates(AnnualkWh) *RangesexcludethehighestandlowestUECestimates. ShadedarearepresenlSjudgementally-preferredrangeofestimates. Figure**1~5.** Ranges ofOther UEC Estimates* Single-Family Dwellings 2,400 2.800

1-5Introductionl
Figure 1-5 depicts UEC estimates for other appliances. Two sets ofestimates 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 ofthe.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 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 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 avery powerful toolofend-use research.

4 -C II/trodliefi,ll1


### Figure 1-5 depicts CEC estimates, for other appliances. Two sets ofestimates are presented. Oneset (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, describesjudgmentalestimates ofreasonableranges 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, partofthe.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 ofmanual-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'atlength in this report. However, the variation in these, estimates undoubtedly can be traced to fundamental differencesinmodelspecification anddataquality. 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 canbea very powerfultoolofend-useresearch.

# 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 ofthe BasicConditional Demand Model 1. MajorDeterminantsof Household Energy Consumption Figure 2-1 provides an overview of a basic conditional demand model. The underlying spirit ofthe approach is that a household's energy consumption is directly related to the stock ofappliances 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 approachby a series ofbinary (0,1) or cardinal (0,1,2,...) indicators, generally defined for fairly specific appliance types. Binary indicators are used toindicate whether ornot 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 maybe 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 _ ··

___ 

# 2

REVIEW OF CONDITIONAL'DEMAND METHODOLOGY

A. Introduction' This section provides a brief review ofConditional 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 deferreduntilthenextsection.



### B. Overviewofthe Basic Conditional Demand Model
1. Major DeterminantsofHousehold Energy Consumption

. Figure 2-1 

### pro\ides an overview of a basic conditional demand model. The underlying spirit of the approachis that a household's energy·consumption is directly relatedto the 

### stock ofappliances 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, dwellingcharacteristics, and the household's utilization patterns.

.Appliance stocks are typically represented in the CDAapproachbya seriesofbinary (Oil)

### orcarCinal (0,1,2,

...) indicators, generallydefined for fairly specific appliance types. 

### Binary indicators are**l.!~ed** 

### to indicate whetherornota particularsystem 

### (e.g., central air conditioning) is preSent, whereas cardi-· nal variables aresometimesused to representaFpliances (say, TVs or refrigerators) where mul:iple units maybepresent. 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 FEATUF'::S
I i ! ! i**~** 'Y 

## I

....PPLIANCE-sPEclrrc 

# .1 APPUANCE HOL'SE¥.OL!) 

# 1-2::

E:->ERGY CONSUMPTIO;-; LEVELS**CO~SU~(PTIO:-:**

# 1- (UECs) 

# ·1 STOCXS

Fjgure2-1. 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 ifthis 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 jin period t S,,. = a binary indicator of household i's owrership of appliancej. 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, = a 0+a, S,, +a 2Si2 +

... 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 ofhousehold i's appliance j STRUC, = pertinent structural features UPi, = utilization patterns relating to appliancej e,, = a random error term for the end use.
i Ofcourse, random-coefficient regression could be

used torecognize randomvariation ofUECs across households. But thepoint here refers to the ability ofthe analyst to model cross- .household variations in terms ofknown variables. Review of'Comnitionai: .'imandiAltltodohlioLy 

## \ \

may be available ior the analysis. However, their

roles can be recognized also indirectly by including variables that aree:-..pected to becorrelated with the features (age ofthestructure, householdsize, 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 ifthis isnot theca?e. Utilization patterns (Quid cover thermostat settings on .....aterheaters, pools/spas and spaceconditioning systems, as well as a variety ofbehavioral patterns relating to thE' use

## or other·appliances. These utilization patterns are not·generally'directly observ**abl~,** 

### 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

# =LUEC", ,5;1 j=l 

### where the**foll()win~** 

### \'ariapledefinitionsareused: HEe, = 

### enerp;con'sumptionby household i 

### inper!l.)d t UECij, 

## = ener:;y consumptionthrough appliance j 

### inperiod t 5", 

## = a bi.nar:· indicatorofhousehold i's ownershipofappliancej. 

### Both HEClI 

### and 5:., dre observable. Information on appliance stocks (5,,J 

### is generally obtained through a utility'sappliance :,aturation survey. Thisinformation 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 associatedwith theuseofCDA. 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 bewrittenas:

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 withoutthis·appiiarice, all**oth~r** 

### things equal, this tendency will be captured by the estimate ofthe 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 holdingsofotherappliances. Ofcourse, the above form ofa conditional demand model is highly stYlize'd, in that ittreats 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 formalizedas:·

where: AFij 

## = features ofhousehold i'sappliancej 

### ·STRUCi

## = pertinentstructural features UPij,

## = utilization patterns relatingto appliancej 

### eill 

## = a randomerrorterm for theend use.

1 Ofcourse,**r~ndom-cocffjci~nt** regression cl.luld be

used to recognize random variation ofIJECs acrosshouseholds. But the point here refers to theabilitv ofthe analvstto model cross-

. househoid variations'in terms ofknown

variables.Revieu' ofConditionalDemnandMethodology

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 ofinterest 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 outend-use consumption levels isdearly easier with the use ofmonthly data. In asense, the monthly variation in weather-sensitive loads (space heating, cooling, water heating and perhaps others) helps to distinguish these loadsfrom 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 ofthe regression to distinguish the impacts of these variables. As a result, it may be difficult to disentangle the influences ofindividual 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 onerefrigerator. Thisparticular problem can be alleviated ifinformation 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 theequation. 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 \.. "



### 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 modelcan be writtenas:

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 purposesoftheanalysis. 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. Estimatingthe Model The CDA model can be estimated statistically by standard multivariate regression analysisusing 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 generalissuesare mentionedbelow. 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 ofinterest 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 applianceswithvery 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, sortingoutend-useconsumrtionlevelsisdearly easier with theuse ofmonthlydata. In a sense, the monthly variation in weather-sensitive loads (space heating, cooling, waterheatingand perhapsothers) helpsto distinguishtheseloadsfromeach**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 oftheregresSion to distinguish the impacts of these'variables. As a result, it may be difficult to disentangle the influences ofindividual appliances or household features on.·total energy consumption. for instance, refrigeratorconsumption may be difficult to distinguish from consumption through unspecified (miscellaneous) appliances because all householdsare likely to own at least onerefrigerator. This particularproblemcan

.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, priorinformation mayhave to be used to constrain the relative or absolute values of some coefficients, or certain terms may simply have to be dropped from theequation. 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, weRtrzw;c of c)f nditionual DkentandMA~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 Si , terms. So it may be advisable to use one of the standard corrections for heteroskedasticitv in order to improve the efficiency ofestimated 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 forremoving 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 ofsuch 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,), forSi = 1, where F, is an estimated form ofF,. 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 = a 0 + a, SOFT, + a HDD - a 3MP,,forSi = 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 ofprice variables is discussed in Section 5. - See. for instance, GCicser (1969).

· See Dubin and McFadden (1984). I.. "**Rl~';fi{1** ofCOl/{iiliollll/Dl!molld

### Mdirodo/v:\y

could write**th,~** 

### household energy consumption equationas:


### J (Sa) HEC" 

## =**~F,(AF",** STRUCj• WCjl' MCj,•EDC;l'S,j 

### +

### e jl 

### i=l
J where

### e'l =

### ~

### e 'llS't 

### i=l Clearly, the variance of the error term e'jt will depend upon the values ofthe Sjj terms. So it may be advisable to use one 9f the standard corrections for heteroskedz1sticity in order to improve the efficiencyofestimated**coefficients.~** 

### Exogeneity of Appliance Stocks. In most COA specifications, appliance stocks are taken as exogenous, orgiven. 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 ofthe same factors thatare .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 conclusiveon thepoten. ofthe associated UEC. In general, this problemcan be minimized with more comprehensive question**nc~ires.** 

### Given a questionnaire and the data set it generates, however, the analyst should exercise caution in.accepting UEe estimates \\;thoutconsideringthe·possibilityofsuchbiases. 

### 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 intothemodeL


### 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 WCREFjand**~1CREFi'** 

### respectively). Using the notation developed above, the UEC equation canbewrittenas: tial size of this bias. In any event, standard approaches ·are 

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

### and for removing them.J 

# " " UECji = .F,(AFil' STRUCj•WCREFj, MCREf,•EDCj). 

### forSj

## =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 ofthese missing appliances is correlated with one 0: 

### moce of the appliances covered 

### bv the survev', theestimated 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 orattitudinal 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 ClfF,. As anexample, 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 

## =a o

### + a1SOFTj 

### +a 2HOD '" 

### a 3MPj•

### forSj

## =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 weatherconditions to derive UECs.· When this practice is followed, the resultantestimatesarecalled weathernonnaiized UECs

.

.

# . 4**Th~** issueof**th~** appropriatespeci.!lcation ofprice

\'ariablesis discussed in Section 3.Review ofConditionalDemandMethodology
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 =( wUEC,,)/ w, i i . where the summation is over the set ofhouseholds with the appliance in question (i.e., for whom 

### si = 1). 5 Another approach to calculating average UECs involves the insertion ofmean 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 + a 2HDD +a 3MP where the respective means are defined for households for whom S, = 1.' One note of caution is
5 This assumes that binary values ofS$, 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 oimeans could involve the useof

probabilistic weights ifpredicted 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 ofthe 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 isextremely valuable insofar as it recognizes the sensitivity of unit energy consumption to household characteristics and market conditions. On the other hand, the dependence ofUECs 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. \" "



### Deriving UECs on a household-by-household basis (by substituting each household's valueofthevariables in the UEC equation) canbe

### a useful intermediatestep; however, estimates are.generallyderived

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

### 5ij 

## = 1).5 

### Another approach to calculating average UECs involvesthe insertionofmeanvalues oftheexplanatory 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 computationalapproach, wecould write:

where the respecti"e**m~an5** 

### are defined for households for whom S, 

## = I." 

### One note of caution is

5 Thisassumes that binary values of5ii areused. If

fractional prediCted \'aluesare used. households with non-zero probabilitiescould be included in thiscalculation and therespective probabilities could be used in weights. 6 Again. thecalculation oimeanscould involve the useof

probabilistic**woei~hts** iipredicted saturationsareused inthe estimation process. . . 

