# EPRl Zlectric Power 3esearch Institute Keywords: Customer attitudes End-use consumption Energy efficiency Load research



# ResidentialEnergyUsage Comparison: Findings

Preparedby Quantum Consulting Inc. Berkeley,California EPRlCU-7392 Project2863-3 Final Report August 1991 

# EPRI
:::Iectric Power 'lesearch Institute Keywords: Customer attitudes End-use consumption Energy efficiency Load research



# Residential Energy Usage Comparison:**Fi~dings**

Prepared by Quantum Consulting Inc. Berkeley, California EPRI CU-7392 Project 2863-3 Final Report August 1991

## DISCLAIMEROFWARRANTIESAND LIMITATIONOFLiABlLlTlES THIS REPOKIWAS PREPAREDBY THE ORGANIZAT.ON(S)hAMEO BELOWAS Ah ACCOUNT OF WORK SPONSOREDOR COSPONSORED BY TrlE ELECTRIC POWER RESEARCHINSTITUTE. INC lEPRll NEITHER EPRI. ANY MEMBER OF EPRI. ANY COSPONSOR THE ORGANI&~~ON~SI NAME0BELOW NORANY PERSONACTING INMAKES AhY WARRANTY OR REPRESENWIONWtiAlSOEVER. EXPRESS OR MPLIED. (I) WITH RESPECT 

### m THE USE OF ANY INFORMATON. APPARATUS. METHOD. PROCESS OR SIMILAR ITEM 0 SCLOSEO INThS REPORT, INCUlDlhG MERChANTABI-INAND

### FITNESSFORA PAAnCU4RPURPOSE.OR (11)ThAT SJCH USE DOES NOIIhFRlNGE IlNIlR INTERFEREWITH PRrVA1E.Y OWNED RIGhTS. INCLUDINGANY 

### PARP('SINTELLE'~UAL 

### PROPERTY.OR(III~THATTHlS REPORT ISSUITABLETO ANY PARTICULAR USER'S CIRCUMSTANCE; OR 10) ASSUMES RESPONSBlLlPl FOR AhY DAMAGES OR OTHER LIABILITY fiHAnOEVER llNCWDlNGANY CONSEOUEhTlALOAMAGES. EVEN IF EPRIORANY EPRI REPRESEI(TATIVEHASBEE~AD~I~E~OF T~E POSSI~~L~~Y OFSJCHDAMAGES) RESULTlhGFROMWURSELECTIONORUSEOFTHSREPORTORANY IhFORMATON. APPARATUS, hIETHO0. PROCESS.OR SIMILAR ITEM DISCLOSED INThS REPORT ORGANIUFIION(S) THAT PREPAREDTHIS REPORt QUANTUMCONSULTING INC.
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# Residential EnergyUsage Comparison: Findings CU-7392 Research Project2863-3 FinalReport,August 1991

Prepared by QUANTUMCONSULTING INC. 2030Addison Street Berkeley, California94704 PrincipalInvestigators 

### 6. A. Smith R.T. Uhlaner T. N. Cason S. Courteau

Preparedfor SouthernCalifornia EdisonCo. 2244Walnut GroveAvenue Rosemead, California91770 Project Manager P. Delaney Electric Power Research Institute 3412 HillviewAvenue PaloAlto, California94304 EPRIProject Managers S. Braithwait P. Cleary P. Meagher Demand-SideManagement Program CustomerSystemsDivision 

## DISCLAIMER OFWARRANTIESAND LIMITATION OF LIABILITIES THIS REPORT WPS PREPARED BYTHE ORGANIZArION(S) NAMED BELOW PS AN ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. (EPRII. NErTHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THE ORGANIZArION(SI NAMED BElOW. NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM: tAl MAKES ANY WARRANi"I OR REPRESENTI!!ION WHATSOEVER, EXPRESS OR IMPUED, 0) WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, METHOD. PROCESE, OR SIMILAR ITEM DISCLOSED IN THIS REPORT INCLUDING MERCHANTABILITYANDATNESS FORAPARl1CULAR PURPOSE, OR (II) THAT SUCH USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED RIGHTS, INCLUDING ANY PARTY'S INTELLECTUAL PROPERTY. OR (III) THATTHIS REPORT IS SUITABLE TO ANY PARTICULAR USER'S CIRCUMSTANCE; OR te) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHi!!SOEVER (INCLUDING ANYCONSEQUENTIAL DAMAGES, EVEN IFEPRIOR ANY EPRI REPRESENTATIVE HAS BEENADViseDOFTHE POSSIBILITYOFSUCHDAMAGES) RESULTING FROMveURSELECTIONORUSEOFTHIS REPORTORANYINFORlloATION, APPARATUS, METHOD, PROCESS, ORSIMILAR ITEM DISCLOSED IN THIS REPORT ORGANIZATION(S) THAT PREPARED THIS REPORT: QIIAlilUM CONSUIJING INC. 

# * Printed

### on Recycled Paper 

# Residential EnergyUsage Comparison: Findings
CU·7392 Research Project2863·3 Final Report, August 1991

Preparedby QUANTUM CONSULTING INC, 2030 Addison Street Berkeley, California94704 Principal Investigators B, A.Smith R. T. Uhlaner T. N, Cason S. Courteau

Prepared for Southern California Edison Co. 2244Walnut Grove Avenue Rosemead, California 91770 Project Manager p, Delaney Electric Power Research Institute 3412 HillviewAvenue PaloAlto, california94304 EPRI Project Managers S. Braithwait P. Cleary P. Meagher Demand-Side Management Program Customer Systems Division

# ResidentialEnergyUsageComparison: Findings The load shapesof major residentialelectric and gas applianceswere compared inan integrated loadand market research project insouthernCalifornia.The energyconsumptiondataprovidea benchmark for comparingthe costsof alternative technologies; the market research, data relate customer attitudeswith appliance loadshapes.

BACKGROUND Comparisonsof the relativeenergy consumptionof gas and 

### INTERESTCATEGORIES electric householdappliances havetendedto relyon laboratory analysisandcom- ~uter simulations. Bycontrast,this study, cosponsored bySouthernCalifornia Edison Co. (SCE), lookedat 92 similar households and measuredthe actual energy 

### Demand-side

### planning consumptionof their appliances, either gas or electric. 

### Marketassessment Lighting

### and 

### appliances OBJECTIVES To measurethe actual energy consumptionof com~arablemod

### KEYWORDS ern electrican0gas appliances; to determinethefactors behindcustomerappliance oecisions: to determinethe relationshipsbetweena customer's characteristics andthe correspondingloadshapeand energy usage. 

### Customerattitudes End-use

### consumption Energy

### efficiency APPROACH The project team identified 92 homesinOrangeCounty, California, 

### Load

### research southeastof Los Angeles,to serveasthe studysample. The homeswere owneroccupied, single-familyhomes ina high-growtharea andwere representativeof typical newer homesinthe SCEservicearea. Ineach home, newappliances were installed forspace conditioning,water heating,cooking, andclothes drying. Half ofthe homes received electrical equipmentand halfgas. Loadmonitoring equipmentwas 'first tested ineight out-of-samplehomes and then installedoneach individual appliance inthe 92-homesample. Energy datawere collected regularly over a periodof approximately twoyears. The projectteam surveyedthe householdsseveraltimesto obtain information on theirenergy needs andtheir acceptance of the new appliances. RESULTS All comparisonsof energy consumptionwere madeinterms of site energy with 1 kwh = 3.412 kBtu. Average annualper-household energyconsumption for cookingwas 3.24 MBtuforthe gassites and 1.31 MBtuforthe electric sites. Clothes drying consumed4.32 MBtufor gas sites and3.66 MBtufor electric sites. Inwinter, gassites consumed an average20.49 MBtufor.spaceconditioning, and electric sites usedanaverageof 7.50 MBtu. Customer surveys showedthat electric heat pumpsfor space heatingwerewell accepted.Of the households whose gasfurnaceswere replaced with heat pumps, 50% changed their preferences from gasto electricity.The acceptance ratings for electric heat pumpwater heaterswere not as highasfor gaswater heaters. Participants found the heat pumpsslow to respond.The acceptance ratingsfor electric cooking applianceswere strong. Ofthe householdsretrofittedfrom gas ranges INTEREST CATEGORIES 

### Demand-side planning Market assessment Lighting and appliances KEYWORDS 

### Customerattitudes End-use consumption Energy efficiency Load research 

# REP 0 R T 

# SUMMARY



# Residential EnergyUsageComparison: Findings The load shapes of major residential electric and gas appliances were compared in an integrated load and market research project in southern California. The energy consumption data provide a benchmarkfor comparing the costs of alternativetechnologies; the market research data relate customer attitudeswith appliance load shapes.

.

BACKGROUND Comparisons ofthe relative energy consumption of gas and electric household appliances havetended to rely on laboratory analysis and computer simulations. By contrast, this study, cosponsored by Southern California Edison Co. (SCE), looked at 92 similar households and measured the actual energy consumption oftheir appliances, either gas orelectric. OBJECTIVES To measurethe actual energyconsumption ofcomparable modern electric and gas appliances; to determine the factors behind customer appli· ance decisions; to determine the relationships between acustomer's characteristics and the corresponding load shape and energy usage. APPROACH The project team identified 92 homes in Orange County, California, southeast of Los Angeles, to serve as the studysample. The homes were owneroccupied, single-family homes in a high-growth area and were representative of typical newer homes in the SCE service area. In each home, new appliances were installed for space conditioning, water heating, cooking, and clothes drying. Half ofthe homes received electrical equipment and halfgas. Load monitoring equipmentwas 'first tested in eight out-of-sample homes and then installed on each individual appliance in the 92-home sample. Energy datawere collected regularly over a period of approximately two years. The projectteam surveyed the households several times to obtain information on their energy needs and their acceptance of the new appliances. RESULTS All comparisons of energy consumption were made in terms ofsite energy with 1 kWh = 3.412 kBtu. Average annual per-household energy consump· tion for cooking was 3.24 MBtu for the gas sites and 1.31 MBtu for the electric sites. Clothes drying consumed 4.32 MBtu for gas sites and 3.66 MBtu for electric sites. In winter, gassites consumed an average 20.49 MBtu for-space conditioning, and electric sites used an average of 7.50 MBtu.
- Customersurveys showed that electric heat pumps for space heating were well accepted. Ofthe households whose gas furnaces were replaced with heat pumps, 50% changed their preferences from gas to electricity. The acceptance ratings for electric heat pump water heaters were not as high as for gas water heaters. Participants found the heat pumps slowto respond. The acceptance ratings for electric cooking appliances were strong. Ofthe households retrofitted from gas rangesto electric inductionstoves, 50°/o expressed a preferencefor electricityas afuel forcooking.

~p EPRI PERSPECTIVE This project providesnew informationabout how comparablegas and electricappliancesare usedandacceptedincustomer homes.An overviewreporton this researchwas published earlier (EPRI reportCU-6952). Utilitiesinsimilar climatezones can usethese dataon actualenergyconsumptionand loadshapes to estimatethe costeffectiveness andattractivenessof different applianceoptionsfortheir customers.Theproject also providesanexampleof how loadshapes maydiffer bymarket segment, includingthe energy-needs-based segmentsdevelopedunder EPRl'scustomer preferenceand behavior project (EPRI report EM-5908).Thiscan help utilities assess the likely impact ofattracting particular typesof customersthroughtargeted marketing programs.

-- - PROJECT RP2863-3 Project Managers: PeterCleary; Steven D. Braithwait;PaulC. Meagher CustomerSystems Division Contractor: QuantumConsultingInc. Forfurther information on EPRI research programs, call EPRITechnical Information Specialists(415) 855-2411. to electric induction stoves, 50% expressed a preference for electricity as afuel for cooking. EPRI PERSPECTIVE This project provides new information about how comparable gas and electric appliances are used and accepted in customer homes. An overview report on this research was published earlier (EPRI report CU-6952). Utilities in similar climate zones qan use these data on actual energy consumption and load shapesto estimate the costeffectiveness and attractiveness ofdifferent appliance options for their customers. The project also provides an example of how load shapes may differby market segment, including the energy-needs-based segments developed under EPRJ's customer preference and behavior project (EPRI report EM-590B). This can help utilities assess the likely impact of attracting particulartypes ofcustomers\hrough targeted marketing programs. PROJECT RP2863-3 Project Managers: Peter Cleary; Steven D. Braithwait; Paul C. Meagher Customer Systems Division Contractor: Quantum Consulting Inc. For further information on EPRI research programs, call EPRI Technical Information Specialists (415) 855-2411.



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### ABSTRACT



### This report presents results from Electric Power Research Institute research project 2863-3, the Residential Energy Usage Comparison (REUC) Project. The REUC project, a 

### joint effort by Southern California Edison Company (SCE) and EPRI, 

### has measured and compared typical energy consumption patterns of energy-efficient residential electric and gas appliances for space conditioning, water heating, cooking and clothes drying. A 

### wealth of appliance (end-use) energy data and customer survey data were gathered between February 1988 and December 1989 from the 92 participating households located in Orange County California, southeast of Los Angeles, Results of analysis regarding the electric and gas appliances' energy usage patterns, load profiles and customer acceptanceare presented inthe report. Analysis of the appliances' energy usage at point-of-use (whichdoes not include losses incurred in generation, transmission or distribution) indicates that the energy-efficient electric appliances used less site energy than gas appliances. Electric heat pumps used 36 percent of the energy used by comparable gas furnaces duringthe winter months. Electric induction stoves used 32 percent of the energy used by gas stoves at point-of-use during the year. Electric water heaters used 40 

### percent of the site energy used by comparablegas water heaters. Periodic surveys were administered to determine which appliances were well accepted and which appliance attributes were important to customers. The survey responses found that participantsfound their heat pumpwater heaters too slow to respond. Acceptance ratingsfor the heat pumpwater heaters were quite low, at 45 

### percent. Incontrast, electric induction stoves were well accepted; 80 percent would recommend their induction stoves to afriend. The REUCproject dbta were also used to develop segmentation approaches that portrayed load shapes for different customer groups. Several segmentation schemes, including one based on EPRl's 

### CLASSIFYTM code and one based on participant's images of electricity and gas, accurately simulatedwater heater load shapes based only on customer attribute information. 

### ABSTRACT

" 

### This report presents results from Electric Power Research Institute research project 2863-3, the Residential Energy Usage Comparison (REUC) Project. The REUC project, a joint effort by Southern California Edison Company (SCE) and EPRI, has measured and compared typical energy consumption patterns of energy-efficient residential electric and gas appliances for space conditioning, water heating, cooking and clothes drying, A wealth of appliance (end-use) energy data and customer survey data were gathered between February 1988 and December 1989 from the 92 participating households located in Orange County California, southeast of Los Angeles. Results of analysis regarding the electric and gas appliances' energy usage patterns, load profiles and customer acceptance are presented in the report. Analysis of the appliances' energy usage at point-of-use (which does not include losses incurred in generation, transmission or distribution) indicates that the energy-efficient electric appliances used less site energy than gas appliances. Electric heat pumps used 36 percent of the energy used by comparable gas furnaces during the winter months, Electric induction stoves used 32 percent of the energy used by gas stoves at point-of-use during the. year, Electric water heaters used 40 percent of the site energy used by comparablegas water heaters. Periodic surveys were administered to determine which appliances were well accepted and which appliance attributes were important to customers. The survey responses found that participants found their heat pump water heaters too slow to respond. Acceptance ratings for the heat pump water heaters were quite low, at 4S percent. In contrast, electric induction stoves were well accepted; 80 percent would recommend their induction stoves to a friend.
. The REUCproject data were also used to deve'lop segmentation approaches that portrayed load shapes for different customer groups. Several segmentation schemes, including one based on EPRI's CLASSIFYTM code and one based on participant's images ofelectricity and gas, accurately simulated water heater load shapes based only on customer attribute information.



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### EPRI FOREWORD

The residentialenergyusagecomparison(REUC)projecthasbeenajointeffortbyEPRI

### and SouthernCaliforniaEdisonCo.tomeasureandcomparetypicalenergycomsurnptionpatterns of energy-efficient, residential electric and gas appliances. This innovative demand-side projecthasproducedvaluable information about howactualhouseholds useelectricityand gasfor spaceconditioning,waterheating, cooking, and clothesdrying. This reportpresents the design, analysis methods, andresultsfromthe REUCproject. Steven D. Braithwait; Peter Cleary; Paul C. 

### Meagher, Prbject Managers Customer Systems Division EPRI FOREWORD



### The residential energy

### usage comparison (REUClprojecthas been ajointeffortby EPRI and SouthernCaliforniaEdisonCo.tomeasureandcomparetypicalenergycomsumptionpatterns ofenergy-efficient, residential electric and gas appliances. This innovative demand-side projecthas producedvaluable information abouthowactual householdsuse electricity

### and gasfor spaceconditioning, waterheating, cooking, andclothes drying. 

### This reportpresents the design, analysis 

### methods, and results from the REUC project.
. Steven D. Braithwait; Peter Cleary; Paul C. 

### Meagher, Project Managers

. Customer Systems Division

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### ACKNOWLEDGEMENTS

The Quantum Consulting project staff wishes to acknowledge the assistance and 

# - of several organizationsand persons inconductingthis research. In particular, we want to acknowledge the substantive contributions of Dr. Steven Braithwait and Mr. Peter Cleary, our EPRl Project Managers, of Mr. Stephan Galanter and Mr. Paul Delaney, our Southern California Edison Project Officers, and of Dr. Mark Shirilau. Their support, guidance and insights have been a valuable ingredient inour research.

vii 

### ACKNOWLEDGEMENTS

The Quantum Consulting project staff wishes to acknowledge the assistance and contributions ofseveral organizations and persons in conducting this research. In particular, we want to acknowledge the s.ubstantive contributions of Dr. Steven Braithwait and Mr. Peter Cleary, our EPRI Project Managers, of Mr. Stephan Galanter and Mr. Paul Delaney, our Southern California Edison Project Officers, and of .Dr. Mark Shirilau. Their support, guidance and insights have been a valuable ingredient in our research.

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### Section SUMMARY 1 INTRODUCTION 

### CONTENTS

Overviewofthe REUC Project EPRl Involvement in.the REUC Project Report .Organization 2 REUC PROJECTDESIGNOBJECTIVES lntroduction REUC Design REUCAnalysis

lntroduction REUC Project Scope History of REUC DataCollection 

# ? THE REUC CUSTOMER SAMPLE lntroduction REUC Sample Design Demographic Description ofthe REUC Sample Representativeness of the REUC Sample 5 

### REUC LOAD DATA ANALYSIS: ELECTRICAND GAS APPLIANCE USAGE lntroduction Load DataCollection andAnalysis Average Yearly Total Usage DistributionofAverage Daily Usage Space Conditioning Weather Model 6 

### REUC APPLIANCE LOAD PROFILES Introduction 

### 6-1 Load Profiles 

### 6-1 Using HouseholdCharacteristics to Explain Load Shapes 6-10 

### Total Energy Regressions 6-1

### 8 CONTENTS
Section



### 1



### 2



### 3

4

5

6 SUMMARY INTRODUCTION Overview ofthe REUC Project EPRI Involvement in .the REUC Project Report .Organization REUC PROJECT DESIGN OBJECTIVES Introduction REUC Design REUC Analysis REUC PROJECT SCOPE AND DATA COLLECTION HISTORY Introduction REUC Project Scope History of REUC Data Collection THE REUC CUSTOMER SAMPLE Introduction REUC Sample Design Demographic Description ofthe REUC Sample Representativeness of the REUC Sample REUC LOAD DATA ANALYSIS: ELECTRIC AND GAS APPLIANCE USAGE Introduction Load Data Collection and Analysis Average Yearly Total Usage Distribution ofAverage Daily Usage Space Conditioning Weather Model REUC APPLIANCE LOAD PROFILES Introduction Load Profiles Using Household Characteristics to Explain Load Shapes Total Energy Regressions S-l 

### 1-1 1-1 1-2 

### 1-5 2-1 2-1 2-1 

### 2-2 

### 3-1 3-1 3-1 3-4 4-1 ·4-1
. 4-1 

### 4-2 4-4 

### 5-1
5-1 5-1 5-4 5-11 

### 5-16 6-1 6-1 6-1 6-10 6-18

### CONTENTS, 

### cont. Section 7 

### CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE Introduction FuelType Image Space Heat.ing Water 

### heat in^ 

# - Cooking Appliance Discount Factors 8 

### INTEGRATION OF LOAD AND SURVEY ANALYSES Introduction Load Shapes by REUC Customer Segment 4 

### Determination of "Optimal" Stratifying Variables Appliance Life CycleCosts APPENDIX A 

### Supplementary Energy Usage Information and Load Profiles APPENDIX 

## B Methodology to Assess 

### the Generalizability of the REUC Customer Sample APPENDIX C Load Data EditingandValidation GLOSSARY OF TERMS 

### CONTENTS, cont. Section 

### 7



### 8 CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE Introduction Fuel Type Image Space Heating Water Heating·

. Cooking Appliance Discour:tt Factors INTEGRATION OF LOAD AND SURVEY ANALYSES Introduction Load Shapes by REUC Customer Segment Determination of "Optimal" Stratifying Variables

. Appliance Life Cycle Costs

APPENDIX A Supplementary Energy Usage Information and Load Profiles APPENDIX B Methodology to Assess the Generalizability ofthe REUC Customer 

## .. Page

7-1 7-1



### . 7-2



### 7-6 7-14 7-18 

### 7-21 8-1 8-1 

### 8-2 8-5 8-7

A-1

Sample B-1 APPENDIX C Load Data Editing and Validation C-1 GLOSSARY OF TERMS G-1

)(

### FIGURES



### S-1 Annual Average Energy Usage

# - Space Conditioningand Water Heating S-2 SCE REUC: Average Load Profiles

# -Winter Weekday Space Conditioning S-3 Customers' Required Return ona Small Riskless Investment S-4 Customers' Required Returnon an lnvestme'ntinAppliance Efficiency S-5 

### REUC Participant Imagesof Electricvs. Gas S-6 SCE REUC: Quality Factor Segments 

### -Winter Weekday Heat Pump S-7 SCE REUC: CP&B Needs-BasedSegments 

# - Summer Weekday Heat Pump S-8 REUC: Heat Pump Water Heater

# - Demographic Scenario 1-1 

### REUC Project Milestones 3-1 REUCSurveyand Load DataCollection History 5-1 

### Annual Average Energy Usage Space Conditioning and Water Heating 5-2 AnnualAverage Energy Usage

### -Clothes Dryingand Cooking 5-3 DifferenceinAnnual Energy Consumption between Gas and ElectricAppliances 5-4 

### Seasonal Average Weekday Electricity Usage Electric Sample End-Uses 5-5 

### Whole House: Average Daily Electricity Usage 5-6 

### SCE REUCWeekdayAverage UEC ~istribition 

### - Winter Electric Heat Pump: Pooled Cities 5-7 

### SCE REUCWeekday Average UEC Distribution 

### - Summer Space Conditioning: Pooled Cities FIGURES Figure Page S-1 Annual Average Energy Usage
- Space Conditioning and S-3 , Water Heating S-2 SCE REUC: Average Load Profiles
- Winter Weekday S-4 Space Conditioning S-3 Customers' Required Return on a Small Riskless Investment 5-6. S-4 Customers' Required .Return on an Investme·nt in Appliance 5-7 Efficiency S-5 REUC Participant Images of Electric vs. Gas 5-8 S-6 SCE REUC: 

### Quality Factor Segments
- Winter Weekday 5-9 Heat Pump S-7 SCE REUC: CP&B Needs-Based Segments
- Summer Weekday 5-10 Heat Pump S-8 REUC: Heat Pump Water Heater
- Demographic Sceriario ·5-11 1-1 REUC Project Milestones 1-3 3-1 REUC Survey and Load DataColiection History 3-4 5-1 Annual**Averag~** Energy Usage Space Conditioning and Water 5-4 Heating 5-2 Annual Average Energy Usage
- Clothes Drying and Cooking 5-5 5-3 Difference in Annual Energy Consumption between Gas 5-7 and Electric Appliances 5-4 Seasonal Average Weekday Electricity Usage Electric Sample End-Uses

. 5-10
5-5 Whole House: Average Daily Electricity Usage 5-11 5-6 SCE REUC Weekday Average UEC Distribution
- Winter 5-13 Electric Heat Pump: Pooled Cities 5-7 SCE REUC Weekday Average UEC Distribution
- Summer Space Conditioning: Pooled Cities 5-13



### xi

### FIGURES, 

### cont.

5-8 

### SCEREUCWeekdayAverage UEC ~istribution 

# - Winter Gas Furnace: Pooled Cities 5-9 SCE REUCWeekdayAverage UEC Distribution

# - Winter Electric Water Heating: Pooled Cities 5-10 SCE REUCWeekdayAverage UEC Distribution

# -Winter Gas Water Heating: pooled Cities 6-1 SCE REUC winter: Space Conditioning 6-2 SCE REUC Summer: Space Conditioning 6-3 SCE REUCWinter: Water Heating 6-4 

### SCE REUC Summer: Water Heating 6-5 SCE REUC: Space Conditioning ElectricSample 6-6 SCE REUC: Heat Pump Space Conditioning 6-7 SCE REUC: Gas/Electric Space Conditioning

,, 

### 6-8 SCE REUCWinter Weekday Electricity: Electric Sample 6-9 SCE REUC.SummerWeekday Electricity: Electric Sample 6-10 SCE REUCWinter: Whole House Electricity 61 1 SCE REUC Summer: Whole House Electricity 6-12 REUC Heat PumpWater Heater

# - House50

# - FirstStep Estimated Loadvs. Average Load 6-1

### 3 REUC Heat PumpWater Heater -.House 86 

### - FirstStep Estimated Loadvs. Average Load 6-14 

### REUC Heat PumpWater Heater

# - House 50

### - Second Step Estimated Load vs. Average Load 6-15 

### REUC Heat Pump Water Heater

# - House 86

# - Second Step Estimated Load vs. Average Load 6-16
, 

### REUC Heat Pump Water Heater Demographic Trend Scenario

xii 

### FIGURES, cont. Figure 5-8 SCE REUC Weekday Average UEC Distribution
- Winter 5-14 Gas Furnace: Pooled Cities 5-9 SCE REUC Weekday Average UEC Distribution
- Winter 5-14 Electric Water Heating: Pooled Cities 5-10 SCE REUC Weekday Average UEC Distribution
- Winter 5-15 Gas Water Heating: Pooled Cities 6-1 SCE REUC Winter: Space Conditioning 6-2 6-2 SCE REUC Summer: Space Conditioning 6-2 6-3 SCE REUC Winter: Water Heating 6-3 (J-4 SCE REUC Summer: Water Heating 6-3 6-5 SCE REUC: Space Conditioning Electric Sample 6-4 6-6 SCE REUC: Heat Pump Space Conditioning 6-6 6-7 SCE REUC: Gas/Electric Space Conditioning 6-7 6-8 SCE REUC Winter Weekday Electricity: Electric Sample 6-8 6-9 SCE REUC Summer Weekday Electricity: Electric Sample6-a 6-10 SCE REUC Winter: Whole House Electricity 6-9 6-11 SCE REUC Summer: Whole House Electricity 6-9 6-12 REUC Heat Pump Water Heater
- House 50 - First Step 6-13 Estimated Load vs. Average Load 6-13 REUC Heat Pump Water Heater
- ·House 86 - First Step 6-13 Estimated Load vs. Average Load 6-14 REUC Heat Pump Water Heater
- House 50 - Secqnd Step 6-1 6 Estimated Load vs. Average Load 6-1 5 REUC Heat Pump Water Heater
- House 86 - Second Step 6-1 6 Estimated Load vs. Average Load 6-16' REUC Heat Pump Water Heater Demographic Trend Scenario 6-18

xii

### FIGURES, cont.



### Effectof HouseholdSize onAverage Weekday. Usage: REUC Heat Pump Water Heater (Model) Effectof Quality Imageof ElectricityonAverageweekday Usage: REUC Heat Pump Water Heater (Model) Effectsof CP&B ClassificationonAverageWeekday Usage; REUC Heat Pump Water Heater (Model) REUC ParticipantImagesof Electricvs. Gas REUC Participants' Comparisonsbetween Oldand NewSpace Heatersby FuelType lmportance of HeatingSystemAttributes by FuelType Electric Sample Importanceof HeatingSystemAttributes by FuelType Gas Sample REUC Participants' Comparisons between Oldand New Water Heatersby FuelType ~on~itudinal change in Electric Induction Range Subsample

### - Participants' Fuel Type Preferencefor Cooking REUC Participants' Required Returnon a Small Riskless lnvestment -7 

### REUC Participants' Required Returnon an Investment 7-24 inAppliance Efficiency -1 

### REUC Summer Weekday Load Shape: Electric Space 8-3 

### Conditioning Customer Preference & 

### Behavior Stratification 

### -2 .REUCSummerWeekday Load Shape: Electric Space 8-4 

### Conditioning Electric ValuelGas Value Stratification -3 

### REUC Winter Weekday Load Shape: Heat Pump Water Heater 8-4 Electric QualitylGas ValueStratification -4 REUC Winter Weekday LoadShape: Heat PumpWater Heater 8-5 Income by Square Feet Stratification

xiii 

### FIGURES, cont. igure Page -17 Effect of Household Size on Average Weekday- Usage: 6-21 REUC Heat Pump Water Heater (Model) -18 Effect ofQuality Image of Electricity on Average Weekday Usage: REUC Heat Pump Water Heater (Model) 6-22
-19 Effects ofCP&B Classification on Average Weekday Usage; 6-23 REUC Heat Pump Water Heater (Model) -1
.REUC Participant Images of Electricvs. Gas 7-2 -2 REUC Participants' Comparisons between Old and New Space 7-10 Heaters by Fuel Type -3a Importance of Heating System Attributes by Fuel Type Electric 7-1

## r Sample -3b Importance of Heating System Attributes by Fuel Type Gas 7-12 Sample -4 REUC Participants' Comparisons between Old and New Water 7-14 Heaters by Fuel Type -5 Longitudinal Change in Electric Induction Range Subsample
- Participants' Fuel Type Preference for Cooking 7-19
-6 REUC Participants' Required Return on a Small Riskless 7-23 Investment -7 REUC Participants' Required Return on an Investment 7-24 in Appliance Efficiency -1 REUC Summer Weekday Load Shape: Electric Space Conditioning Customer Preference & Behavior Stratification 8-3
-2 REUCSummer Weekday Load Shape: Electric Space 8-4 Conditioning Electric 

### Value/Gas Value Stratification -3 REUC Winter Weekday Load Shape: Heat Pump Water Heater Electric 

### Quality/Gas Value Stratification 8-4
-4 REUC Winter Weekday Load Shape: Heat Pump Water Heater 8-5 Income by Square Feet Stratification

xiii

### FIGURES, 

### cont.

REUCParticipants' RequiredPaybackperiods REUCSpace Conditioning Fuel Choice Preference Regions Space Conditioning: Winter Average Daily Usage Water Heating: Average Daily Usage Cooking Range: Average DailyUsage Clothes Drying: Average Daily Usage Space Conditioning: Summer Average Daily Usage 5CE REUCWinter: Clothes Drying SCE REUC Summer: Clothes Drying SCE REUCWinter: Cooking SCE REUC Summer: Cooking SCE REUCWinter: Whole House Electricity SCE REUC Summer: Whole House Electricity sCE REUCWinter: Space Conditioning SCE REUCSummer: Space Conditioning SCE REUCWinter: Water Heating SCE 

### REUC Summer: Water Heating SCE 

### REUCWeekday Average Load: Electric Sample Space Conditioning .;cE 

### REUCWeekday Average Load: ElectricSample Space &>nditioning SiE

### REUCWeekday Average Load: ElectricSampleSpace conditioning

xiv 

### FIGURES, cont. Figure Page 8-5 REUC Participants' Required Payback Periods 8-9 8-6 REUC Space Conditioning Fuel Choice Preference Regions 8-10 A-l Space Conditioning: Winter Average Daily Usage A-2 A-2 Water Heating: Average Daily Usage A-2 A-3 Cooking Range: Average Daily Usage A-3 A-4 Clothes Drying: Average Daily Usage A-3 A-5 Space Coriditioning: Summer Average Daily Usage A-4 A-6 SCE REUC Winter: Clothes Drying A-4 A-7 SCE REUC Summer: Clothes Drying A-S A-8 SCE REUC Winter: Cooking A-5 A-9 SCE REUC Summer: Cooking A-6 A-10 SCE REUC Winter: Whole House Electricity. A-6 A-l1' SCE REUC Summer: Whole House Electricity A-7 A-12

. SCE REUC Winter: Space Conditioning A-7 A-13 SCE REUC Summer: Space Conditioning A-8 A-14 SCE REUC Winter: Water Heating A-8 A-15 SCE REUC Summer: Water Heating A-9 A-16 SCE REUC Weekday Average Load: Electric Sample Space A-9 C,)nditioning A-17 SeE REUC Weekday Average Load: Electric Sample Space A-10 CL)nditioning A-18 SCE REUC Weekday Average Load: Electric Sample Space A-10 C,)nditioning

xiv

### FIGURES, 

### cont.

SCE REUCWeekday Average Load: Gas Sample Space Conditioning SCE REUCWeekday Average Load: Gas Sample Space Conditioning SCE REUCWeekdayAverage Load: Gas Sample Space Conditioning SCE REUCWinter Weekday Electricity: Electric Sample SCE REUC SummerWeekday, Electricity: Gas Sample SCE REUC SummerWeekday Electricity: Electric Sample SCE REUCWhole House Electricity: Electric Sample SCE REUCWhole House Electricity: Gas Sample SCE REUC Space Conditioning: Electric Sample SCE REUCWater Heating: Electric Sample SCE REUCCooking: Ele'ctricSample SCE REUCClothes Drying: Electric Sample SCE REUC: Temperature REUC Sample Assessment Methodology 

### FIGURES, 

### cont. Figure A-19 SCE REUC Weekday Average Load: Gas Sample Space Conditioning A-20 SCE REUC Weekday Average Load: Gas Sample Space Conditioning A-21. SCE REUC Weekday Average Load: Gas Sample Space Conditioning A-22 SCE REUC Winter Weekday Electricity: Electric Sample A-23 SCE REUC Summer Weekday Electricity: Gas Sample A-24 SCE REUC Summer Weekday Electricity: Electric Sample A-25 SCE REUC Whole House Electricity: Electric Sample A-26 SCE REUC Whole House Electricity: Gas Sample A-27 SCE REUC Space Conditioning: Electric Sample A-28 SCE REUC Water Heating: Electric Sample A-29 SCE REUC Cooking: Electric Sample A-30 SCE REUC Clothes Drying: Electric Sample. A-31 SCE REUC: Temperature 8-1 REUC Sample Assessment Methodology



# ..

xv A-11
A-11
A-12
A-12. A-13

.A-13

A-14 A-14 A-15 A-15 A-16 A-16 A-17 8-2

### xvi 

# Thispageleft deliberatelyblank

xvi

### TABLES.

REUC Participant Demographics

### - Sample Design Stratification Variables REUC Participants ~emo~ra~hics 

# - Highest Completed Education LevelAmong Key Household Members REUC sample Representativeness by Income Stratum. REUC sample Representativ6ness by Housesize Strhtum REUC: Table of Month Labelto Date Correspondence AverageAnnual andSeasonal Energy Usage

### - Pooled Cities MonthlyAverage Usagefor PooledCities Estimated Linear Relationship between Average Morning Space Heatingand Morning Heating Degree-Days: All Households Estimated and Simulated Harmonic Model Load Shape Parameters DifferingAverage Characteristicsofthe Households Factor Loadings BasedonAnalysis of FuelTypelmage Question Longitudinal Changes in Electric Sample Participants' FuelType Preferencesfor Space HeatingSystems LongitudinalChanges inGas Sample Participants' FuelType Preferencesfor Space Heating Systems REUC Participants' Willingnessto RecommendTheir Space HeatingSystem to a Friend

### - By Fuel Type Longitudinal Changes in ElectricSample Participants' Fuel Type Preferences for Water Heaters LongitudinalChanges inGas Sample Participants' FuelType Preferences for Water Heaters

xvii

TABLES. Table Page 4-1 REUC Participant Demographics
- Sample Design Stratification Variables 4-3
4-2 REUC Participants Demographics
- Highest Completed 4-4 Education Level Among Key Household Members 4-3 REUC sample Representativeness by Income Stratum, 4-6 4-4 REUC sample Representativeness by House Size Stratum 4-7 5-1 REUC:. Table of Month Label to Date Correspondence 5-3 5-2 Average Annual and Seasonal Energy Usage
- Pooled Cities 5-6 5-3 Monthly Average Usage for Pooled Cities 5-8 5-4 Estimated Linear Relationship between Average Morning Space Heating and Morning Heating Degree-Days: All Households 5-17
6-1 Estimated and Simulated Harmonic Model Load Shape Paramete·rs 6-12
6-2 Differing Average Characteristics ofthe Households 6-17 7-1 Factor Loadings Based on Analysis ofFuel Type Image Question 7-6 7-2a Longitudinal Changes in Electric Sample Participants' Fuel Type Preferences for Space Heating Systems 7-7
7-2b Longitudinal Changes in Gas Sample Participants' Fuel Type 7-8 Preferences for Space Heating Systems 7-3 REUC Participants' Willingness to Recommend Their Space 7-13 Heating System to a Friend
- By Fuel Type 7-4a Longitudinal Changes in Electric Sample Participants' Fuel Type Preferences for Water Heaters 7-16
7-4b Longitudina! Changes in Gas Sample Participants' Fuel Type 7-17 Preferences for Water Heaters

xvii

### Table 

# - 7-5
7-6
7-7 7-8 8-1 A-1 A-2
, 

### A-3 A-4 8-1 8-2 0-3 8-4
C-1 C-2 C-3 C-4 TABLES, cont.



### REUC Participants'Willingness to Recommend Their Water HeaterSystem toa Friend.- By FuelType Electric induction Subsample Participants' Willingness to RecommendTheir Rangeto a Friend REUC Participants' Rangeloven Comparisons between ~uel Types Energy Savings Requirementsof Higher Efficiency Appliances REUC Stratification Scheme Variance Calculations AverageAnnual and Seasonal Energy Usage

### - MissionViejo Households AverageAnnual andSeasonal Energy Usage

### - l~ine Households MonthlyAverage Usagefor MissionViejo Households MonthlyAverage Usagefor lrvineHouseholds Appliance Stock lnventory (ASI)Share bySCE Division Chi-Square Statisticsfor Customer Descriptorsthat Discriminate between Appliance Stock Inventories (ASi)and PoolOwnership RSS-BasedStratification of Customer Descriptors Customer Descriptorsthat Meaningfully Discriminate Annual Usage Profiles byAppliance Stock lnventory (ASI)andSCE Division "Reasonable" Connected Load Estimates "Reasonable" Rangesfor Weekly Appliance Use ACUPA Error FlagCriteria Keyto invalid Data Flagged During DataValidation Process

xviii 

### TABLES, 

### cont. Table Page 7-5 REUC Participants' Willingness to Recommend Their Water 7-18 Heater System to a Friend·- 8y Fuel Type 7-6 Electric Induction Subsample Participants' Willingness to Recommend Their Range to a Friend 7-20
7-7 REUC Participants' Range/Oven**Compa~isons** between Fuel 7-21 Types 7-8 Energy Savings Requirements of Higher Efficiency Appliances 7-25 8-1 REUC Stratification Scheme Variance Calculations 8-6 A-1 Average Annual and Seasonal Energy Usage
- Mission Viejo Households A-18
A-2 Average Annual and Seasonal Energy Usage
- Irvine Households A-19
A-3 MonthlyAverage Usage for MissionViejo Households

.A-20 A-4 MonthlyAverage Usage for Irvine Households A-22 8-1 Appliance Stock Inventory (AS!) Share bySCE Division 8-4 8-2 Chi-Square Statistics for Customer Descriptors that Discriminate 8-5 between Appliance Stock Inventories (ASI) and Pool Ownership 8-3 RSS-8ased Stratification of Customer Descriptors 8-6 8-4 Customer Descriptors that Meaningfully Discriminate Annual Usage Profiles byAppliance Stock Inventory (AS!) and SCE 8-7 Division C-1 "Reasonable" Connected Load Estimates C-4 C-2 "Reasonable" Ranges for Weekly Appliance Use C-4 C-3 ACUPA Error Flag Criteria C-5 C-4 Key to Invalid Data Flagged During Data Validation Process C-6

xviii

### SUMMARY This report presents the research methods and resultsfrom the Residential Energy Usage Comparison (REUC) project, a joint effort by Southern California Edison Company (SCE) and the Electric Power Research Institute (EPRI). The REUC project design activities began inearly 1986. The REUC project is

### an in)lovative demand-side project designed to measure and compare' typical energy consumption patterns of energy efficient residential electric and gas appliances. REUC PROJECTOVERVIEW The REUC project has been designed to assess the energy performance and customer acceptance of new electric and gas appliances. Traditionally, studies that have examined the relative energy efficiencies of electric and gas end-use technologies have relied on laboratory settings or computer simulation models. The nature of the REUC project is 

### to move away from the laboratory setting by relying on the energy demands and customer behavior of actual households instead of simulated "test bench" usage patterns. The REUC project results have thus accounted for many of the subtle but important determinants of appliance usage, including household characteristics, that are not captured in engineering models. The projectdesign is

### discussed morefully inSections 1 

### through 3. 

### The results presented in this report pertain to a 

### sample of 92 households in Orange County, southeast of LOS

### Angeles. Over the pastdecade, Orange County has experienced significant residential and commercial growth. The REUC Participant sample was designed to represent an important segment of SCE's residential customers, namely owner-occupied, new single-family homes. More details about the REUC sample are provided in Section 4. 

### The analysis results illustrate the value of two uniquefacets of the project's design: 'first, end-use (appliance) load data have been collected for both electric andgas 

### appliances; and, second, bothcustomer survey data and end-use data have been collectedfrom the participants. End-use data collection began in late 1986for a Ponionof the OrangeCounty participants; survey research data collection began in 

### mid-1987. Bothend-use data and survey researchdata collection concluded in 

### SUMMARY 

### This report presents the research methods and results from the Residential Energy Usage Comparison (REUC) project, a joint effort by Southern California Edison Company (SCE) and the Electric Power Research Institute (EPRI).**Th~** 

### REUC project design activities began in early 1986. 

### The REUC project is an innovative demand-side project designed to measure and compare-typical energy consumption patterns ofenergy efficient residential electric and gas appliances. REUC PROJECT OVERVIEW 

### The REUC project has been designed to assess the energy performance and customer acceptance of new electric and gas appliances. Traditionally, studies that have examined the relative energy efficiencies of electric and gas end-use technologies have relied On laboratory settings or computer s(mulation models. The.nature of the REUC project is to move away from the laboratory setting by relying on the energy demands and customer behavior of actual households 

### instead of simulated "test bench" usage patterns. The REUC project results have thus accounted for many of the subtle but important determinants of appliance usage, including household characteristics, that are not captured in engineering models. The project design is discussed more fully in Sections 1through 3. The results presented in this report pertain to a sample of 92 

### households in Orange County, southeast of Los Angeles. Over the past decade, Orange County has experienced significant residential and commercial growth. The REUC participant sample was designed to represent an important segment of SCE's residential customers, namely owner-occupied, new Single-family homes. More details about the REUC sample are provided in Section 4. 

