Keywords: Customer attitudes End-use consumption Energyefficiency Load research



# Residential EnergyUsage Comparison: Findings

Prepared by Quantum Consulting Inc. Berkeley, California EPRI CU-7392 Project 2863-3 Final Report August 1991 Keywords: Customerattitudes End-use consumption Energyefficiency Load research



# Residential EnergyUsage Comparison: Findings

Prepared by Quantum Consulting Inc. Berkeley, California EPRI CU-7392 Project2863-3 Final Report August 1991

# REPORT 

# s u M 

# MAR Y

INTERESTCATEGORIES Demand-side planning Marketassessment Lighting and appliances KEYWORDS Customerattitudes End-use consumption Energyefficiency Load research

EPRI CU-7392s 

# ResidentialEnergyUsageComparison: Findings The load shapes of major residential electricand gas applianceswere compared in an integrated load and market research project in southern California. The energyconsumption data provide abenchmarkfor comparing the costs ofalternativetechnologies; the market research data relate customerattitudeswith appliance load shapes.

BACKGROUND Comparisons ofthe relative energy consumption of gas and electric household appliances havetended to rely on laboratoryanalysis and computersimulations. Bycontrast, this study, cosponsored by Southern California Edison Co. (SCE), looked at 92 similar households and measured the actual energy consumption oftheir appliances, eithergas orelectric. OBJECTIVES To measurethe actual energyconsumption ofcomparable modern electricand gas appliances; to determinethe factors behind customerappliance decisions; to determine the relationships between a customer's characteristics and the corresponding load shape and energy usage. APPROACH The projectteam identified 92 homes in Orange County, California, southeastof Los Angeles, to serve asthe studysample. The homeswere owneroccupied, single-family homes in a high-growth area and were representative of typical newer homes in the SCE service area. In each home, newapplianceswere installed forspace conditioning, water heating, cooking, and clothes drying. Half ofthe homes received electrical equipment and halfgas. Load monitoring equipmentwas first tested in eightout-of-sample homes and then installed on each individual appliance in the 92-home sample. Energydata were collected regularly overa period ofapproximatelytwo years. The projectteam surveyed the households several timestoobtain information on theirenergy needs and theiracceptance ofthe newappliances. RESULTS All comparisons ofenergyconsumption were made in terms ofsite energywith 1 kWh = 3.412 kBtu. Average annual per-household energyconsumption for cooking was 3.24 MBtu forthe gas sites and 1.31 MBtu forthe electric sites. Clothes drying consumed 4.32 MBtu forgas sites and 3.66 MBtu for electric sites. In winter, gas sites consumed an average 20.49 MBtu forspace conditioning, and electric sites used an averageof7.50 MBtu. Customersurveys showed that electric heat pumps forspace heating were well accepted. Ofthe householdswhose gasfurnaces were replaced with heat pumps, 50% changed their preferences from gasto electricity. The acceptance ratings for electric heat pump water heaterswere notas high as for gas water heaters. Participants found the heat pumps slowto respond. The acceptance ratings for electric cooking applianceswere strong. Ofthe households retrofitted from gas ranges
Electric Power Research Institute INTERESTCATEGORIES Demand-side planning Marketassessment Lighting and appliances KEYWORDS Customerattitudes End-use consumption Energyefficiency Load research

EPRI CU-7392s 

# RE PORT 

# s u M 

# MAR Y

ResidentialEnergyUsageComparison:Findings The loadshapesof majorresidential electricandgasapplianceswere . compared in an integrated load and market research project in southern California.Theenergyconsumptiondata provideabenchmarkfor comparingthecostsofalternativetechnologies; the market research data relatecustomerattitudeswith appliance loadshapes.