### ReviC!l('

## ofConditio/lal DemandMethodology

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 meansoftheoriginalvariables (5QFfandHOD)

.. 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~** 

### Forinstance, the first' procedure is probably more convenient if theanalyst wants to estimate LJECs for a numberof subpopulations. Once household-specific UECs are calculated, these values can be summarized verj 'easily for household groupings using standard subroutinesinvariousstatisticalprograms. 

### 5.' Comparabilityof UECs Across Service Areas ' Regardless of the approach to extracting estimated UECs from the Conditional Demand model, one point is

### cl~ar: the'values ofthese UECs depend upon the values of the variables in 

### the UEC equations. Thisfeature is extremely,valuableinsofarasit recognizes the sensitivity of unit energy consumption to household characteristics and marketconditions. On the otherhand, the dependenceofUECs 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, comparingUECestimatesacross service areasrequires that theestimatesbe 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 servicearea boundariesis 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 ofthe conditional demand model (e.g., the space heating portion); * a summary ofthe range of UECs estimated by various analysts; * an attempt to standardize for key determinants of the variation ofUECs 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 servicearea 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 ofestimates across service areas. For the sake of illustration, define a household's space heating UEC as UECsH. The space heating UEC equation canbe expressed as: (10) UECsH, = H(STRUC,,EFF, INS,.EDC,.WCi,, MC,)

As noted earlier, STRUC, refers to a set ofstructural features of the housing unit. In the context ofspace heating, these could include: * the square footage ofthe home * the number of windows * the materials used forroofing and siding * the type of foundation
_1- 

# 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:

• spaceheating
• airconditioning
• waterheating
• refrigeration
• cooking
• poolsand spas
• others. For each end use, the presentation entails a series ofsteps:
• a review ofthedetailed form orthe relevant componentoftheconditionaldemand model (e.g., thespace heating portion);
• a summaryoftherange ofVECsestimatedby various analysts;

• an,attemptto standardize for key determinants ofthevariation ofUECs across serviceareas; and

• an assessmentofthe transferabilityofthese estimatesacross service areas. The summaries covera fairly large numberofstudies, some of which relate to all end uses, others of which focus ononlyoneorsome.Table 3-1 lists the studies that will be cited at one point oranother 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, studiescovered in the 1984 Lawrence and Parti review areexcluded from this review. As noted earlier, all studies covered by thesurveyareconductedattheservicearea level. This approach was used in order to facilitate the standardization of estimates for regional weather conditions. As shown, 27 studies arecovered by the sl,lrvey. Thewiderangeofserviceareas encompassed by these studies should provide a goodbasis for thecomparisons thatfollow.



### B. Space Heating
1. General Form ofSpace 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 comparisonsofestimatesacross serviceareas. For the sake of illustration, define a household's space heating UEC as UECsHi
. The space heating VECequationcan.beexpressedas:

As notedearlier, STRUCj 

### referstoa setofstructural**f~atures** 

### ofthe housingunit. In thecontextofspace heating, thesecouldinclude:

• thesquarefootage ofthe home
• thenumberofwindows
• thematerialsusedfor roofing and siding
• thetype offoundation'.

Summ,,ary ofUECEstimates

Table 3-1 Studies Reviewed UtilityService Area Analysts Year Pacific Gas and Electric (PGandE) In-House 1986 Pacific Gasand Electric (PGandE) RER. 1986 Pacific Gas and Electric (PGandE) In-House 1987 San DiegoGas &Electric (SDG&E) CSI 1985 San Diego Gas &Electric (SDG&E) RER 1986 Northwest Power Planning Council (NPPC) CSI 1984 Tennessee ValleyAuthority (TVA) Jackson 1985 Florida Power &Light (FP&L) QED 1986 NewYork 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 offloors * the presence ofan attic (and whether or notitis finished). The generic variable EFF, indicates the efficiency of the heating unititself. 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 ofthe 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 ofheater operation. While desired temperatures are not generally observed (thev are commonly modeled as a function of other variables), a reference temperaturecan be chosen to describe "typical" household behavior. The difference between outside temperatures and this reference temperature over the period being SI/II/II/ary

### ofUEC**E~till/ntl!$**
Table 3-1 Studies Reviewed UtilityServiceArea Analysts 

### Year Pacific Gas and Electric (PGandE) In-House 1986**Pacifi~** Gasand Electric (PGandE) RER. 1986 Pacific Gasand Electric (PGandE) In-House 1987 San DiegoGas & Electric**(SD~&E)** CSI 1985 San DiegoGas & 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) JerseyCentral 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) NewOrleans 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 numberoffloors
• thepresenceofanattic (and whetherornotitis finished). The generic variable EFF; indicates the efficiency of the heating uniit itself_·Some basic efficiency differences areoftentaken.intoaccountby distinguishing between types of heating systems (e.g., resistance heating and heatpumps). But finerefficiency differentials could also be considered (perhaps by incorporating.the**ag'~** 

### oftheheatingunit). 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 intothe 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. Examplesofsuchvariables**incl~de:**



### • income
• agedistribution
• household size
• occupationalstatus. Weather conditions are obviously important determinants ofspace heating consumption. The difference between outside temperatures an.d desired ·interior temperatures isa key driver ofheater operation. While desired temperatures are not generally observed (they are commonly modeled as a function of othervariables), areference temperaturecan be

### chosento describe "typical"householdbehavior. The difference between outside te·mperatures and this reference temperature over the period beingSummaryofUEC Estimates
analyzed is often quantified in one of two general ways: * via the useofa heating degree-day measure * through the use ofa heating-requirements specification. Heating degree-day variables are based on a comparison ofdaily 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 heatingrequirements. 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, forinstance, RER, 1985), the gains from doing so seem to be modest. As suggested by the long list of possible determinants, the modeling ofspace heating UECs can be a complicated process. It is not surprising that estimates of space heating UECs are fairly sensitive to the specification ofthe UEC equation;


### 2. Summary ofSpace 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 ofsaturation 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 thepurposes ofthese 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. 

### analyzed is often quantified in one of two general ways:
• via theuseofaheatingdegree-daymeasure.
• throughtheuseofaheating-requirements specification. Heating degree-day·variables are based on a comparison ofdaily temperatureswithreterencevalues. 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 thoughtofas a thermostat·settingless 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 thereference temperature experienced across hours over I:he cour.se of the day. It 

### reflects, in**ess~nce.** 

### a simplified engineering estimate of heatingrequirements. Of course.**temperatur~s** 

### are not**th~** 

### only weather condition**af:fecti~g** 

### space heating consumption. Other pertinent weather \·ariable:;. are rainfall. humidity, hoursofsunlight, wind**s~ed.** 

### etc While there have been some attempts t..."1 

### include other weathervariables 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. themoqeling ofspace heatinf: UECs can be a**complicat~d** 

### process. It is notsurr:ising that estimates of**spac~** 

### heating UECs are fairly sensitive to .' the specificationofthe UECequatior1;



### 2.Summal:YofSpace Heating UEC**Estimat.~s** 

### This subsection provides both tabuI.:.: and graphical summaries of estimated**space-hea:l~g** 

### UECs. The analysiscovers two majorappliance-:J.tegories:

• primaryconventional electric spa.:.: heating
• heatpump 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.

. . 

### SummaryofUEC Estimates

Tables 3-2 and**3~3** 

### list the estimated weather-normalized space heatingUECs reportedin 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 estimatesarepresented

.. 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. estimatesofconventionalheatingUECs weredevel- "ped from the more disaggregated values. by using information on relative saturations of the heating systems in question. Estimates of conventional and heat pumpheatingUECs wereaggregated toobtain overall·space heating UECs for all types ofelectric systems. In some cases. the process ofaggregating UECs was aided by, the presence ofsaturation data in 

### the**techni~a1** 

### documentati.on ofthe studiescited. In other instances, the subject utilities provided estimated saturations for use in this study. UEC estimates derived for the.purposesofthesecomparisons.are presentedin 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'studiesinwhich separate UECs wereestimated

.. Oneshouldbecautiousininferringrelative 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 withjadiantsystems. 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 UECEstimiates Table3-2 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 6,47 e

. 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 ofequal saturations of central and radiant systems.
c Weighted averages of UECs for individual dwelling types. d Total heat pump usage wasbroken into heating cooling estimates by identifying monthly usage as either

heating!cooling (orboth, in transition months).

e Estimated using saturations from 1984data base.



### Table 3-3 Primary Space Heating UECS: Single-Family Dwellings Type ofHeating 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 Slll/l/llfl/'Y

### ofUEC Estimate:>

3-4 Table 3-2· Primary Space Heating UECS:All Dwellings Typeof 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 ofequal saturations·01 central and radiant systems.
c Weighted averagesof UECsfor 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 usagewas broken into heating cooling estimates by identifying monthly usageas either

.heating!cooling (or both, intransition months).
e Estimated using saturationsfrom 1984data 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 perdwelling.Summnan of UECEstimates

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&Lto almost 7,500kWh for NPPC. Aggregated space heating UECs follow a similar pattern, with three values close to 10,000 kWh and several around orbelow 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 ofthis variation are considered.
3. Standardizationof 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-SquareValue: 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 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 peryearfor Northwest PowerPlanningCouncil (NPPC) to roughly 740 kWh for Florida Power & Light (FP&L). Heatpumpvaluesvary from,a low of roughly 800 

### kWh for FP&L toalmost 7,500kWh for NPPC. Aggregated space heating UECs follow a similar pattern, with three values close to 10,000 

### kWh and severa)around orbelow 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 sourcesofthis variation areconsidered. 