### The analysis results illustrate the value oftwo unique facets ofthe project's design: first, end-use (appliance) load data have been collected for both electric and gas appliances; 

### and,_ second, both 

### customer survey data and end-use data have been collected from the participants. End-use data collection began in late 1986 

### for a POrtion ofthe Orange County participants; survey research data collection began in 

### mia-1987. 

### Both end-use data and survey research data collection concluded in S-l

### December 1989. The project's October 1990 interim report (CU-6952) presented results based on one year of data ending inJanuary 1989. This report presentsanalysis resultsusingallthe available REUCdata. A summaryof the findings presentedinthis report isprovided below. ELECTRICAND GAS APPLIANCE ENERGY CONSUMPTION 

### A principal objective of the REUC project has been to m'easure electric and gas appliance energy consumption, at point of use. All energy inthis Report is given as point-of-usesite energy; i.e., 1 kwh

### =3.412 kBTU. Southern California Edison identified four residential energy uses to be considered in the project: space conditioning, water heating, cooking and clothes drying. These four end-uses were selected because of the direct competition between electricity and natural gas and because of the development of new, high-efficiency electric technologies. Figure S-1 illustratesthe point-of-use annual average energy consumption for the REUC space conditioning appliances (electric heat pumps relative to gas furnaces/electric air conditioners) and water heating appliances (electric heat pump water heatersand gas water heaters). These results are basedon appliance energy data gathered over nearly two years, from February 1988 through December 1989. The figure shows that, at point of use, electric heat pumps consume less than 45 

### percent as much energyas gas space conditioningsystems. During 'be winter season, electric heat pumps consume less than 36 percent as much site energy as gas furnaces. In addition, electric heat pumpwater heaters consumeabout 40 

### percentas muchsite energy as gas water heaters. Additional results for the REUC project electric and gas appliances' energy consumption are presented inSection 5. ELECTRICAND GASAPPLIANCE LOAD PROFILES 

### An uncommonfacet of the REUC project has been the collection of both electric and gas appliance load data. The unique pairing of electric and gas end-uses affords an opportunity to carefully compare the daily usage patterns of appliances with different energy sources. December 1989. The project's October 1990 interim report (CU-6952l presented results based on one year of data ending in January 1989. T,his report presents analysis results using all the available REUC data. A summary ofthe findings presented in this report is provided below. ElECTRIC AND GAS APPLIANCE ENERGY CONSUMPTION A principal objective of the REUC project has been to m'easure electric and gas appliance energy consumption, at point ofuse. All energy in this Report is given as point-of-use site energy; i.e.,l kWh = 3.412 kBTU.Southern California Edison identified four residential energy uses to be considered in the project: space conditioning, water heating, cooking and clothes drying. these four end-uses were selected because of the direct competition between electricity and natural gas and because of the development of new, high-efficiency electric technologies. Figure 5-1 illustrates the point-of-use annual average energy consumption for the REUC space conditioning appliances (electric heat pumps relative to gas furnaces/electric air conditioners) and water heating appliances (electric heat pump water heaters and gas water heaters). These'results are based on appliance energy data gathered over nearly two years, from February 1988 through December 1989. The figure shows that, at point of use, electric heat pumps consume less than 45 percent as much energy as gas space conditioning systems. During 'l]e winter season, electric heat pumps consume less than 36 percent as much site energy as gas furnaces. In addition, electric heat pump water heaters consume about40 

### percent as much site energy as gas water heaters. Additional results for the REUC project electric and gas appliances' energy consumption are presented in Section 5. ELECTRIC AND GAS APPLIANCE LOAD PROFILES An uncommon facet ofthe REUC project has been the collection ofboth electric and gas appliance load data. The unique pairing of electric and gas end-uses affords an opportunity to carefully compare the daily usage patterns ofappliances with different energy sources.

5-2. . FigureS-1 AnnualAverace Enerw Usaee SpaceConditioningand Water Heatine

.(at ~oint 

### ofuse) *+

R ,Gas Sample ElectricSample 

# u !

. .



## i 

# I
!

$

Additional 

### REUC appliance load ~rofiles developed from end-use data are 3 j presented insection 6 and inAppendix A. s,.~. .

;paceConditioning

Water Heating

0 5 10 15 20 25 30 MBTU

Figure

### 5-2 illustratesthe average winter weekday load profiles for both gas and i : :electricspace conditioning appliances (electric heat pumps and gas furnaces). ,These load profiles are based on appliance energy data gathered for nearly two years, from February 1988 through December 1989, for the 92 household 

### smple. As the figure indicates, gas furnaces have a much sharper morning peak (about 11 

### kBTU per hour) .than electric heat pumps, which have a lower diversified load (about

### 3 

### kBTU per houror 1 

### kW). I,.

_ 

### Figure 5-1 Annual Average Energy Usage Space Conditioning and Water Heating (atpointofuse)

;paceConditioning

Water Heating



### o 5 10 15 MOTU I:l!**,~Sam~le** iii Electric Sample 23.4

20 25 "

30



### 'Figure 5-2 illustrates,the average winter weekday load profiles for both gas and electric space conditioning appliances (electric heat pumps and gas furnaces). ,These load profiles are based on appliance energy data gathered for nearly two years, from February 1988 

### through December 1989, 

### for the 92 

### household sample. As the figure indicates, gas furnaces have a much sharper morning peak (about 11 

### kBTU per hour) ,than electric heat pumps, which have a lower diversified load (about 3 kBTU per hour or 1 

### kWl. Additional REUC appliance load profiles developed from end-use data are presented in Section 6and in Appendix A.

5-3FigureS-2 SCE REUC: Avera~e Load Profiles WinterWeekday Soace Conditionine:(at ooint of use)

Elccfric HcalPump Car Furnace

CUSTOMER ACCEPTANCE OF APPLIANCES Measuring customer acceptanceof these energy efficient appliances was another objectiveofthe project. The 

### REUC project included the administration of surveys designed to measurethe level of new appliance acceptanceamong 

### REUC participants. Acceptance was measured 

### by allowing the participants to rate their new appliances amongvarious attributes including reliability, operatingcost, response time and maintenance. kBTU

Three findings were revealedfrom the customer acceptance analysis. First, as customers gain experience with more than one fuel type, fuel type increases in importance as an attribute. Second, actual experience with the appliance is required in order for the participants to make strong recommendations. Third, per 12 -7 Hour 10-

8-

6

# - ('1 11 'I 

# ; ; ; 

# ; I I: !I 'I 

### I I I I

or..I..I.'I~'~~L~'I''8'~I

0 3 6 9 12 15 18 21 24 T~mc of Day 

### Figure 5-2 SCEREUC: Average load Profiles Winter Weekday Space Conditioning (at point ofuse)
kBTU per 12 

# ----- Electric Meat Pump Hour (I I \ 

### ---_. Gas Furnace 10- I \ I I I I I I I I 8 I I I I I I I I & - I \ I I I 

### \ I I \ 4 I I I \**./~-.-'""\** I \ ;' . \ 

## " \ I \ "../ \ I 

# , • 2 ,,,-" \ I 

## '---/'
0 •

0 3 & 9 12 15 18 21 24 Time of Day



### CUSTOMER ACCEPTANCE OF APPLIANCES 

### Measuring customer acceptance ofthese energy efficient appliances was another objective ofthe project. The REUC project included the administration ofsurveys designed to measure the level of new appliance acceptance among REUC participants. Acceptance was measured by allowing the participants to rate their new appliances among various attributes including reliability, operating cost, response time and maintenance
.

.Three findings were revealed from the customer acceptance analysis. First, as

customers gain experience with more than one fuel type, fuel type increases in importance as an attribute. Second, actual experience with the appliance is required in order for the participants to make strong recommendations. Third,

5-4

### ngsystems are more likely to have proponentsthan heat pump systems. 

### s that the causes of the weaker reception of heat pumps for the REUC 

### ntswere the perceived quality problemswith the units installedjn 1986 .L+


### ey responses disclosed that participants found their heat pump water too slow to respond. As a result, acceptance ratings for the heat pump aterswere quite low, at 45 

### percent. Incontrast, electric inductionstoves 

### ell accepted; 80 

### percent would recommend their induction stoves to a ':Among the participants retrofittedfrom gas ranges to electric induction 50

### percentexpressed an opinion prefeFringelectricityas afuelfor cooking. kijonalresultson appliance acceptanceare provided inSection 7.

RATED 

### MARKET RESEARCH 

### oi~ectibn of both end-use load data and participant survey data is 

### yet er

### distinctivefeature of the REUC 

### project. Integrated market research is

### the ve synthesis of load research information with market research/survey infor- 

# " to 

### provide insight aboutcustomers' energy usage behavior patterns.

. , 

### systems are more likely to have proponents than heat pump systems. that the causes of the weaker reception of heat pumps for the REUC

were the perceived quality problems with the units installed in 1986

responses disclosed that participants found their heat pump water slow to respond. As a result, acceptance ratings for the heat pump

were quite low, at 45 percent. In contrast, electric**inductio~** 

### stoves accepted; 80 percent would recommend their induction stoves to a '.' Among the participants retrofitted from gas ranges to electric induction

,50percent expressed an opinion preferring electricityas afuel for cooking.

## ~Ulonal results on appliance acceptance are provided in Section 7.

TED MARKET RESEARCH**:~olilection** 

### of both end-use load data and participant survey data is yet distinctive feature ofthe REUC project. Integrated market research is the synthesis of load research information with market research/survey inforto provide insight about customers' energy usage behavior patterns.



## s-sFigureS-3 Customers' Reauired Returnon a Small Riskless Investment

% of so Participanls

40

30

20

10

0 6qo-nqo g%-lo% 11%-I 3% RequiredReturn 

# - Income: SS0;OOl -SnO,OOO IncomE $30,000-$50,000

The REUC project surveys included questions designed to estimate participants' appliance investment payback requirements based on their required rates of return. The higher an individual customer's required rate of return for an appliance investment, the shorter the required payback period for such investments. Participantswere also asked to state their required return on a small riskless investment, such as a bank certificate ofdeposit. As illustratedby Figures 

### 5-3 and S-4, the REUC participants' required rate of return on appliance efiiciency investments is much higher than that of their riskless rate of return. These high required rates of return implyvery short payback periods; as a result, expensive appliances must demonstrate large efficiencies to compete with cheaper alternatives. Figure S-3 Customers' Required Return on a Small Riskless Investment

% of**50,-------------------------~--------_,** Participants

40

30

20

10



### o



# - G%-8'% 90/0-100/0 Required Return 

## rn Income: >$80,000 Illl Income: $50;001·$80,000**~** Income·: $30,000-$50,000 11%-13%



### The REUC project surveys included questions designed to estimate participants' appliance investment payback requirements based on their required rates of return. The higher an individual customer's required rate of return for an appliance investment, the shorter the required payback period for such investments. Participants were also asked to state their required return on a small riskless investment, such as a bank certificate ofdeposit. As illustrated by Figures 5-3 and 5-4, the REUC participants' required rate of return on appliance efiiciency investments is much higher than that of their riskless rate of return. These high required rates of return imply very short payback periods; as a result, expensive appliances must demonstrate large efficiencies to compete with cheaper alternatives.

5-6

### Figure 5-4 

### Customerst Required Return onan Investment in Aapliance Efficiency

% of 40 Participanls

30

20

10

0 45% 15%-59% >59% RequiredRelurn

Income: $50,001-$80,000 5 Income: $30,000-150,000 

### Figure 5-4 Customers' Required Return on an Investment in Appliance Efficiency

% of**40~----------------------------------'** Participants

Required Return fa lncome: >$80,000 Iii! I""om.:$50,001.$80,000 E!1I Income: $30,000·$50,000FigureS-5 REUC Participant lma~es 

### of Electricvs. Gas

Inconvenient PoorService

LowMonl

The REUC project surveys also included questions designed to elicit participants' images of electricity and gas. Figure S-5 portrays the REUC participants' fuel images for electricity and gas. Fuel images were found to be useful for defining customer segments. Factor analysisof the REUC participants'fuel images resulted in the derivation of a Quality factor (a composite of the Safety, Convenience, Service and Reliability images) and a Value factor (acomposite ofthe Appliance Cost, General ExpenseandMonthly Utility Bill images). Figure 5-5 REUC Participant Images ofElectric V5. Gas Dirly '-" " Clean -,- , 

# J Dangerous !Jj Safe 

## tf 

# I Easy 10 Hookup Hard to Hookup**~** :;:.:'

# ~\ :..I

# - Inconvenient Convenient 

## ....**~** 

# :3**~** 0- Poor Service 

### t Good Service (rom Utility if from Utility 

## " !J Unrcli<lblc }f Reliable 

# dfi/'" Sluggish Heal Fasl Heal-I-**'~-:;:**.. 

# l/**~(,** High Cost 

# .,:t \ lowCost Appliances Appliances 

## .... 

# , ::J



## .... Expensive 

### ,£ Inexpensive

### ~

# ~t 

# \ High Monthly LowMonlhly t:! UtilityBills**UlililyBills_~** Electricity Gas

Image Image

The REUC project surveys also included questions designed to elicit participants' images of electricity and gas. Figure S-5 portrays the REUC participants' fuel images for electricity and gas. Fuel images were found to be useful for defining customer segments. Factor analysis ofthe REUC participants' fuel images resulted in the derivation of a Quality factor (a composite of the Safety, Convenience, Service arid Reliability images) and a Value factor (a composite ofthe Appliance Cost, General Expense and Monthly Utility Bill images).

S-8Figure S-6 SCE REUC: OualityFactor Seements Winter Weekday Heat Pumo

Load 1.4

# -

in 

## kw 1.2- HiElcclridLoGasQualily

0.8-
0.G0.0 ,'I.,.,.,.,.

' 0 3 6 9 12 15 18 21 24 Timc of Day (inHours

### Pm

FigureS-6 

### illustratesthe useofthe Quality 

### factor for customersegmentation. The figure presents average winter weekday load profiles for participants' heat pumps segmentedwith the electricand gas Quality 

### factors. Thisfigureshows the distinct loadshapes for these customersegments. Figure S-6 SCE REUC: Quality

### Factor Segments Winter Weekday Heat Pump

La Electric/Hi GasQuality Hi ElcctridLo GasQuality

6 9 12 1S 18 21 24 Time of Day (in Hours

## pm

Figure 5-6 illustrates the use ofthe Quality

### factor for customersegmentation. The figure presents average winter weekday load profiles for participants' heat pumps segmented with the electric and gas Quality

### factors. This figure shows the distinct load shapes for these customer segments.

5-9FigureS-7 SCE REUC: CP&B Needs-Based Seaments 

### Summer Weekday Heat Pump

,....... v.,*%-. Res.Consrv. 

# ----- ValucSeekers Load 1 .OinkW 

### 0.8-

0.4-
0.2-

0.01 

## .,..,~,.,.,.,. 0 

### 3 6 9 12 15 18 21 24 Timcof Day(inHoursPST)

The EPRl Customer Preference and Behavior (CP&B) CLASSIFYTh' survey was administered to 

### the REUC participants and was alsofound to be a useful instrument for customer segmentation. Figure S-7 presents average summer weekday load profiles for heat pumps segmented by Appearance Conscious, Resource Conserver and Value Seeker needs-basedsegments. Figure 5-7 SCE REUC: CP&B Needs-Based Segments Summer Weekday Heat Pump

Load 

## 1.0,.----------------------...., inkW 0.8
0.6
0.4
0.2 •.•••••, ........"''"'< ..... Appear. Cons. Res. Consrv
. Value Seekers**O.O+-~-,-~-.--.....-** 

### ....**--.----.-~.,-~-r---r-.--....---l** 

## o 3 6 9 12 15 18 21 24 Time ofDay (in HoursPST)

The EPRI Customer Preference and Behavior (CP&B) CLASSIFY'" survey was administered- to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure S-7 presents average summer weekday load profiles for heat pumps segmented by Appearance Conscious, Resource Conserver and Value Seeker needs-based segments.

S-10FigureS-8 

### REUC:Heat PumpWater He* Demopraphic Scenario

ousehold characteristic data from REUC participants were also used inthe :t's load shape modeling research. End-use load shapes yere modeled household characteristics ina two-step regression'procedure: (1)harmonic ling of load shapes, followed by (2) householdcharacteristic regressionson armonic model parameters. Figure S-8 illustrates an application of this ~ach by comparing derived heat pump water heater load shapes for two :hetical demographic market segments produced with the load shape ,ling methodology. This figure shows how the load shapes may differ :en younger and older households. .d 0.8

. . iorial integrated market research results of the REUC project are presented :tion 8.

W

0.6

# - c\ Older~ouseholds I \ 

# ----- Younger Households I \ I \ I \

0.4 

# -

0.2

# -

0.0 I.I.,','I.. 0 3 6 9 12

. 15 18 21 24 Timeof Day(inHours PST)



### Figure S-8 REUC: Heat Pump Water Heater DemographiC Scenario

## .~ 0.8

## J---------::~-r_~~~~~~~~;==jl**~**\ Older Households t' \ 0.6

0.4

0.2 .." 

### .......

# _-

o 3 6 I \ - - - - - Younger Households I \ I \ I \\

9 \\\\. 

## ,

# "

12 

# ,\ \\

15 Time ofDay (in Hours

### psn 18 21 24

ousehold characteristic data from REUC participants were also used in the :t's load shape modeling research. End-use load shapes**~ere** modeled household characteristics in a two-step regression·procedure:
.(1) harmonic ling of load shapes, followed by (2) household characteristic regressions on armonic model parameters. Figure 

### 5-8 illustrates an appli.cation of this ,ach by compar.ing derived heat pump water heater load shapes for two :hetical demographic market segments produced with the load shape ·Iing methodology. This figure shows how the load shapes may differ**~en** younger and older households.
ional integrated market research results of the REUC project are presented :tion 8.

5-11 . 

### .'Section 

### 1 INTRODUCTION This report presents the research activities and findings from the Residential Energy Usage Comparison (REUC) project, a joint effort by Southern California Edison Company (SCE) and the Electric Power Research institute (EPRI). The REUC project 

### is an innovative demand-side project designed to measure and compare typical energy consumption patterns of energy efficient residential electric and gas appliances. OVERVIEW OF THE REUC PROJECT The REUC project has been designedto provide information necessaryfor assessingthe energy performance, the relative effectiveness and the customer acceptance of new electric and gas appliances. Southern California Edison has recognized that evaluating key residential end-uses such as space conditioning, water heating, cooking and clothes drying can provide much needed insight into the energy efficiencies ofthese technologies for the benefit of its customers. Traditionally, studies that have- examined the relative energy efficiencies of electric and gas technologies have relied on laboratorysettings or computersimulation approaches in order to assess these end-use technologies' performances. The nature of the REUCproject 

### isto move away from the laboratory setting by substituting the energy demands of actual households in place of simulated usage patterns. The advantage ofthis approach 

### isthat itaccounts for manyofthe subtle but important determinants of appliance usage not captured in engineering models, including unobservable householdcharacteristics. The advantages of conducting a "field" experiment are reflected in the value of more comprehensive, realistic assessments of actual customers' usage and response to new electrotechnologies. These advantages.offset the reduction in "control" affordedby a laboratorysimulationstudy. 

### Section 1 INTRODUCTION 

### This report presents the research activities and findings from the Residential Energy Usage Comparison (REUC) project, a joint effort by Southern California Edison Company (SCE) and the Electric Power Research Institute (EPRI). The REUC project is an innovative demand-side project designed to measure and compare typical energy consumption patterns of energy efficient residential electric and gas appliances. OVERVIEW OF THE REUC PROJECT 

### The REUC project has been designed to provide information necessary for assessing the energy performance, the relative effectiveness and the customer acceptance ofnew electric and gas appliances. Southern California Edison has recognized that evaluating key residential end-uses such as space conditioning, water heating, cooking and clothes drying can provide much needed insight into the energy efficiencies ofthese technologies for the benefit ofits customers. Traditionally, studies that have examined the relative energy efficiencies of electric and gas technologies haVe relied on laboratory settings or computer simulation approaches in order to assess these end-use technologies' performances. The nature ofthe REUC project is to move away from the laboratory setting by substituting the energy demands of 

### actual households in place of simulated usage patterns. The advantage ofthis approach is that it accounts for many ofthe subtle but important determinants of appliance usage not capt\lred in engineering models, including unobservable household characteristics. The advantages of conducting a "field" experiment are reflected in the value of more comprehensive, realistic assessments of actual customers' usage and response to new electrotechnologies. These advantages-offset the reduction in "control" afforded by a laboratory simulation study.

1.1Proiect Obiectives The REUC project was designed, in part, to meet several of SCE's needs 

### for assessing new electrotechnologies available to residential customers. ,These needs have been satisfied by meetingseveral objectives: measure energy usage of electric and gas residential appliances; compare usage patternsofsimilar electric andgas appliances; assess customer acceptance of new end-use technologies; and generalize resultsto other residential customers. This reportwill illustrate howthese objectives have been met.
Proiect Milestones Since 1985, when itwas first suggested bySCE andthe California Public Utilities, Commission, the REUC project has involved three phases: Phase I, from early 1986 through late 1987, included the experimental design, analysis plan.and instrumentation installations; Phase II, which consisted of initial data collection and analysis, ran through early 1989. Phase Ill has consistedof subsequent data collection and analysis, whichwas completed inthe summer of 1990. The major REU,Cproject milestonesare illustrated in Figure 1-1. EPRl INVOLVEMENT INTHE REUC PROJECT EPRl has contributedto the REUC project inthree.principal areas: projectdesign and planning; projectanalysis; and project management. 

### Project Objectives The REUC project was designed, in part, to meet several of SCE's needs for <;lssessing new electrotechnologies available to residential customers. 'These needs have been satisfied by meeting several objectives:
• measure energy usage of electric and gas residential appliances;
• compareusage patterns ofsimilar electric and gas appliances;
• assess customer acceptance of new end-use technologies; and
• generalize results to other residential customers. This report will illustrate how these objectives have been met. 

### 'Project Milestones Since 1985, when it was first suggested by SCE and the California Public Utilities, Commission, the REUC project has involved three phases: Phase I, from early 1986 through late 1987, included the experimental design, analysis plan 'and instrumentation installations; Phase II, which consisted of initial data collection and analysis, ran through early 1989. Phase III has consisted of subsequent data collection and analYSiS, which was completed in the summer of 1990. The major REU,C project milestOnes are illustrated in Figure 1-1.
EPRI INVOlVEMENT IN THE REUC PROJECT EPRI has contributed to the REUC project in three,principal areas:
• project design and planning;
• projectanalysis; and
• project management.

1-2

# 1985

# - IdeaSuggested by CPUCandSCE 

# q/ Early 1986

# - Discussionswith EPRl 

# 9 Summer 1986

# - Customer Sample Design; REUC-RSSSurvey; lnstallations Begun 

# qfWinter 1986

# - MissionViejo Installations Complete 

# / Fall 1987

# - ProjectAnalysis Plan; First REUCSurvey



# q/Winter 1987

# - lrvine lnstallations Complete 

# q/ Spring 1988

# - Second REUCSurvey Summer 1989

# -Third REUCSurvey; Winter 1989

# - Final REUCSurvey; Equipment Removal 1985 1



# - 00 

## 1986 1987 1988 1989 

# I· 1 1 

### 1 

# ..q 1985 - Idea Suggested by CPUC and SCE

# ~ Early 1986 - Discussionswith EPRI

# ~ Summer 1986 - Customer Sample Design; 1990 

# I

REUC-RSS Survey; Installations Begun 

# -<fI Winter 1986 - Mission Viejo Installations Complete 

# 4t Fall 1987 - ProjectAnalysis Plan; First REUC Survey

# ~ Winter 1987 - Irvine Installations Complete

# ~ Spring 1988 - Second REUC Survey 

# 11 Summer 1989 - Third REUC Survey; 

# -«f Winter 1989 - Final REUC Survey; Equipment Removal.Proiect Desien EPRI first became involved with the REUC project during its experimental design and planning activities. EPRl project staff emphasized the need to focus on'a a> clear set of objectives of the project and then to carefully and systematically design the REUC householdsample accordingly. The participants would needto accurately represent important segments of SCE's residential customer population. In addition, the REUC ex6erimental design needed to determine how much and what type of customer data should becollected inorder to ensure the precisionof subsequent REUC analyses. Finally, the design neededto include a comprehensive data analysis plan, so that the collected REUC data would satisfy theproject objectives. The REUC experimental design activities took advantage of previous EPRIsponsored research results, including the design of the Athens Automation and Control Experiment (EPRI EM-4628) and the model-based sample design techniques in EPRi £A-3286. ProiectAnalvsis The REUC project has pioneered the integration of load research/load shape analysis with market research/customer behavior analysis. The project team has developed "integrated market research" techniques and "behavioral engineering statistical techniques (BEST)" to meet the project objectives. These techniques have leveraged upon research results from several other EPRI projects, including the Customer Preference and Behavior (CP&B) Project (~~2671) and the Load DataAnalysis Workstationproject (RP23421. : Proiect Mana~ement EPRI has assisted SCE staff 

### by offering primary managementof the REUC project design anddata analysestasks. SCE staff provided effective management of the customer contact, equipment installation, anddata collection efforts. Membersof the project team, from EPRI, SCE and the Contractor, have presented preliminary results from the REUC project at numerous industry conferencesand meetings. Project Design EPRI first became involved with the REUC project during its experimental design and planning activities. EPRI project staff emphasized the need to focus on'a 

### ,. clear set of objectives of the project and then to carefully and systematically design the REUC household sample accordingly. The participants would need to accurately represent important segments of SCE's residential customer population. In addition, the REUC experimentai design needed to determine how much and what type of customer data should be collected in order to ensure the precision of subsequent REUC analyses. Finally, the design needed to include a comprehensive data analysis plan, so that the collected REUC data would satisfy the project objectives. The REUC experimental design activities took advantage of previous EPRI-

. sponsored research results, including the design of the Athens Automation and

Control Experiment (EPRI EM-4628l and the model-based sample design techniques in EPRl EA-3286. Project Analysis The REUC project has pioneered the integration of load research/load shape analysis with market research/customer behavior analysis. The project team has developed "integrated market research" techniques and "behavioral engineering statistical techniques (BEST)" to meet the project objectives. These techniques have leveraged upon research results from several other EPRI projects, including the Customer Preference and Behavior (CP&B) Project (RP2671) and the Load Data Analysis Workstation Project (RP2342). Project Management· EPRI has assisted SCE staffby offering primary management ofthe REUC project design and data analyses tasks. SCE staff provided effective management of the customer contact, eqUipment installation, and data collection efforts. Members ofthe project team, from EPRI, SCE and the Contractor, have presented preliminary results from the REUC project at numerous industry conferences and meetings.

1-4isorganized as follows: Section 2, REUC Project s the design of the REUC project. Section 3, REUC 

### ope and Data Collection, describes the history of REUc data collection.

REUCCustomer Sample, provides a summary of the REUC sample description of the REUC sample. Section 5, REUC d Electric Appliance Energy Usage, presents results ce energy data. Section 6, REUC Appliance Load idesappliance load profiles for the REUCelectric and gas appliances. ustomer Preferences and Appliance Acceptance, discusses REUC attitudes toward their new electric and gas appliances. Section 8, 

### n of Load and Survey Analyses, presents results of'integrated market rticipants' fuel images and load profiles b.y customer pendix A provides supplementary energy usage information .load Appendix B contains a description of the kethodologyfor generalizing 

## cCustomer Sample Results. Appendix C describes the load data editing rATION of this report is organized as follows: Section 2, REUC Project 5bj,ectives, describes the design of the REUC project. Section 3, REUC and Data Collection, describes the history ofREUC data collection. REUC Customer Sample, provides a summary of the REUC sample a demographic description of the REUC sample. Section S, REUC 'Analysis: Gas and Electric Appliance Energy Usage, presents results lysis of appliance energy data. Section 6, REUC Appliance load**IrO\lidE~S** appliance load profiles for the REUC electric and gas appliances. Customer Preferences and Appliance Acceptance, discusses REUC

attitudes toward their new electric and gas appliances. Section 8, of Load and Survey Analyses, presents results ofintegrated market an;l.IY!ieS, such as participants' fuel images and load profiles 

### by customer

Appendix A provides supplementary energy usage information ,load 'Appendix Bcontains a description of the methodology for generalizing

Customer Sample Results. Appendix C describes the load data editing 1il1i"""'i;,,n methods.

1-5Section2 REUC PROJECTDESIGN OBJECTIVES

INTRODUCTION AS mentioned in Section 1, one of the REUC project'objectives has been to generalizethe results obtained from the REUC sample itself to other residential customers. In order to satisfy this objective, the project experimental design, projectdata and analysis methods were developed inan integrated fashion. This section suggests how the REUCdesign.andanalysis methods can be used by other utilities. Thus, this section provides a response to the question, "Howcan utilities other than Southern California Edison utilize the REUC project's research results?" These research results include both the analysis methods themselves as well as the analysisfindings. REUC DESIGN The REUC project's experimental design developed a consolidated customer sample design, data collection plan and analysis plan. Project resources limited the sample to no more than 100 households. A model-based sample design was employed in order to produce an efficient customer sample that was consistent with the available project reso~rces.~ The REUC participant sample was not designed to be a completely random sample nor to represent SCE's entire residentialcustomer class of over 3.5 million customers. Rather, the REUC participants were selected to represent owneroccupied, single-family new homes inSCE's Southern Division. Thus, the project sample represents one specific and important segment of residential customers, those most likely to purchase new, energy-efficient electric end-use technologies for space conditioning, water heating, cooking and clothes drying. This segment
1J.T. Powers, M.S. Shirilau, R.T. Uhlaner, B.A. Smith, "Design of a Utility CompetitiveAssessment Experiment: The Residential Energy Usage Comparison Project," lEEE Transactions on Power Systems, Vol. 3, No. 3, August 1988, pp. 1298-1 305.

L. 

### Section 2 REUC 

### PROJECT DESIGN OBJECTIVES

INTRODUCTION 

### As mentioned in Section 1, one of the REUC project'objeciives has been to generalize the results obtained from the REUC sample itself to other residential customers. In order to satisfy this objective, the project experimental design, project data and analysis methods were developed in an integrated fashion. This section suggests how the REUC design ,and analysis methods can be used by other utilities. Thus, this section provides a response to the question, "How can utilities other than Southern California Edison utilize the REUC project's research results?" These research results include both the analysis methods themselves as well as the analysis findings.
REUC 

### DESIGN 

### The REUC project's experimental design developed a consolidated customer sample design, data collection plan and analysis plan. Project resources limited the. sample to no more than 100 households. A 

### model-based sample design was employed in order to produce an efficient customer sample that was consistent with the available project resources. 1 

### The REUC participant sample was not 

### designed to be a completely random sample nor to represent SCE's entire residential customer class ofover 3.5 million customers. Rather, the REUC participants were selected to represent owneroccupied, single-family new homes in SeE's Southern Division. Thus, the project sample represents one specific and important segment of residential customers, those most likely to purchase new, energy-efficient electric end-use technologies for space conditioning, water heating, cooking and clothes drying. This segment
1J.T. Powers, M.S. Shirilau, R.T. Uhlaner, B.A. 

### Smith, "Design of a Utility Competitive Assessment Experiment: The Residential Energy Usage Comparison Project," IEEE Transactions on Power Systems, 

### Vol. 3, No.3, August 1988, pp. 1298-1305.
2-1of residentialcustomers 

### isgrowing rapidly inSCE'saswell as other utilities' service territories. The demographics of the REUC sample are described in Section 4. This segment-specific design, based on information widely available about residential customers

### -- household income and dwellingsize, willfacilitatethe application of the project results to a larger portion of SCE's customers as well as to other utilities. As mentioned above, the REUC project's experimental design linked the sample requirements to the planned data analysis methods using a model-based sample design. Such integrated design 

### is recommended for two reasons on utility projects that assess energy-efficient end-use technologies such as the REUC project. First, such designs specify the type and number of customers needed in the sample, based on accuracy requirements and the analysis methods' data requirements. Second, integrated designs combine the data collection plan, the measurement plan and the evaluation plan requirements so that program planners can understand and account for the inter-relationships between such plansat the design phase. These designs can helpensurethatthe utility'ssizeable investment in such load monitoring projects can provide valuable results on schedule and within budget. REUCANALYSIS The REUC project has collected a wealth of data from the 92 participants' households. The project's data collection plan and subsequent analyses were undertaken to facilitate the generalization of the project results. The collection of both end-use load data and customer survey responses has allowed the extension of load profile results to other residential customers at SCE. As a result of the project activities, many of SCE's other load monitoring customers receive residentialappliancesaturationsurveys. The REUC project has demonstrated that, when end-use loads are being monitored, it 

### is important and cost-effectiveto requestthat these customers also respondto standard saturation survey questions. The generalization of load data and load profile results to the utility's other residential customers requires common descriptors of the two groups of customers. The projectfound that the 

### ofresidential customers is growing rapidly in SCE's as well as otherutilities' servi'ce territories. The demographics of the REUC sample are described in Section 4. This segment-specific design, based on information widely available about residential customers
-- household income and dwelling size, will facilitate the application of the project results to a larger portion of SCE's customers as well as to other utilities. As mentioned above, the REUC project's experimental deSign linked the sample requirements to the planned data analysis methods using a model-based sample design. Such integrated design is recommended for two reasons on utility projects that assess energy-efficient end-use technologies such as the REUC project. ,First, such designs specify the type and number ofcustomers needed in the sample, based on accuracy requirements and the analysis methods' data requirements. Second, integrated designs combine the data collection plan, the measurement plan and the evaluation plan requirements so that program planners can understand and account for the inter-relationships between such plans at the design phase. These deSigns can help ensure thatthe utility's sizeable investment in such load monitoring projects can proVide valuable results on schedule and within budget. REUC ANALYSIS 

### The REUC project has collected a wealth of data from the 92 participants' households. The project's data collection plan and subsequent analyses were undertaken to facilitate the generalization of the project results. The collection of both end-use load data and customer survey responses has allowed the extension of load profile results to other residential customers at SCE. As a result ofthe project activities, many ofSCE's other load monitoring customers receive residential appliance saturation surveys. The REUC project has demonstrated that, when end-use loads are being monitored, it is important and cost-effective to request that these customers also respond to standard saturation survey questions. The generalization of load data and load profile results to the utility's other residential customers requires common descriptors of the two groups of customers. The project found that the
2-2t-ofdescriptors 

### is customer responses to the demographic/charles in utility saturation surveys. Furthermore, the kinds of data techniques employed on the REUC project can allow for the 

# . lication of these results to residential customers served by other 

### *

tered four different survey instruments, as described in was based on SCE's 1985 Residential SaturationSurvey additional survey information, the REUC results were lityto other SCE residentialcustomers. This assessment

. . fwith the detailed electric and gas end-use load shapes that have been on the project, the survey research results determined customers' nd acceptance of these energy-efficient end-use technologies. this

of loadand survey information can be usedto assess the impacts and of utility demand-side programs that emphasize customers' efficient 

### ;e. As discussed inSection 7, the fie1 images of the REUC participants ecorrelatedto customers' energy usage behaviorand, as a result, their

program participation. I the REUC project included load shape modeling analyses that used both -use loaddata and customersurvey responses. Resultsof,severalof these ses are described in Section 6. The load shape modeling linked customer Cteristicsto appliance load shapes and usage patterns. End-use load shapes 

### e modeled using customer characteristics with a two-step procedure. The edure uses customers' demographic, attitudinal and/or needs-based segment racteristics to explain end-use load shapes. These results suggest how other F+"$S .A

# -. . . :lJtllltles mayadaptthe REUC loadshapes to theirservice territories. set"of descriptors is customer responses to the demographic/charriatlles in utility saturation surveys. Furthermore, the kinds of data techniques employed on the REUC project can allow for the 'appli.:atllon of these results to residential customers served by other
project administered four different survey instruments, as described in " first survey was based on SCE's 1985 Residential Saturation Survey this and the additional survey information, the REUC results were " their applicability to otherSCE residential customers. This assessment



### in Section 4. with the detailed electric and gas end-use load shapes that have been

- on the project, the survey research results determined customers' acceptance of these energy-efficient end-use technologies. "This ofload and survey information can be used to assess the impacts and of utility demand-side programs that emphasize customers' efficient As discussed in Section 7, the fuel images of the REUC participants be correlated to customers' energy usage behavior and, as a result, their ""-'.',"VV ofprogram participation. the REUC project included load shape modeling analyses that used both**~n(l-w;e** load data and customer survey responses. Results of-several of these

are described in Section 6. The load shape modeling linked cUstomer irae:t",i<t;,-< to appliance load shapes and usage patterns. End-use load shapes modeled using customer characteristics with a two-step procedure. The**U\.~UUI"P** uses customers' demographic, attitudinal and/or needs-based segment to explain end-use load shapes. These results suggest how other mayadapt the REUC load shapes to their service territories.

2-3 

## ,Section 3 REUC PROjECTSCOPEAND DATA COLLECTION HISTORY
INTRODUCTION The REUC customer sample consists of 100 households. These 100 households are comprisedoftwo groups: eight homes where load monitoring equipment was initially tested, the "equipment test sample"; and 92 homes located in,lrvine and MissionViejo, the "REUC sample." This reportfocuses only on datafrom the REUC's 92-householdsample. lrvine and MissionViejo are located inOrange County alonga high-growthcorridor between San Diego and Los Angeles. Over the past 20 years, this area has undergone a transitionfrom a lightly populated agriculturally based economyto a metropolitan area consisting of high-technology research and manufacturing industries. The population is largely new to the area and represents a diverse cross-section of other parts of the SCE servicearea and of the country. This section discusses the scope of the REUC project followed by the REUC data collection history. REUC PROJECTSCOPE Southern California Edison identifiedfour residential energy uses to be considered inthe REUC project 

### -- space conditioning, water heating, cooking, and clothes drying. They were selected because these end-uses represent a majority of residential energy consumption and because of the development of new, efficient electric technologies. These are also the end-uses that allow direct comparisons between similar electric and gas appliances. Inthe Electric Sample, households .were retrofitted with heat pumps for both space conditioning and water heating. The Gas Sample was retrofitted with efficient gas furnaces, electric air conditioners, and gas water heaters. Set-back thermostats were not used in either sample. These Electric and Gas Samples were selectedfrom the same housingtracts. This INTRODUCTION Section 3 REUC PROJECT SCOPE AND DATA COLLECTION HISTORY



### The REUC customer sample consists of 100 households. These 

### lOa households are comprised oftwo groups: eight homes where load monitoring equipment was initially**t~sted,** 

### the "equipment test sample"; and 92 homes located in 

### .Irvine and Mission Viejo, the "REUC sample." This reportfocuses only on data from the REUC's 92-household sample. Irvine and Mission Viejo are located in Orange County along a high-growth corridor between San Diego and Los Angeles. Over the past 20 years, this area has undergone a transition from a lightly populated agriculturally based economy to a metropolitan area consisting of high-technology research and manufacturing industries. The population is largely new to the area and represents a diverse cross-section of other parts ofthe SCE service area and ofthe country. This section discusses the scope of the REUC project followed by the REUC data collection history.
REUC PROJECT SCOPE 

### Southern California Edison identified four residential energy uses to be considered in 

### the REUC project

-- space conditioning, water heating, cooking, and clothes drying. They were selected because these end-uses represent a majority of residential energy consumption and because of the development of new, efficient electric technologies. These are also the end-uses that allow direct comparisons between similar electric and gas appliances. In the Electric Sample, households were retrofitted with heat pumps for both space conditioning and water heating. The Gas Sample was retrofitted with efficient gas furnaces, electric.air conditioners, and gas water heaters. Set-back thermostats were not used in 

### either sample. These Electric and Gas Samples were selected from the same housing tracts. This

3-1strategy reduced variation induced by building envelope characteristics and weather. The heat pump space conditioning systems have connected loads &fapproximately 6 kW, or 20.5 kBTU/hour. The heat pump systems have a resistance backup system designed to provide supplemental heat on cold days; this system can draw loads up to 16 kW. The gas homes are equipped with efficient gas heatingsystems that have outputcapacities of 58 to 

### 117 kBTU/hr and electricfans that have loads under 800 watts. As mentioned above, the electric homes received new electric heat pumps for space conditioning (typical Seasonal Energy Efficiency Ratio [SEER] of 9, or Heating Season Performance Factor [HSPF] of 7) and heat pump water heating (Coefficient of Performance [COP] of 

### 3). Many of the heat pump water heaters had a resistance backup system for water heating; this system could draw loads of about 2.5 kW. The gas homes received new gas furnaces (80percent efficiency), electric air conditioners (typicalSEER of 9)and gas water heaters (75 percent recovery efficiency). The cooking appliance experiment relies on the existing gas and electric resistance ranges, except in the Mission Viejo electric test sites, where new electric induction ranges were installed. The gas ranges included an electronic ignition system. The installed induction cooktops consisted of one inductiontwo-burner "cartridge"and one resistancetwo-burner cartridge. The induction cooktop uses a high-frequency, electronically controlled electromagnet to produce heat in ferromagnetic cookware. Induction stoves feature the same quick responsiveness as gas ranges because of their low thermal capacity. Also, they are safer than either electric resistance or gas stoves because the cooking surface remains relativelycool. Though no new technologies have been applied to clothes drying, new gas and electric clothes dryers were installed inthe test homes to evaluate customer attitudes and collect end-use consumption information. In addition to energy usage comparisons, the REUC project data can support assessmentof qualitative differences between appliance technologies and fuels. In attempting to measure these differences, the REUC project differs from most other customer load monitoring projects in two key ways. First, all participants have had their homes retrofittedwith new appliances, halfwith gas appliancesand half with electric or electric heat pump appliances. Second, the participants have strategy reduced variation induced by building envelope characteristics and weather. The heat pump space conditioning systems have connected loads of approximately 6 kW,or 20.5 kBTU/hour. The heat pump systems nave a resistance· backup system designed to ·provide supplemental heat on cold days; this system can draw loads up to 16 kW. The gas homes are equipped with efficient gas heating systems that have output capacities of58 to 117 kBTU/hr and electric fans that have loads under 800 watts. As mentioned above, the electric homes received new electric heat pumps for space conditioning (typical Seasonal Energy Efficiency Ratio [SEER] of 9, or Heating Season Performance Factor [HSPF] of 7) and heat pump water heating (Coefficient of Performance [COP] of 3). Many of the heat pump water heaters had a resistance backup system for water heating; this system could draw loads of about 2.5 kW. The gas homes received new gas furnaces (80 percent efficiency), electric air conditioners (typical SEER o(9) and gas water heaters (75 percent recovery efficiency). The cooking appliance experiment relies on the existing gas and electric resistance ranges, except in the Mission Viejo electric test sites, where new electric induction ranges were installed. The gas ranges included an electronic ignition system. The installed induction cooktops consisted of one induction two-burner "cartridge" and one resistance two-burner cartridge. The induction cooktop uses a high-frequency, electronically controlled electromagnet to produce heat in ferromagnetic cookware. Induction stoves feature the same qJJiCk responsiveness as gas ranges because of their low thermal capacity. Also, they are safer than

.either electric resistance or gas stoves because the cooking surface remains

relatively cool. Though no new technologies have been applied to clothes drying, new gas and electric clothes dryers were installed in the test homes to evaluate customer attitudes and collect end-use consumption information. In addition to energy usage comparisons, the REUC project data can support assessment ofqualitative differences between appliance technologies and fuels. In attempting to measure these differences, the REUC project differs from most other customer load monitoring projects in two key ways. First, all participants have had their homes retrofitted with new appliances, halfwith gas appliances and half with electric or electric heat pump appliances. Second, the participants have
3-2been repeatedly surveyed in order to assess their experiences with their new appliancesand the influence these appliances may have hadan their image of gas andelectricity. Since inthe selected test areas almost all households rely on gas 

## . for space and water heating, the REUC experiment in these cases consists of a' sample of fuel-switched former gas customers matched with a "control group" of comparably equippedgas customers. The REUC project analyses cover three main topic areas: realized appliance energy usage comparisons; load profile analysis; and the integrationof these topics with market research data. Each ofthese areas 

### issummarized below. Enerev Usaee Comparisons Comparisons of the appliances' energy usage have been made by customer strata that reflectthe initial sample design, as well as by appropriatealternative stratifications or segmentations of the sample derived from the analysis of the market research data. These energy usage comparisons account for seasonal andweather effects. Load Profiles The REUC load profilescan aid utilities in assessingthe effectivenessof encouraging customers' adoption of energy-efficient appliances. The estimated REUC load profiles also have been combined with the project's market researchlattitudinal data in an attempt to understand the factors influencing the load shapes. The pairing of gas and electric homes affords an opportunity to carefully compare the usage patterns of appliances with differentenergy sources, since gas end-use data are rarely collected. Market ResearchAnalvsis The REUC market research was undertaken to better understand customer attitudes and behaviortoward the electricand gas appliances. Analysis of the market research data has concentrated on three basic issues. First, these data were used to determine which aspects or features of electricity and gas affect customers' images of those energy sources. Are these features limited to cost, or do they includeother qualities associatedwith the twofuels? Second, analysis of customer reactionto the periormance of their new appliances allows qualitative assessment of non-pecuniary benefits, notjust the dollar value of the savings from operating 

### been repeatedly surveyed in order to assess their experiences with their new appliances and the influence these appliances may have had on their image ofgas and electricity. Since in the selected test areas almost all households rely on gas for space and water heating, the REUC experiment in these cases consists of a" sample offuel-switched former gas customers matched with a "control group" of comparably equipped gas customers. The REUC project analyses cover three main topic areas: realized appliance energy usage comparisons; load profile analysis; and the integration ofthese topics with market research data. Each ofthese areas is summarized below. Energy Usage Comparisons Comparisons.ofthe appliances' energy usage have been made by customer strata that reflect the initial sample design, as well as by appropriate alternative stratifications or segmentaiions of the sample derived from the analysis of the market research data. These energy usage comparisons accountfor seasonal and weather effects. Load Profiles The REUC load profiles can aid utilities in assessing the effectiveness ofencouraging customers' adoption ofenergy-efficient appliances. The estimated REUC load profiles also have been combined with the project's market research/attitudinal data in an attempt to understand the factors influencing the load shapes. The pairing of gas and electric homes affords an opportunity to carefully compare the usage patterns ofappliances with different energy sources, since gas end-use data are rarely collected. Market Research Analysis The REUC market research was undertaken to better understand customer attitudesand behavior toward the electric and gas appliances. Analysis ofthe market research data has concentrated on three basic issues. First, these data were used to determine which aspects or features of electricity and gas affect customers' images of those energy sources. Are these features limited to 

### cost, .or do they include other qualities associated with the two fuels? Second, analysis ofcustomer reaction to the performance oftheir new appliances allows qualitative assessment of non-pecuniary benefits, not just the dollar value of the savings from operating

, 3-3and capital costs. Finally, the integration of this expanded set of customer data with the traditionally collected set of household characteristics enhanced the capability to model, or explain, customer energy usage by different market segments. HISTORY OF REUC DATA COLLECTION The REUC data collection activities have consisted of load and survey data collected from two samples: 42 homes in Mission Viejo aod 

### 50 homes in lrvine. Figure 3-1 summarizesthe survey and loaddata collection activities from September 1986 through December 1989 of the REUC project. Figure3-1

REUC Survey and Load DataCollection History 9/86 6187 3/88 6/89 12/89 Mission I I Viejo SO S1 S2 S3 S4 lrvine I I S 0 S1 S2 S3 S4


### 1-1 = Load data collection SO 

### = REUC-RSSsurvey 51-54 = REUC Market Research survey
Four different survey instruments have been administered to each sample. The first survey, REUC-RSS, was designedto gatherspecificappliancesaturation data in order to select prospectiveparticipants. The REUC-RSS survey instrument is based on the SCE 1985 RSS survey but augmented by several attitudinal and demographic questions. The first REUC market research survey, administered in June 1987, contained all new questions that focus on customer attitudes towards attributes of fuel types and end-uses as well as the customer's propensityto "invest" and capital costs. Finally, the integration of this expanded set of customer data with the traditionally collected set of household characteristics enhanced the capability to model, or explain, customer energy usage by different market segments. HISTORY OF REUC DATA COLLECTION The REUC data collection activities have consisted of load and survey data collected from two samples: 42 homes in Mission Viejo al;ld 50 homes in Irvine

.