BACKGROUND Comparisonsofthe relative energyconsumption ofgasand electric household appliances havetendedto relyon laboratoryanalysisand computersimulations
.Bycontrast,thisstudy, cosponsored bySouthern California Edison Co
.(SCE), looked at92 similar householdsand measuredthe actual energy consumptionoftheirappliances, eithergasorelectric. OBJECTIVES To measurethe actual energyconsumptionofcomparable modern electricand gasappliances;to determinethefactors behindcustomerappliancedecisions;to determinethe relationships between a customer'scharacteristicsandthecorresponding loadshapeand energy usage. APPROACH The projectteam identified92 homes in OrangeCounty, California, southeastof LosAngeles, to serveasthe studysample.The homeswere owneroccupied, single-family homes in ahigh-growthareaand were representative of typical newer homes inthe SCE servicearea. In each home, newapplianceswere installedforspaceconditioning, water heating, cooking, andclothesdrying
. Half ofthe homes received electricalequipmentand halfgas. Load monitoring equipmentwasfirsttested in eightout-of-sample homesandthen installedon each individualappliance in the 92-homesample. Energydatawerecollected regularly overa periodofapproximatelytwo years. The projectteamsurveyedthe householdsseveraltimestoobtain informationontheirenergy needsandtheiracceptanceofthe new appliances. RESULTS All comparisonsofenergyconsumptionwere made in termsofsite energywith 1kWh = 3.412 kBtu. Average annual per-householdenergyconsumptionfor cookingwas3.24 MBtuforthegassites and 1.31 MBtuforthe electric sites
.Clothesdryingconsumed4.32 MBtuforgas sitesand3.66 MBtufor electric sites
. In winter, gassites consumed an average 20.49 MBtuforspace conditioning, and electricsites usedan averageof7.50 MBtu
. Customersurveysshowedthat electric heatpumpsforspace heatingwere well accepted
.Ofthe householdswhose gasfurnaces were replacedwith heat pumps, 50%changedtheir preferencesfrom gasto electricity
.Theacceptance ratingsfor electric heat pumpwater heaterswere notas high asfor gaswater heaters
. Participantsfound the heat pumpsslowto respond
.The acceptance ratingsfor electriccookingapplianceswere strong. Ofthe households retrofitted from gas ranges
Electric Power Research Instituteto electric induction stoves, 50% expressed apreference for electricity as afuel for cooking. EPRI PERSPECTIVE This project provides new information about how comparable gas and electricappliances are used and accepted in customer homes. An overview report on this research was published earlier (EPRI report CU-6952).Utilities in similarclimate zones can usethese dataon actual energyconsumption and load shapesto estimatethe costeffectiveness and attractiveness ofdifferentappliance optionsfortheir customers. The projectalso provides an example of how load shapes maydifferby marketsegment, including the energy-needs-based segments developed under EPRI's customerpreference and behavior project (EPRI report EM-5908). This can help utilities assessthe likely impact ofattracting particulartypes ofcustomers through targeted marketing programs. PROJECT RP2863-3 Project Managers: PeterCleary; Steven D. Braithwait; Paul C. Meagher Customer Systems Division Contractor: Quantum Consulting Inc. Forfurther information on EPRI research programs, call EPRI Technical Information Specialists (415) 855-2411. to electric inductionstoves,50%expressed apreferenceforelectricityas afuel forcooking. EPRI PERSPECTIVE Thisproject provides new information about how comparablegasand electricappliancesare usedand accepted in customer homes. An overview report on this research was published earlier (EPRI report CU-6952).Utilitiesin similarclimatezones can usethese dataon actualenergyconsumption and load shapesto estimatethecosteffectiveness and attractivenessofdifferentapplianceoptionsfortheir customers.The projectalso providesan exampleofhow load shapes maydifferby marketsegment, includingthe energy-needs-basedsegmentsdeveloped underEPRI'scustomerpreferenceand behaviorproject (EPRI report EM-5908)
.Thiscan help utilities assessthe likelyimpact ofattracting particulartypesofcustomersthroughtargeted marketing programs. PROJECT RP2863-3 Project Managers:PeterCleary; Steven D. Braithwait; Paul C. Meagher Customer Systems Division Contractor: Quantum Consulting Inc. Forfurther informationon EPRI research programs, call EPRI Technical Information Specialists(415) 855-2411.

## DISCLAIMEROFWARRANTIESAND LIMITATIONOFLIABILITIES THIS REPORT WAS PREPARED BYTHE ORGANIZATION(S) NAMED BELOW AS AN ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. (EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THE ORGANIZATlON(S) NAMED BELOW, NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM: (A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED. (I) WITH RESPECT TO THE USE OF ANY INFORMATION. APPARATUS, METHOD. PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS REPORT, INCLUDING MERCHANTABILITYANDFITNESS FORAPARTICULAR PURPOSE, OR (II)THAI' 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 (B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHATSOEVER (INCLUDING ANYCONSEQUENTIALDAMAGES. EVEN IF EPRI ORANY EPRI REPRESENTATIVE HASBEENADVISEDOFTHE POSSIBILITYOFSUCH DAMAGES) RESULTING FROMYOURSELECTIONORUSEOFTHISREPORTORANYINFORMATION, APPARATUS. METHOD. PROCESS, ORSIMILAR ITEM DISCLOSED INTHIS REPORT. ORGANIZATlON(S) THAT PREPARED THIS REPORT: QUANTUM CONSULTING INC.

Printedon RecycledPaper 

# ResidentialEnergyUsage Comparison: Findings CU-7392 Research Project 2863·3 Final Report, August 1991

Prepared by QUANTUM CONSULTING INC. 2030 Addison Street Berkeley. California94704 Principal Investigators B. A. Smith R. 1. Uhlaner 1. 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 Palo Alto, California 94304 EPRI Project Managers S. Braithwait P. Cleary P. Meagher Demand-Side Management Program CustomerSystems Division 