### 3. Standardization ofSpace Heating UECs for Differences inWeather 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

UnitEnergyConsumption 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. Ofcourse, this analysis requires information on weatherconditions in 

### each of the service areas under consideration. Because most of the

_ studies covered by the analysis used heating-degree-days torepresent 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 weatherstations withintheserviceareasin 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 wasused in thecalculation ofall heatingdegree-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 linkbetween these estimates and the regional values of heating

AllDwelling Types Single-Family

Dwellings



## o---**-...:~.**

## ---------- ------------ ---- ------------------'

o 2,000

Moclels: UEC(AfIDwellings} 

# = -1012 + 1.62(HOO) UEC(SingleFamily)

## = -702+ 1.65(1100) 4,000 6,000 HeatingDegree-Days

Figure 3-1. Conventional Space Heating UECs vs. Heating Degree-Days 8,000
R-SquareValue: AflDwellings: 0.6901 SingleFamily:0.7311



### 3-5Suinrlarlu o(t' UE:CELtia;:lc
degree-days is strong.' Simple regressions of the estimates for single-family dwellings on the degreeday measure yielded an R 2 value over .70. The R 2 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 ofthe 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 thatwould 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 notcollected 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 R 2 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 otherhouseholds 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 ofthe 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 HeatingDegree-Days

= -1268 + 1.95(HDD)
= -1809 + 2.07(11DD) 

### Estimates Across summarized above, it

All Dwelling Types Single-Family

Dwellings

8,000
R-SquareValues: All Dwellings: 0.7634 Single Family: 0.7682 Figure 3-2. Heat Pump Space Heating UECs vs. Heating Degree-Days 3-6 

### 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 estimatestend tohave substantially, torinstance, 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 economiddemographicfeatures. Eviconstructed for thisanalysis 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 holdswithelectric space heating. incomes in some areas. The degree ofthese differF' 3 2 '11 t t th . 1 t' b t heat ences between heatpumpownersandotherhouse-

Igure

- 1 

### us ra es e re a IOn e ween '. , . . holds With electnc space heatmg undoubtedly

1 Fourestimates thatwould havebeenoutliers

wereomitted from these andotherplotsin this Section,Theseestimateswerederivedas part of a feasibility study, usingdata notcollected specifically for CDAestimation. Moreover, the analystwhodeveloped theestimatesrecognized thattheywereunreasonably low,

UnitEnergyConsumption 20,000

-

IS.000

..

10,000

..

5.000

.



### o

### o
:\1odells: 2,000

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

## =-1809+ 2.07(IIDD) 

### varies across service areas, making comparisons of heatpumpUECs verydifficult. 

### 4. Transferabilityof UEC EstimatesAcross ServiceAreas On the basis of the evidence summarized above, it

4,000 HeatingDegree-Days 6,000 

### o

8,000 AU Dwelling Types Single:.Family

Dwellings ---8---

R-SquareValues: AU Dwellings: 0.7634 SingleFamily:0.7682 Figure 3-2. Heat PumpSpace Heating UECs vs. Heating Degree-Days ' 3-6

### SulmmaruofUECEstimates

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 beexpected 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 ofthe AirConditioning UEC Equation This subsection considers thetypical 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 ofUEC 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 setofstructural 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.

UnitEnergyConsumption 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-SquareValues: 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

.---- )---

, _ = 

### appears thatconventional space heating UECs trace out a consistent pattern when mapped against weather conditions. Whether ornot these estimates are "transferable" in a rigorous sense is open to interpretation. Oneonehand, thedegree ofcorrelation 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 otherfactors canbeexpectedto playa substantial role indeterminingspaceheatingusage.**Conse~** 

### quently, these area-specific factors should be 

### taken intoaccountindevelopingend-useestimates.


### C. Air Conditioning
1. General Form oftheAir Conditioning UEC Equation This subsectionconsidersthetypical specification of the space cooling portion of a conditional demand

UnitEnergyConsumption 6,000 ,..
5,000 :- 4,000**~**
3.000**~** "2,000**~**
1,000 :-
0---- 

### Summaryvi 

### 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 comparisonofUEeestimates. A household's space cooling UEC is referred.to as UECsCi
. The general space cooling UEC equation canbe expressed as:

As 

### notedearlier, STRUCi 

### refers to a setofstructural features ofthe 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 onthesefeatures areavailable.



## c "AllDwelling Types Single-Family

Dwellings



### ....**-~)---**



### o 500 1.000 2,000 2,500 3,000 3,500 4,000 CoolingDegree-Days "Models:

UEC(AJIOIfellings) 

## = 565

### +l.18(CDD) UEC(SingleFamily)

## =458+ L'-t(CDD) R-SquareValues: All Dwellings: 0.6225 SingleFamily:0.8726 Figure 3-3. Conventional Central AirConditioning UECs vs. Cooling Degree-Days' 3-7Slumnnmna ofUECEstimates

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 ofair 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 inthe 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 AirConditioning 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 ofthese two customer categories. These studies are listedin only one ofthe 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 ofcorresponding 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 

### Summary afUEC.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'whetherornota particularkind of insulation is present) or continuous (describing R-values). The same weatherization variables could be incorporated into the space cooling UEC equa-' tion as discussedearlierfor theheatingequation. Economic and demographic variables (EDCi) 

### are typically includedtocapturethedeterminantsofair conditioner thermostat settings. As for space heating, these variables might include income, age distribution, andotherhouseholdfeatures. Weather conditions have obviousimpacts 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 temperatureqifferentialfora "typical" householdis derived using a constant reference temperature. The cumulative difference between outside.temperatures and this reference temperature over a distinct time interval (say, a day ora billing period) is usually capturedin theform of:

. • ,a coolingdegree-daymeasure
• a**cooling-requin~ments** 

### specification. These measures are similar to theheati'ng measures described earlier (See Lawrence and Parti, 1984).

, Like space heating. coolingis undoubtedly sensitive

to otherweathE!r conditions like humidity and wind speed. There hav,e been some'attempts to include other weathervariables in CDA models (RER, 1985;. QED, 1986), and these approaches appear to hold some promise. Still, temperature-based weather measures arec1earh' the dominantvariables used in conditionaldemandanalyses.


### 2. SummaryofAir Conditioning UEC Estimates This subsection presents estimates of UECs for the following spacecooling appliances:
• conventionalcentral airconditioning
• room airconditioners
• heat pumpairconditioners. The survey did not uncover enough estimate's of other space cooling appliances (evaporative coolers, attic fans, etc.) to allow meaningfulcomparisons. 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** 

### customercategories. These studiesarefisted inonly,oneofthetwo tables

.. The survey indicated that some studies combine heat pumps with conventionalcentral 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 inTable

### 3-4, conventional centralcooling systems UECs take on a wide range of values. Southern service areas (TVA, 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 obscuredby thelack ofcorrespondingestimates' 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 oftheservicearea (orthesample 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 ofvalues. As discussed below, this pattern is tied stronglyto weatherconditions. 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 pumpcooling than for conventionalcentral 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, specificTable 3-4 Space Cooling UECS: All Dwellings

Convent'l All Roomor 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 2.179a 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 ¢ 232d MetEd (87) -865 27' 240d NPC (86)

- 3,280e 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 withmore thanone 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 400a PGandE (86) 1,475 775 1.425 400a 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 459a LP&L (84) -- 1,834 339a MP&L (84)

- - 1,668 518a NOPS (84) -- 1,306 FP&L (86) -- 5.074 2,439a CommEd (87)

- - 2,030 510a VEPCO (87) 2,124 2,141 2,131 1,057a 709b NPC (86)
- - 4.451 3.264a R6&E (86)

- 1,011
- Sierra (88)

- 1,500 346a 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 Perunit. a All air conditioning, including room/wall units. Summary ofUECEstimates

3-9 

### Suilllllary ofUEC 

### Estimates

Table3-4 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 400a 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 2.179a 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 2,873b



# - - 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 evaporativecoolers. b Per unit. t All airconditioning systems.

C Homes with onlyone unit. 9 First room unit.
d Homes with more than one unit. n Second room unit.

Table3-5 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 531a SDG&E (85) 858 780 847 281 b SOG&E (84) 992 906 980 249b TVA (85) 3,419 4.360 

# - 1,181a AP&L (84) 

# - - 1.658 4598 LP&L (84) 

# - - 1.834 339a MP&L (84) 

### - 

# - 1.668 518B

, 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 709b NPC (86) 

### - 

# - 4,451c 3.264a R6&E (86) 

### - 

# - 1.011 0



### - Sierra (88) 

# - - 1.500 346a AEP (88)


# - - 1,487 335b 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. 3-9Suliinnary of UECEstimates

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 AirConditioning 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 R

2 of the regression linking single-family UECs with CDDs is close to .87. The R: ofthe 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, ofcourse, 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 ofAirConditioning 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 forwindow/wall air conditioning, partly because of the different ways ofmodeling 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 ,INS i ,LOC,, EDCi,WC,, MC i, DW i ,CW,) where EFF, reflects the efficiency of the water heating unit, while INS, indicates the presence of a SUlllmnry ofUEC E:;;tilllntc:'



### microclimate conditions, and thermostat settings of the householdsinquestion. Aggregated central cooling CECs (the'mostbroadly 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, otherfactors like relativeenergy prices probably also play a role. The impact of weatherconditionsis 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 unitspresent) 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 summerconditions.