. Figure 3-1 summarizes the survey and load data collection activities from September 1986 through December 1989 of the REUC project.

Mission Viejo Irvine



### I-I SO 51-54 Figure 3-1

REUC Survey and Load Data Collection History

. 9/86

SO
SO

= Load data collection 

### = REUC-R55 survey 6/87 

### I 51
51



## =REUC Market Research survey 3/88
52 

### I 52 6/89 12/89 

### I 53 54 

### I 53 54

. Four different survey instruments have been administered to each sample. The

first survey, REUC-R55, was designed to gather specific appliance saturation data in order to select prospective participants. The REUC-R55 survey instrument is based on the SCE 1985 R55 survey but augmented by several attitudinal and demographic questions. The first REUC market research survey, administered in june 1987, contained all new questions that focus on customer attitudes towards attributes offuel types and end-uses as well as the customer's propensity to "invest"

3-4. . iin energy-effi~ient appliances. The second REUC survey instrument included ! Irefinedversions of the firsf survey's questions, additional questions that measure acceptance of the new appliances, and the EPRl CLASSIFYTM questionnaire. Inorder to perform longitudinal analysis of the participants' acceptanceof

! the new appliances, the second REUC survey was administeredtwice (March 1988 .andJune 1989). The final survey was ashorterversion of the second REUC survey !andcontained portionsof SCE's residentia! attitudesurvey instrument. Inthis way; the final survey measures the long-term acceptance of the REUC appliances and providesalinkto similar surveysadministeredto other SCE cusiomers. Appliance load data collection continued through December 1989. As 

### is shown, the data collection history for the lrvine sample differs from the Mission Viejo sample. In Phase I, the REUC projectwas limited to the MissionViejo sample and was primarilyenvisioned as an end-use load monitoring project. Once the Mission Viejo sample was selected and retrofitted with new appliances, the scope of the project broadened in Phase II to include a second customer sample in lrvine and to undertake a comprehensive market research study. The load data analysis results contained in Sections 5 and 6 are based on the 22-month period from February 1988 through November 1989 so that results from both cities can be compared. Load DataAnalysis Workstation All ofthe REUC projectdata collection, translation, editing, validation and analysis activities have been performedon microcomputers. The project's sample design and customer load, survey andweather data analyses have been undertakenwith the Load Data Analysis Workstation (LDAW)developed for EPRl under research project 2342-8. The Workstation is a hardwarelsoftware system developed specificallyfor the design, analysis andevaluationofdemand-side programs. With the support 

### oi SCE, the REUC project has provided another opportunity to demonstratethe usefulness of this PC-basedWorkstation. The REUC Workstation is based on the EPRl LDAW and uses an 80386 PC with a floating point (math) co-processor. To accommodate the very large data sets generated in end-use load monitoring experiments, the machine includes a fast internal 600-megabyte hard disk and an external 6250 bpi 9-track tape drive. The internal hard disk is large enough to store on-line the data from an entire



## ri energy-efficient appliances. The second REUC survey instrument included refined versions ofthe first survey's questions, additional questions that measure 'customer acceptance ofthe new appliances, and the EPRI CLASSIFYTM question-

. . 

### naire. In order to perform longitudinal analysis ofthe participants' acceptance of' ,the new appliances, the second REUC survey was administered twice (March 1988 and June 1989l. The final survey was a shorter version ofthe second REUC survey ;and contained portions ofSCE's residential. attitude survey instrument. In this 

### way, the final survey measures the long-term acceptance of the REUC appliances and

.provides a linkto similar surveys administered to other SCE customers.

:Appliance load data collection continued through December 1989. As is shown,

the data collection history for the Irvine sample differs from the Mission Viejo sample. In Phase I, the REUC project was limited to the Mission Viejo sample arid was primarily envisioned as an end-use load monitoring project. Once the Mission Viejo sample was selected and retrofitted with new appliances, the scope of the project broadened in Phase II to include a second customer sample in Irvine and to undertake a comprehensive market research study. The load data analysis results contained in Sections 5 

### and 6 are based on the 22-month period from February 1988 through November 1989 so that results from both cities can be compared. Load Data 

### Analysis Workstation 

### All ofthe REUC project data collection, translation, editing, validation and analysis activities have been performed on microcomputers. The project's sample design and customer load, survey and weather data analyses have been undertaken with the Load Data Analysis Workstation (LDAW) developed for EPRI under research project 2342-8. The Workstation is a hardware/software system developed specificallyfor the design, analysis and evaluation ofdemand-side programs. With the support of 

### SCE, the REUC project has provided another opportunity to demonstrate the usefulness ofthis PC-based Workstation. The REUC Workstation is based on the EPRI LDAW and uses an 80386 PC with a floating point (math) co-processor. To accommodate the very large data sets. generated in end-use load monitoring experiments, the machine includes a fast internal 600-megabyte hard disk and an external 6250 bpi 9-track tape drive. The internal hard disk is large enough 

### to store on-line the data from an entire
3-5season of a 100-household experiment monitoring four to eight channels per household at five-minute intervals. A cartridge tape drive 

### is installed in the Workstation to receive load data from the data collection subsystem,' and a WORM drive 

### is used to store permanent analysis data sets. ~ommunic~tions are handled through a 2400-baud internal modem. A VGA graphics board and highresolution color monitor allow the software to display results in the form of intuitive, presentation-qualitygraphics.' The REUCWorkstationalso has taken advantage of the LDAW software, which 

### is specifically tailored for large-scale end-use load research and demand-side projects. All the LDAW's analysis routinesare written inSAS-PC. The use ofthis statistics package ensures that analysts already familiar with SAS on a mainframe can work productively on the Workstation from thefirst day. Similar to the hardware architecture, the LDAW analysis software 

### is designed with modularity in mind. Each program iswritten for asingle function and can be modifiedto meet the needs of any specific project such as the REUC. Batch processing on the LDAW makes it possible to string together these analytic "building blocks" to accomplish complexanalysis tasks. These programs include routinesto: design load research and demand-side program customer samples; read load data instandard central stationtape formats; edit these data for possibly invalid measurements; estimate average load profiles; performsurvey analysis; undertake market research analysis; estimate appliance-connected loads from metered observations; calculate applianceduty cyclesand duty-cycledistributions; calculate load reductions from direct load control strategies; and 1For more information, see "The Load Data Analysis 'workstation Userls Guide," Version 0.4RG, May 1990, EPRl RP2342-8. 

### season of a 100-household experiment monitoring four to eight channels per household at five-minute intervals. A cartridge tape drive is installed in the Workstation to receive load data from the data collection subsystem," and a WORM drive is used to store permanent analysis data sets. Communications are handled through a 2400-baud internal modem. AVGA graphics board and highresolution color monitor allow the software to display results in the form of intuitive, presentation-quality graphics.' The REUC Workstation also has taken advantage ofthe LDAW software, which is specifically tailored for large-scale end-use load research and demand-side projects. All the LDAW's analysis routines are written in SAS-PC. The use ofthis statistics package ensures that analysts already familiar with SAS on a mainframe can work productively on the Workstation from the first day. Similarto the hardware architecture, the LDAW analysis software is designed with modularity in mind. Each program is written for a single function and can be modified to meet the needs ofany specific project such as the REUC. Batch processing on the LDAW makes it possible to string together these analytic "building blocks" to accomplish complex analysis tasks. These programs include routines to:
• design load research and demand-side program customer samples;
• read load data in standard central station tape formats;
• edit these data for possibly invalid measurements;
• estimate average load profiles;
• performsurvey analysis;
. • undertake market research analysis;
• estimate appliance-connected loads from metered observations;
• calculate appliance duty cycles and duty-cycle distributions;
• calculate load reductions from direct load control strategies; and

,For more information, see "The Load Data Analysis Workstation User's Guide," Version O.4RG, May 1990, EPRI RP2342-8.
3-6Section 4 THE REUC

## CUSTOMER SAMPLE

ibes the REUC project customer sample. The sample design and criteria are described, followed by the demographic characthe REUCsample. This section concludes by summarizingthe resultsof gerieralizability of the REUC sample to other customers and &f SCE's serviceterritory.
identify a potential sample of residential customers, SCE administered survey, calledthe REUC-RSS, to 2,380 potential participants. This survey 85 Residential Saturation Survey (RSS). From the 756 t of potential participantswas visited by SCE project staff dditional information on specific appliances in the household, 

### ld characteristics, and occupancy patterns. elsewhere,' extensive pre-experimental research was undertaken to of customers that could provide the most pertinent and valuable the study, namely electric and gas end-use load shapes and The goal of the REUC sample design was to determine 92 sets of sub-metering equipment and usage between electric and gas measured. This allocation was achieved by characteristics that best predict overall stratifying the sample according to those [+a!acteristics. Required sample sizes were calculated using estimated variances

.- ''Design of a Utility CompetitiveAssessment Experiment: The Residential 

### lmerg~ Usage ComparisonProject," 

### J.T. Powers, M.S. Shirilau, R.T. ~hlaner and @Smith, IEEE Transactions on Power Systems (August 1988),Vol. 3, NO.

### 3, 

## [m.1298-1305. Section 4



### ,THE REUC CUSTOMER SAMPLE



### .-l"':rri'hf>s the REUC project customer sample. The sample design 

### and criteria are described, followed by the demographic charac-

, REUC sample. This section concludes by summarizing the results of the gerieralizability of the REUC sample to other customers and ofSCE's servi,ce territory.**'M'CL~** 

### DESIGN**ide~tify** 

### a potential sample of residential customers, SCE administered sun,.. v. 

### called the REUC-RSS, to 2,380 potential participants. This survey on SCE's 1985 Residential Saturation Survey (RSS)' From the 756 esp<lnSE!S, a subset ofpotential participants was visited by 

## set project staff additional information on specific appliances in the household, characteristics, and occupancy patterns.**r.\S··iI~!;cril,,,rl** 

### elsewhere,l extensive pre-experimental research was undertaken to a sample ofcustomers that could proVide the most pertinent and valuable for the study, namely electric and gas end-use load shapes and characteristics. The goal ofthe REUC sample design was to determine allocation of the available 92 sets of sub-metering equipment arid Jnanr,', 

### so that the differenoe in energy usage between electric and gas i!l?1llia,ncles could be accurately measured. This allocation was achieved by observable household characteristics that best predict overall electric load shape and then stratifying the sample according to those [qJ;ara<:teristi,-s. Required sample sizes were calculated using estimated variances 

## ltil 

### '"Design ofa Utility Competitive Assessment Experiment: The Residential 

# .Blrgl**~sage** 

### Comparison Project," J.T. Powers, M.S. Shirilau, R.T. Uhlaner and F.IID'" l'2

### m1th, IEEE Transactions 

### on Power Systems 

### (August 1988), Vol. 3, No.3, !'. 98-1305.from the pre-experimental study (as a function of household characteristics) and the criterion that a 95 percent confidence interval around the estimated difference inenergy usage betweengas and electric appliances be no wider than 5 percentoftotal loadfor each appliance. In order to develop the customer sample, analysis was performed on key demographic variables used as stratificationvariables in load research projects. Ultimate selection of the stratification variables reflectedthe project objectives of selecting a sample that was statistically supportable and that represented key customer segments of interest to SCE. As described in Section 2, this sample represents customers in the new, owner-occupied, single-family homes in SCE's Southern Division. Household income and square feet were selected as the key stratification variables. Inorderto controlfor the effects of other householdcharacteristics, the sample is limited to single-family homes built between 1975 and 1982. In California, homes constructed after 1978 were subject to building standards that emphasized more energy-efficient thermal characteristics (the "Title 24" standards). In addition, an attempt was made to select households with between two and five members. Age and education level of the head of household were not controlled for inthe sample design. DEMOGRAPHIC DESCRIPTION OF THE REUC SAMPLE Analysis of utility demand-side programs typically can involve relating customer energy usage, appliance choice, and program acceptance to key demographic 

# . variables such as household income, age of head of household, and family size, and dwelling characteristics such as square feet, number of bedrooms, and type. These data describing household characteristics and appliance inventories were collectedfor the REUC by surveyingthe REUC participants. Table 4-1 tabulates the key demographic data for the REUC sample based on the initial stratification variables. This tabulation 

### is based on the March 1988 REUC customer survey. Since the sample was selected in 1986, the household income distribution has changed. Table 4-2 illustrates the distribution of the highest from the pre-experimental study (as a functi.on of household characteristics) and the criterion that a 95 percent confidence interval around the estimated difference in energy usage between gas and electric appliances be no wider than 5 percent oftotal load for each appl.iance. In order to develop the customer sample, analysis was performed on key demographic variables used as stratification variables in load research projects; Ultimate selection of the stratification variables reflected the project objeCtives of selecting a sample that was statistically supportable and that represented key customer segments of interest to SCE. As described in Section 2, this sample represents customers. in the new, owner-oc;:cupied, single-family homes in SCE's Southern Division. Household income and square feet were selected as the key stratification variables. In order to control for the effects ofother household characteristics, the sample is limitedto single-family homes built between 1975 and 1982. In California, homes constructed after 1978 were subject to building standards that emphasized more energy-efficient thermal characteristics (the "Title 24" standards). In addition, an attempt was made to select hOl.lseholds with between two and five members. Age and education level of the head of household were not controlled for in the sample design. 

### DEMOGRAPHIC DESCRIPTION OF THE REUC SAMPLE Analysis of utility demand-side programs typically can involve relating customer energy usage, appliance choice, and program acceptance to key demographic variables such as household income, age of head of household, and family size, and dwelling characteristics such as square feet, number of bedrooms, and type. These data describing household characteristics and appliance jnventories were collected for the REUC by surveying the REUC participants. Table 4-1 tabulates the key demographic data for the REUC sample based on the initial stratification variables. This tabulation is based on the March 1988 REUC customer survey. Since the sample was selected in 1986, the household income distribution has c.hanged. Table 4-2 illustrates the distribution of the highest

4-2education level completed among the heads of household. Educationwas identified as an important potential stratification variable.2 As shown, almost threequarters of the REUC Electric and Gas Sample participants have a Bachelor or graduate degrees. The above results describe the REUC sample at about the same 

### ' time as SCE's 

### 1987 RSS was administered. As described below, the 

### 1987 RSS sampleof single-family homes is used to assess the representativeness of the REUC sample. Table 4-1 

### REUC Partici~ant Demoeraphics Samole Desien Stratification Variables

House Size Medium House 

### 1,250

### - 1,749 sqft Large House 

### 1,750sq ft or more Total

HouseSize Medium House 

### 1,250-1,749 sq ft Large House 

### 1,750sq ft or more ElectricSample Number of Households Mission Income 

## & Vieio 

# Total < $5OK 12 3 

### 15 $50K or more 

### 2 6 

### 8


### < $SOK 2 4' 

### 6 $50K or more 

### 9 8 

# - 17 46 Gas Sample Number of Households Mission Income 

## & Vieio 

## Total < $50K 2 3 

### 5 $50K or more 

### 12 6 

### 18
$50K or more 

### 10 8 

### - 18 Total 

### 46 

### education level completed among the heads of household. Education was identified as an important potential stratification variable.2 

### As, shown, almost threequarters of the REUC Electric and Gas Sample participants have a Bachelor 'or graduate degrees. The above results describe the REUC sample at about the same' time as SCE's 1987 

### RSS was administered. As described below, the 1987 

### RSS sample ofsingle-family homes is used to assess the representativeness ofthe REUC sample.

House Size Medium House 1,250

- 1,749 

### sq ft Large House 1,750 

### sq ft or more Total

House Size Medium House 1,250-1,749 

### sq ft Large House 1,750 

### sq ft ormore Total

2Ibid., p. 1302. 

### Table 4-1 

### REUC Participant Demographics Sample Design Stratification Variables Electric Sample Number of Households Mission Income Irvine Viejo, Total < $50K 12 3 15

, $50K 

### or more 2 6 8



### < $50K 2 4 6 $50K 

### or more 9 8 

### lZ 46 

### Gas Sample Number of Households Mission Income Irvine Viejo Total' < $50K 2 

### 3 5 $50K 

### or more 12 

### 6 18



### < $50K 1 4 5 $50K 

### or more 10 8 l§. 46

<\-3Table 4-2 REUC Particioants Demoeraohics HiehestComoleted Education levelAmone Key Household Members

Level Comoleted High School 2-Year ~ssociate Bachelor Masters Doctorate

Level Comoleted High School 2-Year Associate Bachelor Masters Doctorate ElectricSample Number of Households lwine MissionVieio

2 3 3 4 14 3

5 6 1 2 Gas Sample Number of Households lrvine Mission Vieio 1 2 5 3 8 8 10 

### 2



### - 2

REPRESENTATIVENESS OF THE REUC SAMPLE To perform the assessment of the representativeness of the REUC customer sample, Integrated Market Research (IMR) techniques were applied to analyze the relationships between energy usage and household characteristics. Customer data from both the REUC and 1987 RSS surveys were transformed into customer descriptors and summaries of household appliance holdings. Then, IMR techniques were used to identify those customer descriptors that segment customers into groups that use electricity similarly. Appendix .B describes the methodology developedto asses the REUC sample's representativeness. Two objectives are met by the results presented below. First, the customer descriptorsoriginally used to design the REUCsample are re-examinedinterms of their ability toexplain customer energy usage. This re-examination confirmsthe Table 4-2 REUC Participants Demographics Highest Completed Education Level Among Key Household Members

Level Completed High School 2-Year Associate Bachelor Masters Doctorate

Level Completed High School 2-Year Associate Bachelor Masters Doctorate Electric Sample

Gas Sample

REPRESENTATIVENESS OF THE REUC SAMPLE Number of Households Irvine Mission Viejo

2' 3 3 4 14 3

5 6 1 2
Number of Households

Irvine Mission Viejo 2 5 3 

### 8 8 10 2 2

To perform the assessment of the representativeness of the REUC customer sample, Integrated Market Research (iMR) techniques were applied to analyze the relationships between energy usage and household characteristics. Customer data from both the REUC and 1987 RSS surveys were transformed,into customer descriptors and summaries of household appliance holdings. Then, IMR techniques were used to identify those customer descriptors that segment customers into groups that use electricity similarly. Appendix ,B describes the methodology developed to asses the REUC sample's representativeness. Two objectives are met by the results presented below. First, the customer descriptors originally used to design the REUC sample are re-examined in terms of their ability to explain customer energy usage. This re-examination confirms the

4-4.' !validityof the ~ri~inal'model-based sample design. The second objective 

### ismet 

## iby determiningthe share of SCE's single-family homesfor which the REUC results ;areapplicable. This share represents a quantitative measure of the direct

.. . .applicabilityof the REUC resultstoSCE's residentialcustomers. comparison of

### REUC and RSS Samples Inassessingthe representativenessofthe REUC sample, the original REUC sample stratificationvariables, income and square feet, were used. The principal finding 

### isthat the REUC sample represents 40 percentof SCE's single-family homes using the income variable, and over 55 percent of SCE's single-family homes when using the squarefeet variable. A key concept underlying the comparison of the REUC and RSS customers rests on segmenting these two customer samples by their appliance stock inventories (ASI). This segmentation byAS1 allows the energy usage characteristicsof the two samples to be directly linked. Seventy-eight percent of the RSS single-family subsample fell into one of three AS1 definitions: (1) gas space heating and electric central air-conditioning (GSHICAC); (2) gas space heating and electric wail/window air-conditioning (GSHIWWAC); and (3) gas space heating and noair-conditioning (GSHINOAC). See Appendix Bfor details of the AS1 definitions. InTable 4-3, the Electric and Gas (ELVGAS)Sample sizes for each stratum are shown for the REUC participants usingthe lncomevariable. For the RSS singlefamily subsample, the AS1 shares for each stratum are reported. For the stratum correspondingto the REUC sample, the "REUC Share of ASI," iscomputed. For example, in the case of the lncome variable for the GSHICAC ASI, this share equals 67 percent (25.0 

## + 42.0).3 Only two RSS income strata are used in this summation since there were no REUC customers in the lower two RSS income strata. Thus, 67 percent of the households in this AS1 are represented by the REUC sample using income as the basisfor comparison. The "AS1 Share of SCE
3~ee Appendix B for a definition of each ASI. 

### :validity ofthe original'model-based sample design. The second objective is met :by determining the share ofSCE's single-family homes for which the REUC results :are applicable. This share represents a quantitative measure of the direct ,applicability ofthe REUC results to SCE's residential customers. Comparison of REUC and RSS Samples 

### In,assessing the representativeness ofthe REUC sample, the original REUC sample stratification variables, income and square feet, were used. The principal finding is that the REUC sample represents 40 percentofSCE's single-family homes using' the income variable, and over 55 percent of SCE's single-family homes when using the square feet variable. Akey concept underlying the comparison of the REUC and RSS customers rests on segmenting these two,customer samples by their appliance stock inventories (ASi). This segmentation by AS! allows the energy usage characteristics ofthe two

, samples to be directly linked. Seventy-eight percent of the RSS single-family

subsample fell into one ofthree AS! definitions: (1) 

### gas space heating and electric central air-conditioning (GSH/CAC); (2) gas space heating and electric wall/window air-conditioning (GSH/WWACl; and (3) gas space heating and no air-conditioning (GSH/NOAC). See Appendix Bfor details ofthe AS! definitions. !n Table 4-3, the Electric and Gas (ELE/GAS) Sample sizes for each stratum are "shown for the REUC participants using the Income variable. For the RSS**single~**



### family subsample, the AS! shares for each stratum are reported. For'the stratum corresponding to the REUC sample, the "REUC Share ofAS/," is computed. For example, in the case of the Income variable for the GSH/CAC AS!, this share equals 67 percent (25.0 + 

### 42.0).3 Only two RSS income strata are used in this summation since there were no REUC customers in the lower two RSS income strata. Thus, 67 percent of the households in this AS! are represented by the REUC sample using income as the basis for comparison. The "ASI Share ofSCE
3See Appendix Bfor a definition ofeach ASI.

4-5Single Family" 

### istakenfrom the "Total SCE" column of Table

### B-1. Multiplyingthe "REUC Share ofASI" by the "AS1 Share of SCE Single Family"produces the "REUC Share ofSCE Single Family byASI." Finally, the "REUCTotal Share of SCE Single Family"

### isthe sum across eachAS1 of the REUC Shareof SCE Single Family. Thus, the REUC customers represent 40 percent of SCE's single-family households, by income strata (15.4 

## + 6.0 

## + 18.6), representing over 550,000 households throughout SCE's service territory. Table 4-3 REUC Sample Reoresentativeness by Income Stratum REUC sample RSS Single-Family Subsarnple REUC Income ELEIGAS RSS Income Appliance Stock Inventory (ASI) Size 

### - Stratum CSH/CAC GSHMIWAC GSHINOAC

REUC Shareof AS1 AS1 ShareofSCE Single Family REUCShareof SCE Single Family byAS1 15.4 6.0 18.6 REUCTotal Shareof5CE Single Family 40.0% 

### Single Family" is taken from the "Total SCE" column ofTable 8-1. Multiplying the "REUC Share ofASI" by the "ASI Share ofSCE Single Family" produces the "REUC Share ofSCE Single Family by AS!." Finally, the "REUC Total Share ofSCE Single Family" is the sum across each ASI ofthe REUC Share ofSCE Single Family. Thus, the REUC customers represent 40percent ofSCE's single-family households, by income strata (15.4 + 

### 6.0 + 

### 18.6), representing over 550,000 households throughout SCE's service territory. Table 4-3 REUC Sample Representativeness 

### by Income Stratum 

### RSS Single-Family Subsample REUC sample REUC Income Stratum ELE/GAS RSS Income Size Stratum Appliance Stock Inventory (ASI) GSHiCAC GSHfWWAC GSH/NOAC NA <$30,000 $30,000-$49,999 $50,000+ NA 0/0. 21/10 25/36 <$25,000 $25,000·$34,999 $35,000·$49,999 $50,000+ 19.5% 13.5 25.0 42.0
REUC Share ofASI 67.0 ASI Share ofSCE Single Family 23.0 REUC Share ofSCE Single Family by ASI 15.4 REUC Total Share ofSCE Single Family

4-6 48.8% 31.3% 17.8 18.5 17.4 21.5 16.0 28.7
33.4 50.2 18.0 37.0

6.0 18.6 40.0%Table 4-4 provides similar results of the REUC Sample's representativeness according to the house size (SqFt) strata. From the table, the REUC share of the 

## . GSHICAC AS1 

### isvery high, 86.4 percent. Followingthe calculations discussed


## . above for the income strata, the "REUC Total Share of SCE Single Family"for the GSHICAC AS1 

### is55.3 percent, representing over 750,000 households.
Table 4-4 REUC ~amnle' Representativeness 

### by House Size Stratum REUC sample RSS Single-Family Subsample REUC SqFt ELE/GAS RSS SqFt Appliance Stock Inventory (AS11 

### Stratum 

### - Size CSHICAC CSHIWWAC GSHINOAC
< 1,250 SqFt 010 < 1,250 SqFt 13.6% 45.2% 31.1% 1,250-1,749 SqFt 23/23 1,250-1,749 SqFt 38.2 37.1 34.1 1,7504 SqFt 23/23 1,750+ SqFt 48.2
. 17.7 34.8
REUC Share of AS1 86.4 54.8 68.9 AS1 Share ofSCE Single Family 23.0 18.0 37.0 REUC Share of SCE Single Family byAS1 19.9 9.9 25.5 REUCTotal Share of SCE Single Family 55.3% perhaps the most relevant result presented in Tables 4-3 and 4-4 is the REUC share of the CSHICAC ASI, customers with gas space heat and central airconditioning. This 

### is because these customers would most likely purchase heat pumps to replace their existing space' conditioning appliances.4 For this importantsingle ASI, the REUC 92-household sample representsover 15 percent of SCE's single-family homes by income strata and almost 20 percentof SCE1s single-family homes by square feet, or over 200,000 and 2S0,000 households, by income and square feet strata, respectively.

4~ustomers inthe other two ASls would have to decide to use central'air conditioning alongwith deciding to installa heat pump. 

### Table 4-4 provides similar results of the REUC Sample's representativeness according to the house size (SqFt) strata. From the table, the REUC share ofthe GSH/CAC ASI is very high, 86.4 percent. Following the calculations discussed

, above for the income strata, the "REUC Total Share ofSCE Single Family" for the

GSH/CAC ASI is 55.3 percent, representing over 750,000 households.
Table 4-4 

### REUC Sample' Representativeness 

### by House Size Stratum RSS Single-Family Subsample REUC sample REUC SqFt Stratum ELE/GAS RSS SqFt Size Stratum Appli;lnce Stock Inventory (ASU GSH/CAC GSHIWWAC GSH/NOAC
< 1,250 SqFt 1,250-1,749 SqFt 1,750+ SqFt 0/0 23/23 23/23
REUC Share ofASI < 1,250 SqFt 1,250-1,749 SqFt 1,750+ SqFt

ASI Share ofSCE Single Family REUC Share ofSCE Single Family by ASI REUC Total Share ofSCE Single Family 13.6% 38.2 48.2
86.4 23.0 19.9 45.2% 31.1% 37.1 34.1 17.7 34.8
S4.8 68.9 18.0 37.0

9.9 25.5 55.3% 

### Perhaps the most relevant result presented in Tables 4-3 and 4-4 is the REUC share of the GSH/CAC ASI, customers with gas space heat and central airconditioning. This is because these customers would most likely purchase heat pumps to replace their existing space' conditioning appliances.4 

### For this important single ASI, the REUC 92-household sample represents over 15 percent of SCE's single-family homes by income strata and almost 20 percent,of SCE's single-family homes by square feet, or over 200,000 and 250,000 households, by income and square feet strata, respectively.

4Customers in the other two ASls would have to decide to use central air conditioning along with deciding to install a heat pump.

4-7Conclusions From the above analysis results, it can be seen that the 92 household REUC customer sample is fairly representativeof a number of important segments of SCE's 3.5 million residential customers. This analysis also confirms;hat the REUC sample design, basedon income andsquarefeet, was avalid samplingframework.

. Finally, the IMR techniques applied to this representativeness assessment combined both quantitativeand qualitativeassessments'ofhow survey-based data can be efficiently distilled intoa useful set of customer descriptors. The two-step framework offirst developing simplified appliance stock inventories followed by analyzingthe relationshipofcustomer characteristicsto how customer use energy has provided insights into how to explain customer energy usage. This analysis framework conceptually improves on the specification of conditional demand analysis oftenperformedwith data ofthis type. Conclusions 

### From the above analysis results, it can be seen that the 92 household REUC customer sample is fairly representative of a number of important segments of SCE's 3.5 million residentialcustomers. This analysis also confirms that the REUC sample design, based on inc:ome and square feet, was avalid sampling framework. Finally, the IMR techniques applied to 

### this representativeness assessment combined both quantitative and qualitative assessments'ofhow survey-based data can be efficiently distilled into a useful set ofcustomer descriptors. The two-step framework offirst developing simplified appliance stock inventories followed by analyzing the relationship ofcustomer characteristics to how customer use energy has provided insights into how to explain ,customer energy usage. This analysis framework conceptually improves on the specification of conditional demand analysis often performed with data ofthis type.

4-8turnaround of initial data validation so that malfunctioning monitors and appliances can be quickly identified.' The nearly two years of usage data presented inthis report consist of {he period February 5, 1988 through November 30, 1989. Therefore, the complete 1988 and 1989summer seasons and nearly two complete winter seasons are analyzed. For consistency, a "month" contains either a complete four- or five-week period that closely, though notexactly, correspondsto the true calendar month. Inthis way, the same proportion of week days and weekends enter into each month. Table 5-1 describes exactlywhich days constituteeach month's data.2 Before energy usage estimates were made, the data were extensively screened for validity. Questionable data were removed from the analysis data set, and usage amounts for each channel were calculatedbysumming up all valid usage readings. Average usage figures were calculatedby normalizing each channel by the number of valid days of data, giving the usage of typical appliances for the entire year. Details of allthese validation, editing and analysis procedurescan be found inAppendix C.

'For more information, see John

### T. Powers and Bruce A. Smith, "Load Data Analysis Workstation: DOS-Based Load and Customer Data Analysis System," /FEEComputerApplications in Power, Vol. 1, No. 4 (October 1988). 2~onthly usage estimates were computed by normalizingthe daily usage estimates by the "average" number of days in each month. Thus, the March estimate isbased on a 31-day month, and the April estimate is basedon a 30-day month, etc. 

### turnaround of initial data validation so that malfunctioning monitors and appliances can be quickly identified.1 

### The nearly two years ofusage data presented in this report consist of the period February 5, 1988 through November 30, 1989. Therefore, the complete 1988 and 1989 summer seasons and nearly two complete winter seasons are "analyzed. For consistency, a "month" contains either a complete four- or five-week period" that closely, though not exactly, corresponds to the true calendar month. In this 

### way, the same proportion of week days and weekends enter into each month. Table 5-1 describes exactly which days constitute each month's data.2 

### Before energy usage estimates were made, the data were extensively screened for validity. Questionable data were removed from the analysis data set, and usage amounts for each channel were calculated bysumming up all valid usage readings. Average usage figures were calculated by normalizing each channel by the number of valid days ofdata, giving the usage of typical appliances for the entire year. Details ofall these validation, editing and analysis procedures can be found in Appendix C.



### 1For more information, see John T. Powers and Bruce A. Smith, "Load Data Analysis Workstation: DOS-Based Load and Customer Data Analysis System," IEEE ComputerApplications in Power, 

### Vol. 1, No.4 (October 1988). 2Monthly usage estimates were computed by normalizing the daily usage estimates by the "average" number of days in each month. Thus, the March estimate is based on a 31-day month, and the April estimate is based on a 30-day month, etc.
.

5-2Table 5-1 

### REUC: Table of Month Labelto DateCorrespondence ;Month 

# - Label Ijanuary ! February .March April May June July ,August September October November December Actual Dates Dec 30, 1988- Feb2, 1989 Feb 5- Mar 3, 1988 Feb 3- Mar2, 1989 MarCMar31, 1988 Mar3-Mar 30, 1989 Apr 1 

# - Apr 28, 1988 Mar 31

# - Apr 27, 1989 Apr 29- Jun2, 1988 Apr 28- Jun1, 1989 Jun3- Jun30, 1988 Jun2-Jun29, 1989 

### JuI 1-

### JuI 28, 1988 Jun30- Aug 3, 1989 Jul29- Aug 25, 1988 Aug 4- Aug 31, 1989 Aug 26- Sep 29, 1988 Sep 1 -Sep 28, 1989 Sep 30- Oct 27, 1988 Sep 29- Nov

### 2, 1989 Oct28- Dec 1, 1989 NOV 3- NOV 30, 1989 Dec 2- Dec 29, 1988 Number

### of Davs 35 2 8 28 2 8 

### 2Ba 28 28 2 8 35 28 28 2 8 35a 2 8 28 35 28 2 8 3

### 5 35 28 28 aThe following two weeks are not included inthe Mission Viejo sample because of data quality problems: March24

### - March30, 1989andJuly7 -July13, 1989. iMonth Label 

# r:**lJ~nuary** ffebruary 

## ,. 'March April May june July

.August

.September October November December 

### Table 5-1 REUC: Table ofMonth Label to Date Correspondence
Actual Dates Number of**Da~s** Dec 30, 1988- Feb 2, 1989 35 Feb 5- Mar 3, 1988 28 Feb 3- Mar2, 1989 28· Mar4-Mar 31, 1988 28 Mar3-Mar 30, 1989 28a Apr 1- Apr 28, 1988 28 Mar 31- Apr 27,1989 28 Apr 29- jun 2, 1988 28 Apr 28- jun 1, 1989 35 jun 3- jun 30, 1988 28 jun 2- jun 29, 1989 28 jul 1- jul 28, 1988 28 jun 30- Aug 3, 1989 35a jul 29- Aug 25, 1988 28 Aug 4- Aug 31, 1989 28 Aug 26- Sep 29, 1988 35 Sep 1 -5ep 28, 1989 28 5ep 30- Oct 27, 1988 28 5ep 29- Nov 2, 1989 35 Oct 28- Dec 1, 1989 35 Nov3- Nov30, 1989 28 Dec 2- Dec 29, 1988 28 aThe following two weeks are not included in the Mission Viejo sample because ofdata quality problems: March 24 - March 30, 1989 and july 7 - july 13, 1989.

5-3AVERAGE YEARLY TOTAL USAGE The averageyearly total usagefor space conditioning, water heating, cookingand clothes drying are presented in Figures 5-1 and 5-2, respectively.3 These.values

. . are given in MBTUsfor direct comparison between the gas and electric'samples. Also, these average usagefigures are given intheir natural units (kwhand therrns) and are brokendown by season inTable5-2.4 Figure5-1 AnnualAveraee Enerw Usage SoaceConditionine and Water Heatinga

a At Paintof Use MBTU

3~ll the energy comparisons made in this report are at the point of use; therefore, the energy losses from generation and transmission are not reflected in these numbers. 4~or the two appliances that overlap with SCE's Residential Appliance End-Use Study (RAEUS), the.REUC usages exceed the RAEUS usages (Cooking: 385 kwhvs. 224 kwh; Clothes Drying: 1070 kwhvs. 749 kwh). The usage estimates are further broken down by city, month and day type (weekend vs. weekday) inAppendix A. AVERAGE YEARLY TOTAL USAGE The average yearly total usage for space conditioning, water heating, cooking and clothes drying are presented in Figures 5-1 and 5-2, respectively) Thesevalues are given in MBTUs for direct comparison between the gas and electric'samples. Also, these average usage figures are given in their natural units (kWh and therms) and are broken down by season in Table 5-2.4 

### Figure 5-1 Annual Average Energy Usage SpaceConditioningand Water Heatinga

SpaceConditioning

Wate-r Heating



### o 5 a AI PointofUse 10 1S MBlU 20**~** GasSample

.. Eledri<: Sample 23.4

2S 30

3

All the energy comparisons made in this report are at the point of use; therefore, the energy losses from generation and transmission are not reflected in these numbers. 4For the two appliances that overlap with SCE's Residential Appliance End-Use Study (RAEUS), the REUC usages exceed the RAEUS usages (Cooking: 385 kWh vs. 224 kWh; Clothes Drying: 1070 kWh VS. 749 kWh). The usage estimates are further broken down by city, month and day type (weekend vs. weekday) in Appendix A.
5-4Figure5-2 Annual. Averaee Enerw Usage Clothes Dryineand Cookinga

a At Point of Use ' MBTU

The REUC load data confirm that, with the exception of clothes drying, households with gas appliances consume considerably moreenergy at pointof use than households with electric appliances. As illustrated in Figure 5-1, the Gas Sample consumes two to three times the total energy consumed by the Electric Sample for space conditioning. Figure 5-1 also indicates that gas water heaters consume two-to three times the energy consumed by electric heat pump water heaters at pointof use. Gas ranges, as can be seen by Figure 5-2, consume more energy at point of use than electric ranges by a similar factor. Clothes drying, incontrast, does not appear to differ as much inenergy usage across fuel types as the other appliances. As shown in Figure5-2, gas dryers use.about 25 percent more energy at pointofuse than electric dryers, whichaverage 3.7 MBTUperyear. Clothes Drying

Cooking

o Figure 5-2 Annual.Average Energy Usage Clothes Drying and Cooking"

1 2 4 a At Point ofUse 3 MBTU eJ Gas Sample II Electric Sample 4.7

5 6

The REUC load data confirm that, with the exception of clothes drying, households with gas appliances consume considerably more.energy at point of use than households with electric appliances. As illustrated in Figure**5~**1, the.Gas Si3mple consumes two to three times the total energy consumed by the Electric Sample for space conditioning. Figure 5-1 also indicates that gas water heaters consume two-to three times the energy consumed by electric heat pump water heaters at point of use. Gas ranges, as can be seen by Figure 5-2, consume more energy at point of use than electric ranges by a similar factor. Clothes drying, in contrast, does not appear to differ as much in energy usage across fuel types as the other appliances. As shown in Figure 5-2, gas dryers use ·about 25 percent more energy at point ofuse than eleCtric dryers, which average 3.7 MBTU per year.

5-5Table 5-2 AverageAnnual andSeasonal Ener~v Usa~ea. PooledCities
Electric Sample Gas Sample Gas 'Sample (Gas Components) (ElectricComponents) Appliance kwh MBTUa MBTU kwh MBTUa Annual Average Household Usage (12 months) Whole House 13000 44.40 

### NA 7540 25.70 Space Conditioning 2950 10.10 19.20 1170 3.99 Water Heating 2810 9.60 23.40 

### NA N

### A Clothes Dryer 1070 3.66 4.32 110 0.38 Rangeloven 385 1.31 3.24 NA NA ~interb Average Household Usage (8 months) Whole House 9230 31.50 NA 4720 16.10 Space Conditioning 2190 7.50 1 9.00 437 1.49 Water Heating 2080 7.10 17.00 NA NA Clothes Dryer 760 2.60 2.97 7 5 0.26 Rangeloven 274 0.94 2.24 NA NA SummerC Average Household Usage (4 months) Whole House 3770 12.90 NA 2820 9.60 Space Conditioning 760 2.60 0.20 733 2.50 Water Heating 730 2.50 6.40 NA NA Clothes Dryer 310 1.06 1.35 3 5 0.1 2 Rangeloven 111 0.37 1.OO NA NA

a~t pointof use. b~anuary through May and Octoberthrough December. =Junethrough September.

The annual point-of-use energy savings of operating electric appliances 

### is illustrated in Figure 5-3. This figure indicates that an all-electric end-use home can use nearly 30 MBTUs less per year relative to an all-gas end-use home, a difference of almost450 MBTUs over the assumed 15-year appliance lifetime. Table 5-2 Average Annual and Seasonal Energy Usage>' Pooled Cities

Appliance

Whole House Space Conditioning Water Heating Clothes Dryer Range/Oven

Whole House Space Conditioning Water Heating Clothes Dryer Range/Oven

Whole House Space Conditioning Water Heating Clothes Dryer Range/Oven
aAt point of use. Electric Sample kWh MBTU' Gas Sample (Gas components) MBTU Gas Sample (Eledric Components)

kWh MBTU' Annual Average Household Usage (12 months) 13000 44.40 NA 7540 25.70

2950 10.10 19.20 1170 3.99 2810 9.60 23.40 NA NA 1070 3.66 4.32 110 0.38

385 1.31 3.24 NA NA Winterb Average Household Usage (8 months) 9230 31.50 NA 4720 16.10 2190 7.50 19.00 437 1.49 2080 7.10 17.00 NA NA

760 2.60 2.97 75 0.26 274 0.94 2.24 NA NA

SummerC Average Household Usage (4 months) 3770 12.90 NA 2820 9.60

760 2.60 0.20 733 2.50 730 2,50 6.40 NA NA 310 1.06 1.35 35 0.12 111 0.37 1.00 NA NA

b]anuary through May and October through December. clune through September.