## DISCLAIMEROFWARRANTIESAND LIMITATIONOFLIABILITIES THIS REPORT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. (EPRI)
.NEITHER EPRI,ANY MEMBER OF EPRI, ANY COSPONSOR,THE ORGANIZATlON(S) NAMED BELOW,NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM: (A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED, (I) WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, METHOD,PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS REPORT, INCLUDING MERCHANTABILITYAND FITNESS FORAPARTICULAR PURPOSE,OR(II)THAT SUCH USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED RIGHTS, INCLUDINGANY PARTY'SINTELLECTUAL PROPERTY,OR (III) THATTHIS REPORT IS SUITABLE TO ANY PARTICULAR USER'S C IRCUMSTANCE;OR (B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHATSOEVER (INCLUDINGANYCONSEQUENTIAL DAMAGES,EVEN IF EPRIORANY EPRI REPRESENTATIVEHASBEENADVISEDOFTHE POSSIBILITYOFSUCHDAMAGES) RESULTING FROM'rUURSELECTIONORUSEOFTHIS REPORTORANYINFORMATION, APPARATUS,METHOD,PROCESS,ORSIMILAR ITEM DISCLOSED INTHIS REPORT ORGANIZATION(S) THAT PREPARED THIS REPORT: QUANTUM CONSULTING INC.

Printedon RecycledPaper 

# ResidentialEnergyUsage C

omparison: F indings CU-7392 Research Project2863-3 Final Report, August 1991

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

Preparedfor SouthernCalifornia EdisonCo. 2244WalnutGroveAvenue Rosemead, California91770 Project Manager P. Delaney Electric Power Research Institute 3412 HillviewAvenue Palo Alto, California94304 EPRI Project Managers S. Braithwait P

.Cleary P. Meagher Demand-Side ManagementProgram CustomerSystems DivisionElectric Power Research Institute and EPRI are registered service marks of Electric Power Research Institute. Inc. Copyright © 1991 Electric Power Research Institute. Inc. All rights reserved. ORDERING INFORMATION Requests for copies of this report should be directed to Research Reports Center (RRC), Box 50490, Palo Alto, CA 94303, (415) 965-4081. There is no charge for reports requested by EPRI member utilities and affiliates, U.S. utility associations, U.S. government agencies (federal, state, and local), media, and foreign organizations with which EPRI has an information exchange agreement. On request, RRC will send a catalog of EPRI reports. Electric Power Research Institute and EPRI are regis tered service marks of Electric P ower Research Institute. Inc
. Copyright © 1991 Electric Power Research Institute. Inc. All rights reserved
. ORDERING INFORMATION Requests for copies of this report should be directed to Research Reports Center (RRC), Box 50490, Palo Alto, CA 94303, (415) 965-4081
.There is no charge for reports requested by EPRI member utilities and affiliates, U
.S
. utility associations, U.S
. government agencies (federal, state, and local), media, and foreign organizations with which EPRI has an information exchange agreement. On request, RRC will send acatalog of EPRI reports.

### 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 ofthe site energy used by comparable gas 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 45 percent. In contrast, electric induction stoves were well accepted; 80 percent would recommend their induction stoves to afriend. The REUC project data were also used to develop 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.



### iii 

### 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 ofthe 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 ofthe site energy used by comparable gas 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 

### 45 percent. In contrast, electric induction stoves were well accepted; 

### 80 percent would recommend their induction stoves to afriend. The REUC project data were also used to develop 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.

iiiEPRI FOREWORD


### 'f

### Ii ; re idential energy usage comparison (REUC) project has been ajointeffortby EPRI and


### , uth-rnCalifornia EdisonCo.tomeasureandcomparetypicalenergycomsumption patterns 

### )1 'oergy-efficient, residential electric and gas appliances. This innovative demand-side pr 

### j thas produced valuable information about howactual households use electricityand g for paceconditioning, water heating, cooking, and clothes drying. This report presents the design, analysis methods, and results from the REUC project. 

### Steven D. Braithwait; PeterCleary; Paul C. 

### Meagher, ProjectManagers 

### Customer Systems Division

v EPRI FOREWORD

1

### Ii , r'sidentialenergyusage comparison (REUC) projecthas been ajointeffortby EPRI and 

### llthernCaliforniaEdisonCo.tomeasureandcomparetypicalenergycomsumptionpatterns )1energy-ef ficient, residential electric and gas appliances. This innovative demand-side pr jc thas producedvaluable informationabouthowactualhouseholdsuseelectricityand 

## gt·forspaceconditioning,waterheating,cooking,andclothesdrying
. This reportpresents 

### the :design,analysis methods, and resultsfromthe REUC project. 

### Steven D
. 8raithwait;PeterCleary; Paul C. 

### Meagher, ProjectManagers Customer Systems Division

v

### ACKNOWLEDGEMENTS

The Quantum Consulting project staff wishes to acknowledge the assistance and contributions of several organizations and persons in conducting this research. In particular, we want to acknowledge the substantive 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.

VII 

### ACKNOWLEDGEMENTS

The Quantum Consulting project staffwishes to acknowledge the assistance and contributions ofseveral organizations and persons in conductingthis research. In particular, we want to acknowledge the substantive 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.