### 3. Standardizi3tion ofAirConditioning UECs for Differences inWeather Conditions This subsection considers the relationship between estimated air conditioning CECs and weather conditions in the service areascovered by the analysis.

,To support the analysis, l\:OAA 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 on;ierto preservecomparability across**servicE~,**

### territories. Figure'3-3 ilIustr,ates the**rela~onship'** 

### between cooling degree-days and estimated conventionalcooling UECs. Cooling, degree-days are estimated from NOAA data on individual weather stations and either actual or assumed customer distributions across thesestations. As depicted in Figure 3-3, the correlation between weather'and conventional air conditioning UECs is reasonably strongfor bothresidencecategories. The 

### R2 ofthe regression linking single-family,UECs with COOs is close to .87. TheR: (If

### theregression for all d';velling types is only**.6~.** 

### primarily due to a couple of extreme values. Some variation around the regression lineis to bee:\pected, of("ourse, as a result of all the factors discussed earlier for space heating. In general, the degree ofunexplained 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 otherweatherfactors should havea moreimportant influenceon cooling loadsthanon 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 samplesize.


### 4. Transferability ofAirConditioning UEC EstimatesAcross, ServiceAreas The analysis of the weather sensitivity of cooling UECs allows usto offera preliminaryassessmentof 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 weatherconditions to warrant some confidence in the estimates. Adjusting values for other service areas for differences in weatherconditions (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 canbe said for window/wall air conditioning, partly because of the different ways ofmodelingconsumption throu'gh these appliances.



### D. Water Heating
1. General Specification Electricwater 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) UECWHj = 

### W(EFFj.INS

### j.lOCj.EOCj

.WC"MCj•

### OWj

.CW;l where EFF; retlects the efficiency of the water heating unit, while INSjindicates the presence of aSummaryof UECEstimnates

Unit EnergyConsumption

.5,000

-
4,000

3,000
2,000

.1.000

O

-

0 500 1.000 1,500 2,000 2,500 CoolingDegree-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-SquareValues: All Dwellings: 0.8614 SingleFamily.0.8680 Figure 3-4. Heat Pump Central Air ConditioningUECs 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 ofhousehold 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 ofwater 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, itmay 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. Thepresence ofa dishwasher probably has a more subtle influence on water heating. 3-11 '----- _ I

/L
v UnitEnellO'Consumption

, 5,000

..

4,000"

3,000'

2,000 :

.1,000

. -:

. '**/~.** 

### : 

### Summary ofUEC Estimates

AllDwelling Types. Single-Family

Dwellings '" 

# -

......-:..:;"'

### ..

### -

o**~**

### .....__...**-.L.~._ __~** 

# ___________ .....o.-
__ -, 

### o 500 1.000 1,500 2,000 2,500 3,000 3,500 4,000 CoolingDegree-Days Models: UE.C(A11 Dwellings) 

## = -1359+**~~(CDD)** UEC(SingleFamily)

## =-3028+**~.17(CDD)** R-SquareValues: AllDwellings: 0.8614 SingleFamily:0.8680 Figure3-4. Heat PumpCentralAirConditioning UECs vs. Cooling Degree-Days



### water heater blanket. (Note that i \'ariable representing the presence of a**10w-flC'~\'** 

### showerhead couldalsobe 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 numberofshowers**~~ken,** 

### thenumber ofloadsrun throughthe dishwa,;.her andwashing 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 ofhousehold sizeareI..)itenused. 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 ofwater 'heating UECs downward (and space conditioning UECs upward). Water heatingconsumption 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 adjustingconsumption inthe shortrun, it may stillbenon-trivial. Most conditional demand specifications include binary indicators of'the presence of dishwashers and washing machines. These appliances consume powerdirectly (through motorloads), butthey may also influence theuseofhotwater. The presenceof an automatic washing machine may mean that clothes are washed at home rather than at the laundr9mat. Thepresence ofa dishwasherprobably has a mO.re subtle influence on water heating. 3-11Sum(luaryl of LIEC Estiilates
Theoretically, it could actually conserve water, given the practice ofstoring 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,, Sow 0 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= a o - a,Sow, +a 2Sc i Substituting this expression into (13) yields: (15) (a.- a,So, -'-a 2 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 ofboth 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 waterheating UEC, which depends on dishwasher and washing machine saturations, is:


### a o +a,Sw+ a 2SC * 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:

a 2SWH + 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 ofincremental 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 ofwater associated with these appliances. UECs fordishwashers and washing machines (ifexplicitly derived) would include only motor loads. * Second; consumption relating to the use ofwater for dishwashers and washing machines can be isolated and presented as part of the UECs for those appliances. (The rest oftheir 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.**S[III["/(/"~1** 

### of LlEe [sliwal,'';


### Theoretically, it could actually conserve water,

.given the practice of storing dishesin the unit until

a full load is ac(:umulated. On the other hand, higherwater 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 somewhatinconsistent. The treatment of dishwashers and washing machines in estimat·ingwater 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 theyareobviollsly interrelated):

where UECWlh reflects the water heating UEC, including energyconsumption for heating waterfor 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; arebinary variables reflecting the presence ofelectricwaterheating, adishwasher, and a washing machine, respectively. For now, ignore the influence ofhousehold characteri.stics on water heating loads and focus on the interaction of appliance stocks. To recognize the influence ofdishwashers and washing machines on water heating loads,**th~** 

### water heating'UEC can be expressedas:

Substitutingthis. expression into (13) yields:

Given this specification, the following concepts can be defined (averages are indicated by dropping the subscript i):
• Thecoefficientall retlectsthe waterheating UEC in the absenCE! ofbothdishwashersand washing machines; it is sometimescalleda base water heating VEe.



### • Thecoefficients al and a: representthe incrementa] waterheatingloadsassociated with dishwasherand washingmachineownership (givenelectric waterheating). 3-12 • Thetotal waterheating VEe, 

### which dependson dishwasherandwashingmachinesaturations, 

### is:


### a o+

### a, Sow +

### a 2Sew


### • Theoverallunitelectricityconsumption associated with dishwashers depends. uponelectric waterheatingsaturations, andcan bewritten as: . 

### a, SWH + UECow 

### Note thatoverall dishwasher-relatedelectricitv consumption containsa motor-load UEC aswell as.theIncremental waterheatingload weighted

.by the shareofhomes with electric water heating.
• Theoverall unitelectricity consumption associated with**w~shing** 

### machinesdependsupon electricwaterheatingsaturations, and is given by:


### a 2.SWH + UECew 

### These concepts will be used below in interpreting the resultsofvariousconditionaldemand**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 thedifferential treatmentofincremental water heating associated with dish\

...·ashers.and washing machines makes comparisons very difficult. As explained above, there are 1\\'0 

### ways to presentwaterheatingUECs:

. • First, the waterheating 

### UEe canbe derived usingtherelevant saturationsofdishwashers and washing machines (along with all theother argumentsofthe UEC function). In thiscase, the waterheating UEC encompassesthe**incremen~al** 

### useofwaterassociated with these appliances. UECs for dishwashersand washing machines (ifexplicitlyderived) would includeonlymotor loads.
• Second;consumptionrelating to the useof\'I.·ater for dishwashersand washing machinescan bt' isolated and presented as partofthe CECs for those appliances. (The restoftheirUECs would be motorloads.) 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 DWUsagea CWUsage' 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 loadsonly. Motor loads arenotincluded. D Average of UECs derivedfrom several models.
c Water heating usage other than for dishwasher.

Table 3-7 Conventional Water Heating UECS: Single-Family Dwellings AllH20 H20 Heat Incremental Incremental Utility Heating exc. OW/CW OW Usagea 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. Motorloads arenot included. ° Water heating usage otherthan for dishwasher. Summnrv ofUECEstini:;:es

3-13 

# 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) 3,500b 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 NPC (86) 3,127 APC (83) 2,175c 869 TNP (88) 4,894 BG&E (88) 3.141 2,369 410

a Incrementalwater healing loads only. Motor loads are not included.
D Average of UECs derived from several models.
C Water heating. usage otherthan 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)


### - 4,370b 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. Motorloads are not included. o Water heating usage otherthan lordishwasher.Sunmanrn / I'LIECEstii,:.:c;r


### 3. Standardization of Water Heating UECs for Differences inWeather Conditions Ideally, water heating UECs should bestandardized 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 forthe 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 notplausible 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 

### SU/1/mary o(

### LlEC**E~ti,,:.;~l·~**


### 3. Standardiization ofWater Heating UECs for Differences in Weather Conditions Ideally, waterheatingUECs shouldbe standardized for 

### variations in groundwater temperatures across service areas to facilitate theircomparison. Unfortunately,**reliabl,~** 

### data l10 

### groundwater temperatures are extremely difficult to find. Instead. average annual temperatures. as reported by NOAA, were used to representgroundwatertemperatures for the 'service areas covered by the survey,· In general, there is a close corresFondence (on an annualbasis) between ambient air**~emperatures** 

### and water temperatures at·the sen'ice inlet, so this.approa'ch shouldbe**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 waterheatingconsumption except dishwasherandclothes washerincrements.


### , Estimates for four service areas were omitted from the latter figure because they seem implausibly

2 Oneestimatewas·ornitte-': from the scatter

because it wasnot plau;:!:-le given the average temperaturein the seni.::e territoryin question.

UnitEnergyConsumption ::,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 accountin 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 averagetemperature

. 