The annual point-of-use energy savings of operating electric appliances is illustrated in Figure 5-3. This figure indicates that an all-electric end-use home can use nearly 30 MBTUs less per year relative to an all-gas end-use home, a difference ofalmost 450 MBTUs over the assumed 15-year appliance lifetime.
5-6Figure5-3 Difference inAnnual EnerwConsumotion betweenGasand ElectricADDliancesa

SpaceConditioning '

To show which time of year has the highest and lowest relative energy consumption for the end-uses, Table 5-3 provides daily and monthly average usage 

### by month. ihistable 

### is disaggregated by city in Appendix 

### A so that the readercan make inter-city comparisons.

. .

### Figure 5-3 Difference in Annual Energy Consumption between Gas and Electric Appliancesa

SpaceConditioning'

Water Heating

Clothes Drying

Cooking



### o 5 10 15
a Point ofUse Annual Difference (MBTU) 20



### To show which time of year has the highest and lowest relative energy consumption for the end-uses, Table 5-3 provides daily and monthly average usage by month. 

### This table is disaggregated by city in Appendix A so that the reader can make inter-city comparisons.

5-7Table 5-3 MonthlvAverage Usagefor Pooled Cities ElectricSample Gas Sample Gas Sample.

. (Gas Components) (Electric Components) kwh Therms kwh Weekday Monthly Weekday Monthly Weekday Monthly Averape Averape 

### w-AveraQe January Whole House Space Cond. Water Heating Clothes Dryer RangelOven February Whole House Space Cond. Water Heating Clothes Dryer Range/Oven 1.6 'NA

March Whole House Space Cond. Water Heating Clothes Dryer Range/Oven April Whole House Space Cond. Water Heating Clothes Dryer ' RangeIOven

Whole House Space Cond. Water Heating Clothes Dryer RangelOven June Whole House 27.3 825 NA NA 18.5 558 Space Cond. 2.6 74 0.04 1.6 2.5 71 Water Heating 6.7 204 0.58 17.7 NA NA Clothes Dryer 2.5 80 0.1 1 3.6 0.3 9 Rangeloven 0.9 29 0.08 2.5 NA NA Table 5-3 Monthly Average Usage for Pooled Gties Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) kWh Therms kwh Armliancea Weekday Monthly Weekday Monthly Weekday Monthly Average Average Average Average Average Average January Whole House 50.4 1625 NA NA 19.0 598 Space Condo 19.8 644 1.84 60.2 2.0 65 Water Heating 10.0 314 0.78 24.4 NA NA Clothes Dryer 3.3 108 0.13 4.2 0.3 11 Range/Oven 1.2 38 0.09 2.8 NA NA February Whole House 41.1 1177 NA NA 18.2 517 Space Condo 12.3 344 1.22 34.2 1.6 45 Water Heating 9.2 261 0.77 21.9 'NA NA Clothes Dryer 2.9 92 0.12
. 3.5 0.3 9. Range/Oven 1.1 32 0.08 2.3 NA NA March Whole House 32.3 1048 NA NA 17.6 559 Space Condo 5.0 176 0.47 16.3 1.4 51 Water Heating 8.7 272 0.74 23.4 NA NA Clothes Dryer 2.8 98 0.12 3.8 0.3 9 Range/Oven 1.0 33 0.08 2.5 NA NA April Whole House 31.2 955 NA NA 19.4 579 Space Condo 4.5 131 0.24 7.3 2.8 78 Water Heating 8.3 251 0.67 20.2 NA NA Clothes Dryer

. 2.7 92 0.11 3.5 0.3 9 Range/Oven 1.0 32 0.09 2.8 NA NA May Whole House 28.0 888 NA NA 18.2 569 Space Condo 2.4 79 0.13 4.6 1.6 50 Water Heating 7.8 245 0.64 20.0 NA NA Clothes Dryer 2.7 91 0.11 3.7 0.3 9 Range/Oven 1.0 30 0.08 2.6 NA NA June Whole House 27.3 825 NA NA 18.5 558 Space Condo 2.6 74 0.04 1.6 2.5 71 Water Heating 6.7 204 0.58 17.7 NA NA Clothes Dryer 2.5 80 0.11 3.6 0.3 9 Range/Oven 0.9 29 0.08 2.5 . NA NA

5-8Table 5-3, Cont. Gas Sample (Electric Components) kwh ElectricSample Gas Sample (Gas Components) kwh Therms Weekday Monthly Weekday Monthly Weekday Monthly ~~oliance 

### Averaae AveraQe 

## AveraBe Averaqe

whole House 32.8 1022 NA NA space Cond. 8.3 257 0.00 0.1 Water Heating 5.7 177 0.51 15.8 clothes Dryer 2.5 82 0.1 1 3.4 ~ange/Oven 0.9 27 0.08 2.5 August Whole House 29.6 934 NA NA

. Space Cond. 5.9 191 0.00 0.1 Water Heating 5.5 173 0.50 15.3 Clothes Dryer 2.2 75 0.1 0 3.3 Rangeloven 0.9 27 0.08 2.5 September 999 NA 244 0.01 173 0.51

75 0.10 28 0.08 October 912 NA

74 0.05 204 0.56

86 0.1 0 31 0.09 November 1059 NA

184 0.55 239 0.66 Whole House 32.3 Space Cond. 7.4 Water Heating 5.7 Clothes Dryer 2.3 Rangeloven 0.9

Whole House 28.7 Space Cond. 2.1 Water Heating 6.6 Clothes Dryer 2.5 Rangeloven 1.O

Whole House 34.8 Space Cond. 6.2 Water Heating 7.9 Clothes Dryer 2.7 Rangeloven 1.2 December Whole House 49.9 Space Cond. 18.2 Water Heating 9.4 Clothes Dryer 3.2 Rangeloven 1.3 

### Table 5-3, Cont. Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) kWh Therms kWh .' Weekday Monthly Weekday Monthly Weekday Monthly Appliance Average Average Average· Average Average Average July Whole House 32.8 1022 NA NA 26.1 803 Space Condo 8.3 257 0.00 0.1 8.9 272 Water Heating 5.7 177 0.51 15.8 NA NA Clothes Dryer 2.5 82 0.11 3.4 0.3 9 Range/Oven 0.9 27 0.08 2.5 NA NA August



### , Whole House 29.6 934 NA NA. 22.8 719 Space Condo 5.9 191 0.00 0.1 5.4 177**Wat~r** Heating 5.5 173 0.50 15.3 NA NA Clothes Dryer 2.2 75 0.10 3.3 0.3 9 Range/Oven 0.9 27 0.08 2.5 NA NA September Whole House 32.3 999 NA NA 24.5 748 Space Condo 7.4 244 0.01 0.4 6.6 211 Water Heating 5.7 173 0.51 15.2 NA NA Clothes Dryer 2.3 75 0.10 3.2 0.3 8 Range/Oven 0.9 28 0,08 2.6 NA NA October Whole House 2B.7 912 NA NA 19.3 607 Space Condo 2.1 74 0.05 1.7 1.5 53 Water Heating 6.6 204 0.56 17.2 NA NA Clothes Dryer 2.5 86 0.10 3.4 0.3 9 Range/Oven 1.0 31 0.09 2.7 NA NA November

.Whole House 34.8 1059 NA NA 19.3 582

Space Condo 6.2 184 0.55 16.4 1.2 37 Water Heating 7.9 239 0.66 19.6 NA NA, Clothes Dryer 2.7 90 0.11 3.6 0.3 9 Range/Oven 1.2 35 0.10 2.9 NA NA December Whole House 49.9 1564 NA NA 22.2 704 Space Condo 18.2 556 1.59 49.3 1.9 58 Water Heating 9.4 293 0.74 23.2 NA NA Clothes Dryer 3.2 104 0.13 4.1 0.3 11 Range/Oven 1.3 42 0.11 3.7 NA NA

5-9Not surprisingly, the relativeenergy used by gasfurnaces as compared to electric heat pumps increases in the colder months. This occurs because during the summer heat pumps operate similarly to the air conditioners installed in,the Gas Sample. The usage pattern of the other end-uses is stable throughout the year, although water heating requires somewhat more energy in the colder months. This 

### isfurther illustrated by Figure 5-4, which presents average weekday usage separately by season for the four electric end-uses. Comparisons between the Electric and Gas Samples' average daily energy use by season are included in Figure 5-4 SeasonalAveraeeWeekdav Electricitv Usaee ElectricSamole End-Uses

. 

### -

SpaceConditioning 

### Not surprisingly, the relative energy used by gas furnaces as compared to electric heat pumps increases in the colder months. This occurs because during the sUlilmer heat pumps operate similarly to the air conditioners installed in"the Gas Sample. The usage pattern of the other end-uses is stable throughout the year, although water heating requires somewhat more energy in the colder months. This is further illustrated by Figure 5-4, which presents average weekday usage separately by season for the four electric end-uses. Comparisons between the Electric and Gas· Samples' average daily energy use by .season are included in Figure 5-4 Seasonal Average WeekdayElectricity Usage Electric sample End-Uses

WaterHealing

Space Conditioning

Cooking

Clothes Drying. f2l WinterWeekday II Summ
...Weekday 8.8

o 1 2 3 4 5 6 7· 8 9 10 h

5-10Appendix A. Finally, Figure 5-5 presents weekday and weekend whole house average usage in winter and summer for the Electric and Gas Samples. Usage increases during the summer for the Gas Sample because of electric air conditioning, while itdecreases duringthe summerfor the Electric Sample.
DISTRIBUTION OF AVERAGE DAILY USAGE The average usage tables and figures in the preceding subsection represent averages across a large number of days. As a result, important information regardingthe variability ofaverage usage acrossdifferentdays inaseason oryear is lost. The distribution of average daily usage reveals, for example, that space conditioningseasonal averages do not representusage averagefor extremedays. Figure 5-5 Whole House: Average Daily Electricitv Usage

WinterWeekend

SummerWeekend

kwh

Figures 5-6 through 5-10 plotthe distributions of the sample averageddaily usage for the space conditioning and water heatingend-uses. These figures are divided 

### Appendix A. Finally, Figure 5-5 presents weekday and weekend whole house average usage in winter and summer for the Electric and Gas Samples. Usage increases during the summer for the Gas Sample because of electric air conditioning, while it decreases during the summerfor the Electric Sample.

DISTRIBUTION OF AVERAGE DAILY USAGE 

### The average usage tables and figures in the preceding subsection represent averages across a large number of days. As a result, important information regarding the variability ofaverage usage across different days in a season oryearis lost. The distribution of average daily usage reveals, for example, that space condition'ing seasonal averages do notrepresentusage average for extreme days. Figure 5-5 Whole House: Average Daily Electricity Usage

Winter Weekend

Winter Weekday

Summer Weekend

SummerWeekday

o 10 20 30 40 

### so kWh
GasSample II Electric Sample 

### Figures 5-6 through 5-10 plotthe distributions ofthe sample averaged daily usage for the space conditioning and water heating end-uses. These figures are divided

5-11

### byfuel typeand season. They illustrate the modal usage levelfor typicaldays ina season and the degree of skewness that is 

### attributable to extreme days. Consequently, the usage variance, median and mode across days can be understood moreclearly. The difference between space conditioning and water heating average weekday frequency distributions is 

### immediately obvious. Water heating is 

### generally distributed symmetrically around a central mean, creating a 

### familiar bell shape. Space conditioning, on the other hand, is 

### strongly skewed to the left (low usage) inall cases. That is, 

### on most days, littlespace conditioning is 

### required relative to the demand on a few extreme days during which a large amount of energy is 

### consumedto cool or heat the homes. Typically, the extreme days' averageusage is

### nearly ten timesthe median average usage. The amount of dispersion inthe average daily usage distributions is 

### also very different between the two end-uses. The range for water heating is 

### approximately two-thirds of the mean, while the range for space conditioning is 

### about five times the mean. Both of these observations regardingthe difference in the two end-uses' distributions (skewness and range) reflect their different degrees of weathersensitivity. 

### byfuel type and season. They illustrate the modal usage level for typical days in a season and the degree ofskewness that is attributable to extreme days. Consequently, the usage variance, median and mode across days can be understood more clearly. The difference between space conditioning and water heating average weekday frequency distributions is immediately obvious. Water heating is generally distributed symmetrically around a central mean, creating a familiar bell shape. Space conditioning, on the other hand, is strongly skewed to the left (low usage) in all cases. That is, on most days, little space conditioning is required relative to the demand on a few extreme days during which a large amount of energy is consumed to cool or heat the homes. Typically, the extreme days' average 

### usage is nearly ten times the median average usage. The amount of dispersion in the average daily usage distributions is also very different between the two end-uses. The range.for water heating is approximately two-thirds of the mean, while the range for space conditioning is about five times the mean. Both of these observations regarding the difference in the two end-uses' distributions (skewness and range) reflect their different degrees of weather sensitivity.

5-12Figure5-6 SCE REUCWeekdav Averaae UEC Distribution Winter Electric Heat Puma: PooledCities
PcrcenlofTotal 40
35
30 25
20
15
10 5
0 <2 4 8 12 16 20 24 28 32 36 40 44 DailykwhMidpoinlr Figure 5-7 SCE REUCWeekdav Averaee UEC Distribution

Summer Space Conditionine: PooledCities
PercmlofTotd 40
35
30 25
20 15
10 5
0 <Z 4 8 12 16 20 24 28 3'1 36 40 DdlykwhMidpoints Figure 5-6 SCE REUC Weekday Average UEC Distribution Winter Electric Heat Pump: Pooled Cities·
PffUfltofTob:l 40 35

<2 4 8 12 16 20 24 28 32 36 40 44 DailykWhMidpoints Figure 5·7 SCE REUC Weekday Average UEC Distribution

Summer Space Conditioning: Pooled Cities
PercenlorTotai 40

<2 4 8 12 16 20 24 28**3~** 36 40 DailykWhMidpoinu
5-13Figure5-8 SCE REUCWeekdavAverace UEC Distribution Winter Gas Furnace: PooledCities

DailykBTUMidpointr 

# I Figure5-9 SCE REUCWeekday Averaae UEC Distribution Winter ElectricWater Heatina: PooledCities Figure 5-8 SCE REUC WeekdayAverage UEC Distribution Winter Gas Furnace: Pooled Cities

Percent ofTolal SO

PercentofT olal 45

<2 50 100 150 200 250 300 350 400 450 Daily kBTU Midpoints Figure 5-9 SCE REUC Weekday Average UEC Distribution Winter ElectricWaterHeating: Pooled Cities

20 18 16 14 12 10 8 6 4 2 0 4.5 5 5.5 6 6.5 7 7.S 8 8.5 9 9.5 1010.511 11.512 12.513 13.5 Daily kWhMidpoints

5-14Figure5-10 SCE REUCWeekday Averacre UEC Distribution Winter GasWater Heating: PooledCities
PmcenlofTobl 20 18 16 14 12 10 8 6 4 2 0 30 35 40 45 50 55 60 65 70 75 BO 85 90 95 100 105 110 Daily kBTUMidpoints PercenlorTolal Figure 5-10 SCE REUC Weekday Average UEe Distribution Winter Gas Water Heating: Pooled Cities
20 18 16 14 12 10 8 6 4 2 0 30 3S 40 45 50 5S 60 65 70 7S 80 85 90 95 100 105110 Daily kBTU Midpoints

5-15SPACE CONDITIONING WEATHER MODEL It 

### isevidentfrom the usage tables that heat pumps require less energy at pointof use than gas furnaces. This section explains the usage of each of the space heating appliances as afunction of temperature. The amount of energy required to heat a home on a given day depends on a numberof factors includingthe exteriortemperature. The load shapes in Section 7 show that the usage between 6:00 and 10:OO a.m. 

### isthe most important period for space heating. A model is estimated below that uses the morning heating degree days to explain the morning heatingenergy usage. The model that isestimated has the followinggeneral form:

where 

### ~k isthe morning usage in

### kBTU between 6:00 and 10:OO a.m. for 

### k =gas It or electric, j indexes cities, and t ranges over all days of the winter. Hjtisthe number of heatingdegree days base 68°F in cityj on day t. Heatingdegree days are definedasfollows:
120 

## [ max (0,68-tempti,)]

m=73 Hjt= 288 (5.2) where m=73 to 120 are the five-minute time periods between 6:00 and 10:OO a.m. Both usage and heatingdegree days are subtracted from their meansfor the estimation so that no intercept 

### is required in the model; Ejt is a random 

### "k disturbanceterm. Thus, two 

## PI are estimated, one for each fuel type. Estimation is carried out after first averaging across all of the households. The estimated parameters for Equation 5.1 are given inTable 5-4 for each fuel type, electricity andgas. " ,I 

### SPACE CONDITIONING WEATHER MODEL It is evident from the usage tables that heat pumps require less energy at point of use than gas furnaces. This section explains the usage of each of the space heating appliances as afunction of temperature. The amount of energy required to heat a home on a given day depends on a number offactors including the exterior temperature. The load shapes in Section 7 show that the usage between 6:00 and 10:00 a.m. is the most important period for space heating. A model is estimated below that uses the morning heating degree days to explain the morning heating 'energy usage. The model that is estimated has the following general form: (5.1)
where

# uft is the morning usage in kBTU between 6:00 and 10:00a.m. for k = gas or electric, j indexescities, and t ranges over all days of the winter. Hjt is the' number of heating degree days base 68°F in city j on day t. Heating degree days are defined as follows:
120 

# [ L max (0,68-temptjm)]

m=73 Hjt = 288 (5.2)
where m=73 to 120 are the five-minute time periods between 6:00 and 10:00 a.m. Both usage and heating degree days are subtracted from their means for the estimation so that no intercept is required in the model; Ejt is a randoni disturbance term. Thus, two

# Bf are estimated, one for each fuel type. Estimation is carried out after first averaging across all of the households. The estimated parameters for Equation 5.1 are given in Table 5-4 for each fuel type, electricity and gas.

5-16Table 5-4 Estimated Linear Relationshiv betweenAverage Mornine. Svace

Heatine.and 

### morn in^ heat in^ Degree-Davs: All Households

.# 

### &I 

# fik Std. Error 

# - N 

### - R2 

### :Gas 26.5a 0.45 841 .0.81 'Electricity 7.Sa 0.14 841 0.76
aDenotes significance at the one percent level.
For gas furnaces, the morning usage increases about 25 kBTU for every heating degree day and, for electric heat pumps, the morning usage increases about 7 kBTUfor every heatingdegree day. This relationship islinear; higher order terms do not significantly increase the explanatory power of the model. Furthermore, no significant usage difference between weekends and weekdays is detected, so bothday types are pooledinthe regressions. 

### Table 5-4 

### Estimated Linear Relationship between Average Morning Space

Heating and Morning Heating Degree-Days: 

### All Households

, 

# a k ifiJel Std, Error N 

## g2 'Gas 26.Sa 0.45 841 0.81

.Electricity 7.5a 0.14 841 0.76
aDenotes significance at the one percent level.
For gas furnaces, the morning usage increases about 25 kBTU for every heating degree day and, for electric heat pumps, the morning usage increases about 7 kBTU for every heating degree day. This relationship is linear; higher order terms do not significantly increase the explanatory power of the model. Furthermore, no significant usage difference between weekends and weekdays is detected, so both day types are pooled in the regressions.

5-17Section6 REUC APPLIANCE LOAD PROFILES .!@NTRoDUCTION i .. !rhissection provides the appliance load profiles developedfrom nearly two years !if data collected from the REUC sample. The REUC appliance load data are /;nique in that daily energy usage patterns of both electric 

### and gas residential /appliances have been calculated and compared from field data. 

## he data were (collectedat five-minute intervals and subsequently aggregated over half-hour i ,periodsfor graphical presentation. This section presents load profiles highlighting the key comparisons made for the monitored appliances. Appendix A contains a comprehensiveset of load profiles. Finally, this section concludeswith load shape modeling results. LOAD PROFILES Figures 6-1 through 6-4 contain average load profiles for space heating and water heating, for the summer and winter seasons. These usage profiles indicate the relative energy usage of the different fuel types, as well as the different usage patterns of electric and gas appliances. Cooking and clothes drying usage profiles are provided inAppendix A. The load profile results mirror the seasonal and annual energy usage presented in the previous section. The loadshapesfor electric andgas appliances appear quite similar, differing primarily in the amount of energy under the respective curves. Load profiles expressed as 

### a percentage of total daily usage are provided in Appendix A and generally support this observation. 

### Section 6 REUC APPLIANCE LOAD PROFILES
:INTRODUCTION ' ' 1, .•

-. 

### !rhis section provides the appliance load profiles developed from nearly two years

## ~of data collected from the REUC sample. The REUC appliance load data are !unique in that daily energy usage patterns of both electric and gas 

### residential t··-
. 

### (appliances have been calculated and compared from field data. The data were !collected at five-minute intervals and subsequently aggregated over half-hour 'periods for graphical presentation. This section presents load profiles highlighting th'e key comparisons made for the 'monitored appliances. Appendix Acontains a comprehensive set of load profiles. Finally, this section concludes with load shape modeling results.
LOAD PROFILES 

### Figures 6-1 through 6-4 contain average load profiles for space heating and-water heating, for the summer and winter seasons. These usage profiles indicate the relative energy usage of'the different fuel types, as well as the different usage patterns ofelectric and gas appliances. Cooking and clothes drying usage profiles are provided in Appendix A. 

### The load profile results mirror the 'seasonal and annual energy usage presented in the previous section. The load shapes for electric and gas appliances appear quite Similar, differing primarily in the amount of energy under the respective curves, Load profiles expressed as a percentage of total daily usage are provided in Appendix Aand generally support this observation.

6-1Figure6-1 SCE REUCWinter: SpaceConditionine:(at pointof use)
kBTUpr Hour 12

# - Elec.Wekdryr ?? 

# ------ Elec.Weekends 10- 

## -.-.-.- GasWeekdryr GigWeekends
8-

6- 

## E I 

### '. I 4-

or.. r..c..Ir.I....'*'-~..i

0 3 6 9 12 15 18 21 24 Timof Day Figure 6-2 SCE REUC Summer: SpaceConditionine:(at point of use)

kBTU prHour 3.0-

GasWeeken& 2.0

# -

15-

1.0-

o.o,..I..I..I..I=.I-.I.".'I 0 3 6 9 12 15 18 21 24 Timeof Day Figure 6-1 SCE REUC Winter: Space Conditioning (atpointofuse)

kBTU per Hour 12

10

8

6

4

o 3 6 9 12 limeo(Oay Figure 6-2 15 18 Elec. Weekdays Elec. Weekends GasWeekdays Gas Weekends

21 24

SCE REUC Summer: SpaceConditioning (at point ofuse)

kBTU per Hour 3.0

2.5

2.0

1.5

1.0

0.5 Elec. Weekdays Elec.Weekends Cas Weekdays CasWeekends

.:;>'**.~~** '¥, 111:::...._......- _ 0.0**-I-r--r-r-~ro"'-~~"'-r-T~-r-"""'-r-~-r~,...,** o 3 6 9 12 15 18 21 24 TimeofDay

6-2Figure 6-3 SCE REUCWinter: Water 

### heat in^(at point of use) 

## r- kBTUpr Hour 8

# - !! ! ! 7

# - ! ! Ecc.Wehn* ! ! GaW-kdayr ! 

### !A GaWrrkedl 6

# - ! !I \ 

## i 

# I \.

0 7 I

0 3 ' 6 ' 9 12 IS 18 21 14 Timof Dq Figure 6-4 SCE REUCSummer: Water Heatine(at ooint of use)

k8TUpr Hour 6

# - Ekc. Weekendr 5

### -

3

# -

0

# -, I

0 3 6 9 12 15 18 21 24 Timeof Day 

### - Figure 6-3 SCE REUC Winter: Water Heating (at pointofuse)

kBTU per Hour 8
7 

# •
5
4
1 

## ,. i i i i i " F 

### ! f\ 

### • • ; .j' \ i , \ 8ee.Weekdays Bte.Weekrnds GasWeekdays GasWeekends i " \ iii \ i"'\ i I ; ;",\ i I '. \ 

### r**~** 

### , 

# ;,' '., '\**,,~.**
, I' " ; I " ' •. _,

.• .Ii**~~** 

### i I _\, .' '\, 2

# f \. '**~....**

# .. " ...'

.. 

# ...-...-... : I I ..... .. 1

### ~

### ':0. ,/ ',/ ',----...,-

I**~** ......

'-iOlI""":::t-,..
-

Timeof**D~** Figure 6-4 SCE REUC Summer: Water Heating (at pointofuse) 

# ,'. 

# .

, i "' ; i i i ""/', I \1 \ Elec. Weekd;;ays Elec. Weekends GasWeekdays GasWeekends 

### i ,\ \**~** i \ , ... • ,. \ /A, i I"', \ 

### t

### l' ',\ j J '; \ .,**l\~** ! I \ \ 

### /...-f**''..;~\.** 

# I'

.. '-. ; i I '. '-J .. ; 

### .......... '

### .., ,

### .....

.. I ',"'-'" I I , j I

## ,J "**~,-** 

## -.......**:._~.'** I ----,' 

### ""'- .' 

# • • 12 15 

# ,. 21 24 Time ofDay

6-3Forexample, considerthe water heating load profiles in Figures 6-3 and 6-4. Gas water heaters have a high peak consumption level. Electricwater heaters, on the other hand, have a lower connected load although they sustain this load over longer periods of time. Thus, not only do the total usage amounts diffeiby fuel type, but the load shape differs as well. Interesting comparisons can be made across seasons. By comparing Figure 6-1 with Figure 

### 6-2, the magnitudeof heat pumps' average peak demand 

### is observed to be approximately equal in both summer and winter. Only the timing of this peak differs between the two seasons. However, Figure 6-5, which expresses demand in 

### kW ratherthan kBTU per hour, illustrates that in the months of extreme temperatures (july and January), the peak of the average heating load profile of the heat pump 

### is significantly higher than the peak of the average cooling load profile. Space conditioning average load profiles for the other ten months are included in Appendix A. Figure6-5 SCE REUC: 

## hateConditionina ElectricSample

January Weekends 

## -.-.-. *.- JulyWeekdays 

# ----- JulyWeekends kW Load 2.0

# - 1.8

# - 1.6

# - 1.4

# -

0.2 -%-,

. 

## / 0.0 ..I.-I.sI..I.-I..I"l"i 0 3 6 9 12 15 18 21 24 Timeof Day 

### Forexample, consider the water heating load profiles in Figures 6-3 and 6-4. Gas water heaters have a high peak consumption level. Electric water heaters, on the other hand, have a lower connected load although they sustain this load over longer periods oftime. Thus, not only do the total usage amounts diffe; by fuel type, but the load shape differs as well. Interesting comparisons can be made across seasons. By comparing Figure 6-1 with Figure. 6-2, the magnitude ofheat pumps' average peak demand is observed to be approximately equal in both summer and winter. Only the timing of this peak differs between the two seasons. However, Figure 6-5, which expresses demand in kW rather than kBTU per hour, illustrates that in the months of extreme temperatures (July and January), the peak of the average heating load profile of the heat pump is significantly higher than the peak of the average cooling load profile. Space conditioning average load profiles for the other ten months are included in Appendix A.

kW load 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 

### Figure 6-5 SCE REUC: Space ConditioningElectric Sample·



### • • • • 

### • • 

### , / \ ,/**~~'**



### • • '-


### ...**~ .~-c-**  ...



### ....:-.."::".:.
0 3 6 

### ,.../1
9 II 

### //**I"~**

12 Time ofDay

6-4 15 18 JanuaryWeekdays JanuaryWeekends July Weekdays July Weekends

21 24""re3 6-6 and 6-7 present the average load profiles across all twelve months of 2 year for heat pump and gas space conditioning, respectively. These threemensionalfigures trace,the changing heightof the average load profile peak in ,ch month. 

### 11four electric end-uses' load profiles are presented inone plot in Figures 6-8 ,d 6-9 in order to illustrate the different magnitudes of average appliance load. Figures 6-10 and 6-11 present whole house load shapes for ihe Electric ,d Gas Samples. Space conditioning and whole house load are overlaid in ;gureA-22 inAppendix A. hese load shapes and average load profiles are consistentwith other residential

. . lad metering studies. For example, load profiles produced from the SCE esidential Appliance End-Use Survey (RAEUS) project are consistent with the EUC load profileswhere comparisons can be made. The RAEUS project, while leteringonly electric appliances, has a larger numberof end-use pr&files.l he profiles provided in Appendix A include: (1 load profiles for cooking and lothes drying; (2) load profiles for the electric appliances in kW rather than :BTU; 

### (3)two-dimensional space conditioning average load profiles for each nonth; (4) load profiles.for weekdays expressed as a percentage of total daily sage; 

### (5)whole house, space conditioning and water heating load profiles on a :ommongraph; and (6)a three-dimensional daily temperature profile by month.

'For more information about the RAEUS load profiles, see .southern California Edison, "ResidentialAppliance End-Use Survey: Sample I Final Report," February 1989.



### oures 6-6 and 6-7 present the average load profiles across all twelve months of o eyear for heat pump and gas space conditioning, respectively. These thteemensional figures trace ·the changing height ofthe average load profile peak in lch month. II four electric end-uses' load profiles are presented in one plot in Figures 6-8 ld 6-9 in order to illustrate the different magnitudes of average appliance load. nally, Figures 6-10 and 6-11 present whole house load shapes for the Electric ,d Gas Samples. Space conditioning afld whole house load are overlaid in 'gure A-22 in Appendix A. hese load shapes and average load profiles are consistent with other residential lad metering studies. For example, load profiles produced from the seE esidential Appliance End-Use Survey (RAEUS) project are consistent with the EUC load profiles where comparisons can be made. The RAEUS project, while letering only electric appliances, has a larger number ofend-use profiles.' he profiles provided in Appendix A include: (1) load profiles for cooking and lathes drying; (2) load profiles for the electric appliances in kW rather than :BTU; (3) two-dimensional space cbnditioning average load profiles for each nanth; (4) load profiles·for weekdays expressed as a percentage of total daily :sage; (5) whole house, space conditioning and water heating load profiles on a :ammon graph; and (6) a three-dimensional daily temperature profile by month.

, For more information about the RAEUS load profiles, see .Southern California Edison, "Residential Appliance End-Use Survey: Sample I Final Report," February 1989.
6-5Figure6-6 SCE REUC: Heat Pumo S~ace Conditioning Figure 6-6 SCE REUC: Heat Pump Space Conditioning

kWLood
z.o

L6

1.0

OJ;

21.1>0 18:00 '11m. (PST)

6-6Figure6-7 SCE REUC: CasIElectricSpace Conditioning 

### Figure 6-7 SCE REUC: Gas/ElectricSpace Conditioning

6-7Figure6-8 SCE REUCWinter Weekdav Electricitv: ElectricSam~le

kWLord 1.0- 0.9- 0.8- 0.7-

0 3 6 9 12 15 18 21 24 limeofDay Figure 6-9 SCE REUCSummer Weekdav Electricitv: ~lectric Sample

kWLwd 1.0

# - Spice Conditioning Water Hialing Cmking 0.8 ClothsDrying 0.7 * 0.6

# - 0.5

# - 0.4

# - 0.3

# -

0 3 6 9 12 15 18 21 24 Time of Day Figure 6-8 SCE REUC WinterWeekday Electricity: Electric Sample

kW Load 1.0 

### D.' 0.8 0.7 0.6
. 05 

### D.' 0.3 0.2 0.1 0.0 0 (' 

### • 

### • 

# , • • 

### • • • • • • • • 

### • 

### • • 

### • '
...

### _-,' 

# '" \\

### , \

### ,



## , ",

# ," Space Conditioning**W~ter** Heating Cooking Clothes Drying

!

...

## ,----.....

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

# - ....**~'.'** 3 6 9 12 15 18 21 2. TImeofDay Figure 6-9 SCE REUC Summer Weekday Electricity: Electric Sample

kW Load 1.0 

### D.' 0.8 0.7 0.6 0.5 

### D.' 0.3 0.2 Space Conditioning Water He"aling Cooking Clothes Drying
/\ 

# . , I \ 

# . , : .....

### , • • 

# . 

# . \ 

### ' 

## ,. 

# . , 

# , , 

# . , \ I '.. ,..,/ I**~** \, / / I' \
_ ...___ /.J 0.0

## -F='-.,;:...,:::::~~;~·:-:-~-;:-:-;.-:;-:.·------:-;:.--::;-:.·-·- ..

## ,....--·T'-'---::-:;~ 0.1

o 3 

# • 9 12 15 18 21 2. Time ofDay

6-8Figure 6-10 

### SCEREUCWinter: Whole House Electricity 

# - kWLoad 3.0- 2.8

# - 2.6

# -
2.2

# -
1.8-

0 3 6 9 12 IS 18 21 24 Timeof Day

Figure6-11 

### SCEREUC Summer: Whole House Electricity
kWLoad 2.07 1.8-

0.4

# - 0.2

# - 0.0- ..,..I..,..,.' IIII 0 3 6 9 12 15 18 21 24 Time ofDay Elec. Sample Weekdays 

# ------- Elec..SampleWeekends GasSampleWeekdays GasSampleWeekends kW load 3.0 2.8 2.6 lA 2.2 2.0 1.8 1.6 1.' 1.2 1.0 0.8 0.6 0.' 0.2 0.0 0

kWLoad Figure 6-10 SCE REUC Winter: Whole House Electricity

3 6 

# • ,- , I 

### , , I 

### " ,/
. 

### ....._,

12 15 18 Time of DOIlY

Figure 6-11 21 Elec. S ...mple Weekdays Elec. Sample Weekends G.as Sample Weekdays Gas Sample Weekends 2.

SCE REUC Summer: Whole House Electricity
2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 3 6 9

6-9 r"\**/...-'-'~~** ,...

### --..... ,--' 

### '_

## ..-'

12 lS Time ofDay flee. SampleWeekdoys Elec..sample Weekends CasSample Weekdays Gas Sample**Weekend.~**

18 21 24USING HOUSEHOLDCHARACTERISTICS TO EXPLAIN LOAD SHAPES Traditionally, most load shape modeling approaches have concentrated on describing premise loadcharacteristics as afunction of demographics, household appliance inventories, and time. The REUC project has shown thai' these approaches can be enhanced through the inclusion of other measurable variables, such as a customer's image of electricity. The REUC project has extended these approaches by successfully including these additional variables into appliance (end-use)load shape modeling. A goal of this research isto show that a relationshipexists betweenthe loadshape of an appliance and the characteristics of the householdthat uses the appliance. A two-step regression procedure is used to ascertain this relationship: (1) harmonic modeling of load shapes, followed by (2) household characteristic regressions on the harmonic model parameters. The first step explains the load shape of an appliancewith a harmonic specification based on time of day as the independent variable. For each appliance monitored from each REUC household, a set of parameters was estimated that summarized the information inthe load data. That 

### is, these parameters are said to "summarize" the appliance's load shape. In the second step of the procedure, these estimated parameters were modeled usingthe REUC householdcharacteristicsas the independentvariables. Usingthe parameter estimates from this model, household characteristics can be used to generate "simulated" load shapes. The results presented here are based on electric and gas water heatersfrom the REUC sample. Water heaters were chosen because they consume a large amount of energy relative to other end-uses. Also, water heaters have little weather-sensitivity, so the behavioralaspects of usage may be easierto identify. Stea 1: Harmonic LoadShaae Reeressions Using a technique developed in earlier EPRI researchfor premise (whole house) data,2 appliance load shapes wece here modeled using harmonic functions (i.e.,

2"Residential End-Use Load-Shape Estimation, Volume 1: Methodology and Results of Statistical Disaggregationfrom Whole-House Metered Loads" (EPRI EM-4525), May 1986. 

### USING HOUSEHOLD CHARACTERISTICS TO EXPLAIN LOAD SHAPES 

### Traditionally, most load shape modeling approaches have concentrated on describing premise load characteristics as a function ofdemographics, household appliance inventories, and time. The REUC project has shown that' these

.approaches can be enhanced through the inclusion of other measurable variables, such as a customer's image of electricity. The REUC project has extended these approaches by successfully including these additional va·riables into appliance (end-use) load shape modeling. Agoal ofthis research is to show that a relationship exists between the load shape ofan appliance and the characteristics ofthe household that uses the appliance. A two-step regression procedure. is used to ascertain this relationship: 

## (n harmonic modeling of load shapes, followed by (2) 

### household characteristic regressions on the harmonic model parameters. The first step explains the load shape ofan appliance with a harmonic specification based on time ofday as the independe.nt variable. For each appliance monitored from each REUC household, a set ofparameters was estimated that summarized the information in the load data. That is, these parameters are said to "summarize" the appliance's load shape. In the second step of the procedure, these estimated parameters were modeled using the REUC household characteristics as the independent variables. Using the parameter estimates from this model, household characteristics can be used to generate**"~imulated"** 

### load shapes. The results presented here are based on electric and gas water heaters from the REUC sample. Water heaters were chosen because they consume a large

. amount of energy relative to other end-uses. Also, water heaters have little

weather-sensitivity, so the behavioral aspects ofusage may be easierto identify. Step 1: Harmonic Load Shape Regressions 

### Using a technique developed in earlier EPRI research for premise (whole house) data,2 appliance load shapes were here modeled using harmonic functions (i.e.,

2"Residential End-Use Load-Shape Estimation, Volume 1: Methodology and Results ofStatistical Disaggregation from Whole-House Metered Loads" (EPRI EM-4525), May 1986.

6-10

### sine and cosine). The first-step regression produces a set of parameters that describe the daily loadshapeof an appliancewith respecttotime. Thefirst step is

### accomplished by running regressionsof appliance load shapes on 

### ' water heatersfor each home inthe lwineand MissionViejo samples ofthe REUC database. Usingharmonic functions of various periodicities, the daily loadshape for both electric and gas water heaters is

### described as a function of time. This method of using harmonic functions to model whole house load is

### described in detail elsewhere.3 The REUC project's innovation is

### to applythese techniques to end-use data. The end-use harmonicmodel specificationsare givenasfollows:

wheret 

### = timeofday inhours rangingfrom 1

### to 24, 

### and LOAD, = 

### appliance load inWatts at time t.
Results of the harmonic regressions for the heat pump water heaters in two households are presented in Table 6-1 under the column heading "Estimated." Figures 6-12 and 6-13 illustrate these results by comparing the estimated load shape to the actual daily load shape for the same two homes. The closefit ofthe regression model to the actual daily load shape illustratesthe effectivenessof the harmonic load shape modelingtechnique.

3"~esidential End-Use Load Shape Estimation,"

# a sine and cosine). The first-step regression produces a set of parameters that describe the daily load shape ofan appliance with respect to time. The first step is accomplished by running regressions ofappliance load shapes on water heaters for each home in the Irvine and Mission Viejo samples ofthe REUC database. Using harmonic functions ofvarious periodicities, the daily load shape ·.for both electric and gas water heaters is described as a function of time. This method of using harmonic functions to model whole house load is described in 

### detail elsewhere.3 The REUC project's innovation is to apply these techniques to end-use data. The end-use 

### harmonicmodel specifications are given as follows:

where 

# (2ltt) (2ltt) . (2ltt) (2ltt) LOADt 

## =Ao +

### A1sin 24 

### +

### A 2

### cos 24 

### +

### A3sin 

## U +

### A4COS 

## U +

t= 

### LOADt = 

# (2ltt) (2ltt) (2ltt) (2ltt) +

### Assin 

## 8 +

### A6COS 

## 8 +

### A7Sin 

## 6 +

### Agcos 

## 6 +



### time ofday in hours ranging from 1

### to 24,

### and appliance load in Watts at time t. (Eq.6-1)



### Results of the harmonic regressions for the heat pump water heaters in two households are presented in 

### Table 6-1 

### under the column heading "Estimated." Figures 6-12 

### and 6-13 

### illustrate these results by comparing the estimated load shape to the actual daily load shape for the same two homes. The close fit ofthe regression model to the actual daily load shape illustrates the effectiveness ofthe harmonic load shape modeling technique.