VIICONTENTS 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 Historyof REUC Data Collection THE REUC CUSTOMER SAMPLE Introduction REUC Sample Design Demographic Description of the 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
ix 

### S-1 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 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 REUCAnalysis REUC PROJECT SCOPE AND DATA COLLECTION HISTORY Introduction REUC Project Scope Historyof REUC DataCollection THE REUC CUSTOMER SAMPLE Introduction REUC Sample Design Demographic Description ofthe REUC Sample Representativeness ofthe REUC Sample REUC LOAD DATA ANALYSIS: ELECTRIC AND GAS APPLIANCE USAGE Introduction Load Data Collection andAnalysis AverageYearlyTotal Usage Distribution ofAverage Daily Usage Space Conditioning Weather Model REUC APPLIANCE LOAD PROFILES Introduction Load Profjles Using Household Characteristics to Explain Load Shapes Total Energy Regressions
ix 

### S-1 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



### 8 CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE Introduction**~uel** Type Image Space Heating Water Heating Cooking Appliance Discount Factors INTEGRATION OF LOAD AND SURVEY ANALYSES Introduction Load Shapes by REUC Customer Segment Determination of 

### 1I0ptimai ll Stratifying Variables Appliance Life Cycle Costs APPENDIX A Supplementary Energy Usage Information and Load Profiles APPENDIX B 

### 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-l Methodology to Assess the Generalizabilityofthe REUC Customer Sample B-1 APPENDIX C Load Data Editing and Validation GLOSSARY OF TERMS



### x C-l G-l 

### CONTENTS, 

### cont. Section 

### 7

8 CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE Introduction r::uel Type Image Space Heating Water Heating Cooking Appliance Discount Factors INTEGRATION OF LOAD AND SURVEY ANALYSES Introduction Load Shapes by REUC Customer Segment Determination of

### 1I0ptimai li StratifyingVariables Appliance Life Cycle Costs APPENDIX A Supplementary Energy Usage Information and Load Profiles APPENDIX 8 Methodologyto Assess the Generalizabilityofthe REUC Customer 

### 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-l
Sample 8-1 APPENDIX C Load Data Editing and Validation C-1 GLOSSARY OF TERMS 

### G-l



### x'-, I
'-, )

S-·1 

## s-') S-h
S-7
5-8 1-1 3-1 5-1 

### 5-2 5-3 

### 5-4 5-5 5-6 5-7 FIGURES
Annual Average Energy Usage - Space Conditioning and Water Heating SCE REUC: Average Load Profiles - WinterWeekday Space Conditioning Customers' Required Return on a Small Riskless Investment Customers l Required Return on an Investment in Appliance Efficiency REUC Participant Images of Electric

### vs. Gas SCE REUC: 

### Quality Factor Segments - Winter Weekday Heat Pump SCE REUC: CP&B Needs-Based Segments - Summer Weekday Heat Pump REUC: Heat Pump Water Heater - Demographic Scenario REUC Project Milestones REUC Survey and Load Data Collection History Annual 

### Average Energy Usage Space Conditioning and Water Heating Annual 

### Average Energy Usage - Clothes Drying and Cooking Difference in Annual Energy Consumption between Gas and Electric Appliances Seasonal 

### Average Weekday Electricity Usage Electric Sample End-Uses Whole House: 

### Average Daily Electricity Usage SCE REUC Weekday 

### Average UEC Distribution - Winter Electric Heat Pump: Pooled Cities SCE REUC Weekday 

### Average UEC Distribution - Summer Space Conditioning: Pooled Cities

XI 

### S-3 S-4 S-6 S-7 

### S-8 S-9 5-10 5-11 

### 1-3 3-4 

### 5-4 5-5 5-7 5-10 5-11 

### 5-13 

### 5-13 " I '> )
'> 1

. 

### ,



## s·s S

-h
S

-7
5-8 1

-1 3-1 5-1 

### 5-2 

### 5-3 5-4 5-5 5-6 5-7 FIGURES

Annual Average Energy Usage - Space Conditioningand Water Heating SCEREUC: Average Load Profiles - WinterWeekday Space Conditioning Customersl Required Return on a Small Riskless Investment Customers l Required Return on an Investment in Appliance E

fficiency REUC Participant Images ofElectricvs. Gas SCE REUC: 

### Quality Factor Segments - WinterWeekday Heat Pump SCE REUC: CP&B Needs-Based Segments - Summer Weekday Heat Pump REUC:Heat Pump Water Heater - Demographic Scenario REUC Project Milestones REUC Survey and Load Data Collection History Annual Average Energy Usage Space Conditioning and Water Heating Annual Average Energy Usage - Clothes Dryingand Cooking Difference in Annual Energy Consumption between Gas and Electric Appliances Seasonal Average Weekday Electricity Usage Electric Sample End-Uses Whole House: Average Daily Electricity Usage SCE REUC Weekday Average UEC Distribution - Winter Electric Heat Pump: Pooled Cities SCE REUC Weekday Average UEC Distribution - Summer Space Conditioning: Pooled Cities

XI 

### S-3 S-4 S-6 S-7 5-8 

### 5

-9 5-10 5-11 

### 1-3 3-4 

### 5-4 

### 5

-5 5-7
5-10 5-11 

### 5-13 5-13

### 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 WaterHeating: 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 6-4 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 Sample 6-8 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 - Second Step 6-16