### 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 AverageTemPerature(DegreesFahrenheit) Models: UEC(AlI**Dwelli~s,** 

## = 5145

-34.37(Deg.F) UEC(SingleFamily) = 7293

-70.85(Deg.F) 70 75
R·Square.Value: AllDwellings: 0.0623 SingleFamily:0.1860 Figure 3·5. Conventional Water Heating 

### UECs (All H20 Heating) vs. Average TemperatureSummary ofUECEstimates

Unit EnergyConsumption 5,000

- 4,500
. 4,000 - 3,500
- 3,000
- 2,500
- 2,000 - 1,500 AllDwelling Types Single-Family

Dwellings
--- & 0



### --., z

1,00040 45 50 55 60 65 AverageTemperature (Degrees Fahrenheit) Models: UEC(AII Dwellings) =7053

-69.75(Deg.F) UEC(SingleFamily) =6881

-6123(Deg. F) 70 75
R-Square Value: All Dwellings: 05062 SingleFamily: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 ofwater 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 ofthis 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 R 1, is the usage ofa manual defrost refrigerator, SFFR, indicates the ownership of a frost-free unit, and f 2 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 ofrefrigerators. The important thing to note is that Ro, and R 1l 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 - -- UnitEnergyConsumption 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 'SlIllImaryof

### U£C Estimates



## -.

75 AllDwelling Types----E::rSingle-Family

Dwellings ---0---

AverageTemperature(DegreesFahrenheit) Models: UEC(AII Owellinj!s) 

## = 7053

- 69.75(Deg.F) UEC(SingleFamily) = 6881

-61.23(Deg.F) R-SquareValue: AllDwellings: 0.5062 SingleFamily:0.4286 Figure3-6. Conventional Water Heating (H20 heatexc. DW/CW) vs. AverageTemperature



### should not simply borrow a water heating UEC from'another, At best, estimates from**comparabl~** 

### service areas could provide rough reference points for ananalysis ofwaterheatingconsumption. ,


### E. ,Refrigeration, '
1. General Specification This subsection focuses on energy consumption through refrigerators and freezers. Deriving UECs for refrigeratorsis very difficult as a consequence of the widespread ownershipofthis 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 energyconsumption resulting from a second ,or subsequent refrigerator, or a frost-free unit (as

opposed to a manual defrost unit). One standard specificationcould 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. Anothercommon specification is:



### where R\i 

### is theusageofa manualdefrostrefrigerator, SFFRi 

### indicates the ownership of a frost-free unit, and**f~** 

### is the incremental L1.sage of a frost-free unitrelative to a manual defrost refrigerator. Of course, other variations of these specifications could be used to distinguish among various numbers and types ofrefrigerators. 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.). 

### 3-156 I i ! I : i

i i

I :uflfllarifeiJ

### UEC Etiiwtes

FREQUENCY

9

4

i 2

900-1099 1100-1299 1300-1499 UECs (UsageofFirstRefrigeratororAverage perUnitofAllRefrigerators) .. . i 3
I1
I

Figure 3-7. Distributionof 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 ofUECEstimatLes

< 900 1500-1699 1700 +

2

i ::,:wllIll7ry

### of

### UEC**E~tilllalL':;**

3-16 FREQUENCY

3

; 1 1 r(

------ i i <900 900·1099 9 r----·

6

1100-1299 • I ; 

## : II ! :

UECs1300-1499, 4

1500-1699
(UsageofFirstRefrigeratororAverageperUnitofAll Refrigerators)

FREQliENCY

2 Figure3-7. Distribution ofRefrigerator UECs Single-Family Dwellingsand AII'Dwellings

11

9

5

2 

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

### _ .. , . : 1


# -----_. __..

# _._.......

# __. <700 700-899 900-1099 1100-1299 1300-1499 UECs

Figure3-8_ Distribution of Freezer UECs Single-Family Dwellings and All Dwellings 2

1700+

/

3

1500

### +

### Summaryof UECEstimates

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 ofestimated 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 bythe 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 firstunits, with a difference of roughly 300-400 kWh. * Ifall ofthe 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, ofcourse, without saturation data), the range ofUECs would besomething 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 useover and above that fora manual defrost refrigerator. b For first unit. c Estimates total usage of 3.429 if two ormore units arepresent. d Estimates total usage of3,111 if two ormore units are present. e Manual unit. f Frost-free unit. 3-17 

### i L'ECs for stand-alone freezers can generallY be modeled fairly simply. A traditional CDA specification wouldbe:

,,'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. Summaryof Estimated UECs Tables 3-8 and 3-9 present summaries ofestimated 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 inconditionaldemandmodelspecifications. However, some general patterns are revealed by thedata: SUllllllary

### ofUECEstimates

• Manual defrostrefrigerators appearto haveusage levelsinthe770-1,300 kWh range.
• Theincrementin usageassociated witha frostfree refrigeratoris generallyestimatedto fall in the600-850 kWh interval.

• Estimates ofUECs for first and subsequent refrigerators (withoutregard to de(rostfeatures) vary too much to produce a clearpattern. As expected, though, second and subsequentunits generally have lowerestimatedconsumption levels than first units, with a differenceof

- roughly 300-400-kWh.

• Ifall oftheestimatesinTables 3-8and 3-9 were converted to singlevalues for refrigeratorsin general (something that cannotbedonewithany greatprecision, ofcourse, withoutsaturation data), the rangeofUECs would besomethingon theorderof 1,200-1,500 kWh. Qifferencescould reflect weather\'ariation alongwithdifferences inhousehold features.

• Estimated freezer UECs alsocovera widespan; howe\'er, a substantialportion oftheestimates fall in the900-1,100 range. Table 3-8

, i Refrigeratorand 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.

772b 836

7,297 Incr.for Frost-Free First

Refrig.a Refrig.

622b 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,406b 1,184b 1,045 1,209 1,5769 '1,809'

804 1,225

3-17Sumtinai ofUECEstimates

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 1.043e 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 useover and above thatfora manual defrost refrigerator. " Forfirst unit. c Estimatesforage-specific groupings were the same for all residence types. The figures here were calculated on the

assumption that relative saturations are also thesame 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(EDCi , 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. 

### Summary ofUEC Estimaks

Table 3-9 Refrigeratorand 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,007tl '480b



# - '- 1,385 820d l,225e SDG&E (84) 836 611


### - - 1,317 880 TVA (86)


# - - 

# - - 

# - 1,420 AP&L (84) , 

### - 995 

# - - 

# - 933d t,043e LP&L (84) 

## _. 1,493


### - - 

# - 497d 1,441e 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 

# - 1,259tl 1,238 NPC (86)


### - 

# - - 1,621 

# - 1,045 RG&E (86) 1,297c 802c 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 abovethat 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 acrossservice 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.Sumimnryof UEC Estimates

As a result, an additional UEC equation is necessary for this appliance. A standard form is: (20) UECM,,,, = m 4(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 thisappliance.


### 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. Poolsand 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 dueto microwave ownership.
3-19 

### As a result, an additional DEC equation is necessaryfor thisappliarice. A standardform is:



### 2. Summaryof 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, multipleovens maybe present. Second, electric ranges tend to be owned by relatively high income households. If 

### thesehouseholds 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 respectto thecoverageofspecified appliances, SlImmary ofUEC**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 tocook out oreat out than those facing inclementweather much of the year. For instance, estimated UECs tend to'be fairly low in the California andFlorida service areas. . Microwave estimates are much more consistent, with UECs in the \icinity of 200 to 300 kWh being estimated for this appliance. 

### 3. Transferabilityof Estimates The microwa\'e CEespresented above are probably reasonable estima:es ofthe 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 operaTable3-10 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) 593a 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.
3-19Table 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 tomicrowave 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. Distributionof Range/Oven UECs

Single-Family Dwellings and All Dwellings

3-20 6
·.i i i
i i i I

f Summnlry ofUECEstimates SUlIZmary UEC 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 87b Sierra (88) 848 284 AEP (88) 1.040 

### - BG&E (88) 606 200

a Without microwave.**C)~~** b Incremental electricity use dueto 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 Dwellingsand 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 ofUEC 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 oftheir 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 SpaPumps 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 includesome electric spa heating. 

### 3-21 Figure 3-10. Distribution ofMicrowave Oven UECs Single-Family Dwellingsand All Dwellings tion of cleaning systems, could be specified as a (22) UECsp,, = p 2(SEAS,, WC,, MC,, EOC,, CONS,) function of several variables. A reasonable specification might be: '---

,

---- I

I I: i.:I FREQUENCY

3

< 100 100-199 9

200-299

UECs I ',I

2

300-399

Figure3-10. Distribution of MicrowaveOven UECs Single-Family Dwellingsand All Dwellings 

### Summary ofUEC Estimates

400+

tion of cleaning systems, could be specified as a function ofseveral 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 energyconservation 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 theseestimates: the unde'rstanding that some spa usage may take, • Pool pumping takesroughly 1,700 to 2,700kWh the form ofspa heating: in service areaswith 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 electricspa heating, 3-21Suimmaryf oI' UECE: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 touse approximately

700-1,350 kWh annually. 

### 3. Transferabilityof 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 ofsaturations, the design ofutility 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 ofdecomposing dishwasher consumption (see our earlier discussion on water heating specifications), dishwasher UECs differ dramatically across studies. Ifattention 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 ColorTV 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 1.218a

- - 380b MetEd (87) 1,012 1.194a

- -432 b NPC (86)

- 423a

- - 498 RG&E (86) 828 93 80 160c 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. thiswould include some incremental electric water heating. Insofaras

some homes with theappliance have non-electricwater heating, however, thisestimate should not be interpreted as the sum ofthe motor load and the incremental electric water heating load. b For firstunit. c Derived from relativesaturations with and withoutVCR present. d For households with no children and at least one non-working adult. e All TVs. 3-22 

### summers. Th<:! low estimate for RG&E andthe high value for Nevada Powerare unsurprising, given the weatherconditionsin Rochesterand Las Vegas.
• Spa pumpsappearto useapproximately 700-1,350 

### kWh annually. 

### 3. Transferabilityof Estimates The spa and**po~1** 

### pump UECs are consistent enough to be used as reference values.in service areas with similar weatherconditions. 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 ofsat!lrations, the designofutility marketingprograms, regulatory directives, and so on. In general, these other UEC equations**includ~** 

### relatively few arguments

- typically, household size, income, square footage, and a few othervariables. 