3"Residential End-Use Load Shape Estimation," op cit.
6-11Table 6-1 Estimatedand Simulated ~armonic Model LoadShaoe Parameters HOUSE 50 Estimatedb Simulated

367.32 414.62 (8.47) 34.28 39.56 (11.98) -128.01 -76.53

(11.99) -167.75 -235.92

(11.98)

3.28 17.04 (11.99) 30.28 50.61 (11.98) -35.41 0.30 (11.99) 43.50 49.62 (11.98)

2.88 7.1 5 (11.99) -40.56 -49.73 (11.98) 54.84 46.02 (11.99) 

## aAo-Ale are defined in

## quat ti on 6-1. HOUSE 86 Estirnatedb Simulated

350.08 385.29 (7.37) -59.53 -41.60 (10.42) -80.02 -162.76 

### (1

0.42) -164.53 -155.10

(1

0.42) 95.97 166.89 (10.42) 58.97 79.00 (1

0.42) 16.87 -14.96 (10.42)

-9.89 -72.30 (10.42) -45.47 -54.32 (10.42)

19.27 23.67 (1

0.42) 11.89 -3.70 (10.42)

b~tandard errors are given in parentheses. Table 6-1 Estimated and Simulated Harmonic Model load Shape Parameters
HOUSE 50 HOUSE 86 Variable

### a Estimatedb Simulated Estimatedb Simulated 

### Ao 367.32 414.62 350.08 385.29 (8.47) (7.37) 

### Al 34.28 39.56 -59.53 -41.60 (11.98) (10.42) 

### A2 -128.01 -76.53 -80.02 -162.76 (11.99) (10.4:2) 

### A3 -167.75 -235.92 -164.53 -155.10 (11.98) (10.42) 

### A4 3.28 17.04 95.97 166.89 (11.99) (10.42) 

### As 30.28 50.61 58.97 79.00 (11.98) (10.42) 

### A6 -35.41 0.30 16.87 -14.96 (11.99) (10.42) 

### A7 43.50 49.62 -9.89 -72.30 (11.98) (10.42) 

### A8 2.88 7.15 -45.47 -54.32 (11.99) (10.42) 

### A9 -40.56 -49.73 19.27 23.67 (11.98) (10.42) 

### AIO 54.84 46.02 11.89 -3.70 (11.99) (10.42) 

### aAo-AIO are defined in Equation 6-1. bStandard errors are given in parentheses.

6-12Figure6-12 REUC Heat Pump Water Heater

# - House 50

First Steo Estimated Loadvs. Actual Load

Figure 6-13 

# .

REUC HeatPumpWater Heater

# - House 86 First Step Estimated Loadvs. Actual Load 08-

06-

036 g 12 15 18 21 24 rimeol nay (in Hours PST1

0.6'

0.4-

. . 036g 12 15 i8 21 24 . ~ime 01 Day (in Hours PST1 

### Figure 6-12 REUC Heat Pump Water Heater

- House 50

First Step Estimated loadvs. Actual load

kWLoad**"o,----------~------_,** 0.6



### o. 

# I Estlmaled

# L1

---, Actualload_



# -

0.0+--....--
..............--..... - ..... - ..... - .....--1 a 3 6 9 12 15 16 21 24 Time of Day (In Hours PST)


### Figure 6-13 REUC Heat Pump Water Heater

- House 86

First Step Estimated loadvs. Actual load

,WL"'" 0.6,------------------,
0.6

0.4

0.2**o.o.J--.,....-.,....-.,....-.---.---,....~.---I** a 3 6 9 12 15 16 21 24 Time 01 Day (in Hours PST)

6-13Step 2: Characteristic Regressions The second step 

### is conducted on the estimated parameters of the water heater loadshapes, from step one. The purposeof this analysis 

### isto assess the ability of customer characteristics to explain the step 

### 1 parameters, the parameters that describe periodicities in the appliance load shapes. Estimated load shape parameters were used as the dependent variables in a model where customer characteristics, includingthe CP&B and REUC attitudinal variables, were used as the explanatory variables. The development of the attitudinal variables is explained inSection 7. The end-usecharacteristicsmodelspecificationsare givenasfollows:
Ai 

### = Bo

## + BI 

### * MAXHHAGE

## + B2

### * SQFT 

## + B3

## * MAXEDLEV

## + 

## + B4

### * HHSIZE

## + B5

### * HHlNC

## + B6

### * CASVAL

### + B7

### * GASQUA t 

## + Bg

## * ELECVAL

## + Bg

### * ELECQUA

## + B10

### *QUALGUY 

## +

whereAi 

### = MAXHHAGE= SQFT 

### = MAXEDLEV= HHSIZE= HHlNC= GASVAL =

ELECVAL 

### = one of the eleven parameters estimated by the harmonic model (i.e., 

### A. 

### -Alo in

### Eq. 6-I), categorical variable representing the age of the oldest member of the household, area measure (insquare feet) of the home,' categorical variable representing the highest level of education attained by members of the household, number of occupants inthe household, and

.. categorical variable representingthe combined income of members of the household, , variable representinga customer's image of gas as a high -valuefuel, variable representinga customer's image of gas as a high qualityfuel, variable representinga customer's image of electricity as ahighvaluefuel, 

### Step 2: Characteristic Regressions The second step is conducted on the estimated parameters of the water heater load shapes, from step one. The purpose ofthis analysis is to assess the ability of customer characteristics to explain the step 1 parameters, the parameters that describe periodicities in the appliance load shapes. Estimated load shape parameters were used as the dependent variables in a model where customer characteristics, including the CP&B and REUC attitudinal variables, were used as the explanatory variables. The development of the attitudinal variables is explained in Section 7. The end-use characteristics model specifications are given as follows:

whereAi'" Bo + BI 

### * MAXHHAGE + B z 

### * SQFT + B3 

### * MAXEDLEV + + B4 

### * HHSIZE + B5 

### * HHINC + B6 

### * GASVAL + B7 • GASQUA + + B8 

### * ELECVAL + B9 • ELECQUA + BIO 

### *QUALGUY + 

### . + BII • CPBN02 + BIZ 

### *CPBN03 

### + BI3 

### *CPBN04 + (Eq.6-2)

one of the eleven parameters estimated by the harmonic model (i.e.,

## An

-AIO in Eq. 6-1), MAXHHAGE '" categorical variable representing the age of the oldest member of the household, SQFT = area measure (in square feet) ofthe home; MAXEDLEV = categorical variable representing the highest level of education attained by members of the household, HHSIZE = number of occupants in the household, and HHINC = categorical variable representing the combined income of members of the household, / GASVAL = variable representing a customer's image ofgas as a high value fuel,

. GASQUA = variable representing a customer's image of gas as a high qualityfuel, ELECVAL = variable representing a customer's image of electricity as a high value fuel,
6-14ELECQUA= variable representinga customer's image of electricity as ahigh qualityfuel, and

.,QuALGUY = indicator variable representing whether a customer's ELECQUA score is higher than the customer's ELECVAL score, CPBN02 = indicator variable representing, a customer being classifiedas "AppearanceConscious," CPBN03 

### = indicator variable representing a customer being classifiedas "ResourceConserver,"

.CPBN04 = indicator variable representing a customer being classifiedas "LifestyleSimplifier," CPBN06 = indicator variable representing a customer being classified as "ValueSeeker." Simulated parameters for the two households mentioned above are presented in. Table 6-1 under the column heading "Simulated." These parameters were produced by substitutingthe actual customer characteristics of each household into the end-use characteristic model (Equation 6-21 estimated using the entire REUC sample of heat pump water heaters. Note the similarity between these simulated parameters, generated from the characteristic model, and the estimated parameters, developedfrom the harmonic model. These results are illustratedin Figures 6-14 and 6-15for the same two homes. For these two homes, simulated loadshapes are generated intwo steps: Steo 1: Use characteristics regression results and actual household characteristics to estimate harmonic model parameters; and Step 2: Use estimated harmonic model parameters to simulate a load shapefor each home's water heater. In other words, the simulated load shapes are generated using only household characteristic information collected inthe surveys. Also plottedon this figure are actual average loadshapesfor the two homes. The relativelyclose fit ofthese two shapes demonstrates the potentialof this load shape modelingapproach. 

### ELECQUA = 

### .' QUALGUY =



### CPBN02 = 

### CPBN03 = 

### .CPBN04 = 

### CPBN06 = 

### variable representing a customer's image ofelectricity as ahigh qualityfuel, and indicator variable representing whether a customer's ELECQUA score is higher than the customer's ElECVAl score, indicator variable representing. a customer being classified as "Appearance Conscious," indicator variable· representing a customer being classified as "Resource Conserver," indicator variable representing a customer being classified as "Lifestyle Simplifier," indicator variable representing a customer being classified as "Value Seeker."

.Simulated parameters for the two households mentioned above are presented in·

Table 6-1 under the column heading "Simulated." These parameters were produced by substituting the actual customer characteristics of each household into the end-use characteristic model (Equation 6-2) estimated using the entire REUC sample of heat pump water heaters. Note the similarity between these simulated parameters, generated from the characteristic model, and the estimated parameters, developed from the harmonic l1)odel. These results are illustrated in Figures 6-14 and 6-15 for the same two homes. For these two homes, simulated load shapes are generated in two steps: Step 1: Use characteristics regression results and actual household characteristics to estimate harmonic model parameters; and Step 2: Use estimated harmonic model parameters to simulate a load shape for each home's water heater. In other words, the simulated load shapes are generated using only 

### household characteristic information collected in the suryeys. Also plotted on this figure are actual average load shapes for the two homes. The relatively close fit ofthese two. shapes demonstrates the potential ofthis load shape modeling approach.

6-15Figure 6-1 4 REUC Heat PumpWater Heater

# - House 50 Second Step Simulated Load

### vs. Actual Load

0.6-

0 3 6 9 12 15 18 21 24 Timeof Csy (in Hours PST)

Figure 6-15 REUC Heat PumpWater Heater

# - House 86 Second Step Simulated Loadvs. Actual Load

0.b

O.C

0 3 6 9 12 15 18 21 24 Time of Day (in Hwrs PSTJ



### Figure 6-14 REUC Heat Pump Water Heater

- House 50 Second Step Simulated loadvs. Actual load**kWl~ 1..1r--------------------;.;;;;:;;;;;;~1**  o.

o.

o. \'-

...

o 3 6 9 12 15 

# ,. 21 24 Time of Day (in Hours PST)



### Figure 6-15 REUC Heat Pump Water Heater

- House 86 Second Step Simulated Load vs. Actual Load

kWLoad 0.0,-----------------------------------,

o.

o.

o.

o 3 6 9 12 15 Time of Day (in Hours PST)Simulated Loa Acrual Load



# ,. 21 24

# he REUCpro;- :4:z 

### in extensive demographic, aititudinal and load data, is 

## p.
. . ;$ifiicularly \VE i:.:22 to evaluate how changes in customer population

. iharacteristics E: :?<l;ence utility load-shzpes. The end-usecharacteristic model 

# p 4' is,effectivefor eizazringthe effects oichangingdemographicsonappliance load Cipes. 'Figure 6-16 illu:~=ss -ow an "aging" residential customer population can effect 

## k heater loac ihax~. Inthis example, customers are divided'into twogroups. that the xetz:e household in each group differs only by those variables [given inTable 

### 0-2.

### :i~ average age oithe headof the household, the number oi

. . persons in the lousehold, and the square feet'of the household. All other thousehold charzeris:io are the same betweenthe two groups. Load shapes are igiven in Figure 6-16 kr!ounger and older households, "sing the characteristic f land load shape 5armznic models estimated with REUC data. As is shown, the I ;load shape of the olcer. smaller households have somewhat earlier and lower !,peaks. This exar~le illustrates both how differences incustomer characteristics :caninfluence end-are loadshapes and howutility planningmay benefitfrom load I. Ishape models.

Table 6-2 DiherineAveraee Characteristics ofthe Households Younger. Households Older Households Average Age of Headoi

Household Persons in Household 3.7 2.3 Square Feet 1900 1550
Note: All other household characteristics are assumed equal. 

## !file REUC proj:-:: ",':;, its e:xtensive demographic, attitudinal and load data, is

### ~articularly we

### s~::e-:i to evaluate how changes in customer population @iaracteristics**~~** 

### i:<:':.;ence utilitv load-shapes. The end-use characteristic model 

# I !seffectivefor e\'2;ua::ng the

## eiie~ts ofchanging demographics on appliance"ioad shapes,
. ' ;Figure 6-16 

### illu;:c;es**~ow** 

### an "aging" residential customer population can effect 

# l iwater heater loac shaDeS. In this example, customers are divided 'into two groups. Assume that the**2';erc~e** 

### household in each group differs only by those variables

### (gi~en in Table 6-2. the 

### al'erage age ofthe head of the household, the number of ipersons in the**~ousenold.** 

### and the square feet' of the household. All other !

### household charac:erislic; are the same between the two groups, Load shapes are !given in Figure 

### c-i 6 :or \ounger and older households. using the characteristic r: 

### land load shape**"2rrro~ic** 

### models estimated with REUC data. As is shown, the :load shape of the oleer. smaller households have somewhat earlier and lower [,peaks, This exarple iilustrates both how differences in customer characteristics ican influence ene-use load shapes and how utility pranning may benefitfrom load .' shape models.
Table 6-2 

### Differing Average Characteristics ofthe Households Younger, Households Older Households Average Age of Head of 39 61



### . Household Persons in Household 3.7 2.3 

### Square Feet 1900 1550 

### Note: All other household characteristics are assumed equal.

f:,-t'"

## . Figure6-16 REUC Heat PumoWater Heater DemoeraohicTrendScenario

--

TOTAL ENERGY REGRESSIONS Exploratory modelingwas carried out on the total amount ofenergy used by heat pump water heaters on an average day inthe pooled lrvine and Mission Viejo samples of the REUC households. Threetypes of analysis took place: 

### (1) modeling 

### of the energy statistic based on the traditional household demographic variables (i.e., square footage, number of occupants); 

### (2)modelingof the energy statistic based on the REUC attitudinal variables (a set of five variables designed to measure customers' image of different fuels); and 

### (3) modeling of the energy statistic based on the customer's Customer Preference and Behavior (CP&B) classification. Each isdescribed below. Load 0.8 InkW

0.6

# - ,\ Older Households I \ 

# ----- Younger Households I \ I \ I ! 

### ", I ! 0.4

# -

0.2

# -

0.0 I.,-b. 0 3 6 9 12 15 18 21 24 T~me ofDay (~n HoursPST) Figure 6-16

. REUC Heat Pump 

### Water Heater Demographic Trend 

### Scenario

load inkW

## 0.8r----~r.~~~~~11**~**\ Older Households 

### r \
0.6

0.4

0.2



### o 3 6

TOTAL ENERGY REGRESSIONS I \ I \ I \ I \



## .............. \



## l \\\ Younger Households

I I I I " 1', " I \ 

### I

9 12 , I \ \ I \

\ I \ \ I \ \. "I \



# ,**~'"**

......

## -,

15 18 21 Time ofDa (in Hours PST) 24



### Explora\ory modeling was carried out on the total amount ofenergy used by heat pump water heaters on an average day in the pooled Irvine and Mission Viejo samples ofthe REUC households. Three types ofanalysis took place: (1) 

### modeling of the energy statistic based on the traditional household demographic variables (i.e., square footage, number of occupants); (2) 

### modeling of the energy statistic based on the REUC attitudinal variables (a set of five variables designed to measure customers' image of different fuels); and (3) 

### modeling of the energy statistic based on the customer's Customer Preference and Behavior (CP&B) classification. Each is described below.

6-18heat pumpwater heater demographic model specificationsfor the daily UEC

?,.A thewinter season are givenasfollows: AVEUSE = 29.83 

### - 3.72 

## * MAXHHAGE

## + (12.60) (1.39) (



### - 2.28 

### * MAXEDLEV

## + 2.05 

## * HHSIZE

## + 0.54 

## * HHINC

(1.03) (0.95) (2.07)

- R2=0.24 

### SSE = 1437.65 n

### =38 whereAVEUSE 

### = average energy usageon awinter weekday; MAXHHAGE

### = categorical variable representingthe age ofthe oldest member of the household, SQFT = area measure (insquare feet) of the home, MAXEDLEV= categorical variable representingthe highest level of education attained by members of the household, HHSIZE= number of occupants inthe household, and HHlNC= categorical variable representingthe combined income of members of the household, standard errors or given in( ).
The R2statistic presented for Eq. 6-3 (as well as each of the other regression models discussed in this report) measures the "totalfit" of the specification. It is derived from equally weighting all observations, therefore providing valuable information on total fit. For evaluating load shape regressions, it may be more relevant to weight peak and near-peak observations more heavily and to graphically compare the estimated (synthetic) load shape with the actual appliance loadshape, see below. The heat pumpwater heater attitudinal model specificationsfor the daily UEC in thewinter season are given as follows: AVEUSE =28.62 

### - 4.10 

## 'GASVAL

## + 0.98 

### * GASQUA (1.78) (1.96) (1.84) (Eq. 6-4)



### - 2.93 

### * ELECVAL

## + 4.04 

### * ELECQUA

## + 2.94 

### * QUALGUY

(1.43)
, (1.44) (2.48)

- R2 ~0.22 SSE =2016.79 - n=40 

## iThe heat pump water heater 

### demographic model specifications for the daily UEC 

## rInthe winter season are given as follows:

whereAVEUSE = 29.83

- 3.72 • MAXHHAGE

### + 0.007 • SQFT (12.60) (1.39) (0.004) (Eq. 6-3)

- 2.28 • MAXEDLEV 

### + 2.05 • HHSIZE 

### + 0.54 • HHINC

(1.03) (0.95) (2.07)
R2 =0.24 AVEUSE = MAXHHAGE = SQFT= MAXEDLEV = SSE= 1437.65 n

### = 38

.average energy usage on a winter weekday;

categorical variable representing the age ofthe oldest member ofthe household, area measure (in square feet) of the home" categorical variable representing the highest level of education attained by members of the household, HHSIZE=

. HHINC= number of occupants in the household, and categorical variable representing the combined income ofmembers ofthe household, standard errors or given in ( ).
The R2 statistic presented for Eq. 6-3 (as well as each of the other regression models discussed in this report)" measures -the "total fit" of the specification. It is derived from equally weighting all observations, therefore providing valuable information on total fit. For evaluating load shape regressions, it may be more· relevant to weight peak and neaf-peak observations more heavily.and to graphically compare the estimated (synthetic) load shape with the actual • appliance load shape, see below

.. The heat pump water heater 

### attitudinal model specifications for the daily UEC in the winter season are given as follows: AVEUSE = 28.62

- 4.10 • GASVAL 

### + 0.98 • GASQUA (1.78) (1.96) (1.84) (Eq. 6-4)

- 2.93 

### * ELECVAL 

### + 4.04 • ELECQUA 

### + 2.94 

### *QUALGUY

(1.43)
. (1.44) (2.48) R2 =0.22 SSE

## =2016.79· n =40

6-19 .,.'whereAVEUSE 

### = average energy usageon a winter weekday, GASVAL 

### = variable representingacustomer's imageofgas as a high valuefuel,

., , . . ~. GASQUA = variable representingacustomer's imageof .ga'sas a qualityfuel, ELECVAL

### = variable representingacustomer's imageof electricity as a highvaluefuel, ELECQUA= variable representingacustomer's imageof electricity as aqualityfuel, and QUALGUY 

### = indicator variable representing whether a customer's ELECQUA score is higher than the customer's ELECVAL score, standard errorsaregiven in( ). The heat pump water heater Customer Preference & Behavior model specifications for the daily UEC inthe winter season are givenasfollows: AVEUSE 

### =24.02 

# - 3.97 

## * CPBN02

### + 4.56 

### * CPBN03 (2.57) (4.60) (5.56) (Eq. 6-51 

## + 5.16 

### * CPBN04

## + 8.42 

## * CPBN06

(3.39) (3.73)

whereAVEUSE =
. average energy usage on a winter weekday, CPBN02 

### = indicator variable representinga customer being classifiedas "AppearanceConscious," CPBN03 

### = indicatorvariable representingacustomer being classifiedas "ResourceConserver," CPBN04 = indicator variable representinga customer being classifiedas "LifestyleSimplifier," CPBN06 = indicator variable representing a customerbeing classifiedas "Value Seeker," standarderrors aregiven in ( 

### 1. .whereAVEUSE '" GASVAL '" GASQUA '" ELECVAL '" ELECQUA", QUALGUY= average energy usage on.a winterweekday, variable representing a customer's image ofgas as a high. value fuel,

.. . . . .. . variable representing a customer's image of

### g~s as a qualityfuel, variable representing a customer's image ofelectricity as a high value fuel, variable representing a customer's image of electricity as aqualityfuel, and indicator variable representing whether a customer's ELECQUA score is higher than the customer's ELECVAL score, standard errors are given in ( ).
The heat pump water heater Customer Preference & Behavi()r model specifications for the daily UEC in the winter season are given as follows:

·whereAVEUSE = 24.02 - 3.97 • CPBN02 + 4.56 • CPBN03 (2.57) (4.60) (5.56) 

### + 5.16 

### *CPBN04 

### + 8.42 

### * CPBN06

(3.39) (3.73) 

## R2 =0.03 SSE", 2841.12 n = 43 AVEUSE = CPBN02 = average energy usage on a winter weekday, indicator variable representing 

### a customer being classified as "Appearance Conscious," CPBN03 = indicator variable representing a customer being classified as "Resource Conserver," CPBN04 = indicator variable representing a customer being classified as "Lifestyle Simplifier," CPBN06 '" ·indicator variable representing a customer being classified as "Value Seeker," standard errors are given in ( ).

6-20 (Eq. 6-5)

### nergy use models described above are displayed in Figures 6-17 

### -19. 

### Figure6-17 

### shows the marginal effectonenergy use of increasing of household occupants from two to four to six with the household cs model. As intuition would suggest, the modeled average daily s' 

### usage for the heat pump water heater increases when the number of ccupants increases. Figure 6-17



### Effect of HouseholdSize onAveraceWeekdav Usage: REUC Heat PumpWater Heater (Model) energy use models described above are displayed in Figures 6-17 

### "6-19. Figure 

### 6·17 shows the marginal effect on energy use ofincreasing 

### ofhousehold occupants from two to four to 

### six with the household**~raplhi,'CS** 

### model. 

### As intuition would suggest, the modeled average daily usage for the heat pump water heater increases when the number of occupants increases.



### o figure 6,-17 Effect ofHousehold Size on AverageWeekday Usage: REUC Heat Pump Water Heater (Model)

10 20 30

6-21 Summer Winter

31

40Figure 6-18 shows the effects on energy usage brought about by an increase in customers' perceptions of electricity as a quality fuel with the attitudinal model. This figure modeled estimated energy usage inboth winter and summerfor three types of customers: (1) customers with a below average image of electricity as a Qualityfuel; 

### (2)customerswith an average image of electricity as a Quality fuel; and 

### (3) customers with an above average Quality image for electricity. As the figure illustrates, a negative relationship exists between energy usage and electricityquality image. That is, as quality image increases, energy usedecreases. Additional research on other appliances will help identify the source of this and other image relationships.

. . . .. .. Figure6-18

. . Effectof Quality lmaeeof Electricity on Average Weekday Usage: 

### REUCHeat PumpWater Heater (Model)

1 S.D. BelowMean

1S.D. Above Mean 

### Figure 6-18 

### shows the effects on energy usage brought about by an increase in customers' perceptions ofelectricity as a quality fuel with the attitudinal model. This figure modeled estimated energy usage in both winter and summerfor three types ofcustomers: (1) 

### customers with a below average image ofelectrjcity as a Quality

### fuel; (2) customers with an average image ofelectricity as a Quality

### fuel; and (3) customers with an above average Quality 

### image for electricity. As the figure illustrates, a negative relationship exists between energy usage and electricity quality image. That is, as quality image increases, energy use decreases. Additiona"1 research on other appliances will help identify the source ofthis and other image relationships. Figure 6-18 

### Effect ofQuality Image ofElectricity on Average Weekday Usage: REUC Heat Pump Water Heater (Moden

1 S.D. Below Mean

Mean

1 S.D. Above Mean



### o 10 20 30



### 6-22 Summer Winter

kBTU 40re 6-19 shows the effects of the CP&B variable on energy usage for heat mp water heaters under the 

### CP&B 'regression model. This figure presents $mated usage in both winter and summer for customers classified as

. ppearance Conscious" and "Value Seekers." In each case, Value Seeker 

### ' ustomers consume more energy than Appearance Conscious customers. Figure6-19 Effectsof CP&B Classification onAverage Weekdav Usape: REUC Heat Puma Water Heater (Model)

, . 

# 1

8ure 6-19 

### shows the effects of the CP&B 

### variable on energy usage for heat**':.U.~~p** 

### water heate.rs under**t~e** 

### CP&B regression model. This figure**~~esents** 

### estimated usage In 

### both winter and summer for customers classified as

### ~ppearance Conscious'" and "Value Seekers." In each case, Valu'e Seeker

## ~~stomers consume more energy than Appearance Conscious customers. Figure 6-19 Effects ofCP&B Classification on Average Weekday Usage:

Appear.

Value Seeker



### o REUC Heat Pump Water Heater (Model)

18

24

10 20

6-23 30 

### r2I Summer 

### l1li Winter

32

kBTU 40Section7 CUSTOMER PREFERENCESAND APPLIANCE ACCEPTANCE
INTRODUCTION The design of the REUC project allowed for research directed at understanding customer preferences for and acceptanceof new energy-efficientappliances. As part of the survey research component of the project, participants were asked to periodically rate their new appliances along various attributes as well as to respondto questions that assess appliance performance andvalue. In particular, emphasis was placed on identifying the relationship between appliance preference and fuel type (i.e., electric or gas). See Figure 3-1 for the REUC survey research timeline. This section discusses the evolution of customers' attitudes toward their electric and gas appliances. The lrvine participants answered two surveys before being retrofittedwith either new gas or new electric appliances and three surveys after receiving new appliances, whereas the Mission Viejo participants answered only one survey before and fourafter their appliance retrofit. Manyof the survey questions relatedto the discussion inthis section arefound inSurveys 

### 2, 3, and 4. In these cases, only lrvine participants provide before and after retrofit data. Consequently, only lrvine data are presented in many instances. Some participants moved inthe middle of the study and, consequently, longitudinal data exist both for the original participants (i.e., Surveys 1-3) and for 

### the home's new residents (i.e., Surveys 3-5). As a result, data will also sometimes be presented with more observations than retrofitted houses. This section describes the longitudinal changes in customers' image of electricity and gas and in their preferences in space heating, water heating, and cooking, respectively. The section conc!udes with analysis of the effect of customers' discount rates on their perceptions of conservation and efficiency as investment Opportunities. 

### INTRODUCTION Section 7 

### CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE

The design of the REUC project allowed for research directed at understanding customer preferences for and acceptance of new energy-efficient appliances. As part of the survey research component of the project, participants were asked to periodically rate their new appliances along various attributes as well as to respond to questions that assess appliance performance and value. In particular, emphasis was placed on identifying the relationship between appliance preference and fuel· type (i.e., electric or gas). See Figure 3-1 for the REUC survey research timeline. This section discusses the evolution of customers' attitudes toward their electric and gas appliances. The Irvine participants answered two surveys before being retrofitted with either new gas or new electric appliances and three surveys after receiving new appliances, whereas the Mission Viejo participants answered only one survey before and four after their appliance retrofit. Many ofthe survey questions related to the discussion in this section are found in Surveys 2, 3, and 4. In these cases, only Irvine participants provide before and after retrofit data. Consequently, only Irvine data are presented in many instances. Some participants moved in the middle of the study and, consequently, longitudinal data exist both for the original participants (i.e., Surveys 1-3) and for the home's new residents (i.e., Surveys 3-5). As a result, data will also sometimes be presented with more observations than retrofitted houses. This section describes the longitudinal changes in customers' image of electricity and gas and in their preferences in space heating, water heating, and cooking, respectively. The section concludes with analysis of the effect of customers' discount rates on their perceptions of conservation and efficiency as investment opportunities.

7-1

### FUEL NPEIMAGE REUC

### participantswere askedto rateon ascale of one to seven which of apair of antonyms best describes electricity and gas. Substan:ial differences appear between images of electricity and gas; the means are plotted along the scales in Figure 7-1. 

### Here, the "hour-glass" shape of the electricitylgas graphs inthis chart illustrate the different images these fuels project to utility customers. For example, participants on average considered electricity a "cleaner" and "safer" fuel, andthey perceivedgas asa lessexpensive fuel. Figure7-1 

### REUC Participant lmazesof 

### Electricvs. Gas Dirty Clean 

# - Dangerous Safe
Hard to Hookup Easyto Hookup
Inconvenient Convenient PoorService Good Service from Utility from Utility Unreliable Reliable Sluggish Heat Fast Heat-- HighCost LowCost Appliances Appliances Expensive Inexpensive HighMonthly LowMonthly UtilityBills Utility BillsL 

### k 4 g 

### 3 

### 0.



# 2 2 L

Electricity Gas

Image Image 

### FUEL TYPE IMAGE REUC participants were asked to rate on ascale ofone to seven which ofapair of antonyms best describes electricity and gas. Substantial differenj:es appear between images ofelectricity and gas; the means are plotted along the scales in Figure 7-1. Here, the "hour-glass" shape ofthe electricity/gas graphs in this chart illustrate the different images these fuels project to utility customers. For example, participants on average considered electricity a "!:leaner" and "safer" fuel, and they perceived gas asa less expensivefuel.

Dirty Dangerous
Hard to HooKup
Inconvenient

- Poor Service

from Utility Unreliable Sluggish Heat High Cost Appliances Expensive High Monthly Utility Bills 

### Figure 7-1 REUC Participant Images ofElectric vs. Gas 

# I 

# , ir 

# I.

# ~**~k** i**~** #ii

## ff 

# J Clean
Safe Easy to Hookup :..**~** 

# - Convenient 

## .... 

## :§ Good Service 

### 0'-

from Utility
Reliable

# ~,~/ 

# "/ 11 11**t~ ~**  

# / \ '

- 

## ¥ 

# , \ 

# , Fast Heat Low Cost Appliances 

### ..... :::J



# .... Inexpensive

### ~ La:" Monthly

Utility Bills Electricity Gas

Image Image

7-2-

### research analysis also includedfactor analysis todetermine how of gas and electricity alongvarious specific attributes(e.g.,, safety) can be reduced to a 

### more 

## . .

### parsimonious set of "factors." Before the REUC project's analyses, a brief overview of factor ispresented.. 

## L.. :Factoranalysis isa statisticaltechnique for analyzingthe interdependence among I /a

### set of observed variables.' Factor analysis is 

### based on the assumption that an iunobserved, reducedset of dimensions (or factors) captures most of the process ! 

### (thatgenerates the larger set of observed variables. Inthe case of a 

### data set ]consistingof responsesto asurvey, thedimensionsare assumedto bethe samefor :all respondents, but each respondent will 

### be positioned differently along the ,dimensions. Forexample, it 

### may be desirableto analyze a sample of responsesto questions that required individuals to rankthe quality of a 

### product based on its performance across various attributes. Factor analysis is

### applied to reducethe extensive list of attributes intoasmaller numberof composite attributes that still largely preserves the overall image each respondent has of the quality of the product. Ingeneral, the objective is

### to identify a smaller, manageable set of factors that captures a significant percentage of the information contained in the larger set of survey responses. The method of factor analysis consists of identifyingfactors that capture a 

### large percentage of this data set's variability. Eachfactor produced is

### a lineartransformation inwhich each variable is

### weighted by a value called the "factor loading." Thefactor loadingis

### the correlation betweenthe variable and thefactor. In order. to form interpretable factors, the researcher transforms or "rotates" the factor loadingsso that each variable significantly loads onto only onefactor. Variables that load significantlyonto afactor, i.e., have a loading

### > 0.5, 

### are consideredto be that subset of the original data set "captured" by that factor. The researcher 'For more details about factor analysis, see "Market Segmentation Research, Volumes 1 and 2," 

### preparedfor the Electric Power Research Institute by DipankarChakravarti, Philip.E. 

### Hendrix, and William L. 

### Wilkie, EPRl Research Proj'ect 1537, 1987.**'~Ctor** Anal sis 

### ,- "e REUC market research analysis also includedfactor analysis to determine how**~krticipants'** 

### assessment ofgas and electricity**al~ng ~arious**  

### specific attributes (e.g., '**~ost,** 

### safety) can be reduced to a more parsimonious set of "factors.", Before

## ~'isCussing the results of the REUC project's analyses, a brief overview offactor**r~nalysis** 

### ispresented. ' ,**~"r** • • 

### [Factor analysis is a statistical techniquefor analyzing the interdependence among 

# i a set ofobserved variables. 1 

### Factor analysis is based on the assumption that an iunobserved, reduced 

### set of dimensions (or factors) capfures most of the process 

### , !that generates the larger set of ob'served variables. In the case of a data set Iconsisting ofresponses to a survey, the dimensions are assumed to be the same for ;all respondents, but each respondent will be positioned differently along the ,dimensions. For example, it may be desirable to analyze a sample of responses to' questions thatrequired individuals to rank the quality,ofa product based on its performance across various attributes. Factor analysis is applied to reduce the extensive list of attributes into a smaller number ofcomposite attributes that still largely preserves the overall image each respondent has ofthe quality of the product. In general, the objective is to identify a smalier, manageable set offactors that captures a significant percentage of the information contained in the larger set of survey responses. The method offactor analysis consists of identifying factors that capture a large percentage ofthis data set's variability. Each factor produced is a linear transformation in which each variable is weighted by a value called the "factor loading." Thefactor loading is the correlation between the variable and the factor. In order. to form interpretable factors, the researcher transforms or "rotates" the factor loadings so that each variable significantly loads onto only one factor. Variables that load significantly onto afactor, i.e., have a loading> 0.5, are consideredto be that subset of the original data set "captu'red" by that factor. The researcher

1

### For more details about factor analysis, see "Market Segmentation Research, Volumes 1 and 2," prepared for the Electric Power Research Institute by Dipankar Chakravarti, Philip. E. Hendrix, and William L. 

### Wilkie, EPRI Research Project 1537,1987. 'applies a qualitative assessment of the commonality of this subset of variables in order to "name"the factor.

: . ~.

~ . ..~.
. i Rotation methodsare essential in creating interpretable or "nameable" factors. 

### A rotation 

### is a linear transformation of the matrix of factor loadings. Orthogonal rotations do not change the total explanatory power of the original factors.2 Rotation methods could be thought of as "moving" information from one factor intoanother inorderthat each factor has a particular identity. The application of a factor analysis often follows a two-stage process. The first stage consists of performingfactor analysis in order to determine a manageable setoffactors as describedabove. The secondstage involves applyingthe factorsto classify respondents into groups which are "meaningful." "Meaningful"groups in the context of market research tend. to be customer segments that respond to attributesofa particularclassof products ina similarway. For example, inthe case of automobiles, factor analysis may reveal that a car has two basic composite attributes, economy and performance. Some respondents mayvalue economy highly, resultingina high factor scorefor these respondents alongthat dimension. Other respondents may value performance and have correspondingly highfactor scores alongthat dimension. Next, these factors would be used to classify these respondents into "economy-minded"and "performanceoriented" groups. Itwould then be important to determine if infact these groups conform to behavior that is consistentwith their classification. That is, whether the "economy-minded" group would be observed to purchase economical smaller cars (e.g., Ford Escort), while the "performance-oriented" group would tendtopurchase high performancecars (e.g., Porsche 

### 91

### 1 ).

20ne of the most commonly used rotation methods is the Varimax rotation. This isthe rotationused inthe REUCanalysis. applies a qualitative assessment of the commonality of this subset ofvariables in order to "name" the factor.

,

## , Rotation methods are essential in creating interpretable or "nameable" factors. A rotation is a linear tr<:tnsformation of the matrix of factor loadings. Orthogonal rotations do not change the total explanatory power of the original factors.2 Rotation methods could be thought of as "moving" information from one factor into another in order that each factor has a particular identity. The application of a factor analysis often follows a two-stage process. The first stage consists of performing factor analysis in order to determine a manageable

, set offactors as described above. The second stage involves applying the factors to

classify respondents into groups which are "meaningful." "Meaningful" groups in the context of market research tend, to be customer segments that respond to attributes ofa particular class ofproducts in asimilar way. For example, in the case ofautomobiles, factor analysis may reveal that a car has two basic composite attributes, economy and performance. Some respon'dents may value economy highly, resulting in a high factor score for these respondents along that dimension. Other respondents may value performance and have correspondingly high factor scores along that dimension. Next, these factors would be used to classify these respondents into "economy-minded" and "performanceoriented" groups. It would then be important to determine if in fact these groups conform to behavior that is consistent with their classification. That is, whether the "economy-minded" group would be observed to purchase economical smaller cars (e.g., Ford Escort), while the "performance-oriented" group would tend to purchase high performance cars (e.g., Porsche 911).

20ne of the most commonly used rotation methods is the Varimax rotation. This is the rotation used in the REUC analysis.

# Gelimage Results Inthe REUC project, for each fuel type, factor analysis was used to identify the betweenseveral variables. Table 7-1 

### repo'rts the factor loadings of the two significant factors derived from the factor analysis of each fuel. significant factor loadings are asterisked. Eachcolumn inthis tabkreportsthe factor loading of the survey question item onto the factor named inthe column heading. That is, 

### the resultsof performing factor analysis of the REUC

### participants' ratings of their image of electricity, based on various qualit'ies, resulted inthe derivationof a "Quality" factor which is

### a composite of the Safety, Convenience,

, , 

### service, and Reliability survey items. The' other electricity factor is 

### "Value," a compositeof the ratings alongthe Appliance Cost, General Expense, and Monthly Utility Bill qualities. Each fuel has a Quality and Value dimension which combined capture about 45 

### percent of the variation inthe responses to the ten survey items for fuel. Inthe case of gas, the "Value" and "Quality" factors do not work out as well. Safety loads onto the "Value" factor while one of the cost attributes, level of monthlybills, loadsonto "Quality." Nevertheless, the other key attributes load ontothe appropriate factor. The Quality and Valuefactor scores are "normalized"so that their distribution is 

### described bythe standard normal distribution. Thisdistribution is

### symmetricwith a 

### standard deviation of 1.0, 

### and approximately two-thirds of its weight is

### within onestandarddeviation of the mean. Forexample, a Qualityfactor score of 0.0 is at

### the fiftieth percentile; i.e., exactly one-half of the sample has a lower Quality factor score: A 

### Gas Quality factor score of -1.0 is

### one standard deviation below the mean score, indicatingthat this participant gave lower ratings for gas on the quality attributes'of cleanliness, service and reliability relative to other REUC participants. Section 8 

### uses these factor scores to segment customers for the purposeof explainingenergy usage. 

### fuel image Results 

### In the REUC project, for each fuel type, factor analysis was used to identify the interdependence between several variables. Table 7-1 repo'rts the factor loadings of the two significant factors derived from the factor analysis of each fuel. Significant factor loadings are asterisked. Each column in this tab"le'reports the factor loading ofthe survey question item onto the factor named in the column heading. Thatis, the results ofperformingfactor analysis ofthe REUC participants' ratings of their image of electricity, based on various qualiti'es, resulted in the derivation ofa "Quality" 

### factor which is a composite ofthe Safety, Convenience, Service, and Reliability survey items. The' other electricity factor is "Value," 

### a composite ofthe ratings along the Appliance Cost, General Expense, and Monthly Utility Bill qualities. Each fuel has a Quality 

### and Value 

### dimension which combined capture about 4S percent of the variation in the responses to the ten survey items for fuel. In the case ofgas, the "Value" 

### and "Quality" 

### factors do not

, work out as well. Safety loads onto the "Value" 

### factor while one of the cost

attributes, level ofmonthly bills, loads onto "Quality." 

### Nevertheless, the otherkey attributes load onto the appropriate factor. The Quality

### and Value 

### factor scores are "normalized" so that their distribution is described by the standard normal distribution. This distribution is symmetriC with a standard deviation of 1.0, and approximately two-thirds of its weight is within one standard deviation ofthe mean. For example, a Quality

### factor score of0.0 is at the fiftieth percentile; i.e., exactly one-half of the sample has a lower Quality



### , factor score.' AGas Quality

### factor score of-1.0 is one standard deviation below

the, mean score, indicating that this participant gave lower ratings for gas on the quality attributes of cleanliness, service and reliability relative to other REUC participants. Section 8 uses these factor scores to segment customers for the purpose ofexplaining energy usage.



## 1-5Table 7-1 Factor Loadin~s Based onAnalvsis of FuelTvpe lmageOuestiona

Fuel Oualities Electricity Image Gas Image Quality Value Quality Value 

# Factor Factor Factor Factor

Hard to hookup1Easyto hookup 

### .37 .12 

### .I8 .67*
Poorservicefrom utility1 

### .76* 

### -.I 5 .75* .04 Goodservicefrom utility Sluggish heat/Fast heat 

### .10 .49 .76* .002 Highcost appliances1 

### .02 .56* .03 .59* Low cost appliances High monthly utility bills1 

### .13 .78* .64* .17 Lowmonthly utility bills %Total Variance Explained 

### 43% 46% aseparatefactor analyseswere applied to eachfuel type. In both cases, two significantfactors were found, Quality and Value, These results are based on Survey 53. *Significantfactor loadingsat the 

### 95 percent level.

SPACE HEATING Space heating appliances, viewed as a product, are interesting because they .provide a servicevalued by allcustomers and are often perceivedas a necessity. Three questions are explored in this subsection. First, how did the retrofit fuel type affect REUC participants' fuel type preferences? Second, how didfuel type Table 7-1 Factor Loadings Based on Analysis of Fuel Type Image Questiona Electricity Image Gas Image 

### Quality Value Qualify Value Fuel Qualities Factor Factor Factor Factor Dirty/Clean .54' -.27 .57" .32 Dangerous/Safe .52- -.01 .12 .76- Hard to hookup/Easy to hookup .37 .12 .18 .67* Inconvenient/Convenient .62* .26 .25 .18 Poor service from utility/ .76* -.15 .75* .04 Good service from utility Unreliable/Reliable .81 * 

### .13 .87* -.06 Sluggish heat/Fast heat .10 .49 .76* .002 High cost appliances/ Low cost appliances .02 .56* .03 .59* Expensive/Inexpensive -.21 .76* .01 .59* High monthly utility bills/ lowmonthly utilitybills 

### .13 .78' .64* .17

% Total Variance Explained 43% 46% aSeparate factor analyses were applied to each fuel type. In both cases, two significant factors were found, 

### Qualjtyand 

### Value. These results are based on Survey S3. 'Significant factor loadings at the 95 percent level.

SPACE HEATING Space heating appliances, viewed as a product, are interesting because they

.provide a service valued by all .customers and are often perceived as a necessity. Three questions are explored in this subsection. First, how did the retrofit fuel type affect REUC participants' fuel type preferences? Second, how did fuel type&&ct customers' perceptionsof their new appliances? And, third, howdidthese i'berceptiorisaffect the importanceto custorndrs of various characteristics

### of space i heatingappliances?
,.., *+ Tables 7-2a and 7-2b summarizethe effect upon participants' preferences for fuel type of retrofittingthemwith eithergas or heat pumpspace heatingappliances. Table 7-2a Lonzitudinal Chan~es inElectricSamole Participants' FuelType Preferencesfor Space Heatina Systems 

### First Survey After Retrofit Before Retrofit Prefer Gas Prefer Electricitv Ambivalent 

## Total PreferGas 

### 5 1 7 13 Prefer Electricity 1 4 3 8 Ambivalent 6 2 8 16 Total 12 7 18 37 Second Survey Afier Retrofit First Survey After Retrofit PreferGas Fi+fer Electricitv Ambivalent 

## Total PreferGas 7 0 5 12 Prefer Electricity 0 4 3 7 Ambivalent 2 2 14 18 Total 9 6 

### 22 37 Third Survey After Retrofit (MissionViejo only)a Second Survey After Retrofit PreferGas Prefer Electricitv Ambivalent 

## Total Prefer Gas 5 0 0 

### 5. Prefer Electricity 0 3 0 3 Ambivalent 

### 3 1 7 11 Total 8 4 7 19 aOnly MissionViejo participantswere surveyed a thirdtimeafter receivingtheir new appliances. Consequently, the third survey 

### isbasedon amuch smaller sample than the firsttwo surveys, above.**~ffect** customers' perceptions of their new appliances? And, third, howdid these ;perceptiOriS affect the importance to customers ofvarious characteristics of space 'heating appliances? Tables 7-2a and 7-2b summarize the effect upon participants' preferences for fuel type ofretrofitting them with eithergas or heat pump space heating appliances. Table 7-2a longitudinal Changes in Electric Samllie Particillants' Fuel T),lle Preferencesfor Sllace Heating S),stems
. First Survey After Retrofit Before Retrofit Prefer Gas· Prefer Electricity Ambivalent Total Prefer Gas 5 1 7 13 Prefer Electricity 1 4 3 8 Ambivalent 6 2 8 16 Total 12 7 18 37 Second Survey After Retrofit FirstSurvey After Retrofit Prefer Gas "PrHer Electricity Ambivalent Total Prefer Gas 7 

### 0 5 12 Prefer Electricity 

### 0 4 3 7 Ambivalent 2 2 14 18 Total 9 6 

### 22 37 Third Survey After Retrofit (Mission Viejo only)a Second Survey After Retrofit Prefer Gas Prefer Electricity Ambivalent Total PreferGas 5 0 0 5 Prefer Electricity 0 3 0 

### 3 Ambivalent 3 7 11 Total 8 4 7 19 aOnly Mission Viejo participants were surveyed a third time after receiving their new appliances. Consequently, the third survey is based on a much smaller sample than the first two surveys, above.

7-7Table 7-2b LongitudinalChanges in

# as Sample Participants' FuelTvoe Preferencesfor Space Heating Systems
FirstSurveyAfter Retrofit
Before Retrofit Prefer Gas Prefer Electricitv Ambivalent 

## Total PreferGas 15 

### 1 2 18 Prefer Electricity 0 1 0, 1 Ambivalent 13 1 7 2

### 1 Total 28 

### 3 9 40 Second Survey After 

### Retrofits First Survey After Retrofit Prefer Gas Prefer Electricitv Ambivalent 

## Total Prefer Gas 2 0 1 7 28 Prefer Electricity 2 0 2 4 Ambivalent 5 0 4 9 Total 27 1 13 41 Third Survey After Retrofit (MissionViejo only)b Second Survey After Retrofit Prefer Gas Prefer Electricitv Ambivalent 

## Total Prefer Gas 3 1 2 6 Prefer Electricity 1 0 0 1 Ambivalent 2 1 3 6 Total 6 2 

### 5 13 aThe second survey containsone moresurvey responsethan thefirst survey, above. Thus, the number of participantswho strqngly preferredelectricity isone higher. MissionViejo participantswere surveyed a third time after receivingtheir new appliances. Consequently, the third survey is basedon amuchsmaller sample than the first two surveys, above. 

### Table 7-2b longitudinal Changes in Gas Sample Participants' Fuel Type Preferencesfor Space Heating Systems ., First Survey After Retrofit
Before Retrofit Prefer Gas Prefer Electricity Ambivalent Total Prefer Gas 15 1 2 18 

### Prefer Electricity 0 0 

### Ambivalent 13 

### 7 21 

### Total 28 3 9 40 

### Second Survey After Retrofita FirstSurvey After Retrofit Prefer Gas Prefer Electricity Ambivalent Total Prefer Gas 20 1 7 28 

### Prefer Electricity 2 0 2 4 

### Ambivalent 5 0 4 9 

### Total 27 1 13 41 

### Third Survey After Retrofit (Mission Viejo only)b Second Survey After Retrofit Prefer Gas Prefer Electricity Ambivalent Total Prefer Gas 3 1 2 6 

### Prefer Electricity . 1 0 0 1 

### Ambivalent 2 1 

### 3 6 

### Total 6 

### 2 5 

### 13 

### aThe second survey contains one more survey response than thefirst survey, above. Thus, the number ofparticipants who strongly preferred electricity is one higher. bOn!y Mission Viejo

### participa~ts were surveyed athird time after receiving their· new appliances. Consequently, the third survey is based on a much smaller sample than the first two surveys, above.