. Estimated Load vs. Average Load 6-15 REUC Heat Pump Water Heater - House 86 - Second Step 6-16 Estimated Load vs. Average Load 6-16 REUC Heat Pump Water Heater Demographic Trend Scenario 6-18



### 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 WeekdayAverage UEC Distribution - Winter 5-15 Gas WaterHeating: 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 6-4 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 WinterWeekday Electricity: Electric Sample 6-8 6-9 SCE REUC Summer Weekday Electricity: Electric Sample 6-8 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 - Second Step 6-16

. Estimated Load vs. Average Load 6-15 REUC Heat Pump Water Heater - House 86 - Second Step 6-16 Estimated Load vs. Average Load 6-16 REUC Heat Pump Water Heater Demographic Trend Scenario 6-18



### xii

### FIGURES, 

### cont. 

## Lill ure Page b-17 Effect of Household Size on Average Weekday Usage: 6-21 REUC Heat Pump Water Heater (Model) 6-18 EffectofQuality Image of Electricity on Average Weekday Usage: 6-22 REUC Heat Pump Water Heater (Model) 6-19 Effects ofCP&B Classification on Average Weekday Usage: 6-23 REUC Heat Pump Water Heater (Model) 7-1 REUC Participant Images of Electricvs. Gas 7-2 7-2 REUC Participants' Comparisons between Old and New Space 7-10 Heaters by Fuel Type 7-3a Importance of Heating System Attributes by Fuel Type Electric 7-11 Sample 7-3b Importance of Heating System Attributes by Fuel Type Gas 7-12 Sample 7-4 REUC Participants' Comparisons between Old and New Water 7-14 Heaters by Fuel Type 7-5 Longitudinal Change in Electric Induction Range Subsample - 7-19 Participants' Fuel Type Preference for Cooking 7-6 REUC Participants' Required Return on a Small Riskless 7-23 Investment 7-7 REUC Participants' Required Return on an Investment 7-24 in Appliance Efficiency 8-1 REUC Summer Weekday Load Shape: Electric Space 8-3 Conditioning Customer Preference & Behavior Stratification 8-2 REUC Summer Weekday Load Shape: Electric Space 8-4 Conditioning Electric Value/Gas Value Stratification 8-3 REUC Winter Weekday Load Shape: Heat Pump Water Heater 8-4 Electric Quality/Gas ValueStratification 8-4 REUC Winter Weekday Load Shape: Heat Pump Water Heater Income by Square Feet Stratification

xiii 8-5 

## Lill ure b

-17 6-18 6-19
7-1 7-2 7-3a 7-3b 7-4 7-5 7-6 7-7 8-1 8-2 8-3 8-4 

### FIGURES, 

### cont.
Effect of Household Size on Average Weekday Usage: REUC Heat Pump Water Heater (Model) EffectofQuality Image of Electricityon Average Weekday Usage: REUC Heat Pump Water Heater (Model) Effects ofCP&B Classificationon Average Weekday Usage: REUC Heat Pump Water Heater (Model) REUC Participant Images ofElectricvs. Gas REUC Participants' Comparisons between Old and NewSpace Heaters by Fuel Type Importance of Heating System Attributes by Fuel Type Electric Sample Importance of Heating System Attributes by Fuel Type Gas Sample REUC Participants' Comparisons between Old and NewWater Heaters by Fuel Type Longitudinal Change in Electric Induction Range Subsample - Participants' Fuel Type Preferencefor Cooking REUC Participants' Required Return on aSmall Riskless Investment REUC Participants' Required Return on an Investment in Appliance Efficiency REUC Summer Weekday Load Shape: Electric Space Conditioning Customer Preference & Behavior Stratification REUC Summer Weekday Load Shape: Electric Space Conditioning Electric Value/Gas Value Stratification REUC WinterWeekday Load Shape: Heat Pump Water Heater Electric Quality/Gas ValueStratification REUC WinterWeekday Load Shape: Heat Pump Water Heater Income by Square Feet Stratification

xiii Page 6-21 6-22 6-23 7-2 7-10 7-11 7-12 7-14 7-19 7-23 7-24 8-3 8-4 8-4 8-5

### 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-1 Space Conditioning: WinterAverage 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 Conditioning: Summer Average Daily Usage A-4 A-6 SCE REUC Winter: Clothes Drying A-4 A-7 SCE REUC Summer: Clothes Drying A-5 A-8 SCE REUC Winter: Cooking A-5 A-9 SCE REUC Summer: Cooking A-6 A-10 SCE REUCWinter: Whole House Electricity A-6 A-11 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 Conditioning A-17 SCE REUC Weekday Average Load: Electric Sample Space A-l0 Conditioning A-18 SCE REUC Weekday Average Load: Electric Sample Space A-l0 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-1 Space Conditioning: WinterAverage 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 Conditioning: SummerAverage Daily Usage A-4 A-6 SCE REUC Winter: Clothes Drying A-4 A-7 SCE REUC Summer: Clothes Drying A-5 A-8 SCE REUC Winter: Cooking A-5 A-9 SCE REUC Summer: Cooking A-6 A-l0 SCE REUCWinter: Whole House Electricity A-6 A-ll 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: Conditioning
. Electric Sample Space A-9 A-17 SCE REUC Weekday Average Load: Electric Sample Space A-l0 Conditioning A-18 SCE REUC Weekday Average Load: Electric Sample Space A-l0 Conditioning