### 2. SummaryofSelected

### UEC Estim.ates A full listing of all UEC estimates covered 

## by the analyses surveyed would be extremely tedious. However, Tables**3~13** 

### and3-14 

### presentestimatesfor 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 variationsin usage levels, butprobably also indicates theimpactof differences in model specifications. Like electric ranges, dryers mayactasproxies for a hostof excludedappliances.

. • Duelargely to different waysofdecomposing dishwasherconsumption (seeourearlier discussion on waterheatingspecifications), dishwasher UECs differdramaticallyacross studies. If

### attention is restricted to studies explicitly estimating motorloads, however, the
Table3-13 Other UEC Estimates: All Dwellings Clothes D'Wash C'Wash B&WTV ColorTV Utility Dryer Motor Motor all units allunits 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 modelspecification. this would include some incremental electric water heating. Insofar as

some homes withtheappliance have non-electricwater 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 saturationswith and without VCR present. . d For households with no children and·at least one non-working adult.
e All TVs. 3-22Sum:mary of UEC Estimates



### Table 3-14 Other UEC Estimates: Single-Family Dwellings

Clothes D'Wash C'Wash B&W TV ColorTV Utility Dryer Motor Motor all units allunits 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 612a AP&L (84) 781 840a
- - - LP&L (84) 325 1,467a

- - - 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 5 70b Sierra (88) 728 559 -69

- BG&E (88) 1,301 1,053 513
- 938

a According to model specification, thiswould include some incremental electric water heating: Insofar as

some homes withthe appliance have non-electric water heating, however, this estimate should not be interpreted as the sumof the motor load andthe incremental electric water heating load. b Derived using relative saturations with and without VCR, from data on all dwellings.



### Figure 3-11. Distributionof 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 

### Summary ofUEC Estimates

Table 3-14 Other UEC Estimates: Single-Family Dwellings

Clothes O'Wash C'Wash B&WTV ColorTV 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, thiswould include'someincrementalelectricwater heating. Insofar.as

sclme homeswiththe appliance have non-electricwater heating, however, this estimate should not be interpreted·asthe sum of the motor load andthe incremental electricwater heating load. b**D«~rived** using relative saturationswith andwithout 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. DistributionofClothes Dryer UECs Single-Family Dwellingsand All Dwellings 4

1300

### +

3-23FREQUENCY

7 | 

### .1

. i 4

i 3
I :

'.

iI 2

0 200-399 400-599 600-799 UECs (Excludingthosewithsomeincremental water heating) Figure 3-12. Distributionof Dishwasher Motor UECs Single-Family Dwellings and All Dwellings

FREQUENCY

6

5 i
42

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 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 (ExciudiDgthosewithsomeincrementalwaterheating) Figure 3-12_ Distribution of Dishwasher Motor UECs Single-Family Dwellingsand All Dwellings

6
5

3,

1



### ----

. , 

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

# _ ..

## __..

## _--_.

# __..--.--...."-.--_._-_._
..._-.- ....

### -

... 200-299 300-399 400-499 500-599 UECs 600-699 700-799.

Figure 3-13. Distributionof ColorTV UECs Single-Family Dwelltngs and All Dwellings 3

1000-1199

4

800+Summary of UECEstimates

results are relatively consistent. Most ofthese 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. ColorTVs (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. Transferabilityof 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 

### resultsare relal:ively consistent. Mostofthese estimates(those not markedwith a footnote in Tables3-13 and 3-14) fall in the vicinityof 250 kWh.

. .'

• There is insufficientinformation on clothes washermotorloadsto warrantgeneralization.

• Black & 

### whiteTVs (all units present) seem to use 120-200 kWh peryear. ColorTVs (again, all units in thehome) aregenerallyestimated to use 

### SUl1lmaryof

### UEC**~~/imates**


### between400 and 600 kWh, although a few estimatesfaIl outside this range. 

### 3. Transferabilityof 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 ofsome ofthose 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 ofwhole-house consumption 3. comparison with appliance metering results. Perhaps the least satisfying of these alternatives is the first. Engineeringestimates certainlyprovide 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 ofhouseholds 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 notused 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 comparisonwith actualmetereddata 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 PacificGas and ElectricAppliance 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 

# 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 studiesin whichreference data ha\re been used to assess the general accuracy of CDA estimates. Theresultsofsomeofthose studiesare re\;ewed

### in this section. Several approaches havebeen used 

### to validate CDA estimates, including: 1. 

### comparisonv ...ith engineering estimate;: 2.**out-of~sampl,~** 

### simulation of**\\'hole-hou~e** 

### consumption 

### age, the total level ofconsumptionofhouseholds 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 ofthe CDA model to data on households not used to estimate the model. Thesesimulations are useful, in that they indicate the general ability of CDA estimates to describe overall household consumption behavior. However, they do not directly test UEeestimates 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 thesestudies.



### B. Review 

## of Comparisons With End-Use Metering Data 3. comparisonwith 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 meteringestimates 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 ofhouseholds. 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 problemsin theCDA approach. Any CDA model will correctly predict, ,": 

### the aver

### 1. 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 data 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 forall households in the AMP population, whereas notall 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 thehouseholds covered by the two sets ofestimates. 2 Fifty percent of the compressor usage and 10

percent ofthe 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 ofheat pump usage by allocating compressor

and heatstrip usage. 3 Includes incremental water heating loads associated with dishwashers

and clothes washers. c First unit. Note thatthe sample in question included a high percentage of

frost-tree units, so the UEC may not berepresentative of thebroader population. 4-2 'Valitntionw (of CDA Estimates 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 ofUECs 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 estimateswerecalibrated to 1985-1986 weather conditions. The results of this comparison are presented. inTable 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 ofthe 'overall PGandE customer population. Still, given .that the CDA and AMP E!stimates were developed for the same group of households, their comparison yields someextremelyuseful**i~sights.l**

1 [t is our understandingthat CDA UECs were

developed for all households in the AMP population. whereas notall 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 condi**ti(lnin~** subsample and t.he overall AMP sample (which was used to generate CDAestimates). 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 differencesamountingtoonly 

### 11-13'70. TheCDAestimate for conventional water heating is lower than the AMP estimate. and the opposite·occurs with solarassisted .watcr heating. The twoclothesdryer UECs**md~'r~'d** fur ('\

....ry AMP household. Thlis, there

art! S(IIllI.' diffl'rl.'nces between the households' CQvt!rl.'d by tlwtwo sets ofestimates. 2 Fifty pt!rn'nt ofthe 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,815a ·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 incrementalwater 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.ValidationofCDA Estimates

are very similar. The CDA model seems to perform very well for these enduses. 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 ofthe 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 Junethrough

September.

= Conventional central air conditioning. c Heat pump air conditioning.

= Heating usage coversthe period December through February. 4-3 

### are very similar. The CDA model seems to perform very well for theseend 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 applIancecove'red by·the UEC estimates. The implications'ofthe PGandEcomparison

### 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 ofCDA 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 CDAapproach. Certainly, it indicates.that further improvements in the CDA specification are needed. The feedback prodded by metering experiments should aid this process considerably.



### 2. TheTVA 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 TVA CDA estimates devel**op~d** 

### for .the same time period. A comparison of eshma.tes IS 

### presentedin Table4-2. 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 onlytheJune-Septemberperiod. 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 estimate is less than 7%

### high~r than the
Table 4-2 Results ofTVA 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 1,830a 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 Junethrough

September. ::: Conventional central air conditioning.
c**~eat** pump airconditioning. c Heating usage covers the period DecemberthroughFebruary.
4-3ValidationofCDA 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 isbased 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 ofa 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 ofthese 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 Dishasherc 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 inthe course of the project. The

ores used here are the results of the random-coefficient regression (RCR) analysis. C Or me basis ofthe specification. this should represent motor loads only. 4-4 

### , Validatioll ofCDA 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 thanthe metered estimate (which is based ona large sample consisting of 90 household:.). Electric ranges and clothes dryers tend to be luxury appliances, sotheir high CDA UEC estimates suggest that they act as proxies for other'appliances omitted from the CDA model. In general, theTVA 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**~ccu** 

### racy of CDA. HLw;·ev.er, it is fairly reassurIng for analysts who do not have the option to access enduse meteredd,ata. 

### 3. The Sierra Pacific Energy Information Project Sierra Pacific PowerCompa: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 ProjectJ!.!.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 wintertemperaturesin 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 earlierin 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 maybe theresultofa 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 ofthese 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 1.1SO/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 ofthe 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.ValidationofCDA 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, forcooking 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 

### 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 estimatesdiffersignificantly across thetwo**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. Ingeneral, UEC estimates (especially, it seems, for cookingand dishwashers} are fairly sensitive to model specifications. A m<ire fully specified model might yield Validation ofCDA Estimates

..


### CDA estimates more in line with appliance meteringresults.


### 4. Summary Thethree studiescited aboveofferinterestinginformation, 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 meteredand predicted UECs to specific aspects ofCDA 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 Demandmethodology.