7-8(he Electric Sample (Table 7-2a), seven of the 13 participants who originally istronglypreferred gas became ambivalent immediately after retrofit: Over the lentiretime period,3 a strong preference for

. gas
, .-... dropped

.. by 31 percent and ambivalence rose by 38 percent. By comparison, two-thirds of those who were 

### ' briginallyambivalent came to strongly prefer gas immediately after retrofit in the Gas Sample (Table 7-2b). After another year, a strong preference for gas had increased by 50 percent over its original figure and the number of ambivalent prticipants had dripped by 38 per;ent. Thus, participants who were retrofitted with heat pumps cameto prefer gas less, but did not necessarily prefer electricity more. Those retrofittedwith gas systems preferredgas even more strongly than at first. In another set of questions, participants were asked to compare their new space heaters to their old space heaters on five attributes. Figure 7-2 summarizesthese results. Gas Sample customers agreed that their new systems heated more quickly, heated moreevenly, were less noisy, andturnedonand off lessfrequently than their old heaters. Conversely, Electric Sample customers agreed that their new heat pumps heated less quickly, were more noisy, and turned on and off morefrequently than their old heaters. Not surprisingly, the type of fuel with which participantswere retrofitted affected the importancethey gave to various attributes of a heating system. Figures 7-3a and 7-3b summarize these effects as reflected in participants' longitudinal responses. The Electric Sample (Figure 7-3a) did not believe fuel type to be importantbefore retrofit (Survey2). After retrofitwith heat pumps, however, they found it to be increasingly important (Surveys 3 and 4). In contrast, the Gas Sample (Figure 7-3b) accorded somewhat less importance to fuel type after retrofit.

3Seedata collection timeline in Figure 3-1. 

## !In the Electric Sample (Table 7-2a), seven ofthe 13 participants who originally tstrongly preferred gas became ambivalent immediately after retrofit. Over the :entire time period,3 a strong preference for gas_.df()pped by 31 percent and ambivalence rose by 38 percent. By comparison, two-thirds ofthose who were 'originally ambivalent came to strongly prefer gas immediately after retrofit in the Gas Sample (Table 7-2b). After another year, a strong preference for gas had increased by 50 percent over its original figure and the number of ambivalent participants had dropped by 38

### per~ent. Thus, participants who were retrofitted with heat pumps came to prefergas less, but did not necessarily prefer !electricity more. Those retrofitted with gas systems preferred gas even more strongly than at 

### first. In another set of questions, participants were asked to compare their new space heaters to their old space heaters on five attributes. Figure 7-2 summarizes these results. Gas Sample customers agreed that their new systems heated more quickly, heated more evenly, were less noisy, and turned on and offless frequently' than their old heaters. Conversely, Electric Sample customers agreed that their new heat pumps heated less quickly, were more noisy, and turned on and off more frequently than their old heaters. Not surprisingly, the type offuel with which participants were retrofitted affected the importance they gave to various attributes ofa heating system. Figures 7-3a and 7-3b summarize these effects as reflected in participants' longitudinal responses. The Electric Sample (Figure 7-3a) did not believe fuel type to be important before retrofit (Survey 2). After retrofit with heat pumps, however, they found it to be increasingly important (Surveys 3 and 4). In contrast, the Gas Sample (Figure 7-3b) accorded somewhat less importance to fuel type after retrofit.

3See data collection timeline in Figure 3·1.
7-9Figure 7-2 

### REUC Participants' Comoarisons betweenOldand New Space 

## eaters bv FuelTvoe

Other attributes were also affected. Operating costs and monthly utility bills, although still very important, were seen as less important over time in both samples. On the other hand, only response time became less important for Gas Sample participants. Similarly, accuracy of heat control became much more important to Electric Sample participantswhile remainingstable for those in the Gas Sample. 

# - Slmngly 

# , Disagree
6

# -

/ -4 4

# - --. 

# -__---
3

# -

2

# - Strongly Heat Pump
Agree 

### , Heats Produces Makes TurnsOn/Off Heats Less Less Less Less More Quickly Odor Noise Frequently Evenly 

### figure 7-2 REUC Participants' Comparisons between Old and New Space Heaters by fuel Type
Strongly 7 Disagree
6- 

### " 

# , ...... 5 - 

# , ... 

# ,
...... 

# , ... 

# , ........... 

# , , 

# ...... , ...... 4 - 

# ..._----J

3

2 Gas Furnace 

### ---_. Heat Pump Strongly 1 Agree • Heats Produces Makes Turns On/Off Heats Less Less Less Less More Quickly Odor Noise Frequently Evenly



### Other attributes were also affected. Operating costs and monthly utility bills, although still very important,. were seen as less important over time in both samples. On the other hand, only response time became less important for Gas Sample participants. Similarly, accuracy of heat control became much more important to Electric Sample participantswhile remaining stable for those in the Gas Sample.

7-10

### ants were also asked to indicate their willingness to recommendtheir new heaterto afriend. ASshown inTable 7-3, 80 

### percent of the Gas Sample &pants said they would recommendtheir heating system, and 75.6 

### percent


### .. 'they would recommend it strongly. Not a single participant declined to + 

### mmend their gas heating system, and over time participants became less ivalent and more likely to recommendtheir gas heatingsystems. ApproxiFigure 7-3a 

### Importanceof HeatingSystem 

### Attributes bv FuelTvoe 

### ElectricSample Very Unimportant 4 - =7

3

# -

2

# -

Very 

# -


# .



# ..

# . '.



# . .



# .

.

... '

# . .

.

. ..*. 

# .

. 

# .

.. 

# .

.. 

# . . . 

# .

'... 

# . .
. 

# .

-.

-.

-.
..-.-._._. -.-.-.-.-._._._ 

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

Important 1

# - , Survey 2 Survey3 Survey4

ResponseTime AccuracyofTemperature Control 

# ----- Typeof Fuel 

### cip,lnts were also asked to indicate their willingness to recommend their new

heater to 

### afriend. As shown in Table 7-3, 80 percent ofthe Gas Sample cip,mts said they would recommend their heating system, and 75.6 percent

.they would recommend it strongly. Not a single participant declined to .'

. their gas heating system, and over time participants became less and more likely to recommend their gas heating systems. ApproxiFigure 7-3a Importance of Heating System Attributes by Fuel Type Electric Sample Very Unimportant 4 =7 

### , 

### , ,

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

### , 

### , '" " 

## ,,," 3 '., 

## , ". 

### ""

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

### "

## ,,
............. 

### "...

### ...


### ...........'.**~.-.-.-.-.-.-.-.-.-.-.-** 2**-----~-------------------------** ---.



# - Very 1 Important 

### , , , Survey 2 Survey3 Survey 4
OperatingCosts/Monthly Bills

..------. ResponseTime

.............. Accuracy ofTemperatureControl



### ---- ... Type ofFuel

7-11Figure7-3b Importance 

### of Heatiner SystemAttributes bv Fuel Tv~e Gas Sam~le Very Unimportant 4

# - =7

3

# -

2

# -

Very Important 1 

## , 

# .
,



## ,

' 

# . . 4' 

# ----__ 

# . ,' -----4 

## -.-.-._. 

# -.-._._ 

# .-.

-.-.._ ._.- 

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

I Survey2 Survey3 Survey 4

ResponseTime Accuracy ofTemperatureControl 

### Figure 7-3b Importance of Heating System Attributes by Fuel Type Gas Sample " Very Unimportant 4,----------------------, =7

Very Important 3

2 / 

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

# ...-



# ----- '" 

### ------...". 

# .' .' 

## .'

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

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

### -_..- 

# .'..'

# .'.' ."

..'


## ,L-----.-----------r-----------.----J Survey 2 Survey 3 Survey 4 OperatingCosts/Monthly Bills Response Time Accuracy ofTemperature Control TypeofFuel

7-12Table 7-3 participants1 Willingness to RecommendTheir Space 

### heat in^ System 

### - TOa Friend

# - By FuelTvpe No. of Participants(%) ,Recommend Don't Recommend

Very Very =am& Strongly Weaklv Weaklv Stronnlv

?mately40 percentof the Electric Sample participants respondedthat they would 

### i" 'recommendtheir heating system, with the number of respondents who would :.; istrongly recommend it increasing over time from 21.7 percent to 30.8 percent. 

## i. .However, the number of respondents who'declined to recommend their heat i, pump heatingsystem also increasedfrom 19.6.percentto 33.3 percent. Three findings can be summarized from this analysis. First, as customers gain experience with more than one fuel type, fuel type increases in importance. Second, gas systems are more likely to have proponentsthan heat pump systems. It appears that the causes of the weaker reception of heat pumps in this case were the quality problems that the Electric Sample participants perceived. Third, experience wit'h an appliance is required in order to make strong recommendations, as demonstrated by the consistently higher number of respondentsexpressingopinions inSurvey 4. Table 7·3 

# i!!Jc'Particillants' Willingness to Recommend Their Sllace Heating System
- . To a Friend
- By Fuel TYlle .' No. of Participants ("!o) .Recommend Don't Recommend

Very Very rm'(Sampie Strongly Weakly Unsure Weakly Strongly

.**-·T~~,**

## 1Il~)3 34

. 2 9 0 0 (75.6) (4.4) (20.0) (0.0)

. (0.0)
fF1J4 .22 8 4 0 0 (64.7) (23.5) (11.8) (0.0) (0.0)

..**,;~**
• j£Teciric Sample lfR.rvey 3 10 8 19 1 8

";46)

. (21.7) (17.4) (41.3) (2.2) (17.4) lsurvey 4 12 5 9 3 

### TO [(nb:39) (30.8) (12.8) (23.1 ) (7.7) (25.6)
[mately 40 percent of the Electric Sample participants responded that they would**~** . ;recommend their heating system, with the number of respondents who would**~** . . [strongly recommend it increasing over time from 21.7 percent to 30.8 percent. I_However, the number of respondents who" declined to recommend their heat 

# . '
. p\Jmp heating system also increased from 19.6·percent to 33.3 percent. Three findings can be summarized from this analysis. First, as customers gain experience with more than one fuel tYPe, fuel type increases in importance. Second, gas systems are more likely to have proponents than heat pump systems. It appears that the causes of the weaker reception of heat pumps in this case were the quality proplems that the Electric Sample participants perceived. Third, experience with an appliance is required in order to make strong recommendations, as demonstrated by the consistently higher number of respondents expressing opinions in Survey 4.

7-13WATER HEATING This section examines customers' reactionsto their new water heaters. As with the space heaters, the REUC participants were retrofitted with either new heat pump or gas water heaters. The results from 

### this section indicate that the heat pump water heaters were not received well by most customers. As with the space heaters, their poor reception was attributable not only to their cost performancebut alsoto their inabilityto provide quality service. Figure 7-4 summarizes participants1comparisons of their new water heaters with their old water heaters. The Gas Sample participants agreed that their new gas water heaters heated water more quickly and cost less to operate. In contrast, the Electric Sample participantsgave lower ratings for their new heat pumpwater ' heaters on both of these quality attributes. Figure

### 7-4 REUC Participants' Comparisons betweenOldand New .water Heaters 

### bv FuelTvpe Strongly 

# , - Disagree
6

# -

5

# -

' 4-

3

# -

2

# - Strongly 

# , ----- HeatPumpWater Heater


# -_------------__ 

# -----___

Agree 4 I RunsOut Heats Costs ofWater Water More LessTo

Slower Quickly Operate 

# - WATER HEATING 

### This section examines customers' reactions to their new water heaters. As with the space heaters, the REUC participants were retrofitted with either new heat pump or gas water heaters. The results from this section indicate that the heat pump water heaters were noi received well by most customers. As with the space'heaters, their poor reception was attributable not only to their cost performance but also to their inability to provide quality service. Figure 7-4 

### summarizes participants' comparisons oftheir new water heaters with their old water heaters. The Gas Sample participants agreed that their new gas water heaters heated water more quickly and cost less to operate. In contrast, the Electric Sample participants gave lower ratings Tor their new heat pump water ; heaters on both ofthese quality attributes. Figure 7-4 REUC Participants' Comparisons between Old and New

. Water Heaters by Fuel Type Strongly 7 Disagree GasWater Heater 6 - 

### ----- Heat PumpWater Heater
5



# ------------------------ 4

3

2 Strongly 1

Agree Runs Out Heats Costs ofWater WaterMore Less To

Slower Quickly Operate

7-14Tables 7-4a and 7-4b detail longitudinal changes in participant fuel type prefer- :encesfor water heaters. Immediately after being retrofitted, ambivalence toward /fueltype doubledfrom 10to 20 among Electric Sample participants(~ablk.7-4a), 

### ..

. .. I /whilethe number strongly preferring electricity dropped by over one-fdurth and

### ' 'the numberstrongly preferringgas plummeted by one-half. within the nextyear, !participants1 ambivalence remained fairly stable, the number of participants .stronglypreferring electricity dropped yet further, and the number of participants. ' 'strongly preferringgas rose, but not back to its original level. Inthe Gas Sample I (Table 7-4b), the number of ambivalent participants dropped slightly, yet, over

time, a full 44 percent of the Gas Sample participants remained ambivalent. 'preferencesfor both gas and electricity rose slightly, but no significant changes .occurred. The ambivalence manifested in both the Gas and Electric Samples 

## , 'resulted in some participants (30percentto 45 percent)strongly recommending their heat pumps, while an equivalent number (40 percent,-to45 percent) strongly declined to recommendthem, as shown inTable 7-5. This section has shown that water heater quality attributes strongly affected fuel type preferences. Overall, Gas Sample customers were more satisfied with their new water heaters than Electric Sample customers; however, both groups were primarily ambivalent. There may have been some factors affecting the Electric Sample participants' reactions that should be mentioned. First, the heat pump recipients may have been unawareof any cost savings attributed to their water heaters, since they were probably unableto separate this cost out oftheir electric bills, which had increaied while their gas bills decreased. With regard to appliance quality, participantswere probably aware of such noticeable performance attributes of water heaters as the quickness of heating water; however, it might have been possible to sidestep these negative perceptionsof performancequality by using larger-capacity heat pump water heaters. Although participants may have been unaware.of the cost-effectiveness of their new electric heat pumps, they experienced their quality. It should also be pointed out that the heat pump water heaters installedfor this study were amongthe first commerciallyavailable, and recent quality improvements may have overcome many of the performance problems perceived by the Electric Sample. 

### ;Tables 1-4a 

### and 7-4b detail longitudinal changes in participant fuel type prefer- :ences for water heaters. Immediately after being retrofitted, ambivalence toward [fuel type doubled from 10 to 20 among Electric Sample participants (Table 7-4a), !while

### th~ number strongly preferring electricity dropped byover one-fourth and' the number strongly preferring gas plummeted by one-half. Within the next year, !participants' ambivalence remained fairly stable, the number of participants 'strongly preferring electricity dropped yet further, and the number ofparticipants' strongly preferring gas rose, but not back to its original level. In the Gas Sample I

. 

### (Table 7-4b), the number of ambivalent participants dropped slightly, yet, over ,time, a full 44 percent of the Gas Sample participants remained ambivalent. 'Preferences for both gas and electricity rose slightly, but no significant changes ·occurred. The ambivalence manifested in both the Gas and Electric Samples 

### , . 

### . 'resulted in some participants (30 percent to 4S percent) strongly recommending

.their heat pumps, while an equivalent number (40 percent.·to 45 percent)

strongly declined to recommend them, as shown in Table 7-5. This section has shown that water heater quality attributes strongly affected fuel type preferences. Overall, Gas Sample customers were more satisfied with their new water heaters than Electric Sample customers; however, both groups were primarily ambivalent. There may have been some factors affecting the Electric Sample participants' reactions that should be mentioned. First, the heat pump recipients m.ay have been unaware of any cost savings attributed to**t~eir** 

### water heaters, since they were probably unable to separate this cost out oftheir electric bills, which had increased while their gas bills decreased. With regard to appliance quality, participants were probably aware of such noticeable performance attributes of water heaters as the quickness of heating water; however, it might have been possible to sidestep these negative perceptions ofperformance quality by using larger-capacity heat pump water heaters. Although participants may have been unaware.of the cost-effectiveness of their new electric heat pumps, they experienced their quality. It should also be pointed out that the heat pump water heaters installed for this study were among the first commercially available, and recent quality improvements may have overcome many of the performance problems perceived by the Electric Sample.

7-15Table 7-4a Longitudinal Changes inElectric Sarnole Particioants' FuelTyoe Preferencesfor Water Heaters
FirstSurveyAfter Retrofit Before Retrofit Prefer Gas Prefer Electricitv Ambivalent 

## Total Prefer Gas 3 3. 6 12 Prefer Electricity 3 2 10 15 Ambivalent 0 6 4 10 I Total 6 11 20 37 Second SurveyAfter Retrofit FirstSurvey After Retrofit Prefer Gas Prefer Electricity Ambivalent 

## Total

. . PreferGas 4 0 2 6 Prefer Electricity 2 5 4 11 ~mbivalent 4 4 12 20 Total 10 9 18 37 Third Survey After Retrofit (MissionViejo on1y)a Second Survey After Retrofit Prefer Gas Prefer Electricitv Ambivalent 

## Total Prefer Gas 5 0 2 7 Prefer Electricity 0 3 1 4 Ambivalent 4 0 4 8 Total 9 3 7 19 aOnly MissionViejo participantswere surveyed athird time after receiving their new appliances. Consequently,the third survey 

### isbasedon a muchsmaller samplethan thefirst two, above. 

### Table 7-4a Longitudinal Changes in Electric Sample Participants' Fuel Type Preferencesfor Water Heaters
First Survey After Retrofit
Before Retrofit Prefer Gas Prefer Electricity Ambivalent Prefer Gas 3 3, 

### 6 

### Prefer Electricity 3 2 10 

### Ambivalent 0 6 4 

### Total 6 11 20 

### Second Survey After Retrofit FirstSurvey After Retrofit Prefer Gas Prefer Electricity Ambivalent Prefer Gas 4 0 2 

### Prefer Electricity 2 5 4



### . Ambivalent 4 4 12



### Total 10 9 18 

### Third Survey After Retrofit (Mission Viejo only)a Second Survey After Retrofit Prefer Gas Prefer Electricity Ambivalent Prefer Gas· 5 0 2 

### Prefer Electricity 0 3 

### Ambivalent 4 0 4 

### Total 9 3 7 

### Total 12 15 10 37



### Total 6 11 20 37



### Total 7 4 8 19 

### aOnly Mission Viejo participants were surveyed a third time after receiving their new appliances, Consequently, the third survey is based on a much smaller sample than the first two, above.

7-16Lon~itudinal Chances inGkSample Participants' FuelType

Preferencesfor Water Heaters + FirstSurvey'After Retrofit Before Retrofit PreferGas Prefer Electricity Ambivalent 

# Total Prefer Gas 11 1 4 16 Prefer Electricity 

### 1 0 0 1 Ambivalent 13 1 9 23 Total 

### 25 2 13 40 SecondSurvey After ~etrhfita First Survey After Retrofit PreferGas Prefer Electricitv Ambivalent 

## Total PreferGas 16 2 7 25 Prefer Electricity 0 0 3 3 Ambivalent 4 1 8 13 Total 

### 20 3 18 41 Third SurveyAfter Retrofit (MissionViejo only)b Second Survey After Retrofit Prefer Gas Prefer Electricitv Ambivalent 

## Total Prefer Gas 2 

### 1 3 6 Prefer Electricity 2 0 0 2 Ambivalent 3 1 

### 1 5 Total 7 2 4 .' 13 aSurvey contains one more responsethan thefirst survey, above; thus, the number of participants who strongly preferredelectricity is one higher. bOnly Mission Viejo participants were surveyed athird time after receiving their newappliances. Consequently, this survey 

### isbasedona muchsmaller sample than thefirst two, above. 

### Table 7-4b 

### Longitudinal Changes in

# Gas Sample Participants' Fuel Type Preferencesfor Water Heaters
First Survey After Retrofit Before Retrofit Prefer Gas Prefer Electricity Ambivalent Prefer Gas 11 1 4 

### Prefer Electricity 1 0 0 

### Ambivalent 13 1 9 

### Total 25 2 13. 

### Second Survey After Retrofita FirstSurvey After Retrofit Prefer Gas Prefer Electricity Ambivalent Prefer Gas 16 2 7 

### Prefer Electricity 0 0 3 

### Ambivalent A 1 8 

### Total 20 3 18 

### Third Survey After Retrofit {Mission Viejo only)b Second Survey After Retrofit Prefer Gas Prefer Electricity Ambivalent Prefer Gas 2 1 3 

### Prefer Electricity 2 0 0 

### Ambivalent 3 1 . 1 

### Total 7 2 4 

### aSurvey contains one more response than the first survey, above; 

### thus, the number ofparticipants who strongly preferred electricity is one higher. Total 16.

1 23 40



### Total

25

3 13 41



### Total 6 2 5 13



### bOnlyMission Viejo participants were surveyed a third time after receiving their newappliances. Consequently, this survey is based on a much smaller sample than the first two, above.Table 

### 7-5 

### REUC Particinants' WillingnesstoRecommendTheir Water Heater

-- 

### - - - To a Friend

# - BY FuelTv~e No. of Participants (%) Recommend Don't Recommend

Very Very Gas Samole Stron~lv 

## Weaklv Unsure 

## Weaklv Strongly Survey3 2 2 10 12 0 2 (n=46) (47.8) (21.7) (26.1) (0.0) (4.4) Survey4 2 1 4 5 4 1 (n=35) (60.0) (11.4) (14.3) 

### (11.4) (2.9): Electric Samole Survey3 12 2 13 1 18 (n=46) (26.1) (4.3) (28.31, (2.2) (39.2) Survey4 14 4 4 3 15 (n=40) (35.0) (1 0.0) 

### (10.0) (7.5) (37.5)
COOKING This section discusses customer reactions to the cooking appliances. The experimental groupfor this appliance 

### isdifferentfrom the other appliances. Here, only the 22 households in the Mission Viejo Electric Sample received new electric induction ranges. Customers retrofittedwith electric induction ranges were very pleasedwith their performance. Figure 7-5 shows that one-third of the Mission Viejo Electric Sample customers who originally strongly preferredgas rangesswitchedtostrongly preferringelectric ranges, allowing the number who preferredelectric ranges to quadruple. Before retrofit, over 75 percent of the participants strongly preferred gas and approximately 10 percent strongly strongly preferred electricity. However, immediately after retrofit, the number of participants preferring gas plummeted to approximately25 percent. At the same time, those strongly preferring electricity rose to approximately 25 percent. Thus, exposure to electric ranges resulted in strong changes infuel type preferencesfavoring electricity. Table 7-5 REUC Particillants' Willingness to Recommend Their Water Heater To a Friend By Fuel TYlle 

### .. "'-.:... No. of Participants (%) Recommend Don't Recommend

Very Very Gas Sample Strongly Weakly Unsure Weakly Strongly Survey 3 22 10 12 0 2 (n=46) (47.8) (21.7) (26.1 ) (0.0) (4.4) Survey 4 21 4 5 4 1 (n=35) (60.0) (11.4) (14.3) (11.4) (2.9)' Electric Sample Survey 3 12 2 13 1 18 (n=46) (26.1) (4.3) (28.3) (2.2) (39.2) Survey 4 14 4 4 3 15 (n=40) (35.0) (10.0) (10.0) (7.5) (37.5)
COOKING This section discusses customer reactions to the cooking appliances. The experimental group for this appliance is different from the other appliances. Here, only the 22 households in the Mission Viejo Electric Sample received new electric induction ranges. Customers retrofitted with electric induction ranges were'very pleased with their performance. Figure 7-5 shows that one-third of the Mission Viejo Electric Sample customers who originally strongly preferred gas ranges switched to strongly preferring electric ranges, allowing the number who preferred electric ranges to quadruple. Before retrofit, over 75 percent of the participants strongly preferred gas and approximately 10 percent strongly strongly preferred electricity. However, immediately after retrofit, the number of participants preferring gas plummeted to approximately 25 percent. At the same time, those strongly preferring electricity rose to approximately 25 percent. Thus, exposure to electric ranges resulted in strong changes in fuel type preferences favoring electricity.

7-18Figure 7-5 LoneitudinalChanze inElectric InductionRaneeSubsamole Particioants' FuelTvoe Preferencefor Cooking
% 100 Preferring



# -------. 80

# -

----------.-------------- 20 

# ,.-- 

# -- *.*' 

# . 0 I Before Fir3Survey SecondSurvey ThirdSuwey Retrofit After Retroffi After Retrofit After Retrofit Additionally, as shown inTable 7-6, Mission Viejo Electric Sample participants were likely to recommend their new ranges to friends. Over time, however, the number who would recommend the ranges decreased from 80 percent to 68.4 percent, and.the number who declined to recommend them increased from 10 percentto 21.1 percent. But the number of participantswho would recommend their range very strongly remainedstable at approximately60 percent. Table 7-7 compares participants"attitudes toward electric and gas ranges between the Mission Viejo Electric Sample (new electric induction ranges) and the lrvine Sample (no new ranges or new information).4 Significant differences
4This comparison 

### is not being used as a beforelaftertemporal comparison of one test group. Rather, it

### isa comparison betweenone "fully informedgroup and another group with less information. The lrvine sample contains 44 gas rangesand six conventional electric ranges. 

### Figure 7-5 longitudinal Change in Electric Induction Range Subsample Participants' fuel Type·Preferencefor Cooking
'Yo 100 Preferring StronglyPreferGas 

# --'""---

..**~** StronglyPreferElectricity 80

60•

40 •**----~~-----~--p--------.~** .' " 20

- ,.' " 

# ..

# - .-"
0 Before First Survey Second Survey Third Survey Retrofit AfterRetrofit

.AfterRetrofit After Retrofit 

### Additionally, as shown in Table 7-6, Mission Viejo Electric Sample participants were .likely to recommend their 

### new ranges to friends. 

### Over time, however, the number who would recommend the ranges decreased from 80 percent to 68.4 percent, and·the number who declined to

### recommend them increased from 10 percent to 21.1 percent. But the numberofparticipants who would recommend their range 

### very strongly remained stable atapproximately 60percent. Table 7-7 compares participants'·attitudes toward electric and gas ranges between the Mission Viejo Electric Sample (new electric induction ranges) and the Irvine Sample (no new ranges or new informationl.4 

### Signifrcant differences
4This comparison is not being used as a before/after temporal comparison ofone test group. Rather, it is a comparison between one "fully informed" group and another group with less information. The Irvine sample contains 44 

### gas ranges and six conventional electric ranges.

71floccurredfor onlytwo of the nine attributes. The lrvine Sample disagreed that gas ranges/ovens hadan unpleasant smell, whereas the Electric lnductionSample was unsure. Conversely, the Electric lnduction Subsample agreed that electric ranges - 

### -

- and ovens are safer, while the lrvineSamplewas unsure. In both samples, customers agreed that they do not know what itcoststo operate a rangeor an oven. This 

### isan indication that customers ingeneral are unawareof the cost efficiencies oftheir cookingappliances. It impliesthattheir valuationsof these appliances are driven by quality, notoperatingcost.

Table 7-6 Electric lnductionSubsamole Participants'Willinenessto Recommend Their RanveTo a Friend

No. of Participants(%) Recommend Don't Recomniend

Very Very Strongly 

## Weaklv Unsure 

## Weaklv Strongly Survey3 (n=20) Survey 4 (n=19) 

### occurred for only two ofthe nine attributes. The Irvine Sample disagreed that gas ranges/ovens had an unpleasant smell, whereas the Electric Induction Samplewas unsure. Conversely, the Electric Induction Subsample agreed that electric ranges and.ovensare safer, while the Irvine Sample was unsure. '
In both samples, customers agreed that they do not know what it coststo operate a range or an oven. This is an indication that customers in general are unaware of the c9stefficiencies oftheir cooking appliances. It implies thattheir valuations of these appliances are driven by quality, not operatingcost.

Table 7-6 Electric Induction Subsample Participants' Willingness to Recommend Their Range To a Friend

No. ofParticipants ('Yo) 

### Recommend Don't Recommend

Very Very Strongly Weakly Unsure Weakly Strongly Survey 3 12 4 

### 2 1 1 (n=20) (60.0) (20.0) (10.0) (5.0) (5.0) Survey 4 11 

### 2 2 1 3 (n=19) (57.9) (10.5) (10.5) (5.3) (15.8)



### 7-20Table 7-7 REUC Participants' Rangeloven Comparisons between FuelTvpes Means (standard errors) Electric 

### (1).

# .

# .. . ..

# ..

# ...

# .

# ..

# . ....

# .(7) lrvine Samole Induction Subsam~le strongly Strongly (n=50) 

### (n=2S) Agree Disagree Gas rangeloven has unpleasant 

### 5.19* (.25) 3.92 (.35) smell Electric rangeloven less expensive 

### 4.91 (.22) ' 

### 4.78 (.24) to operate Electric ovens cook more evenly 

### 4.30 (.29) 4.08 (.31) Electricovens heat upkitchen less 

### 4.28 (.25) 4.04 (.34) Electric rangelovensafer 

### 4.12* (.24) 

## . 2.88 (.36) Gas rangelovenlessexpensive 

### 3.77 (.21) 4.05 

### (.28) to buy Electric ovens easierto clean 

### 3.56 (.27) 3.09 (.33) No idea of cost to operate 

### 2.86 (.29) 3.42 (.42) Easier to control temperature on 

### 2.74 (.30) 

### 3.25 (.43) agas range *Difference 

### issignificant at at leastthe 

### 5% level.
APPLIANCE DISCOUNT FACTORS An important consideration inthe planning of a demand-side program or development of a new end-use technology is an assessment of the market participants' financial expectations from energy-related investments. The customer acceptance of a utility demand-side program may be disappointing because the rate at which individuals would like to be paid back for their conservation or efficiency investments is underestimated. The higher an individual customer's discount rate, or required annualized return, the shorter the requiredpayback periodfor a fixed investment. 

### Table 7-7 REUC Participants' Range/Oven Comparisons between Fuel Types Means (standard errors) Electric (1) ..•....•..•..•.......(7) Irvine Sam(:lle Induction Subsamf;lle Strongly Strongly (n=50) (n=25) Agree Disagree

> Gas range/oven has unpleasant 5.19* (.25) 3.92 (.35) smell Electric range/oven less expensive 4.91 (.22) 4.78 (.24) to operate Electric ovens cook more evenly 4.30 (.29) 4.08 (.31) Electric ovens heat up kitchen less 4.28 (.25) 4.04 (.34) Electric range/oven safer 4.12* (.24)

. 2.88 (.36) Gas range/oven less expensive 3.77 (.21) 4.05 (.28) to buy Electric ovens easier to clean 3.56 (.27) 3.09 (.33) No idea of cost to operate 2.86 1.29) 3.42 (.42) Easier to control temperature on 2.74 (.30) 3.25 (.43) agas range 'Difference is significant at at least the 5% level.


### APPLIANCE DISCOUNT FACTORS An important consideration in the planning of a demand-side program or development of a new end-use technology is an assessment of the market participants' financial expectations from energy-related investments. The customer acceptance of a utility demand-side program may be disappointing because the rate at which individuals would like to be paid back for their conservation or efficiency investments is underestimated. The higher an individual customer's discount rate, or reqUired annualized return, the shorter the required payback period for a fixed investment.


### 7-21The REUC project analyses have included the development and administrationof survey questions designed to calculate the participants' implied discount rates. When this economic information about the participants

### iscombinedwith analysis of the project's load data, results can be drawn regardingthe potential penetration of the REUC's end-use technologies such as high efficiency heat pumps. Thus, if the REUC project data analysis leads to the determination that space heating with such heat pumps is substantially more energy efficient than comparable gas space heating systems, calculation of the participants' discount ratesshouldprovidesome revealinganswers about how individuals maychoose to make such appliance efficiency investments. An individual's appliance choice is often modeled as a tradeoff between the initialcapital investmentand ongoing operatingcosts. The principle 

### isthat more energy-efficient appliances are typically based on more capital-intensive, and hence more expensive, technologies. Formally, Life Cycle Cost (LCC) models' of appliance choice compute for various appliance optionsthe discountedstream of capital and operatingcost expenditures. An important assumption inthese calculations is the choice of discount rate. An individual's required return on an investment increases as the uncertainty of the stream of cash flows, or savings, associated with the investment increases. Money inthe bank earning a guaranteed rate of return is afar less risky prospect than investing in higher efficiency appliances that may reduce monthly energy bills. Therefore, the discount rate applied to energy investments should be high enough to compensate for the uncertainty of the savings in monthly energy costs. Surveys 2 and 3 included several questions that permit estimation of respondents' discount rates.

5End-use forecasting models such as REEPS and COMMEND rely on LCCtype calculations for forecasting appliance choice. See, "Household Appliance Choice: Revisionof REEPS BehavioralModels," EPRl EA2512, February 1984; and "COMMEND Planning System: National and Regional Data and Analysis," prepared by Georgia Institute of Technology for.the Electric Power Research Institute, 1986. The REUC project analyses have included the development andadministration of survey questions designed to ca!culate the participants' implied discount rates. When this economic information about th.e participants is combined wiifl analysis of the project's load data, results can be drawn regarding the potential penetra-. tion of the REUC's end-use technologies such as high efficiency heat pumps. Thus, if the REUC project da.ta analysis leads to the determination that space heating with such heat pumps is substantially more energy efficient than comparable gas space heating systems, calculation of the participants' discount rates should provide some revealing answers about how individuals may choose to make such appliance efficiency investments. An individual's appliance choice is often modeled as a tradeoff between .the initial capital investment and ongoing operating costs. the principle is that more energy-efficient appliances are typically based on more capital-intensive, and hence more expensive, technologies. Formally, life Cycle Cost (LCc) models5 of appliance choice compute for various appliance options the discounted stream of capital and operating cost expenditures. An important assumption in these calculations is the choice of discount rate. An individual's required return on an investment increases as the uncertainty of the stream of cash flows, or savings, associated with the investment increases. Money in the bank earning a guaranteed rate of return is a far less risky prospect than investing in higher efficiency appliances that may reduce monthly energy bills. Therefore, the discount rate applied to energy investments should be high enough t?·compensate for the uncertainty of the savings in monthly energy costs. Surveys 2 and 3 included several questions that permit estimation of respondents' discount rates.

SEnd-use forecasting models such as REEPS and COMMEND rely on LCCtype calculations for forecasting appliance choice. See, "Household Appliance Choice: Revision of REEPS Behavioral Models," EPRI EA2512, February 1984; and "COMMEND Planning System: National and Regional Data and Analysis," prepared by Georgia Institute of Technology for'the Electric Power Research Institute, 1986.

.the majority of the 

### REUC sample, the relatively small, long-term required 

### m on a riskless investment is,estimated to be around the expected 9 to 10 ent, as illustratedby Figure7-6. Incontrast, Figure 7-7 demonstratesthat the ired.returnon an energy investment that yields an expected monthly savings


## . 0 over aten-year period ranged between 15and60 percent. The "Implied lscount Rate" calculated inthis table is the Internal Rate of Return of the initial gative cash flow.ofthe "Maximum Capital Investment"followed by 120 months 

### $10positivecashflows derivedfromenergysavings. Figure 7-6 

### i REUC Participants' Reauired Returnon a Small Riskless Investment

RequiredReturn Income: $50,001-$80,000 

### majority of the REUC sample, the relatively small, long-term required on a riskless investment is estimated to be around the expected 9 to 10

as illustrated by Figure 7-6. In contrast, Figure 7-7 demonstrates that the UII,:u.""urn on an energy investment that yields an expected monthly savings $10 over a ten-year period ranged between 15and 60 percent. The "Implied Rate" calculated in this table is the Internal Rate of Return ofthe initial cash flow ofthe "Maximum Capital Investment" followed by 120 months .'$10positive cash flows derivedfrom energy savings.

% 

### Figure 7-6 REUC Participants' Required Return on a Small Riskless Investment

of**50~------------------------------------~** Participants

40

30

20

10



### o 6%-8% Required Return 

# rn Income: >$80,000 I!III Income: $50,001-$80,000 

### II Income: $30,000-$50,000 11%-13%Figure7-7 REUC Participants' Rewired Returnonan Investment inAppliance Efficiency

"lo of Participants

15%-59% Required Return 

### & Income: $50,001-$80,000

At discount rates as high as 60 percent, expensive appliances must demonstrate large efficiencies before they can compete with cheaper alternatives. For example, ifthe household discounts cash savingsfrom energy investments atthe rate of 60 percent, then in order to equate the Net Present Value of real cash savings to the investment, the improved appliance must use 84 percent less energy than the standard appliance. Table 

### 7-8 illustrates an example that calculates the required efficiency of an appliance costing $500 more than the "standard" alternative. The efficiency iscalculated basedon assuming an annual real energy cost of $360 per year for operating the standard appliance. For % Figure 7-7 REUC Participants' Required Return on an Investment in Appliance Efficiency
of 

# 40,--------------------------------------, Participants

30

20

10



### o <15% 15%·59% Required Return**~** Income: >$80,000 

### III Income: $50,001·$80,000 

### III Income: $30,000·$50,000 >59%



### At discount rates as· high as 60 percent, expensive appliances must demonstrate large efficiencies before ihey can· compete with cheaper alternatives. For example, ifthe household discounts cash .savings from energy investments at the rate of 60 percent, then in order to equate the Net Present Value of real cash savings to the investment, the improved appliance must use 84 percent less energy than the standard appliance. Table 7-8 illustrates an example that calculates the required effiCiency of an appliance costing $500 more than the "standard" alternative. The efficiency is calculated based on assuming an annual real energy cost of $360 per year for operating the standard appliance. For**7~?** tl

### xample, the 28 percent figure inthe middle column indicatesthat an individual [ith a 15 

### percent discount rate will be indifferent between the standard pplianceandan appliance that is

### 28 percent more efficient. This 

### isbased ona .n-year appliance life; for longer-livedappliances, the required efficiency level is 

### ower (see the 15-year column on the far right). The table illustrates that :ustomers with higher discount rates (i.e., shorter required payback periods) need yeater efficiency levels to make investments in high-efficiency appliances. This 2fficiencypaybackis

### discussedfurther inthe nextsection.
Table 7-8 Enerav Savinxs Requirements of

Higher EfficiencyAppliances


### Marginal CapitalCostof HigherEfficiency Appliance: $500 Real Annual Energy Costfor StandardAppliance: $360 Energy Investment

Discount Rate Required Energy Reduction Relative to 

### StandardAppliance 10-Year Life 15-Year Life 

### xample, the 28 percent figure in 

### the middle column indicates that an individual lith a 15 

### percent discount rate will be indifferent between the standard ppliance and an appliance that 

### is 28 

### percent more efficient. This 

### is based on a ::I)-year appliance life; for longer-lived appliances, the reqL!ired effidency level is ower (see the 15-year column on 

### the far 

### right). The table illustrates that 

### ;ustomers with higher discount rates (i.e., shorter required payback periods) need $reater efficiency levels'to make investments in highcefficiency appliances. This "fficiencypayback is discussedfurther in the nextsection.

Assumptions: Table 7-8 

### Energy Savings Requirements of



### Higher Efficiency Appliances



## • Marginal Capital CostofHigherEfficiencyAppliance: Real Annual Energy Costfor Standard Appliance: $500 $360 

## • Energy Investment Required Energy Reduction Relative to StandardAppliance DiscountRate lO-Year life 15-Year life 15%
. 

### 28% 24% 20% 

### 33% 30% 40% 

### 58% 56% 

### 60% 

### 84·% 83%

7-25Section8 INTEGRATIONOF LOADAND SURVEY ANALYSES

This section discusses the integration of the REUC's market-research based customer data with the REUC participants' end-use load data. The integrated market 'research (IMR) techniques employed in the analysis of the REUC data have providedthefollowing benefits: They allow identification of more homogeneous customer types than classificationsbased only on demographicdata. In doing so, the statistical estimation of load shapes is made more preciseand hence more usefulfor utility planning. They provide knowledge of the load shapes for different market-research based customer segments. This gives utility market planners the capability to assess the potential load and energy impacts of targeting demand-side programs to that segment. The results shown here establish the value of developing databases for demandside programs consistingof both behavioral (survey)and engineering (load)data. These techniques use existing utility customer data as a foundation to systematically address both "traditional" as well as emerging demand-side planning issues, producingenriched characterizationsof customer segments. The survey variables and classification schemes used for the market research dimension ofthe REUCare explained inthe previous section. Inthis section, average loadshapes are calculated across separate customer subgroups of the sample. Three different types of variables are employed in the stratification schemes below: the Customer Preference and Behavior (CP&B) CLASSIFYTM method,' the factor analysis results from Section 7, and the traditional income and square feet variables often used to define residential customer segments.

1Residential Customer Preference and Behavior: Market Segmentation Using"CLASSIFYTM," EPRl EM-5908, March 1989. Section 8 

### INTEGRATION OF LOAD AND SURVEY ANALYSES
INTRODUCTION 

### This section discusses the integration of the REUC's market-research based customer data with the REUC participants' end-use load data. The integrated market research (iMR) techniques employed in the analysis of the REUC data have provided the following benefits:
• They allow i,dentification of more homogeneous customer types than classifications based only on demographic data. In doing so, the statistical estimation of load shapes is' made more precise and hence more useful for utility planning. '
• ,They provide knowledge of the load shapes for different market-research based customer segments. This gives utility market planners the ,capability to assess the potential load and energy impacts of targeting demand-side programs to that segment. The results shown here establish the value of developing databases for demandside programs consisting ofboth 

### behavioral (survey) and engineering (load) data. These techniques use existing utility customer data as a foundation to systematically address both "traditional" as well as emerging demand-side planning issues,

,producing enriched characterizations ofcustomer segments.

The survey variables and classification schemes used for the market research dimension ofthe REUC are explained in the previous section. In this section, average load shapes are calculated across separate customer subgroups ofthe sample. Three different types of variables are employed in the stratification schemes below: the Customer Preference and Behavior (CP&B) CLASSIFYTM method,l the factor analysis results from Section 7, and the traditional income and square feet variables often used to define resideiltial customer segments.

1Residential Customer Preference and Behavior: Market Segmentation Using "CLASSIFYTM," EPRI EM-59GB, 

### March 19B9.

### LOAD SHAPES BY 

### REUC CUSTOMER SEGMENT A number of different characteristics were used for ex stratification of the REUC sample, generating a large number of load profiles for different customer segments and end-uses. Four of the stratification schemes are presentedfor two electric end-uses, space conditioning and water heating, in this section. The CP&B stratification has four valid customer segments.2 The other three stratification schemes (fuel Quality factor score, fuel Value factor score, and income-by-squarefeet) are two-by-two stratifications, each definingfour customer segments. The fuel image questions as discussed inSection 7 indicate that customers have distinct images of electricity and gas. The various image responses were successfully reduced to two dimensions (orfactors):. fuel 

### qual lit^ (a composite of the

. . Safety, Convenience, Service and Reliability survey items) and fuel "Value" (a composite of the Appliance Cost, Expense, and Monthly Utility Bill items). For each REUC respondent, the factor score representingthe image of gas and electricity iscalculatedfor both Quality and Value. As illustrated below, these scores are an excellent method for stratifying the participant's load shapes of their appliances. Summer weekday average electric space conditioning load shapes are shown in Figures 8-1 and 8-2. The CP&B stratification is presented in Figure 8-1. As expected, the "Resource Conservers" consume the least amount of energy for air conditioning. The Value factor score stratification is presented in Figure 8-2. In this two-by-two stratification, the customers are grouped by their electric and gas Value scores (highversus low), generatinga total of four strata. The average load shapes across these subgroups are clearly distinct. For example, a customer inthe "Low ElecILow Gas Value" segment believes bothfuels are expensive relative to a customer in the "High EledHigh Gas Value" segment. Those customers who perceive electricity as low Value consistently use more electricity. Perhaps high


## TWO CP&B needs-based classification groups were placed into the "unclassifiable" category (Pleasure Seekers and Hassle Avoiders) because not enough REUC households were classified into these categories to make valid subgroups. 