xiv

### FIGURES, cont. '-igure /\- 19 SCE REUC Weekday Average Load: Gas Sample Space A-11 Conditioning A-20 SCE REUC Weekday Average Load: Gas Sample Space A-11 Conditioning A-21 SCE REUC Weekday Average Load: Gas Sample Space A-12 Conditioning A-22 SCE REUC Winter Weekday Electricity: Electric Sample A-12 A-23 SCE REUC Summer Weekday Electricity: Gas Sample A-13 A-24 SCE REUC Summer Weekday Electricity: Electric Sample A-13 A-25 SCE REUC Whole House Electricity: Electric Sample A-14 A-26 SCE REUC Whole House Electricity: Gas Sample A-14 A-27 SCE REUC Space Conditioning: Electric Sample A-15 A-28 SCE REUC Water Heating: Electric Sample A-15 A-29 SCE REUC Cooking: Electric Sample A-16 A-30 SCE REUC Clothes Drying: Electric Sample A-16 A-31 SCE REUC: Temperature A-17 B-1 REUC Sample Assessment Methodology B-2



### xv FIGURES, cont.
/\-19 SCE REUC Weekday Average Load: Gas Sample Space A-11 Conditioning A-20 SCE REUC Weekday Average Load: Gas Sample Space A-11 Conditioning A-21 SCE REUC Weekday Average Load: Gas Sample Space A-12 Conditioning A-22 SCE REUC WinterWeekday Electricity: Electric Sample A-12 A-23 SCE REUC Summer Weekday Electricity: Gas Sample A-13 A-24 SCE REUC Summer Weekday Electricity: Electric Sample A-13 A-25 SCE REUC Whole House Electricity: Electric Sample A-14 A-26 SCE REUC Whole House Electricity: Gas Sample A-14 A-27 SCE REUC Space Conditioning: Electric Sample A-15 A-28 SCE REUC Water Heating: Electric Sample A-15 A-29 SCE REUC Cooking: Electric Sample A-16 A-30 SCE REUC Clothes Drying: Electric Sample A-16 A-31 SCE REUC: Temperature A-17 8-1 REUC Sample Assessment Methodology 8-2



### x

v

### TABLES I.dd/' Page ·1 I REUC Participant Demographics - Sample Design Stratification 4-3 Variables ·1-:? REUC Participants Demographics - Highest Completed 4-4 Education Level Among Key Household Members 4-3 REUC sample Representativeness by Income Stratum 4-6 ·1-4 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 5-17 Heating and Morning Heating Degree-Days: All Households 6-1 Estimated and Simulated Harmonic Model Load Shape 6-12 Parameters 6-2 Differing Average Characteristics ofthe Households 6-17 7-1 Factor Loadings Based on Analysis of Fuel Type Image Question 7-6 7-2a Longitudinal Changes in Electric Sample Participants' Fuel Type 7-7 Preferences for Space Heating Systems 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 7-16 Preferences for Water Heaters 7-4b Longitudinal Changes in Gas Sample Participants' Fuel Type 7-17 Preferences for Water Heaters

XVII



### TABLES I.dd(' Page -4 I REUC Participant Demographics - Sample Design Stratification 4-3 Variables

-1**~**:! REUC Participants Demographics - HighestCompleted 4-4 Education Level Among Key Household Members 4-3 REUC sample Representativeness by Income Stratum 4-6

- 1

-4 REUC sample Representativeness by House Size Stratum 4-7 5-1 REUC: Table ofMonth Label to Date Correspondence 5-3 5-2 Average Annual and Seasonal Energy Usage - Pooled Cities 5-6 5-3 MonthlyAverage Usagefor Pooled Cities 5-8 5-4 Estimated Linear Relationship between Average Morning Space 5-17 Heatingand Morning Heating Degree-Days: All Households 6-1 Estimated and Simulated Harmonic Model Load Shape 6-12 Parameters 6-2 DifferingAverage Characteristics ofthe Households 6-17 7-1 Factor Loadings Based on Analysis of Fuel Type Image Question 7-6 7-2a Longitudinal Changes in Electric Sample Participants' Fuel Type 7-7 Preferences for Space Heating Systems 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 HeatingSystem to a Friend - By Fuel Type 7-4a Longitudinal Changes in Electric Sample Participants' Fuel Type 7-16 Preferences for Water Heaters 7-4b Longitudinal Changes in Gas Sample Participants' Fuel Type 7-17 Preferences for Water Heaters