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 ofconsumer 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 appliancepurchasesis obviously ignored. The impacts ofprices and income on conservationdecisionsmayor 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 

# 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 incomeand price are key variables in any theoretical model ofhousehold 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 ofCDA 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 hasnotbeen 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, separateend-useincome 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 ofpriceelasticity estimates. When current energy prices are included in CDA models, theyare 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 importantin this context:
• thedimension ofconsum.erbehavior encQmpassedby theelasticity measure; • thespecification ofthe price term; • theestimation technique. Theseissuesareconsideredbriefly 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 conservationdevices, and appliance utilization: Acomprehensive model like REEPS takes all three.aspects ofbehaviorinto account. Stand-aloneCDA anah-sis focuses ona limited range ofhousehold responses, the specific coverage depending upon the specification of the model and the kinds ofdata 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 thedwelling. Efficiency elasticities capture induced changes in efficiencies and conservation measures (e.g., theeffectof increased energyprices on appliance efficiency levels, installation of insulation, set-back thermostats, etc.). Ownershipelasticities refertoinducedchangesinapplianceholdings. To the extent that appliunce stocks are taken as given in CDA, the impact ofincome and prices on 

### npplinllcepurchases is obviouslyignored. The imracts ofpricesandincomeon

### consen.'atioll decisiolls mayor may not be reflected in CDA-based elasticities. If 

### conservation measuresare 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 mod5-1ri.- ,: ;dIn.come Elasticities

est, given the likely lags in the response ofconservation 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 ,wtincorporated 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 ofVCRs 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. Specificationsofthe 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 ofcharges and rate blocks. Including all features ofa 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 inthis regard. The mostpopular price variants arediscussed 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 aresult 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 ofrates 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 ofalleviating 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 ofincome for most 5-2 

### est, given the likely lags in the response ofconservation 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 andefficiencyeblsticities. 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 influenceon ownership ofVCRs 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-

.pretedas short-runmeasures. 5-2 

### 3. SpeCifications ofthe Priceterm a. Measurementof 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 ofcharges and rate blocks. Including all features ofa statutoryrate 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 inthis regard. Themostpopularprice variants are discussedbelow.

. Household-Specific Average Price. 

### Oneoptionis 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 evenjust the total bill) when looking atrate 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 consumptiondecisions.

. Furthermore, a household's average price isdetermined partly by its own consumption'as a resultof the'use offixed chargesand varyingmarginal rates. Thus, any association between average price and quantity (the stuffof which estimated price elasticities are made)"would reflect the structureofratesas 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, thereare means ofalleviating 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 mostPrice andIncome 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 ofintramarginal expenditures. The use of household-specific marginal price could entail the same statistical problems as average price. Ifthe 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 ata reference level ofconsumption. 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 ofincome 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-periodprice 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 ofutilization and efficiency elasticities.) As shown, the variation in estimates is considerable. To some extent, though, this is explained
5-3 

### 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 ofintramarginal expenditures. The use of household-specific marginal price could enta'il the same statistical problemsasaverage price. If the household'!5 marginalpricedependsupon the level of consumption (t.hat is, there is more than one marginal rate), the use ofthatspecific marginal rate will cause simultaneity bias. In subsection 4, a standard approach to mitigating this problem is considered. Effective Pric,e. 

### Another alternativefound in CDA literature is a variable that canbetailed an effective price. It is defined asthe averagepriceata reference level ofcOllsllmption. 

### 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 rateat the highestconsumptionblock, 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 oftherate 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 monthortwo) could be

.appropriate. However, ifthe.impactsofincomeand price on decisions concerning the integrity of the thermal shell and/or major appliance efficiencies is . ' being considered. the levels experienced atthe 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 incomevariabledoesnot seem feasible atall.


### 4. EstimationTechniques 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. theuseofhousehold-specific price variables wiU cause simultaneity bias. Consequently, aA estimation technique designed to mitigate this problem shouldbe.used. In the case of the household-specific price variable. a two-stage least-squares approach 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 marginalprice 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 stockandothercharacteristics. These estimated probabilities could be used to construct an expected marginal price (the probability-weighted average ofindividual marginal rates). Thisexpected marginal price is thenusedinthe.CDAmodel.



### 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 theyare 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 ofthe whole-house elasticities presented in the studiesrevie\,·ed. (Note again that these are mixes ofutilization and efficiency elasticities.) As shown, the variation in estimates is considerable. To someextent, though, this is explained
5-3Priceand.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 aspart ofa 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 -. 28to -. 89 Nevada Power (1986b) marginal actual -.14 RochesterGas and Electric (1986) average actual -. 21 San Diego Gasand Electric (1986) average predicted -.32 effective actual -. 29

a Tail-block rate.

Defined as the average price at a fixed reference level ofconsumption.

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 

### Pricealid.Il/colI/eflasticities
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 responsesthan 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 listingis hardlycomprehensive 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.~** 

### sOniethingdn the orderof0.1. 

### 3. End-Use Elasticities Relatively few conditional demand studies derive

### end~use price elasticities.IThe primary reason for the paucity ofsuch estimatesis thatfew conditional demand data sets incorporate enough price variation to support this level ofdetail 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 onend-useincomeand price elasticities. Two studies in which end-use price and income elasticities wereestimated are reviewedbelow. 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 elasticitiesas partofaconditionaldemand analysis. Price and income elasticities derived from theTNP study are presented in Table 5-3. As shown, there is a 'substantial amountofvariation in price elasticities across the end uses·considered. Evaporative coolers and non-central electric resistance heating have the highest priceelasticities, with estimateson the orderof-0.7 to -0.8. Waterheatingandconventional central air conditioning have moderate elasticities, with valuesclose to -0.5. Othercovered 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 ofthe consumptionhistory and the associated variation in market conqitions allowed
Table5-1

5-4 Overall Price Elasticities (Current Prices) Service Area Price Actual or (StudyYear) 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 effectiveb actual " Tail-block rate.
o Defined as the average price at afixed 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 .10Priceand Income Elasticitics
Table 5-3 End-Use Price and Income Elasticities Texas-New Mexico Study Price Income End Use Elasticity Elasticity AirConditioning

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 effectoperates through the effect of the pricechange on the household's real (price-adjusted) income level; the substitution effect is transmitted through the impact ofa price change on relative prices. For normal goods, these effects reinforce each other. In general,. the income effect is likelyto 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 enduses 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 

### Pricea1/d 11/come Elast

### leilics
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 resultsoftheanalysis are presented in Table5-4. Refer first to the end-use price elasticity estimates. As shown, all of the specifiedend-usepriceelastici**t~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 someextent, 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** ofimp''';ts. The income effectoperates

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 Impactofa price change on relative prices. For normal goods, these effects reinforce each other. In general,.the income effeCt is likelvto be largest when**l~xpenditures** on agood 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 somethingabouttheextentto which certainendusesare considered discretionary by households. Water heating and lighting (the latter a major element of**un~pecified** 

### usage) are notgenerallyconsidereddiscretionary. However, pool pumping and spaceconditioning may be treated as more flexible uses of pov\er. Pool pumping'schedules can be altered; the!TIlostat settingscan 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 Priceand 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 5-5PriceandIncome Elasticities

on the ownership of unspecified appliances (VCRs, personal computers, etc.). Air conditioning income elasticities are somewhat ofa 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 

### PricealldIllcoml! Elasticities

on the ownership of unspecified appliances (VCRs, personal computers, etc.). Air conditioning income elasticities are somewhatof**~** 

### puzzle. The elasticity for centra! air conditioning is unexpectedly low, and the elasticity for room airconditioning 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 thisend usecould indicate that higher income households living in these dwellings tend to be home less often than their lower income counterparts. 

### 4. Summary Conditional d€'mandstudies can yield useful evidence onincome 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 thedata 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 valuesatthe 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 acrossenduses. It seems safe to saythatmuch more work needs to be done on this issue before othermore useful generalizationscanbe 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 ofa conditional demand model, summarizes estimates ofend-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 ofthe model to infer UECs, and discusses a number oftheoretical 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 ofresidential end-use consumption behavior in a variety of settings. Of course, these estimates vary across studies because ofvariations 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 ofweather. Several conclusions and recommendations arise from the comparison ofUEC estimates: * Most ofthe variation in space heating and air conditioning UECs across studies is attributable to differences in weather. Once the influence of weather isconsidered, 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 CDAanalysts. * 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 (ifthe latter are not captured very well by average air temperatures). But it is also likely that differences in model specifications are largely responsible forthese 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 ofdishwashers and washing machines. Because ofthe 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 ofthe inherent difficulties ofdistinguishing 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 '.