### LOAD SHAPES BY REUC CUSTOMER SEGMENT A number of different characteristics were used for ex post stratification of the REUC sample, generating a large number of load profiles for different customer segments and end-uses. Four of the stratification schemes are presented for two electric end-uses, space conditioning and water heating, in this section. The CP&B stratification has four valid customer segments.2 The ,other three stratification schemes (fuel 

### Quality factor score, fuel 

### Value factor score, and income-by-square feet) are two-by-two stratifications, each defining four customer segments. The fuel image questions as discussed in Section 7 indicate that customers have distinct images of electricity and gas. The various image responses were succes'sfully reduced to two dimensions (or factors):' fuel 

### "Quality" (a composite of the Safety, Convenience, Service and Reliability survey item's) and fuel 

### "Value" (a composite of the Appliance Cost, Expense, and Monthly Utility Bill items). For each REUC respondent, the factor score representing the image of gas and electricity is calculated for both 

### Qualityand 

### Value. As illustrated below, these scores are an excellent method for stratifying the participant's load shapes of their appli-

, ances.

Summer weekday average electric space conditioning load shapes are shown in Figures 8-1 and 8-2. The CP&B stratification is presented in Figure 8-1. As expected, the "Resource Conservers" consume the least amount of energy for air conditioning. The 

### Value factor score stratification is presented in,Figure 8-2. In this two-by-two stratification, the customers are grouped by their electricand gas 

### Value scores (high versus low), generating a total offour strata. The average load shapes across these subgroups are clearly distinct. For example, a customer in the "Low Elec/Low Gas 

### Value"segment believes both fuels are expensive relative to a customer in the "High Elec/High Gas 

### Value" segment. Those customers who perceive electricity as low 

### Value consistently 

### use more electricity. Perhaps high

2Two CP&B needs-based classification groups were placed into the "unclassifiable" category (Pleasure Seekers and Hassle Avoiders) because not enough REUC households were classified into these categories to make valid subgroups.

8-2

### F1ls affect customer image morethan their image affects usage. Though this conllusionmay be obvious inthis context, this result 

### iscontrary to what one would Lpectfor standard household productsthat cbmpete inthe market. 

## t... i. ' ! .winterweekday average electric water heating load shapes are shown in Figures ~. 

### 18-3 and8-4. The 

### Qualityfactor score stratification

### ispresented in Figure8-3. This !. 'aimension of fuel image also groups customers into distinct load shapes. As :: ;before, the same result 

### is observed; customers who perceive electricity as low



# . lquality use more energy. A more traditional stratification scheme, income-bysquare feet, 

### is presented in Figure 8-4. The estimated load shapes by these :;ustomer segments, as defined in Section 4, also have different shapes, but, as 'shown below, this traditional stratification 

### is generally inferior to the factor score based stratificationschemes. Figure8-1 REUC Summer Weekday Load Shape: ElectricS~ace Conditioning Customer Preference & Behavior Stratification

0.5

# -

0.4-

0.2

# -

0 3 6 9 12 15 I8 21 24 Time 01Day(inHours PSTI**~iJls** 

### affect customer image more than their image affects usage. Though this conclusion may be obvious in this context, this result is contrary to what one would'

### ~pectfor standard household products that

### c~mpete in the market. 

# , r","· p 

### Winter weekday average 

### electric water heating load shapes are shown in Figures 

## l8-3 and 8-4. The Quality

### factor score stratification is presented in Figure 8-3. This I. 

### ,'dimension of fuel image also groups customers into distinct load shapes. As (before, the same resuIt is observed; customers who perceive electricity as low '.quality use more energy. A more traditional stratification scheme, income-by- .square feet, is presented in Figure 8-4. The estimated load shapes by these :customer segments, as defined in Section 4, also have different shapes, but, as 'shown below, this traditional stratification is generally inferior to the factor score based stratification schemes. Figure 8-1 REUC Summer Weekday load Shape: Electric Space Conditioning Customer Preference & 

### Behavior Stratification

kW Load 1.0**~--------------------,**

0.8

0.6

0.4

0.2



### o 3 Appeer. Con

. Res, Consry. V.1ue Seeker

6 9 12 15 Time of Dey (in Hours PST)

B-3 18 21 24Figure8-2 REUCSummer Weekdav LoadShave: Electric Soace Conditioning Electric ValuelGas ValueStratification

0.6

# -

0.4

# -

0 3 6 9 12 15 18 21 24 TlmsorDay (innounPST)

Figure 8-3 REUCWinter Weekday LoadShaoe: HeatPumv Water Heater Electric Oua/itv/Gas OualitvStratification

LW Load 1.0

0.8- Hi EleclLo~ G059UPIit

0.6-

0.4-

0.0- 

## , 0 3 6 g 12 15 18 21 24 Time ofDay(inHours PST) 

### Figure 8-2 REUC 

### SummerWeekday load Shape: Electric Space Conditioning Electric Value/Gas 

### Value Stratification



### t:.WI.Oe4 10..--------------------. 

# ••

0.•

0.4

•.

2 l.OYf EleclLowGbS Val " Low Elec1Hl GO$volu HI E;locll..ow GOiVolu HI EJec1H1 Gus lJell.lO



# • 02



### figure 8-3 IS 16 21 24



### REUC Winter Weekday loadShape: Heat Pump WaterHeater Electric Quality/Gas Quality

### Stratification

5:.'"**I.o~d** 

# 1.0'T---------;::========::::;;-, low EleclLowGO$ Qual t lowElee/H.**G(I~** Quallt 0.0 Hj Elee/low Gas Qualil Hi Elee/Hi Gas Quality 

### e.'



# , 

# . 

# " 0.1**o.o~**__ -....,,....-.,..-...,..-.... --,....-.,..-"" o 6 9 12 Ie 21 24 Timaor Dey (in Hours PST)

.8-4Figure8-4 REUC Winter Weekdav Load Shaoe: Heat Pump Water Heater Income bv Sauare Feet Stratification

kW Load 10

LOWSqft/HI lncom 08- HiSqft/Low lncom

06-

04-

00 -,.,,,.I. I.,. I.,. 0 3 6 9 12 15 18 21 24 Ttme of Day dnHours PST)

DETERMINATIONOF "OPTIMAL" STRATIFYING VARIABLES Stratifying a sample into groups of households with similar characteristics can produce more homogeneous load shapes for each group, thereby decreasing the total variance ofthe average load shape across the householdsineach group. This 

### is the general principle used to determine "optimal" stratification in traditional load researchanalysis when, for example, acustomer sample 

### isdivided intosmall and large kwhusage strata. Important customer characteristics that determine energy usage may ,not be limited to variables such as kwhusage and householddemographics, which are typically used for stratifying load data. Customer needs and attitudes are measurable customer characteristics that may serve as better determinants of energy usage. This presents an interesting question: Howdoes one determine the "best" 

### Figure 8-4 REUC Winter Weekday Load Shape:' Heat 

### Pump Water Heater Income 

### by Square Feet Stratification

kW LoM 1.0..--------------------.

0.8

0.4

0.2 lowSQftllow Inco e Low SqftlHI Incom HI SqftlLow Incom HI SQft/HI Income

0.0

### -!-....--,-...,........,--._..--.--,--...-.-...,..-,.-...-..,.....
.........-I 

### o 9 12 15 Ie 21 24 Tim. of**D.~** On Hours PST)



### DETERMINATION OF "OPTIMAL" STRATIFYING VARIABLES 

### Stratifying a sample into groups of households with similar characteristics can produce more homogeneous load shapes for each group, thereby decreasing the total variance ofthe average load shape across the households in each group. This is the general principle used to de.termine "optimal" stratification in traditional load research analySiS when, for example, a customer sample is divided into small and large kWh usage strata. Important customer characteristics that determine energy usage may .not be limited to variables such as kWh usage and household demographics, which are typically used for stratifying load data. Customer needs and attitudes are measurable customer characteristics that may serve as better determinants' of energy usage. This presents an interesting question: How does one determine the "best"characteristicsex post by which to stratify the sample data? The method applied to the REUC data for determining the optimal stratifying variables isa "least variance" criterion. That 

### is, the best grouping of sample data 

### isthe one with the least total variancefor allofthe average loadshapesfor each customerstratum. Test results,for the water heating and space conditioning end-uses, as well as whole house electricity usage, are presented in Table 8-1. The sample was separated by season and pooled across the two Orange County cities for these tests. Only weekday average load profileswere used.
Table 8-1 REUC Stratification SchemeVarianceCalculations Appliance Income-byLoad Sha~e~ 

## CP&B Souare Feet 

## Oual,tv

Whole Houseb 78.16* 84.60 84.82 86.44 Space Conditioningc 27.30 26.63 25.14 24.71

## * Water ~eatingd 9.34 7.24* 8.48 8.38 Summer Whole House 71.28 71.09 68.12* 68.43 Space Conditioning 21.45 20.67 18.39 18.12* Water Heating 11.47 8.29* 10.51 10.45 *Indicates the stratification variablethat results inthe least totalvariance. aWhole House, Space Conditioning and Water Heatingare from the REUC electric sample households. b~he Whole House isthe electricity usagefor the entire home. CSpace Conditioning 

### is a heat pump. d~ater Heating is a heat pumpwater heater. characteristics ex post by which to stratify the sample data? The method applied to the REUC data for determining the optimal stratifying variables is a "least variance" criterion. That is, the bestgrouping ofsample data is the one with the least 

## , total variance for all ofthe average load shapes for. each customer stratum. Test resultsfor the water heating and space conditioning end-uses, as well as whole house electricity usage, are presented in Table 8-1. The sample was separated by season and pooled across the two Orange County cities for these tests. Only weekday average load profiles were used.

Table 8-1 REUC Stratification Scheme Variance Calculations Appliance Load Shape' CP&B

Whole Houseb 78.16* Space ConditioningC 27.30 Water Heatingd 9.34

Whole House 71.28 Space Conditioning 21.45 Water Heating 11.47 Income-bySquare Feet Winter 84.60 26.63 7.24* Summer 71.09 20.67 8.29* Quality

84.82 25.14 8.48

68.12* 18.39 10.51 86.44 24.71* 8.38

68.43 18.12* 10.45
*Indicates the stratification variable that results in the least total variance. 'Whole House, Space Conditioning and Water Heating are from the REUC electric sample households. bThe Whole House is the electricity

### u~age for the entire home. CSpace Conditioning is a heat pump. dWater Heating is a heat pump water heater.

### /fourstratificationschemes were consideredfor these end-uses and whole-house CP&B, Value image, Quality image and income-by-square feet. The measure shown in.Table

.

### 8-1 is 

### interpreted as follows: lower values :indicatelower overall variance. Thus, the lowestvalue byappliance indicatesthe most effective stratification variable. As Table 8-1 

### indicates, the factor analysis 'methods generally work best, as the Quality and Value stratifications have the lowestoverall variance inthreeofthe six loadshapetypes. The Valuestratification works well, producing estimates with the least variance in two cases. 

## he traditional income-by-Square Feet stratification performs about as well as the Value stratification inthis ranking. The CP&B stratification performs bestfor the winter weekday whole-house loadshape type. The results of these limited tests show promise inthe development of new, nontraditional, stratificationschemes. While additional research isneededto develop procedures to test the significance of these stratification schemes more broadly, the techniques developed here illustrate howoptimal stratificationvariables can be identifiedfor end-use loadshapes.
APPLIANCE LIFE-CYCLE 

### COSTS One of the principal objectives of the REUC project was to collect sufficient information to permit-acomparison of the ,range of overall customer costs of .operating comparable sets of electric and gas .appliances.. Another application of the integrated market research data has been comparison of electric and gas appliance life-cycle-costs.Thefollowing discussion summarizes several aspectsof these analyses. The cost comparison approach developed for these 'analyses, termed the Cost Payback Approach, combines appliance purchase.price, energy consumption, fuel price and customer survey data collected on the project to compare the life-cycle costs of the monitored appliances. The results for space conditioning appliances are summarized here. Water heating, drying, cooking and whole house life-cycle costs also were analyzed, though these results are not ', 

### presented'in this report. The cost comparison method is

### termed the Cost PaybackApproach because it is 

### based on calculating a range of life-cycle costs for electric and gas appliances based on a 

### rangeof customer payback periods. The approach accounts for the 

### iFour stratification schemes were considered for these end-uses and whole-house Iload: CP&B, Value 

### image, Quality 

### image and income-by-square feet. The ;'variance measure shown**il).Ja?l~** 

### B-1 is interpreted as follows: lower values !indicate lower overall variance. Thus, the lowest value by appliance indicates the most effective stratification variable. As Table B-1 indicates, the factor analysis

.methods generally work best, as the Quality

### and Value 

### stratifications have the lowestoverall variance in three ofthe six load shape types. The Value 

### stratification works well, producing estimates with the least variance in two cases. The traditional Income-by-Square Feet stratification performs about as well as the Value 

### stratification in this ranking. The CP&B stratification performs best for the winter weekday whole-house load shape type. The results ofthese limited tests show promise in the development of new, nontraditional, stratification schemes. While additional research is needed to develop procedures to test the significance ofthese stratification schemes more broadly, the techniques developed here illustrate how .optimal stratification variables can

. be identified for end-use load shapes.

APPLIANCE LIfE-CYCLE COSTS 

### One of the principal objectives of the REUC

### project was to collect sufficient information to permit·a comparison of the .range of overall customer costs of ·operating comparable sets ofelectric and gas .appliances
.. Another application of the integrated market research data has been comparison of electric and gas appliance life-cycle.costs. The following discussion summarizes several aspects of these analyses. The cost comparison approach developed for these analyses, termed the Cost Payback Approach, combines appliance purchase.price, energy consumption, fuel price and customer survey data collected on the project to compare the life-cycle costs of the monitored appliances. The results for space conditioning appliances are summarized here. Water heating, drying, cooking and whole house life-cycle costs also were analyzed, though these results are not presented in this report. The .cost comparison method is ter.med the Cost Payback Approach because it is based on calculating a range 

### of life-cycle costs for electric and gas appliances based .on

# arange of customer payback periods. The approach accounts for the

8-7three components of a customer's appliance investment decision: (1) appliance

, ' operatingcosts; 

### (2)the appliance's capital costs; ,and(3)the customer's required. payback period. The cost information has been derivedfrom the REUC end-use load data and other sources. The customer required payback period'; have been derivedfrom ~~~REUC survey responses. As used here, customer payback periods represent the amount of time needed before an initial energy investment 

### is recoveredthrough reduced operationcosts. Required payback period representsthe maximum length of payback period that a customer will accept in deciding upon an energy investment. Required payback period 

### is closely relatedto the concept of implicit discount rate.3 Higher consumer implicit discount rates imply shorter required paybackperiods. Analyses of.survey responsestoaseries of questionson possibleenergy investment decisions indicate that the required payback period on an energy investmentfor almost three-quarters ofthe REUC customers 

### is less thanfive years. The distribution of these customers' required payback periods 

### is illustrated in Figure 8-5, rangingfrom lessthantwoyears to20years. In many areas, natural gas for residential customers costs less per BTU than electricity. Thus, gas systems (gasfurnacelelectric air conditioner)generally cost lessto operatethan standard electric (resistance heatingair conditioning) systems. However, the improved energy efficiency of heat pumps over standard electric systems closes the gas-electric cost gap considerably. For customers with a short payback period(implying less importanceon future operatingcost savings), asmall capital cost advantagefor heat pumps over a gas system will result in lower lifecycle costs for the heat pumpsystem. That 

### is, the somewhat lower operatingcost ofthegas system can beoffset by a lower initial cost heat pump system to result in lower life-cyclecosts.

3~ee "Implicit Discount Rates in Residential Customer Choices," February 1988, EPRl EM-5587, for asurvey of estimates.
8-8 

### three components of a customer's appliance investment decision: (1) 

### appliance operatingcosts; (2) the appliance's capital costs; and (3) the customer's required payback period. The cost information has been derived from the REUC end-use load data and other sources. The customer required payback period; have been derived from the REUC survey responses. As used here, customer payback periods represent the amount of time needed before'an initial energy investment is recovered through reduced operation costs. Required

### payback period represents the maximum length ofpayback period that a customer will accept in deciding upon an energy investment. Required payback period is closely related to the concept of implicit discount rate.3 

### Higher, consumer implicit discount rates imply shorter required payback periods. Analyses of.survey responses to a series of questions on possible energy investment decisions indicate that the required payback period on an energy investment for almost three-quarters ofthe REUC customers is less than five years. The distribution of these customers' required payback periods is illustrated in Figure 8-5, ranging from less than two years to 20

### years. In many areas, natural gas for residential customers costs less per BTU than electricity. Thus, gas systems (gas furnace/electric air conditioner) generally cost less to operate than standard electric (resistance heating/airconditioning) systems. However, the improved energy efficiency of heat pumps over standard electric systems closes the gas-electric cost gap considerably. For customers with a short payback Period (implying less importance on future operating cost savings), asmall capital cost advantage for heat pumps over a gas system will result in lower lifecycle costs for the heat"pump system. That is, the somewhat lower operating cost ofthegas system can be offsetby a lowerinitial costheat pump system to result in lowerlife-cyclecosts. '

3S

### ee "Implicit Discount Rates in Residential Customer Choices," February 1988, EPRI EM-SS87, for a survey ofestimates. 8-8Figure 8-5 REUC Particioants' Rewired

### Payback Periods



### - . .... Percentoi 52 panicipanrs 

# I

<2 " 

### - --3 6-12 13-20 PavbruPeriod (Yrs.)

The Cost PaybackApproach results illusrrate this finding. Figure8-6 indicatesthe combinationsof appliance capital cost differences and paybackperiodsfor which electric heat pumps have a lower life-wcle cost than comparable gas systems. Each point on the curve has been derived by combining the REUC appliance energy data averages and payback information. It should be notedthat the estimates oielectricity.and gas consumption exhibit a range aroundan averagevalue. The curve shown in Figure 8-6 represents the fuel choice "indifference curve" based on these average values. The shaded region on the figure indicates the combinations oicapital cost differences and payback periods for which the electric heat pump has a lower life-cyclecost than the gas system. Figure 8-5 REUC Participants' Required Payback Periods

Percent

### or 00 Particip.ants

<2 6-12 Period (Yrs.) 13-20

The Cost Payback Approach results illustrate this finding. Figure 8-6 indicates the combinations oiappliancecapital cost difierences and payback periods forwhich electric heat pumps have a lower liie-cvcle cost than comparable gas systems. Each point on the curve has been derived by combining the REUC appliance energy data averages and payback iniormation. It should be noted that the estimates oi electricity.and gas consumption exhibit a range around an average value. The curve shown in Figure 8-6 represents the fuel choice "indifference curve" based on these average values. The shaded region on the figure indicates the combinations oi capital cost differences and payback periods for which the electric heat pump has a lower life-cycle cost than the gas system.

8-'1Figure 8-6 REUC SpaceConditionine FuelChoice Preference Regions.

Payback Discount CapitalCon (Yean)

s447

For example, a customerwho requires a payback periodoftwoyears would see a lower electric heat pump life-cycle cost ifthe capital costs for the gas system exceededthose of the heat pump by more than $142. The capital cost difference between the gas system and the heat pump system has been estimated to be approximately $130. Customers with longer payback periods would require the heat pump to have lower capital costs relativeto the gas system (e.g., the gas vs. electriccost differencewould begreater). By usingsuch an approach, a wide rangeof customer paybackperiods can be considered inappliance life-cycle cost comparisons so that utility program planners can target those customers most likely to prefer high-efficiency electricappliances. 

### Figure 8-6 REUC SpaceConditioning Fuel Choice Preference Regions.
Required 15 payback 14 in Payback Discount Capital Cost Years 13 (Yean:) Rate Difference 12 2 s.% $142 11 5 2.% 

# $:l., 10 

# ,. ,.% 5+47

9 8 7 6 5 4 3 2
0 100 200 300 400 500 Required Capital.COS! Difference in 1987$ (Gas
- Electric) 

### Ell Electric Preferred Region



### For example, a customerwhorequires a payback period oftwo years would 

### see a lower electric heat pump life-cycle cost if the capital costs for the gas system 

### exceeded those of the heat pump by 

### more than $142. The capital cost difference between the gas system and the heat pump system has been estimated to be approximately $130. Customers with longer payback periods would require the heat pump to have lower capital costs relative to the gas system (e.g., the gas vs. electric cost difference would be greater>. By using such an approach, a wide range ofcustomer payback periods can be considered in appliance life-cycle cost comparisons so that utility program planners can target those customers most likely to prefer high-efficiency electric appliances.

8-10

### eroperatingcosts ot higher efficiency electric appliances relative to stan-

. . (b.g., resistance inthe case of space conditioning)electricappliances narrows 

# ap between the operating costs of electric and gas appliances. Using the.

ecustomer operatingcosts and distribution of required paybacks, the Cost k Approach analyzes the life cycle costs of the newer electric appliances ve to gas appliances. This approachcombines information measureddirectly

. . e REUCproject

### -- energy usage and required paybacks

## -- and leaves assump- ';about relativecapital costs as an unknown that can befilled inbythe reader bnexamining Figure 8-6.

important to remember that under1ying.a life cycle cost analysis are various about customer appliance utilization, required paybacks, and the installation costs of the appliances themselves. The results of this

individual customers will vary. Each customer utilizes appliances @ifferentlyaid has a differentforecast of energy prices and overall assessment of 

### k., ;futureuncertainty that affects its required payback. 

### r ;Furthermore, life cycle costs are only one of many appliance attributes that may $e considered by a customer selecting an appliance. The fuel image results discussed before support the conclusion that customers value non-cost 'characteristics of 

### fuels that are the energy source for appliances. These'non-cost

' attributes can be given a dollar value and can be used to adjust the "Required

Capital Cost Difference." For example, if electricity is perceived to be more reliable than gas, a dollar premium for this attribute can be used to lower the required capital cost difference to equalize the life cycle costs, since customers would bewilling to pay morefor electric technologies. 

### operatmg costs ot higher efficiency electric appliances relative to s.tanresistance in the case ofspace conditioning) electric appliances narrows between the operating costs of electric and gas appliances. Using the. customer operating costs and distribution ofrequired paybacks, the Cost Approach analyzes the life cycle costs of the newerelectric appliances to 

### gas appliances. This approach combines information measured directly RfUC project

-- energy usage and required paybacks

-- and leaves assumpabout relative capital costs as an unknown that can be filled in by the reader examining Figure 8-6.

# ~s**imp~rtant** to remember that

# u~derlyin~.. a

# li~e cycle c.ost analysis are various**~ssumptlons** 

### about customer appliance utilization, required paybacks, and the rgpital and installation costs of the appliances themselves. The results of this

## ~pproach for individual customers will vary. Each customer utilizes appliances (differently

### a~d has a different forecast ofenergy prices and overall assessment of

## ~ture uncertainty that affects its required payback. 

### r. . . iFurthermore, life cycle costs are only one of many appliance attributes that may ,be considered by a customer selecting an appliance. The fuel image results 'discussed

### bef~re support the conclusion that customers value non-cost 'characteristics of fuels 

### that are the energy source for appliances. These·non-cost attributes can be given a dollar valu.e and can be used to adjust the "Required Capital Cost Difference." For example, if electricity is perceived to be more reliable than gas, a dollar premium for this attribute can be used to lower the

. required capital cost difference to equalize the life cycle costs, since customers

would be Willing to pay more for electric technologies.APPENDIX A SUPPLEMENTARY ENERGY USAGE INFORMATION AND LOAD PROFILES 

### APPENDIX A SUPPLEMENTARY ENERGY USAGE INFORMATION AND LOAD PROFILESFigureA-1 SpaceConditioning: Winter Averaee Dailv Usage

. (at pointof use)



### .... ".

Winter Wcrlrend

kBTU

Figure A-2 Water Heating: Averaee Dailv Usage (at ~oint ofuse)

Wintcr Weekend

SummaWeekend

kBTU 

### Figure A-l Space Conditioning: WinterAverage Daily Usage (at point ofuse)

89.9 Winter Weekend

Winter**W~"day**

o 10 20 30 40 SO 60 70 80 90 100 k8TU 122 CasS
...**m~e** II Electric Sample 

### Figure A-2 Water Heating: Average Daily Usage (at point ofuse)

Winter Weekend

Winter Wee"-d;ay

Sommer Weekend

Summer Weekday

0 10 20 30 40 SO 60 70 80 kBTU 

### r:a CuSample 

# • Electric S;ample
A-7

# This page left deliberatelyblank Thispageleft deliberatelyblank

A-3FigureA-5 . . Soace Conditioning: Summer Average Dailv Usage (at pointof use)

Summer Weekend

bSTU

Figure A-6 SCE REUC Winter: Clothes Drvine(at ooint of use) 

### Figure A·S Space Conditioning: SummerAverage Daily Usage (atpoint ofuse)

Summer**Wil~kend**



# • 5 10



## e 

# • IS idlTlI



### Figure A·6 23.7

2. 25 3 •



### SCE REUC Winter: Clothes Drying (at pointofuse)
kBTU p«HOUf 2.0 

# - Elec.w"'da),> 1.8 

## ....

# _-_.. EIee. W
.. """'" 

### ""._

### ....... Gasw.. ",",),> 1.6 

# ---- GasW
..... ds 1.4 

### 1';1
. 1.2

## I"~ 

# "

....", 1,0 

## ¥ \ 'V"'" 

### i'P,........ _, ..." .......,.<':<.., \ O,S" ill .., "''''... 1**-'~~** ;1' \.!- 0.6 

# !/ '\ 1/ 0.4 

# l

. 0.2

# "-- • I ,// :;-;, 0.0 0 3 6 9 12 15 18 21 24 TifMofDay

A-4FigureA-7

FigureA-8 SCE REUC Summer: Clothes Drying(at ~oint of use)

Elu.Week&+ 

# ------ Elu.Weeken& GasWeekdays Gz Weekmds k8TUper Hour 2.0- 1.8- 1.6- 1.4- 1.2- Elec. Wnkendr 

# -.-.-.- GasWeekday3 GasWeken& A I' I\ 1.0- 0.8

# - 0.6

# - 0.4-

0 3 6 9 12 15 18 21 24 TimofDay

SCE REUCWinter: 

### cook in^(at ~oint of use)
kBTUper Hour 2.0- 1.8- 1.6- 1.4- 

# I 1.2- 

### ?! ; ! ; ! 

### i 

### ;, b\ 

### , I \\ i 11. 1.0- 0.8- 0.6- 0.4

# -

0 3 6 9 12 15 18 21 24 Timeof Day Figure 1>.-7 S.CE REUC Summer: Clothes Drying (atpoint ofuse)
kBiUpn- Hour' '1..0 1.8 1.6 1.4 1.2 1.0 0.8' 0.6 0.4 0,2 £IK.W
..**~** £lee.Week
.. d. CasW
.. kdal'" GasWeekends**o.o.j........:s~-~:;....**

### ......**.....,,..-......,-....,.-....,.~..,** o 3 12 15 18 21 Time ofOay Figure A·8 SCE REUC Winter: Cooking (atpointofuse)
UTUperHour 2.0 1.8 I.G 1.4 1.2 1.0 0.8 0•• 0.4 0.2, 0,0 0 3 £lee.WeekdaY, Bee.Weekends GasW
.. kdays**CasW~kmds**

9 12 TimeofDay

A-S 15 1. 21 24

14FigureA-9

Ek Weekdays 

# ------ Eke.Weekd 

## -.-.-.-, GuWeekdays CuWeek&

TimofDay SCE REUCSummer: Cooking(at pointof usel
k8TUperHour 1.5-

FigureA-10 1.2-

0.9

# -

SCE REUCWinter: Whole House Electricity(at ~oint of use)
Pncenl of Total 

# :.
,

# '. 

# p,

# \ ;\i 1 I! 1,

Tim of~ay 0.6

# -

0.3

# - Figure A-9 SCE REUC Summer: Cooking (atpointofyse)
kBTU pet Hour 1.5

0.9

0.3 Eh!c. W
..

lo!ays El<c.W

..k_ G

..W..lo!a)s GasWeektruls 

### ., 

# . \

### ~ 

# " !,' I • I • I \\ 1 \ i 

### I 

# I, t I! 

# , \\ 

# J

' , \



# ..."" J**/~** 

# \ / ""'" ;""-.;...1/ , \ /'"'. .; I I \ ,I -..... .....-............ I \. 

### i"'

.... 

# _--<","'. " ......:::.. "'"**~** 

# -.......

### _.,... --

## ...., ...... 

## o.o..f::=:;=;=:¢'.:.:....,r--......,-....,.-....,..-....,..-..::; o 3 6 12· 1$ 18 21 24 Timeoftny Figure A-10 SCE REUC Winter: Whole House Electricity (atpoint ofuse)
Pet'Cenl ofTotal Daily £nergy USf! 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3,5 3.0 2.5 2.0 1.5 1.0 0.5 flee. Sample**W~kday$** Gas. $.amp'eWeekdays

0.0

### -h-.....,....,....... ..,...,........,...,........

### -r-,....,..-.-.......**-r-,.....-r~.,** 

### o 3 6 9 12 IS 18 21 2.

A-6FigureA-11 

### XE REUCSummer: Whole House Electricity (at point

### of use)

.~

... .~<
r PcrccntofTotal wlyEnergyuse 7.0 

## :

5.0

# - 4.5

# -

1.5

# - 1.0- 0.5

# - 0.07.
. 8 I 0 3 6 9 12 15 18 21 24 limeof Day FigureA-12 SCE REUCWinter: Space Conditioning(at point of use)

, PercentofTotal DailyEnrayU* l4 13- 12

# - 11

# - 10

# - ('1 I \ 

# :: I I1 I I 9- 8- 7- 6

# - 5- 4- 3

# - 2

# - 1

# -

Ol..I..L..1III. 18

0 3 6 9 12 15 18 21 24
TimofDay Figure

### A-ll SCE REUC Summer: Whole House Electricity (atpointofuse)

f1f:tcent ofTolill**~Iy** Energy Use 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.00 3 Elee. Sample Weekdays Gas $ample Weelatt)1l

6 9 12 limeofOar Figure A-12 I I

15 

### /

### i~,.-,~ 

## .. I

18 21 24

SCE REUC Winter: Space Conditioning (at point ofuse)
PercentofTotal DiljlyEnergy Use 14 (' 13 

# ,' 

# ,. 

# , 12 

# , \ Elee. Weekdays 11 

# , ' CuWHkdays , I 10 I I I I 9 

# , 

# , \ 8 

# , 7 I 

# , , • I I 5 I 

# , 4 I I 3 L.

## -' 2

o 3 6 9 12 15 18 21
Time orOay A-7 24FigureA-13 SCE REUC Summer: Space Conditioninp;(at ooint

### of use)
PercmlofTohl DilyEnergyUse 11

# - 10

# - 9

# - 8

# - 7

# - 6

# - 5

# - 4

# - 3

# - 2

# - 1

# - 0 1

0 3 6 9 12 15 18 21 24 I

Tim ofDay FigureA-14 SCE REUCWinter: Water Heatine(at oointof use)
Percent ofTolrl DailyEnqyUV~ 12

# - 11

# - 10

# -

9- r\ I I 1 I I\ I' 

# ; 8- 

### I I
7

# - 6

# - 5-

0 7

0 3 6 I 9 12 15 18 21 24

Timeof Day FigureA-13 SCE REUC Summer: Space Conditioning (at point ofuse)
PercentorTotal 12 Daily EnergyUse 11 ·1. 

# 1----

9 8 7 6 5 4 3 2 1

# • 3 EI«.Weekdays**~Weekdays**

6 9 12 Timeo(Oay FigureA-14 15 18 21

SCE REUC Winter: Water Heating (at point ofuse)
Percentoflotal Daily Energy Use 12 11 

# ,. • 8 7 6 5 4 3 \

# , 2 \\., I 

### ----' 

## ,.., I , I , I \ I I I I J I J J \\ \\

# , \

# "- Elee. Weekdays Ga.s WHkda)'S 24**.+---'---~~-T--~--~r---r---T-~** 

# • 3 6 

# • 12 15 18 21 24
Time ofDar A-8FigureA-1

### 5 SCE REUCSummer: Water Heating(at pointof use)
Percenloiiolrl DailyEnagy Use 12 

# : 11

# - 10

# - fi r 8

# - 7

# -

5-

0 I

0 3 6 9 12 15 18 21 24 Timof Day

Februay 

# -----

# -. -.-.-.-.,April 

## ----. i L. FigureA-16 ! SCE REUCWeekdayAverage Load: ElectricSample Space Conditioning i ',

I

, !

.

.

,

# . kW Load 1.4-
1.2-
1.0-
0.8

# -

.

0.0 ..*~~I.~,~~a~.~~~#~~,~~r

. i; 0 3 6 9 12 15 18 21 24

. C Time of Day A9 i '.. Figure A-15 SCE REUC Summer: Water Heating (at pointofuse)
Percent ofTolal DailyEnergy Use 12 11 10 9 8 7 6 5 4 3 2

o 3 6 9 

# 1==

\1..., \

### \

12

# "',,<:

15
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18 21 24

SCE REUC WeekdayAverage Load: Electric Sample Space Conditioning
kW Load 1.4

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FigureA-18 SCE REUCWeekday Average Load: ElectricSampleSoaceConditionin~ LNV Load 2.0

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kWL

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SCE REUC Weekday Average load: Electric Sample SpaceConditioning kW lo.ad 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 • • • 0 3 6 9

A-l0 • 12 15 TimeofDay •Odober November December January

18 21 24 IFigureA-19 SCE REUCWeekday Averave Load: Gas Sample Space Conditioning
kBTUper Hwr 20

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### Figure A-19 SCE REUC Weekday Average load: Gas Sample Space Conditioning
kBTU per Hour 20 18 16 14 12 10 8 6 4 2 oo 3 6 9 12 TimeofDay 

### Figure A-20 lS 18 February March April May

21 24



### SCE REUC Weekday Average load: Gas Sample Space Conditioning

kBTU per Hour 4.0
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kBru per Hour 27 24 21 18 15. 12 

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21 24

SCE REUC WinterWeekday Electricity: Electric Sample
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kW load 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 3 6

A-H 9 12 15 Time ofDay Whole House Space Conditioning Waler Healing

18 21 24FigureA-25

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## ------. WinterWeekends 

## -.-.-.-. SummerWeekdays SummerWeekends

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# I FigureA-26 SCE REUC Whole House Electricitv:

### Gas Sample



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. 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 3 WinterWeekdays WinterWeekends Summer Weekdays SummerWeekends

6 9 12 TImeofDay A-14 lS 18 21 24FigureA-27 SCE REUC Svace Conditionin~: ElectricSamvle



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kW Load 1.0 WinterWeekdays Winter**W~ken~s** SummerWeekdays SummerWeekends

kW Load 0.9 0.8 0.7 0.6 0.5 0.4 0.3 \ 

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0 3 6 9 12 15 18 21 24 rimeofDay 

# . Figure A-29 SCE REUC Cooking: Electric Sample
kW Load 0.30

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### SCE

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2H)() 1&00 16:00 Figure A-31 SCE REUC: Temperature.**12.~** 'lime (PST) 9<lO S<lO !.lay 

# i.I.r Febnwy J",=>, Decemb
... Nbvemw Odobor September August JnJy 3;00 June O<lOAppliance

Whole House Space Conditioning Water Heating Clothes Dryer RangelOven Table A-1 Average Annual and Seasonal Enerw Usace MissionVieio Households

Whole House Space Conditioning Water Heating

. Clothes Dryer RangelOven

Whole House Space Conditioning Water Heating Clothes Dryer Rangeloven Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) kwh MBTUa MBTU kwh MBTUa Annual Average Household Usage (12 months)

Winter Average Household Usage (8 months)

Summer Average Household Usage (4 months)

a At point

### of use. Table A-1 Average Annual and Seasonal Energy Usage Mission Viejo Households

Appliance

Whole House Space Conditioning Water Heating Clothes Dryer Range/Oven

Whole House Space Conditioning Water Heating Clothes Dryer Range/Oven

Whole House Space Conditioning Water Heating Clothes Drier Range/Oven

a At point of use. Electric Sample kWh MBTUa Gas Sample (Gas Components) MBTU Gas Sample (Electric Components)

kWh MBTUa Annual Average Household Usage (12 months) 12240 41.80 .NA 8050 27.50

2770 9.50 19.30 1110 3.78 2870 9.80 25.50 NA NA

913 3.12 4.71 1 1 1 0.38 350 1.20 3.70 NA NA Winter Average Household Usage (8 months) 8690 29.70 'NA 5000 17.10 2050 7.05. 19.00 390 

### 1.33 2110 7.20 18.51 NA NA

646 2.21 .3.26 77 0.26 247 0.85 2.56 NA NA Summer Average Household Usage (4 months) 3550 12.10 NA 3050 10.40

720 2.45 0.30 720 2.45 ·760 2.60 6.99 NA NA

267 0.91 1.45 34 0.12 103 0.35 1.14 NA NATable A-2

Appliance Averaee Annual andSeasonal EnergyUsage lrvine Households

Whole House Space Conditioning Water Heating Clothes Dryer Rangeloven

Whole House Space Conditioning 'Water Heating Clothes Dryer Rangeloven

Whole House Space Conditioning Water Heating Clothes Dryer Rangeloven Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) kwh MBTUa MBTU kwh MBTUa AnnualAverage Household Usage (12 months)

Winter Average Household Usage (8 months)

Summer Average ~ousehoid Usage (4 months)

-- a At pointof use. Table A-2 Average Annual and Seasonal Energy Usage Irvine Households

Appliance

Whole House Space Conditioning

.Water Heating Clothes Dryer Range/Oven

Whole House Space Conditioning Water Heating Clothes Dryer Range/Oven

Whole House Space Conditioning Water Heating Clothes Dryer Range/Oven



### a At point of use. Electric Sample kWh MBTU

### a Gas Sample (Gas Components) MBTU Gas Sample (Elearic Components)

kWh MBTU

### a Annual Average Household Usage (12 months) 13660

3110 2750 1210

414 46.60 10.60

9.40 4.12 1.41 NA 19.20 ·21.60 4.00 2.86 7120 1220

NA 110 NA 24.30 4.16

NA 0.37

NA Winter Average Household Usage (8 months) 9680 33.00 NA 4480 15.30 2300 7.85 19.05 478 1.63 2060 7.03 15.70 NA NA

860 2.92 2.73 74 0.25 297 1.01 1.97

. NA NA Summer Average Househoid Usage (4 months) 

### 3980 13.60 NA 

### 2640 9.00

810 2.75 0.15 742 2.53 690 2.37 S.90 NA NA 350 1.20

. 1.27 36 0.12 117 0.40 0.89 NA NA

A-19Whole House Space Cond. Water Heating Clothes Dryer RangelOven Table A-3 Monthlv Averaae Usaaefor MissionVieio Households

Whole House Space Cond. Water Heating Clothes Dryer RangelOven
Whole House Space Cond. Water Heating Clothes Dryer Rangeloven
Whole House Space Cond. Water Heating Clothes Dryer Rangeloven
Whole House Space Cond. Water Heating Clothes Dryer Rangeloven
Whole House Space Cond. Water Heating Clothes Dryer RangelOven 

### Electric Sample

Weekday Monthly



### M Averaee 

### Gas Sample Gas Sample (Gas Components) (ElectricComponents) Therrns kwh Weekday &nlhly Weekday Monthly



# -

-.-

February

March

April

&Y NA

0.1 5 0.73 0.1 3 0.10 lune NA 0.05 0.64 0.12 0.09 Table A-3 

### Monthly Average Usage for Mission Viejo Households Electric Sample
- Gas Sample Gas Sample kWh (Gas Components) (Electric Components) Therms kWh AImliancea Weekday Monthly Weekday Monthly Weekday Monthly Average Average Average Average Average Average January Whole House 44.6 1461 NA NA 19.8 621 Space Condo 16.8 551 1.81 58.9 1.8 57 Water Heating 10.3 329 0.83 25.7 NA NA Clothes Dryer 2.4 87 0.14 4.5 0.3 11 Range/Oven 1.0 33 0.11 3.4 NA NA February. Whole House 39.2 1136 NA NA 19.0 543 Space Condo 12.3 345 1.27 34.9 1.4 39 Water Heating 8.8 255 0.84 23.8 NA NA Clothes Dryer 2.3 78 0.13 3.9 0.3 9 Range/Oven 0.9 28 0.10 2.8 NA NA March Whole House 31.0 1011 NA NA 18.5 590 Space Condo 5.3 187 0.52 17.3 1.1 41 Water Heating 8.5 270 0.82 25.9 NA NA Clothes Dryer 2.4 85 0.12 4.2 0.3 10 Range/Oven 0.9 30 0.09 3.0 NA NA April Whole House 29.6 922 NA NA 20.5 614 Space Condo 4.9 142 0.28 8.3 2.7 71 Water Heating 8.0 248 0.73 22.0 NA NA Clothes Dryer 2.1 76 0.12 3.9 0.3 9 Range/Oven 0.9 31 0.10 2.9 NA NA May Whole House 26.6 854 NA NA 19.7 621 Space Condo 2.5 83 0.15 5.5 1.6 50 Water Heating 7.7 244 0.73 22.7 NA NA Clothes Dryer**2~3** 80 0.13 4.2 0.3 9 Range/Oven 0.9 28 0.10 3.1 NA NA lune Whole House 25.1 777 NA NA 19.5 590 Space Condo 2.1 61 0.05 2.3 2.5 71 Water Heating 6.8 209 0.64 19.4 NA NA Clothes Dryer 2.0 71 0.12 4.1 0.3 9 Range/Oven 0.9 28 0.09 2.9 NA NA
A,20Table A-3, 

### Cont.

Whole House Space Cond. Water Heating Clothes Dryer RangelOven
Whole House Space Cond. Water Heating Clothes Dryer RangelOven
Whole House Space Cond. Water Heating Clothes Dryer RangelOven
Whole House Space Cond. Water Heating Clothes Dryer Rangeloven
Whole House Space Cond. Water Heating Clothes Dryer RangelOven
Whole House Space Cond. Water Heating Clothes Dryer Rangeloven Electric Sample Gas Sample Gas Sample (GasComponents) (Electric Components) 

### kwh Therms 

### kwh Weekday Monthly Weekday Monthly Weekday Monthly AveraeemwAveraaeAveraQem

August

September

October NA 0.09 0.62 0.1 1 0.10 November NA 0.58 0.72 0.12 0.1 1 December NA 1.52 0.78 0.14 0.12 Table A-3, Cont. Electric Sample Gas Sample Gas Sample kwh (Gas Components) Therms (Electric Components) kWh Weekday Monthly Weekday Monthly Weekday Monthly APgliance Average Average Average Average Average Average July Whole House 30.2 956 NA NA 27.7 860 Space Condo 7.6 241 0.00 0.1 8.6 263 Water Heating 6.1 194 0.55 17.0 NA NA Clothes Dryer 2.1 70 0.11 3.5 0.3 8 Range/Oven 0.8 26 0.09 2.9 NA NA August Whole House 26.6 857 NA NA 24.7 778 Space Condo 5.1 168 0.00 0.1 5.3 170 Water Heating 5.7 179 0.54 16.8 NA NA Clothes Dryer 1.7 63 0.11 3.6 0.3 9 Range/Oven 0.7 24 0.09 2.9 NA NA September Whole House 30.4 956 NA NA 26.9 .824 Space Condo 7.5 247 0.02 0.6 6.7 214 Water Heating 6.0 184 0.56 1&.7 NA NA Clothes Dryer 1.8 &2 0.10 3.4 0.2 8 Range/Oven 0.7 25 0.09 2.8 NA NA October Whole House 26.3 859 NA NA 20.5 &47 Space Condo 2.0 71 0.09 2.8 1.4 49

. Water Heating &.7 210 0.&2 19.0 NA NA

Clothes Dryer 1.9 73 0.11 3.& 0.3 8 Range/Oven 0.9 29 0.10 3.2 NA NA November Whole House 32.6 996 NA NA 20.4 616 Space Condo 6.1 178 0.58 16.8 1.0 32 Water Heating 8.0 244 0.72 21.4 NA NA Clothes Dryer 