X

VII

### TABLES, cont. Table Page 7-5 REUC Participants' Willingness to Recommend Their Water 7-18 Heater System to a Friend - By Fuel Type 7-6 Electric Induction Subsample Participants I Willingness to 7-20 Recommend Their Range to a Friend 7-7 REUC Participants· Range/Oven Comparisons 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 A-18 Households A-2 Average Annual and Seasonal Energy Usage - Irvine A-19 Households A-3 MonthlyAverage Usage for Mission Viejo Households .A-20 A-4 MonthlyAverage Usagefor Irvine Households A-22 B-1 Appliance Stock Inventory (AS!) Share by SCE Division B-4 B-2 Chi-Square Statistics for Customer Descriptors that Discriminate B-5 between Appliance Stock Inventories (AS!) and Pool Ownership B-3 RSS-Based Stratification of Customer Descriptors B-6 B-4 Customer Descriptors that Meaningfully Discriminate Annual B-7 Usage Profiles by Appliance Stock Inventory (AS!) and SCE Division C-1 IIReasonableli Connected Load Estimates C-4 C-2 IIReasonablell Ranges for Weekly Appliance Use C-4 C-3 ACUPA Error Flag Criteria C-5 C-4 Keyto Invalid Data Flagged During Data Validation Process C-6

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' Willingnessto 7-20 Recommend Their Range to a Friend 7-7 REUC Participants' Range/Oven Comparisons between Fuel 7-21 Types 7-8 Energy Savings Requirements ofHigher EfficiencyAppliances 7-25 8-1 REUC Stratification Scheme Variance Calculations 8-6 A-1 Average Annual and Seasonal Energy Usage - Mission Viejo A-18 Households A-2 Average Annual and Seasonal Energy Usage - Irvine A-19 Households A-3 MonthlyAverage UsageforMissionViejo Households .A-20 A-4 MonthlyAverage Usagefor Irvine Households A-22 8-1 Appliance Stock Inventory(AS!) Share by SCE Division 8-4 8-2 Chi-Square Statistics for Customer Descriptors that Discriminate 8-5 between Appliance Stock Inventories (AS!) and Pool Ownership 8-3 RSS-8ased Stratification ofCustomer Descriptors 8-6 8-4 Customer Descriptors that Meaningfully DiscriminateAnnual 8-7 Usage Profiles byAppliance Stock Inventory (AS!) and SCE Division C-1 "Reasonable" Connected Load Estimates C-4 C-2 "Reasonable" Ranges forWeeklyAppliance Use C-4 C-3 ACUPA Error Flag Criteria C-5 C-4 Keyto Invalid Data Flagged During DataValidation Process C-6

xviiiSUMMARY
Ihis report presents the research methods and results from the Residential Energy USJge Comparison (REUC) project, a joint effort by Southern California Edison Company (SCE) and the Electric Power Research Institute (EPR!). The 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 of energy 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 Ih3t 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 project design is discussed more fully in Sections 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 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 electricandgas 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 of the Orange County participants; survey research data collection began in mid:'1987. Both end-use data and survey research data collection concluded in 

### 5-1 SUMMARY Ihis report presents the research methods and results from the Residential Energy l)SJge Comparison (REUC) project, a joint effort by Southern California Edison Company (SCE) and the Electric Power Research Institute (EPR!). The 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 simulation models. The nature ofthe 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 

### 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 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 illustratethe value oftwo uniquefacets ofthe project'sdesign: first, end-use (appliance) load data have been collected for 

### both electricandgas appliances; and, second, 

### both customer survey data and end-use data have been collected from the participants. End-use data collection began in late 1986for a portion of the Orange County participants; survey research data collection began in mid:"1987. Both end-use data and survey research data collection concluded in S-1

### December 1989. The project's October 1990 interim report (CU-6952) presented results based on one year of data ending in January 1989. This 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 measure electric and gas appliance energy consumption, at point of use. All energy in this Report is given as point-of-us·e site 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 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 the 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 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 ofappliances with different energy sources.

S-2 December 1989. The project's October 1990 interim report (CU-6952) presented results based on one year ofdata ending in January 1989. This report presents analysis results usingall the available REUC data. A summary ofthefindings presented in this report is provided below. ELECTRIC AND GAS APPLIANCE ENERGY CONSUMPTION 

### A principal objective ofthe REUC project has been to measure electric and gas appliance energy consumption, at pointofuse. All energy in this Report is given as point-of-us· esite 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 ofthe direct competition between electricity and natural gas and because of the development of new, high-efficiency electric technologies. Figure S-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 the 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 opportunityto carefully compare the daily usage patterns ofappliances with differentenergy sources.

S-2

### Figure S-l Annual Average Energy Usage SpaceConditioningand WaterHeating (atpointofuse)

SpaceConditioning

WaterHeating 23.2

23.4



### o 5 10 15 MBTU 20 25 30

[!a Gas Sample
• ElectricSample 

### I"igure 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 houror 1 kW). I\dditional REUC appliance load profiles developed from end-use data are presented in Section 6 and in Appendix A.