# 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-useconsumption 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. explainsthe useofthe model to inferUECs, and discusses 21 

### numberoftheoretical 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€'fuldesign 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 ofresidential end-use consumption behavior in a variety of settings. Ofcourse, theseestimates vary across studies because ofvariationsinweather. marketconditions, 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 ofweather. Several conclusions and recommendations arise from thecomparison ofVEC estimates:
• Most ofthevariationin space heating and air conditioning UECs across studiesis attributable to differencesin weather. Once the influenceof weatheris considered. estimated UECs seem reasonablyconsistentacross service areas. Clearly, CDA spaceconditioningestimatesfrom otherservice areas. adjusted for differences in weatherconditions. provideuseful reference points for CDAanalysts.
• Estimated waterheating UECs are not particul¥lrly consistentacross'service areas, even afterdifferences in weatherconditionsare

,considered.This may be due to variationsin

marketconditions, customercharacteristics, or . watertemperatures(ifthe latterare notcaptured very well byaverageairtemperatures). Butitis also likely thatdifferences in model specifications are largely responsible for these unexplained

.**variatio~s** 

### across serviceareas. Few studies account for the influenceofinlet temperatureson waterheating usage; some ighore the interdependenceofwaterheating

### usage with, ownersl1ip ofdishwashers and washing mClchines. Because ofthe importance ofwater heatingenergy consumption, particularcare shouldbe taken in specifyingthis portionofthe model.
• Estimated refrigeration UECs tend to be fairly similaracross studies. in spiteofthe inherent difficulties ofdistinguishing refrigeratorloads from other miscellaneousloadslike lighting. To improve the abilityofthe CDA model to discern' refrigeration consumption. more information concernirg thecharacteristics of these appliances should be gathered inthe saturation survey.
• Cookingand clothesdryer UECs are fairly volatile. the probableresult ofdifferences in

6-1Sutnlttnary/ anti Conclusions

model specification. These appliances undoubtedly act as proxies for a host of other excluded appliances, thus leading to biased estimates oftheir 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 ofhousehold consumption into end-use levels. and CDA samples diminish the direct comparability of the two sets ofestimates. Furthermore, attempts should be made to ensure representativeness of the sample used in validation studies. Conditional demand analysis should be capable ofyielding 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 4discusses 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

### model specification. These appliances· undoubtedlyactas proxies for a host ofother excluded appliances, thusleading to biased estimatesoftheir UECs. Theway to minimize this problem is to incorporate information on it 

### wide variety ofappliances intothe model.
• Otherestimzlted UECs from studies surveyed here vary considerably; still, they should provide useful general pointsofdeparture ior the dccompo!!ition ofhouseholdconsumption into end-uselevels.



### 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-usepatterns. However, they also revealed some marked differences between conditional demand estimates and metered loads for someenduses. It seems safe to say thatconsiderably more workon 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 samplesdiminish the direct comparability of the ·two sets ofestimates. Furthermore, attempts should be made to ensure representativeness of the sample used in validation studies. Conditional demand analysis should be capable ofyielding 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-specificCDA studieshave notbeen, suited well to this purpose. Most studies use relatively shortconsumption 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 madeto incorporate sufficient price variation to accommodate the estimation ofelasticities. 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 DemandAnalysis ofResidentialElectricApliance Usein the Southwest U.S. Report to the Public Ltility Commission of Texas, February 1988. Barnes, Richard S. and Cathy R. Zoi. Unit Energy Consumption.ofResidentialAppliances. Economics and Forecasting Department, Pacific Gas and Electric Company, April 1986. Barrar, Jack, E. "Estimation ofResidential 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 andElectricCompany ResidentialAppliance LoadStudy, 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 ForResidential ElectricAnd 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 ConditionalEnergy DenmandApproach.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 DemandModel.ng!andAnalysis 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. EstimatingResidential 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." Journalof theAmericanStatisticalAssociation, 64, 1969. Graham, John, R. UsingConditionalDemandAnalysis to Estimate Appliance Electricity Usage. Prepared for Virginia Power, 1987. Greenberg, J., B. Reeder and S. Silberstein. A Review of Energy Use Factors.forSelected 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 Usefor Residential Water Heating: The HoodRiverConservationProject.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. ResidentialAppliance Electricity Demands in the TVA PowerServiceArea:A Conditional DemandAnalysis Based on TVA'S ResidentialSurveys Conductedin .1979,1981, 1982and 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. RegressionAnalysis Using Appliance SaturationSurvey Data.El Paso Electric Company, April 1987. Klien, Yehuda. "Residential Demand for Space Heat." University of California at Berkeley, Department ofEconomics, draft paper, August 1985. 7-1 

## " 

# 7

BIBLIOGRAPHY

Bachmeier, Richard and Mike Robinson. A CLllldi

### tiollal DemandAl1alysis ofResidential Electric Ar;'iiatlcc Use in the**SOlltlll~'est** 

### U.S. Report to the Public Uility CommissionofTexas, February 1988. Barnes," Richard S. and Cathy R. Zoi. 

### Ullit EI/ergy Consumptioll.ofResidentialAppliances. 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** 

### LoadStudy, 1985-1986. Prepared for theRegulatory Cost ofService 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 ElectricAnd"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 DemalldAppr.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 DemandModt?6rg mid A,!a/ysis Project. Technical ReportIII-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 ofEnergy, Washington, 

### p.c., contractno. DE-ACOl-79EI-I0S09, September 1982. Glejser, H. "A New Test for Heteroskedasticity." 10UT/1iI1

### oftheAmerican StatisticalAssociation, 64, 1969. Graham, John, R. 

### Using Conditional DemandAnalysis 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. Preparedfor the U.5. DepartmentofEnergy, NBSIR85-3220, Washington, D.C, August 1985. Hirst, E., R.Goeltz and M. Hubbard. 

### Determinants of Electricity Use for Residential Water Heating: Tlte HoodRiverConseroatiori Project. Draftpaper, 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 SeroiceArea: 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, presentedto 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 ofCalifornia at Berkeley, DepartmentofEconomics, draftpaper, August 1985. 7-1Bibliio rillIhyI

.
Lawrence, A.G. and M. Parti. Survey of Conditional Energy DemandModelsfor EstimatingResidential Unit EnergyConsumption 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 ConditionalDemand Study ofthe Unit Energy ConsumptionperAppliance. Presented to Nevada Power Company, February 1986. Parti, Michael, Iftekharul B. Chaudhury and Anthony Sebald. A ConditionalDemand Study of ApplianceLoad ShapesandAnnual Energy Usagein the NevadaPowerCompany Service Territory.Presented to Nevada Power Company, May 1986. Parti, Michael, Anthony V. Sebald, Chester Pabniak and Iftekharul Bar Chaudhury. Residential End-Use Consumptionand Conservationin the RochesterGasand ElectricService Territory. Prepared for Rochester Gas and Electric Corporation, June 1986. Pacific Gas And Electric Company, Economics and Statistics Department. Unit Energy Consumption of ResidentialAppliances. August 1982. Pacific Gas And Electric Company, Economics and Statistics Department. Residential ConservationServices Audit ProgramEvaluationEnergy SavingsAnalysis. July 1983. -Pacific Gas And Electric Company, Economics and

Statistics Department. Unit Energy Consumption Of ResidentialAppliances. April 1986. QED Research, Inc. A Model ofResidentialEnergy ConsumptionandAppliance Ownership, FinalReport, Volume II: Project Descriptionand Findings. Prepared for Florida Power &Light, June 1986. Regional Economic Research, Inc. Energy Usage Analysis of ResidentialAppliances. Prepared for San Diego Gas & Electric Company, December 1985. Regional Economic Research, Inc. Analysis of the Energy-Related Characteristics ofNew Homes in the PacificGasandElectricCompanyService Territory:Task4: Conditional Demand Analysis. Prepared for Pacific Gas and Electric Company, April 1986. Regional Economic Research, Inc. Residential Conditional DemandAnalysis: FinalReport. Prepared for Baltimore Gas &Electric Company, September 1988. Research Triangle Institute. Residential Conservation Strategies:An Experimental Evaluation of Consumer Responses. FinalReport, Volume 2:Analysis Results. Prepared for Florida Power & Light Company, Mav 1985. Roddy, Michael, G. An Application of Conditional DemandAnalysis. Prepared for GPU Service Corporation, 1987. Russ, Michael, John Fritz, Bill Diehl, Dean Stathis and Gordy Meyer (Middle South Utilities). "End Use Estimation: AnAlternative to Direct Metering." Presented to Southeastern AEIC Meeting, Atlanta, Georgia, August 1984. United Power Association. ResidentialApplianceConsumrtion:ReportAndRecommendations. 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 N I



### 1 a

I 

### Lawrence, A.G.**~nd** 

### M.**P~lrti.** 

### Survey ofConditional. Energy Demand Modelsfor Estimating Residential Unit 

### Ellergy Consumption Coefficiellts. Prepared for Electric Power Research·Institute under EA-3410 Research Project576-3, 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 ofthe Unit Energy Consumptioll perAppliance. Presented to Ne:-,ada PowerCompany, February 1986. Parti, Michael, Iftekharul 

### B. Chaudhury and Anthony Sebald. 

### A Conditional Demand Study of Appliance Load Shapes andAnnual Energy USage in the Nevada Power CompanyService Territory. Presented to NevadaPowerCompany, May 1986. 'Parti, MichaeIr Anthony V. 

### Sebald, ChesterPabniak and Iftekharul Bar Chaudhury. 

### Residential End-Use Consumption and Conservation in 

### tile RochesterGas 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 ResidentialAppliances. August 1982. Pacific Gas And Electric Company, Economics and Statistics Department. 

### Residential Conservation Services Audit Program Evaluation Energy Savings Analysis. 

### July 1983. PacificGas And Electric Company, Economics and Statistics Department. 

### Ullit Energy Consumption 

### Of ResidentialApplia/lCes. April 1986. QED Research, Inc. 

### A Model ofResidential 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 ofReside11tial Appliances. Prepared for San DiegoGas & 

### Electric Company, December 1985.

7-2 

### Regional Economic Research, Inc. 

### Analysis of the. Energy-Related Characteristics ofNew Homes in the Pacific Gas andElectric Company Service Territory: Task4: Condi:ional Demand Analysis. Prepared for Pacific'

## i

.Gasarid ElectricCompany, April 1986. 

### I Regional' Economic Research, Inc. 

### Residential Conditionai Demand Analysis: Final Report. Prepared for Baltimore Gas & 

### ElectricCompany, September1988. 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. 

### AnApplication ofConditional 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: AnAlternative to DirectMetering." Presented to Southeastern AEIC Meeting, Atlanta, Georgia, August 1984. United Power AssoCiation. 

### ResidentialAppliance Con**sum~ion:** 

### ReportAndRecommendations. 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.