### n 77 0.12 3.9 0.3 9 Range/Oven 1.1 32 0.11 3.1 NA NA December Whole House 45.8 1452 NA NA 23.4 74& Space Corid. 16.4 495 1.52 45.4 1.7 50 Water Heating 9.7 308 0.78 24.5 NA NA Clothes Dryer 2.6 90 0.14 4.4 0.3 11 Range/Oven 1.1 37 0.12 4.1 NA NATable A-4 Monthly Average Usagefor lwine Households 

### Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) Therms kwh Weekday Monthly Weekday Monthly Weekday Monthly 

### Averanem'-- January Whole House Space Cond. Water Heating Clothes Dryer Rangeloven February Whole House Space Cond. Water Heating Clothes Dryer RangelOven March Whole House Space Cond. Water Heating Clothes Dryer RangelOven April Whole House Space Cond. Water Heating Clothes Dryer Rangeloven

Whole House Space Cond. Water Heating Clothes Dryer Rangeloven

Whole House Space Cond. Water Heating Clothes Dryer Rangeloven Table A·4 Monthly Average Usage for Irvine Households Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) 

### kWh Therms 

### kWh AQQliancea Weekday Monthly Weekday Monthly Weekday Monthly Average Average Average Average
. Average Average January Whole House 55.3 1764 NA NA 18.2 579 Space Condo 22.2 722 1.86 61.3 2.2 71 Water Heating 9.6 301 0.74 23.3 NA NA Clothes Dryer 4.0 125 0.12 4.0 0.3 10 Range/Oven 1.3 42 0.08 2.3 NA NA February Whole House 42.6 1212 NA NA 17.4 495 Space Condo 12.3 343 1.18 33.7 1.8 51 Water Heating 9.5 266 0.71 20.3 NA NA Clothes Dryer 3.4 104 0.11 3.2 0.3 9 Range/Oven 1.3 36 0.06 1.8 NA NA March Whole House 33.5 1078 NA NA 16.9 533 Space Condo 4.8 168 0.43 15.4 1.7 58 Water Heating 8.B 274 0.68 21.3 NA NA Clothes Dryer 3.2 109 0.12 3.5 0.3 9 Range/Oven 1.2 37 0.07 2.2 NA NA April Whole House 32.6 983 NA NA 18.5 550 Space Condo 4.2 122 0.21 6.5 3.0 84 Water Heating 8.6 255 0.62 18.7 NA NA Clothes Dryer 3.3 105 0.10 3.1 0.3 9 Range/Oven 1.1 34 0.09 2.7 NA NA May Whole House 29.2 916 NA NA 16.9 526 Space Condo 3.1 76 0.11 3.8 1.6 50 Water Heating 6.6 246 0.57 17.7 NA NA Clothes Dryer 3.0 101 0.10 3.2 0.3 9 Range/Oven 1.0 32 0.07 2.3 NA NA June Whole House 29.2 865 NA NA 17.7 530 Space Condo 3.1 85 0.04 1.1 2.6 71 Water Heating 6.6 200 0.53 16.2 NA NA Clothes Dryer 3.0 87 0.10 3.2 0.3 9 Range/Oven 1.0 30 0.07 2.1 NA NA

A-22

### Table A-4, Cont. 

### Electric Sample Gas sample Gas Sample (Gas Components) (Electric Components) ' 

### kwh Therms kwh Weekday Monlhly Weekday Monthly Weekday Monthly



### WWWw-Averaae

Whole House Space Cond. Water Heating Clothes Dryer RangelOven August Whole House Space Cond. Water Heating Clothes Dryer RangelOven September Whole House Space Cond. Water Heating Clothes Dryer RangelOven October Whole House Space Cond. Water Heating Clothes Dryer RangelOven November Whole House Space Cond. Water Heating Clothes Dryer RangelOven December Whole House Space Cond. Water Heating Clothes Dryer RangelOven Table A-4, Cont. Electric Sample Gas Sample

.Gas Sample (Gas Components) (Electric Components) kWh Therms kWh Weekday Monthly Weekday Monthly Weekday Monthly Appliance Average Average Average Average Average Average July Whole House

. 35.0 1077 NA NA 24.7 755 Space Condo 8.9 270 0.01 0.2 9.2 279 Water Heating 5.3 164 0.48 14.8 NA NA Clothes Dryer 2.9 92 0.11 3.2 0.3 9 Range/Oven 0.9 27 0.07 2.1 NA NA August Whole House 32.1 999 NA NA 21.3 669 Space Condo 6.7 211 0.00 0.1 5.5 183 Water Heating
. 5.4 167 0.46 14.1 NA NA Clothes Dryer 2.6 85 0.10 3.1 0.3 9 Range/Oven 1.0 30 0.07 2.2 NA NA**Sep~ember** Whole House 33.8 1034 NA NA 22.5 685 Space Condo 6.7 241 0.01 0.2 6.6 209 Water Heating 5.4 164 0.47 13.9 NA NA Clothes Dryer 2.6 86 0.11 3.1 0.3 9 Range/Oven 1.0 30 0.08 2.4 NA NA October Whole House 30.6 956 NA NA· 18.3 573· Space Condo 2.2 77 0.02 0.7 1.6 56 Water Heating 6.5 200 0.51 15.7 NA NA Clothes Dryer 3.0 96 0.10 3.2 0.3 9 Range/Oven 1.1 33 0.07 2.4 NA NA November Whole House 36.6 1111 NA NA 18.5 553 Space Condo 6.2 190 0.53 16.0 1.4 41 Water Heating 7.9 235 0.61 18.1 NA NA Clothes Dryer 3.2 101 0.11 3.3 0.3 9 Range/Oven 1.3 37 0.09 2.8 NA NA December Whole House 53.4 1658 NA NA 21.2 667 Space Condo 19.7 607 1.65 52.6 2.1 65 Water Heating 9.2 281 0.70 22.1 NA NA Clothes Dryer 3.7 115 0.13 3.8 0.3 11 Range/Oven 1.4 47 0.10 3.3 NA NA

A-23Appendix B METHODOLOGY TO ASSESS THE GENERALlZABlLlN 

### OF THE REUC CUSTOMER SAMPLE This appendix describes the methodology for assessing the generalizability of the REUC findings to other residential customers discussed in Section 4. The characteristics of REUC participantswere comparedwith SCE's 1987 RSS sample of single-family, detached homes. The samples were compared by identifying importantcustomer descriptorsand appropriate appliance stock inventories (ASI). Figure 6-1 illustrates the steps taken in performing the sample assessment. These stepswere asfollows: Step 1: Develop RepresentativeAS1 Definitions; Step 2: Identify ImportantCustomer Descriptors; Step 3: Determine Customer Descriptors that Predict Household'sASI; Step 4: Determine Customer Descriptors that Discriminate Energy Usage Profile; and Step 5: Assess REUC sample by Relevant Segmentation Criteria. This methodology involved first developing a set of representative ASls and, second, aset of customer descriptors. The descriptors identifiedto be most useful inthis analysis are income, squarefeet (SqFt), housevintage (Vintage), numberof residents (NumRes), and resident'sethnicity (Ethnic). Absent from this analysis is education level, noted earlier for beingan important customer descriptor, since it was not included inthe 1987 RSS Survey. A two-step analysis of customer energy usage, comprised of Steps 3 and 4, was undertaken. Step 

### 3 determined those customer descriptors that were correlated with ASI; that is, customer descriptors related to appliance holdings. Step 4 identified those descriptorsthat explain intensity of energy usage. Annual energy usage profiles based on monthly bills for the RSS customersample were stratified 

### Appendix B ' METHODOlOGY TO ASSESS THE GENERALIZABILITY OF THE REUC CUSTOMER SAMPLE This appendix describes the methodology for assessing the generalizability ofthe REUC findings to other residential customers discussed in Section 4. The' characteristics of REUC participants were compared with SCE's'1987 RSS sample of single-family, detached homes. The samples were compared by identifying important customer descriptors and appropriate appliance stock inventories (AS!). Figure B-1 illustrates the steps taken in performing the sample assessment. These steps were as follows: Step 1: Step 2: Step 

### 3: Step 4: Step 5: Develop Representative ASI Definitions; Identify Important Customer Descriptors; Determine Customer Descriptors that Predict Household's ASI; Determine Customer Descriptors that Discriminate Energy Usage Profile; and Assess RE,UC sample by Relevant Segmentation Criteria. This methodology'involved first developing a set of representative ASls and, second, a set ofcustomer descriptors. The descriptors identified to be most useful in this analysis are income, square feet (SqFt), house vintage (Vintage), number of residents (NumRes), and resident'S ethnicity (Ethnic). Absent from this ani3lysis is education level, noted earlier for being an important customer descriptor, since it was not included in the 1987 RSS Survey.
A two-step analysis of customer energy usage, comprised of Steps 3 and 4, was undertaken. Step 

### 3 determined those customer descriptors that were correlated with ASI; that is, customer descriptors related to appliance holdings. Step 4 identified those descriptors that explain intensity of energy usage. Annual energy usage profiles based on monthly bills for the RSS customer sample were stratified

B-1Figure

### B-1 REUC Sample Assessment Methodoloey



### RSSSurvey

Segmentation Criteria, REUC Customers Representativeness Assessment 4 

# * Representative Customer Descriptors 

# 4

Program Planning System/Assessment Customer Descriptors Discriminate Usage Customer Descriptors DiscriminateAS1 Figure 8-1 REUC Sample Assessl1'!ent Methodology



## , RSS Survey

........ 

# I**~** 

# + + Representative Representative Customer ASI Descriptors Definitions 

# + + Customer Customer Descriptors Descriptors Discriminate Discriminate ASI Usage

# • • Segmentation Criteria. REUC Customers 

# f "- 

# .,,- Representativeness Assessment 

# - + Program Planning System/Assessment

8-2by the customer descriptors within each AS1 grouping. The analysis was petiormed at the SCE Division level, although after controlling for key customer descriptors and ASI, distinctions across SCE Divisions were mostly attributable to

+ weather related effects. Appliance Stock lnventorv Definitions A key concept underlying this analysis rests on segmenting RSS and REUC customers bytheir AS!. 

## his segmentation allows the linkageof the energy usage characteristics for the two customer samples. The 1987 RSS provides appliance inventory data includingthe major appliances that were monitored and analyzed in the REUC project. Appliance penetrations were calculated for the singlefamily RSS subsample. Based on these penetrations, AS1 definitions were developed as combinations of gas and electric appliances that characterize a large majority of this subsample's applianceholdings. Appliances withthe highest penetrationsand influenceon energy usage consisted mainly of gas space heaters, gas water heaters and electric space cooling.

. , Seventy-eight percent of the RSS single-family subsample fell into one of three AS1 definitions: gas space heating and electric central (GSHICAC); air-conditioning (2) gas space heating and electric wall/window air-conditioning (GSHIWWAC); and 

### (3) gas space heatingand no air-conditioning (GSHINOAC). In each AS1 definition, gas space heating includes both wall and floor furnaces. Walllwindow air-conditioning includes electric wall or window air-conditioner units and evaporative coolers. Evaporative coolers were included with WWAC because of the similarity of customers' usage profiles owningthis appliance and the profiles of customers owningwall air-conditioners. The AS1 definitions do not include consideration of water heater ownership, since gas water heaters tend to be present in homes with gas space heating. Therefore, omitting water heating iromtheASI definitionsaffords simplicity without sacrificing analyticaccuracy. by the customer descriptors within each ASI grouping. The analysis was performed at the SCE Division level, although after controlling for key customer descriptors and ASI, distinctions across SCE Divisions were mostly attributable to
. weather related effects. Appliance Stock Inventory Definitions A key concept underlying this analysis rests on segmenting RSS and REUC customers by their ASI. This segmentation allows the linkage ofthe energy usage characteristics for the two customer samples. The 1987 RSS provides appliance inventory data including the major appliances that were monitored and analyzed in the REUC project. Appliance penetrations were calculated for the singlefamily RSS subsample. Based on these penetrations, ASI definitions were developed as combinations of gas and electric appliances that characterize a large majority ofthis subsample's appliance holdings. Appliances with the highest penetrations and influence on energy usage consisted mainly ofgas space heaters, gas water heaters and electric space cooling. Seventy-eight percent of the RS$ single-family subsample fell into one of three ASI definitions; (1) gas space heating and electric central air-conditioning (GSH/CAC); (2) gas space heating and electric wall/window air-conditioning (GSH/WWAC); and (3) gas space heating and no air-conditioning (GSH/NOAC). In each ASI definition, gas space heating includes both wall and floor furnaces. Wall/window air-conditioning includes electric wall or window air-conditioner units and evaporative coolers. Evaporative coolers were included with WWAC because of the similarity of customers' usage profiles owning this appliance and the profiles of customers owning wall air-conditioners. The ASI definitions do not include consideration of water heater ownership, since gas water heaters tend to be present in homes with gas space heating. Therefore, omitting water heating. irom the ASI definitions affords simplicity without sacrificing analytic accuracy.

B-3Table 

### B-1 shows the shares of each of the three ASls bySCE Division. Customers with electric heating fell into the "Othe~" appliance stock category. Electric heatingcustomers were not used inthis analysis becauseof the small size 

### of this group. In addition to the AS1 definitions above, in some customer segments it was necessaryto considerthe additional effectsof poolownership.
Table 8-1 Appliance Stock inventory (ASI) Share 

### by SCE Division

GSHINOAC Other Total 

## North - East 

# - West 

# - South 

# - SCE 18% 40% 11% 

### 26% 23% 13 21 

### 20 16 18 

### 39 

### 12 51 40 

### 37 30 

### 2 7 18 18 

### 22 100 100 100 100 100

GSH

# - Gas Space Heating CAC.-Central Air Conditioner WWAC 

### - Window and/or Wall Air Conditioner NOAC

# - No Air Conditioner Customer Descriptors Demographic variables can account for energy usage through customers' appliance choice and desired levels of energy consumption. A number of demographic variables were available from the 

### RSS data, including other householdcharacteristics, dwelling characteristicsand implemented conservation measures. These customer data were used inthe analysis of the RSS data. Two'approacheswere used to identify a parsimoniousset of customer descriptors. First, regressions explaining monthly electricity usage were used to screen the characteristic variables. Second, annual energy usage profiles were computed Table 8-1 shows the shares ofeach ofthe three ASls by SCE Division. Customers with electric heating fell into the "Other" appliance stock category. Electric heating customers were not used in this analysis because ofthe small size ofthis 

## , group. In addition to the ASI definitions above, in some customer segments it was necessary to consider the additional effects ofpool ownership.
Table B-1 Appliance Stock Inventory (ASH Share

### by SCE Division

ASI North East West South SCE GSH/CAC 18% 40% 11 % 26% 23% GSH/WWAC 

### 13 21 20 16 18 GSH/NOAC 39 12 51 40 37 Other 30 27 18 18 22 Total 100 100 100· 100 100
Notes: GSH
- Gas Space Heating CAC.- Central Air Conditioner WWAC
- Window and/or Wall Air Conditioner

. NOAC
- No Air Conditioner

Customer Descriptors Demographic variables can account for energy usage through customers' appliance choice and desired levels of energy consumption. A number of demographic variables were available from the RSS data, including other household characteristics, dwelling characteristics and implemented conservation measures. These customer data were used in the analysis ofthe RSS data. Two·approaches were used to identify a parsimonious set ofcustomer descriptors. First, regressions explaining monthly electricity usage were used to screen the characteristic variables. Second, annual energy usage profiles were computedusing stratifications based on the customer descriptors. This second approach allowed further screeningof the explanatory power ofthe variables and provided independent validation of the regression results. Among the significant variables, affecting energy usage were Income, SqFt, Vintage and NumRes. Customers' reports of their implemented conservation actions was also usefulfor explaining usage insomecases. Customer characteristics can affect energy usage in two ways. First, customer characteristics may be related to the appliance holdings of a household. For example, if high income customers tend to own central air conditioners, the Incomevariablewould be useful in predictinga customer'sASI. Second, given a household's appliance inventory, some customer characteristicsmay be relatedto the intensity that appliances are used. Again, income level may influence a customer's decisionto coolto 68°F versus 70°F. Chi-square statistics were calculated to identify those customer descriptors that were correlated with ASI. Table 

### B-2 reports the chi-square statistic for each variable with respect to the three-way AS1 categorization. Inaddition, chi-square statistics were computed to determine the customer descriptors that explained pool ownership. All the statistics are highly significant; Income, SqFt, andVintage appear to demonstrate the strongest relationships. This result suggests that these customer descriptors could be used to predicta customer'sASI. Table B-2 Chi-Square Statisticsfor Customer Descri~tors that Discriminate between Appliance Stock Inventories (ASI)and PoolOwnership Customer Descri~tor 

# - AS1

. Pool

### owners hi^ Income 204* 240* Sq.Ft. 252* 245* Vintage 641

### * 83* NumRes 30** 43' Ethnic 40* 28* *Significant at the 0.001 probability level. **Significant at the 0.005 probability level. 

### using stratifications based 

### on the customer descriptors. This second approach allowed further screening ofthe explanatory power ofthe variables and provided independent validation ofthe regression results. Among the significant variables, affecting energy usage were Income, SqFt, Vintage and NumRes. Customers' reports oftheir implemented conservation actions was also useful for explaining usage in some cases. Customer characteristics can affect energy usage in two ways. First, customer characteristics may be related to the appliance holdings of a household. For example, if high income customers tend to 

### own central air conditioners, the Income variable would beuseful in predicting a customer's ASI. Second, given a household's appliance inventory, some customercharacteristics may be related to the intensity that appliances are used. Again, income level may influence a customer's decision to cool to 68°F versus 70°F. Chi-square statistics were calculated to identify those customer descriptors that were correlated with AS!. Table B-2 

### reports the chi-square statistic.for each variable with respect to thethree-way ASI categorization. In addition, chi-square statistics were computed to determine the customer descriptors that explained pool ownership. All the statistics are highly significant; Income, SqFt, and Vintage appear to demonstrate the strongest relationships. This result suggests that these customer descriptors could beusedto predict a customer's ASI. Table 8-2 

### Chi-Square Statistics for Customer Descriptors that Discriminate between Appliance Stock Inventories (AS!) and Pool Ownership !.:ustQmer**Descri~tor** 

### ASI Income 204* Sq:Ft. 252* Vintage 641' NumRes 30*' Ethnic 40* 'Significant at the.O.OOl probability level. "Significantilt the 0.005 probability level. Pool**Qwnershi~** 

### 240* 245*

83' 43* 28*Determining the important customer descriptors that influence intensity of appliance usage initially involves~identifying strata for each of the characteristic variables. The purpose ofthis-step 

### isto partially aggregate the categorical data collectedfor each 

### RSS survey variable without losing information that may help explain energy usage. Table 

### 8-3 lists for each variable, three ranges that were derivedfromanalyzingenergy usage profiles. Forexample, the lncome variable 

### is split into three categories "Low lncome," "Medium lncome," and "High income," corresponding to "under 

### $25,000," "$25,000 to 

### $49,999," and 

### "$50,000 and over." The resultsfor the lncome and SqFt variables are consistent with the cell definitions applied inthe designofthe REUCsample. Table B-3 RSS-Based Stratification of Customer Descri~tors Customer Descri~tor

Income

Vintage Stratum Low Medium High Income Income Income 

### <$25,000 $25,000-49,999 $50,000-t Small House Medium House Large House



### <I,250 1,250-1,749 1,750+ Very Old Old 

### Pre-1960 196x9 75 Recent 

### 1975+ NumRes Small Family Medium Familv Larve Familv 

### 1-2 3-4 5+

The strata listed for each characteristic variable inTable 

### 8-3are usedto segment the 

### RSS customers. Table 

### 8-4 lists for each subsample of customers stratified by division and AS1 those variables that, when used to segment customers, resulted in significant differences in annual energy usage profiles. In almost every case, Income, NumRes, and SqFt continue to explain differences in energy usage. In the cases of the GSHNWAC and GSHINOAC ASIS,vintage 

### of dwelling, when split before and after 

### 1960, also helped meaningfully segment annual usage profiles. This result 

### is interesting in that the expected effects of Title 

### 24, California buildingefficiencystandards implementedin 

### 1978,are not significantly Determining the important customer descriptors that influence intensity of appliance usage initially involves·identifying strata for each of the characteristic variables. The purpose of--this,step is to partially aggregate the ca,tegorical data collected for each RSS survey variable without losing information that may help explain energy usage. Table B-3 lists for each variable, three ranges that were derived from analyzing energy usage profiles. For example, the Income variable is split into three categories "Low Income," "Medium Income," and "High Income," corresponding to "under $25,000," "$25.000 to $49,999," and "$50,000 and over." The results for the Income and SqFt variables are consistent with the cell definitions applied in the design ofthe REUC sample.

Customer

. Descriptor

Income Sq Ft Vintage
NumRes 

### Table B-3 

### RSS-Based Stratification ofCustomer Descriptors Stratum Low Medium High Income Income Income <$25,000 $25,000-49,999 $50,000+ Small House Medium House Large House

<1,250 1,250-1,749 1,750+ VeOC Old Old Recent Pre-1960 1960-1975 1975+ Small**Famil~** Medium**Famil~** Large**Famil~** 1-2 3-4 5+

The strata listed for each characteristic variable in Table B-3 are used to segment the RSS customers. Table 8-4 lists for each subsample ofcustomers stratified by division and ASI those variables that, when used to segment customers, resulted in significant differences in annual energy usage profiles. In almost every case, Income, NumRes, and SqFt continue to explain differences in energy usage. In the cases of the GSH/WWAC and GSH/NOAC ASls, vintage of dwelling, when split before and after 1960, also helped meaningfully segment annual usage 

### profiles. This result is interesting in that the expected effects of Title 24, California building efficiency standards implemented in 1978, are not significantly
8-6observed in the data with the exception of the GSHICAC AS1 in the SCE Northern Division.

. - 

# - - Table B-4 Customer Descriptors that Meanindully DiscriminateAnnual Usage Profilesby Appliance Stock Inventory (ASI) and SCE Division

GSHICAC SqFt SqFt NumRes NumRes Income income Pool Pool Vintage GSHIWWAC NumRes SqFt NumRes Income Pool GSHINOAC SqFt SqFt NumRes NumRes income Pool Pool Vintage East 

# - SqFt NumRes Income Pool' SqFt NumRes Income Vintage SqFt NumRes income VintageSqFtNumRes Income Vintage *Samplesize too small for analysis. observed in the data with the exception of the GSH/CAC ASI in the SCE Northern Division. Table 8-4 Customer Descriptors that Meaningfully Discriminate Annual Usage Profiles by Appliance Stock Inventory (ASI) and SCE Division.
ASI North GSH/CAC SqFt NumRes Income Pool Vintage GSH/WWAC NumRes

GSH/NOAC SqFt NumRes Income Pool 'Sample size too small for analysis. South SqFt NumRes Income Pool SqFt NumRes Income Pool SqFt NumRes Pool Vintage East Sq Ft NumRes Income poor SqFt NumRes Income Vintage 

## •

SqFt NumRes Income· Vintage SqFt NumRes Income VintageAppendix C LOAD DATA EDITINGAND VALIDATION REUC DATA VALIDATION METHODS Before REUC appliance load profiles and UEC estimates are calculated, it is necessary to extensively screen the entire data set. for validity. Invalid and misleadingdata can arise because ofthefollowingfactors: Defective or Inaccurate Load Monitoring Devices. It 

### is possiblethat an appliance load monitor, inconstant use over an extended period of time, can fail or can provide inaccurate readings. Inappropriate Pulse Multipliers. The monitors record energy usage with pulses that are converted to the appropriate units (i.e., watt-hours or cubic feet)' with pulse multipliers. Extensivevalidation of these multipliers 

### is required to ensure the validity ofthe load research results. System Failure. Misleading load readings can result from voltage surges and power spikes. In addition, a source of possiblesystem failure is inthe transmission and recording link between the load monitor device and the REUC computer system. Three different levels of damage can result from errors that are found ir; end-use load data. First, 

### a 5-minute interval can be found to 

### be invalid for a single monitoring channel. This can occur because of system failure. Second, a 

### 5- minute interval can be found to be invalidfor all channels inthe entire data set. This again is usually attributed to system failure. Finally, all readings for a long period of time (a month or more) can be invalidfor 

### a particular channel. This most serious type of error can occur due to misidentification of channels or becauseof defective load monitoringdevices. DATA VALIDATION AND EDITING PROCESS The data validation process takes place in three phases in order to screen out inaccurate readings that can result from.data collection problems described .above. Appendix C 

### LOAD DATA EDITING AND VALIDATION REUC DATA VALIDATION METHODS Before REUC appliance load profiles and UEC estimates are calculated, it is necessary to extensively screen the entire data set for validity. Invalid and misleading data can arise because ofthe following factors:
• Defective or Inaccurate Load Monitoring Devices. It is possible that an appliance load monitor, in constant use over an extended period of time, can fail or can provide inaccurate readings.
• Inappropriate Pulse Multipliers. The monitors record energy usage with pulses that are converted to the appropriate units (i.e., watt-hours or cubic feet)' with pulse multipliers. Extensive validation of these multipliers is required to ensure the validity ofthe load research results.
. • System Failure. Misleading load readings can result from voltage surges and power spikes. In addition, a source of possible system failure is in the transmission and recording link between the load monitor device and the REUC computer system. Three different levels of damage can result from errors that are found in end-use load data. First, a 5-minute interval can be found to be invalid for a single monitoring channel, This can occur because of system failure. Second, a 5- minute interval can be found to be invalid for all channels in the entire data set. This again is usually attributed to system failure. Finally, all readings for a long period of time '(a month or more) can be invalid for a particular channel. This most serious type of error can occur due to misidentification of channels or because ofdefective load 'monitoring devices. 

### DATA VALIDATION AND EDITING PROCESS The data validation process takes place in three phases in order to screen out inaccurate readings that can result from. data collection problems described ·above.

r.1

# . Phase 1

### : 

### Self-validation. Comparison of load readings against a credible estimateof connected load.



# . Phase 2: 

### Cross-Validation. ' 

### Comparison of load readings versuswhole house readings. Phase 3: 

### Raw Data Testingand Editing. Testingfor monthly appliance range accuracy and appliance usage pattern detection. The Self-validation phase compares load readings against a credible estimate of connected load. The entire set is checkedfor unreasonable readingsor spikes. Maximum "reasonable" connected loads for each appliance are shown in Table C-1. 

### Data readingsthat exceedthese values are markedas missing inthe source data set. These limits are conservative, set high to ensure that this phase of validationonlyexcludesvalues thatareclearly invalid. Furthermore, as part of the Self-Validation phase, a minimum of one week of each channel inthe REUC sample was graphically screened by a 

### load research analyst using ~oad~iew?~ This MicrosoftWindows-based loaddataanalysis tool allows the researcher to determine if the raw load data reflect appropriate patternsof usage by graphingone day of data at atime. The researchercan also quickly scroll throughsuccessivedays ofdata. LoadViewwas invaluable for verifyingand reconciling the pulse multipliers described above. By viewing all major end-usechannels andwhole house load on one screen for many days of data, it 

### was possible to refine estimates of the pulse multipliers by matching estimated usage patternsto knownappropriate appliance usage patterns. Duringthe Cross-Validation phase, the eniire data set is checked to make sure that no appliance load reading is 

### greater than the whole house reading. Data thatfail thistest are markedas missing inthe source dataset. Duringthe Raw DataTesting and Editingphase, the entire data set is

### subjectedto additional rigoroustestsfor validity. Thefirst set of tests compares appliance load data with reasonable range estimates of likely usage. Inorder to easily manage the first set of tests, the original data set consisting of 5-minute observationsfor each of the monitored appliances is 

### reducedto a data set containing the weekly 

### • Phase 1: Self-Validation. Comparison of load readings against a credible estimate ofconnected load.
• Phase 2: Cross-Validation.' Comparison of load readings versus whole house readings.
• Phase 3: Raw Data Testing and Editing. Testing for monthly appliance range accuracy and appliance usage pattern detection. The Self-Validation phase compares load readings against a credible estimate of connected load. The entire set is checked for unreasonable readings or spikes. Maximum "reasonable" connected loads for each appliance are shown in Table C-1. Data readings that exceed these values are marked as missing,in the source data set. These limits are conservative, set high to ensure that this phase of validation onlyexcludesvalues thatareclearly invalid. Furthermore, as part of the Self-Validation phase, a minimum of one week of each channel in the REUC sample was graphically screened by a load research analystusing LoadView.TM 

### This MicrosoftWindows-based load data analysis tool allows the researcher to determine if the raw load data reflect appropriate patterns ofusage by graphing one day ofdata at a time. The researcher can also quickly scroll through successive days ofdata. LoadView

### was invaluableforverifying and reconciling the pulse multipliers described above. By viewing all major end-use channels and whole house load on one screen for many days ofdata, it was possible to refine estimates of the pulse multipliers by matching estimated usage patterns to known appropriate applianceusage patterns
.

. During the Cross-Validation phase, the entire data set is checked to make sure

that no appliance load reading is greater than the whole house reading. Data thatfail this test are marked as missing in the source data set. During the Raw Data Testing and Editing phase, the entire data set is subjected to additional rigorous tests for validity. The first set oftests compares appliance load data with reasonable range estimates of likely usage. In order to easily rnanage the first set oftests, the original data set consisting of 5-minute observations for each of the monitored appliances is reduced to a data set containing the weekly

C-2usagefor eachappliance. This provides aweekly totalwhole householduse anda weekly total usefor each appliance. During Phase 3, the first test compares weekly appliance total usage (UECSUM) with a "reasonable" range to determine whether it should be included in the REUC study. Valid usage readingsfor particular appliances should fall into the "reasonable" ranges as defined in Table C-2. Data that do not fall into these ranges are markedas ipvalidand removedfrom the data set. Because of the possibility of misidentified channels within the data set, Phase 3 also determines whether a given set of daily load observations for an appliance reflects behavior expected of that appliance. This test uses the Appliance Consecutive Use Period Analyzer (ACUPA), a software package that calculates the amount of time a given appliance is drawing current and the amount of time it is off. The ACUPA incorporates a complex system for filtering out small current draws from, for example, a micro'waveoven clock. By screeningout these small current draws, ACUPA prevents inaccurateusage pattern profiles from entering the data set. ACUPA works by takingadvantage of the fact that certain aspects of usage patterns for specific appliances are well known, and any deviation is unlikely. For example, refrigerators and freezers cannot have load readings of zero for 24 consecutive hours. It is also extremely unlikely that heating and cooling appliances will be on continuously for 24 consecutive hours. Table C-3 lists the usage patterncriteriathat ACUPA employsto flag invalid or misidentified appliance channels. ONTIME refers to the mean numbero'f consecutivehoursan applianceison. 

### Ifan appliance channelhasan ONTIMEgreater'than

### is listed, it

### is flagged. OFFTIME similarly refers tothe meannumber of consecutive hours an appliance isoff. Ifan appliance channel has an OFFTIME greater than orequal to that listed, itis alsoflagged. Data that fail tests during Phase 3 of the Data Validation Process are flagged according to the Problem Codes listedinTable C-4. The three-phase data "alidation system thoroughly tests the data set to remove inaccurate load readings. With the data set'screened in this manner, highly accurate load profiles for each applianceare then created. 

### usagefor each appliance. This provides aweekly total whole household use and a weekly total use for each appliance. .' 

### During Phase 3, the first test compares weekly appliance total usage (UECSUM) with 

### a "reasonable" range to determine whether it should**b~** 

### included in the REUC study. Valid usage readings for particular appliances should fall into the "reasonable" ranges as defined in Table C-2. Data that do not f<:lll into these ranges are marked as invalid and removed from the data set. Because of the possibility of misidentified channels within the data set, Phase 3 also determines whether a given set of daily load observations for an appliance reflects behavior expected of that appliance. This test uses the Appliance Consecutive Use Period Analyzer (ACUPA), a software package that calculates the amount oftime a given appliance is drawing current and the amount oftime it is off. The ACUPA iricorporates a complex system for filtering out small current draws from, for example, a microwave oven clock. By screening out these small current draws, ACUPA prevents inaccurateusage pattern prqfiles from entering the data set. ACUPA works by taking advantage ofthe fact that certain aspects of usage patterns for specific appliances are well known, and any deviation is unlikely. For example, refrigerators and freezers cannot have load readings of zero for 24 consecutive hours. It is also extremely unlikely that heating and cooling appliances will be on continuously for 24 consecutive hours. Table C-3 lists the usage pattern criteria that ACUPA employs to flag invalid or misidentified appliance channels. ONTIME refers to 

### the mean number ofconsecutive hours an appliance is on. Ifan appliance channel has an ONTIME greaterthan is listed, it is flagged. OFFTIME Similarly refers to the mean number ofconsecutive hours an appliance is off. Ifan appli'ance channel has an OFFTIME greater than orequal to thatlisted, itis alsoflagged. Data that fail tests during phase 3 of the Data Validation Process are flagged according to the Problem Codes listed in Table C-4. The three-phase data validation system thoroughly tests the data set to remove inaccurate load readings. With the data set'screened in this manner, highly accurate load profiles for each appliance are ,then created.Table C-1 "Reasonable" Connected Load Estimates

A~oliance Electric Clothes Dryer Electric Oven Range GasClothes Dryer Gas'Water Heater Gas Furnace Electric HVAC Electric Heat Pump AC Heat Pump Water Heater 

### - Gas Range Maximum Acceptable Connected Load Estimate CONL 

### = 7kWhIhour CONL = 

### 1OkWh/hour CONL =40 c.f./hour CONL =59 c.f./hour CONL = 150c.f./hour CONL 

### = 12kWli/hour CONL = 12kWhlhour CONL =6kWh CONL 

### =50c.f./hqur
Table C-2 "Reasonable" RangesforWeeklvAodiance Use
Aooliance Electric Clothes Dryer Electric Range Oven Whole House GasClothes Dryer Gas Water Heater Heat Pump Water Heatel Gas Range Lower Bound N/A N/A UECSUM

### < 1kwh N/A N/A N/A 

### N

/A Uoper Bound 4 UECSUM->2OOkWh UECSUM

### > 70 kwh UECSUM 

### > 1000kWh 

### UECSUM

### > 800cf UECSUM

### > 2000cf UECSUM

### > 200kWh UECSUM

### > 700cf 

### Table C-l
. "Reasonable" Connected load Estimates
Appliance Electric Clothes Dryer Electric Oven Range Gas Clothes Dryer Gas· Water Heater Gas Furnace Electric HVAC Electric Heat Pump AC Heat Pump Water Heater

_ Gas·Range


### Table C-2 Maximum Acceptable Connected Load Estimate CO,,!L 

### = 7kWh/hour CONL = lOkWh/hour. CONL = 40 c.f./hour CONL

## =59 c.f./hour CONL

## =150c.f./hour CONL 

### = 12kWh/hour CONL 

## = 12kWh/hour CONL 

### '= 6kWh CONL 

### = 50 c.f./hqur



### "Reasonable" Ranges for Weekly Appliance Use
Appliance Lower Bound Upper Bound Electric Clothes Dryer N/A UECSUM> 200kWh Electric Range Oven N/A UECSUM > 70 kWh Whole House UECSUM 

### < 1kWh UECSUM > 1000kWh Gas Clothes Dryer N/A UECSUM > 

### BOOd Gas Water Heater N/A UECSUM > 2000cf Heat Pump Water Heater N/A UECSUM > 200kWh Gas Range N/A UECSUM> 

### lOOd

C-4 

## •Table C-3' ACUPA Error FlagCriteria

ADoliance Electric Clothes Dryer Electric Oven Range Whole House Gas Clothes Dryer Gas Water Heater Heat Pump Water Heater Ga's Range ONTIME ONTIME

### > 15 hours ONTIME

### > 12 hours NIA ONTIME

### > 15 hours N/A NIA ONTIME

### > 12 hours OFFTIME N/A N/A OFFTIME=0.5 hours N/A OFFTIME=24 hours OFFTIME

### =24 hours NIA Table C-3 ACUPA Error Flag Criteria
Appliance ONTIME OFFTIME Electric Clothes Dryer ONTIME.> 15 hours N/A Electric Oven Range ONTIME > 12 hours N/A Whole House N/A OFFTIME

## =0.5 hours Gas Clothes Dryer ONTIME > 15 hours N/A Gas Water Heater N/A OFFTIME

## =24 hours Heat Pump Water Heater N/A OFFTIME

## =24 hours Gas Range ONTIME > 12 hours N/A



# ..

C-5Table C-4 Kevto Invalid Data FlaggedDuringDataValidation 

### Process
Problem Code 

# - Test PROBUEC=O The appliance had an acceptable weekly usage. (acceptableWEEKLY USAGE) PROBUEC=1 The appliance had an inordinately high weekly usage. (WEEKLY USAGEtoo high) PROBUEC=2 The appliance had an inordinately lo'w weekly usage. (WEEKLY USAGEtoo low) PROBCONL=O The appliance had an acceptable connected load estimate. (acceptableCONNECTED LOAD) PROBCONL=l The appliance had an inordinately high connected load estimate. (CONNECTEDLOADtoo high) PROBCONL=2 The appliance had an inordinately low connected load estimate. (CONNECTEDLOAD too low) PROBON=O The appliance had an acceptable mean number of consecutive usage hours. (acceptableONtime) PROBON=l The appliance had an inordinately high mean number of consecutive usage hours. 

### (ONtoo much) PROBON=2 The appliance had an inordinately low mean number of consecutiveusage hours. (notONenough) PROBOFF=O The appliance had an acceptable meannumber of consecutive non-usage hours. (acceptableOFF time) PROBOFF=l The appliance had an inordinately high mean number of consecutive non-usage hours. (OFFtoo much) PROBOFF=2 The appliance had an inordinately low mean number of consecutive non-usage hours. (not OFF enough) 

### Table C-4 Key to Invalid Data Flagged During DataValidation Pr()cess
Problem Code PROBUEC=O PROBUEC=l
PROBUEC=2
PROBCONl=O PROBCONl=l

. PROBCONL=2

PROBON=O
PROBON=l PROBON=2 PROBOFF=O PROBOFF=l
PROBOFF=2 Test The appliance had an acceptable weekly usage. (acceptable WEEKLY USAGE) The appliance had an inordinately high weekly usage. (WEEKLY USAGE too high) The appliance had an inordinately low weekly usage. (WEEKLY USAGE too low) The appliance had an acceptable connected load estimate. (acceptable CONNECTED LOAD) The appliance had an inordinately high connected load estimate. (CONNECTED LOAD too high) The appliance had an inordinately low connected load estimate. (CONNECTED LOAD too low) The appliance had an acceptable mean number of

### c~nsecutive usage hours. (acceptable ON time) The appliance had an inordinately high mean number of consecutive usage hours. (ON too much) The appliance had an inordinately low mean number of consecutive usage hours. (not ON enough) The appliance had an acceptable mean. number of consecutive non-usage hours. (acceptable OFF time) The appliance had an inordinately high mean number of. consecutive non-usage hours. (OFF too much) The appliance had an inordinately low mean number of consecutive non-usage hours. (not OFF enough)

C-6GLOSSARY OFTERMS AS1 Appliance Stock Inventory. BEST Behavioral Engineering StatisticalTechniques. CAC Central Air ~ondhionin~. COP Coefficient of Performance. Cost Payback Approach REUC Project methodology for comparing the cost and


## . energy efficiency investment characteristics of gas and CP&B Customer Preference and Behavior (from CLASSIFYTM, EPRl EM-5908). Electric Induction Subsample Mission Viejo participants that received new electric induction ranges. Electric Sample REUC Participants that received new electric heat.pumps, heat pump water heaters, electric clothes dryers and (for the MissionViejo Sample)electric induction ranges. Energy At Pointof Use 1 kwh = 3.412 kBTU (i.e. not including generation and transmission losses). Ethnic Variable representing resident's ethnicity. Gas Sample REUC Participantsthat received new gas furnace/electric air conditioners, gas water heaters, gas clothes dryers and.gas ranges. GSH Gas Space Heating. HSPF HeatingSeason Performance Factor. IFS Image Factor Score. IMR Integrated Market Research. lrvine Sample The second 50 REUC participant households, located in Iwine, California(inOrangeCounty). ASI BEST CAC COP Cost Payback Approach

CP&B
Electric Induction Subsample
Electric Sample
Energy At Point of Use
Ethnic Gas Sample

GSH HSPF IFS IMR Irvine Sample 

### GLOSSARY OF TERMS Appliance Stock Inventory. Behavioral Engineering Statistical Techniques. Central Air Conditioning. Coefficient of Performance.
REUC Project methodology for comparing the cost and energy efficiency investment characteristics of gas and electric appliances. Customer Preference and Behavior (from CLASSIFYTM, EPRI EM-5908).
Mission Viejo participants that received new electric induction ranges. REUC Participants that received new electric heat.pumps, heat pump water heaters, electric clothes dryers and (for the Mission Viejo Sample) electric induction ranges.
1 kWh = 3.412 kBTU (i.e. not including generation and transmission losses). Variable representing resident's ethnicity. REUC Participants that received new gas furnace/electric air conditioners, gas water heaters, gas clothes dryers and'gas ranges. Gas Space Heating. Heating Season Performance Factor. Image Factor Score. Integrated Market Research. The second 50 REUC participant households, located in Irvine, California (in Orange County).

G-l

### LCC LDAW Load Data Load Profile Load Shape Load Shape Modeling

MBTU MissionViejo SampleNOACNumRes 

### Quality Image RAEUS RSS SEER SqFt Therm 

### U

### EC Value Image Thousand ~ritish thermal units; kBTU=lOO*Therms; kBTU=3.4127*kWh. Life CycleCost. The Load DataAnalysis Workstation(EPRI RP2342-8). Five-minute readings ofenergy usage. Hourly (or half-hourly) average load over the 24-hour period from midnight to midnight; same as load shape. Hourly (or half-hourly)average load over the 24-h~ur'~eriod from midnight to midnight; same as load profile. The process of using customer characteristics to explain customer energy usage. Softwaretoolfor visually screeningraw loaddata. Million British thermal units; MBTU=lOOO*kBTU. The first 42 REUC participant households, located inMission Viejo, California (inOrangeCounty). NoAir Conditioning. Number of Household Residents. 

### A composite of the survey respondent's Safety, Convenience, Service and Reliability responses. ResidentialAppliance End-Use Survey. Residential Saturation Survey. Seasonal Energy Efficiency Ratio. Household Square.Feet. A quantity of energy equal to 100,000 British thermal units, typical billingunitfor naturalgas. Unit Energy Consumption. A composite of the survey respondent's Appliance Cost, General Expenseand Monthly Utility Bill responses. kBTU
LCC LDAW Load Data Load Profile
Load Shape
Load Shape Modeling
LoadViewTM MBTU Mission Viejo Sample NOAC

, NumRes



### Quality Image RAEUS RSS SEER SqFt Therm
UEC 

### Value Image Thousand British ther,mal units; kBTU=lOO*Therms; kBTU=3.4127*kWh. Life Cycle Cost. The Load Data Analysis Workstation (EPRI RP2342-8l. Five-minute readings ofenergy usage. Hourly (or half-hourly) average load over the 24-hour period from midnight to midnight; same as load shape. Hourly (or half-hourly) average load over the 24-hour'period from midnight to midnight; same as load profiIe.
The process of using customer characteristics to explain customer energy usage. Software toolforvisually screening raw load data. Million British thermal units; MBTU=1000*kBTU.
The first 42 REUC participant households, located in Mission Viejo, California (in Orange County). No Air Conditioning. Number of Household Residents. A composite of the survey respondent's Safety, Convenience, Service and Reliability responses. Residential Appliance End-Use Survey. Residential Saturation Survey. Seasonal Energy Efficiency Ratio. Household Square ,Feet. A quantity of energy equal to 100,000 British thermal units, typical billing unitfor natural gas. Unit Energy Consumption. A composite of the survey respondent's Appliance Cost, General Expense and Monthly Utility Bill responses.

G-2Vintage WWAC House Vintage. WalliWindow Air Conditioning. Vintage WWAC House Vintage. WallIW.indow Air Conditioning.

G-3