### 5-3 FigureS-1 Annual AverageEnergy Usage SpaceConditioningandWaterHeating(atpointofuse)

23.2 SpaceConditioning

23.4 WaterHeating



### o 5 10 15 20 25 30 MBTU**~** GasSample 

### III ElectricSample

I"igure 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 s

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



### 5-3

### FigureS-2 SCE REUC: Average Load Profiles WinterWeekday SpaceConditioning (atpointofuse)

12 Electric Heat Pump Gas Furnace

6 8 (\ 

# , \ I \ 

# , 

# ' , ' 

# , ' I ' I , 

# , 

# , , ' I ' 

### I \ I , I \ 

# "
\ I \ I \ ./"-_
...... I \ ;' \ / \ I \**~~** \ I 2**,~** \ 1 '- ",1 

# --- 4 10 kBTU per Hour

24 21 18 15 12 9 6 3 O+-........,r-r-r--r-r-r-r-....-..--.r-r-r-or-,:--r.......-,......,
........."""T""-r-..--,



### o Time of Day



### CUSTOMER ACCEPTANCE OF APPLIANCES Measuring customer acceptance of these energy efficient appliances was another objective ofthe project. The REUC project included the administration of surveys 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,



### S-4 kBTU per Hour 

### FigureS-2 SCE REUC: Average Load Profiles WinterWeekdaySpaceConditioning(atpointofuse)

12
10
8
6
4 (\ 

# , \ 

# , \ 

# , 

# ' , ' 

# , ' 

# , ' , I 

# , 

# , , I I I 

### I

### ~ 

# , , , \ 

# "
\ Electric HeatPump GasFurnace



# , \ 

# ,\ ../"'-_

....... 

# , \ ;' \

### ~/ 

## \ 

## " \ ",- 

# , 2 ,,,,-/ \' '- 

# .....

# ' 

# ---
o 3 6 9 12 15 18 21 24 TimeofDay



### CUSTOMER ACCEPTANCE OF APPLIANCES Measuring customer acceptance ofthese energy efficient appliances was another objectiveofthe 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 amongvarious 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,



### S-4gas heating systems are more likely to have proponents than heat pump systems. It appears that the causes of the weaker reception of heat pumps for the REUC participants were the perceived quality problems with the units installed in 1986 and 1987. The survey responses disclosed that participants found their heat pump water heaters too slow to respond. As a result, acceptance ratings for the heat pump water heaters were quite low, at 45 percent. In contrast, electric induction stoves were well accepted; 80 percent would recommend their induction stoves to a friend. Among the participants retrofitted from gas ranges to electric induction stoves, 50 percent expressed an opinion preferring electricity as afuel for cooking. Additional results on appliance acceptance are provided in Section 7.

INTEGRATED MARKET RESEARCH The collection of both end-use load data and participant survey data is yet another distinctive feature of the REUC project. Integrated market research is the creative synthesis of load research information with market research/survey information to provide insight about customers l energy usage behavior patterns.



### S-5 gasheating systems are more likely to have proponents than heat pump systems. it appears that the causes of the weaker reception of heat pumps for the REUC participants were the perceived quality problems with the units installed in 

### 1986 and 

### 1987. The survey responses disclosed that participants found their heat pump water heaters too slow to respond. As a result, acceptance ratings for the heat pump water heaters were quite low, at 

### 45 percent. In contrast, electric induction stoves were well accepted; 80 percent would recommend their induction stoves to a friend. Among the participants retrofitted from gas ranges to electric induction s

toves, 50 percent expressed an opinion preferring electricityas afuel forcooking. Additional results on appliance acceptance are provided in Section 7.

INTEGRATED MARKET RESEARCH The collection of both end-use load data and participant survey data is yet another distinctivefeature ofthe REUC project. Integrated market research is the creative synthesis of load research information with market research/survey information to provide insight about customers l energy usage behavior patterns
.



### 5-5

### Figure S-3 Customers' Required Return on a Small Riskless Investment

DID of 50

# -r---------------------., Participants
40

30

20

10



### o 6%-8% 9%-10% Required Return Income: >$80,000 1m 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 efficiency 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 

### Figure5-3 Customers' Required Return on a Small Riskless Investment

0/0 of 50

## -r---------------------, Participants
40

30

20

10



# ° 6%-8%



# - 9%-10% Required Return**~** Income:>$80,000 1m Income:$50,001-$80,000 

# m 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 abank certificate ofdeposit. As illustrated by Figures S-3 and 5-4, the 

### REUC participants' required rate of return on appliance effiCiency 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 Customers· Required Return on an Investmentin Appliance Efficiency

% of 

# 40..,..-----------------------, Participants

30

20

10



### o <15% 15%-59% Required Return E21 Income: >$80,000 

## m Income: $50,001-$80,000 Income: $30,000-$50,000



### 5-7 >59% Figure 5-4 

### Customers' Required Return on an Investment in Appliance Efficiency

% of**40,-----------------------------------------~** Participants

30

20

10

<15% 15%-59% Required Return 12l Income:>$80,000 f3 Income: $50,001-$80,000**~** Income: $30,000.$50,000



### 5-7 >59%Figure 5-5 REUC Participant Images of Electric vs. Gas Dirty Dangerous Hard to Hookup Inconvenient PoorService from Utility Unreliable Sluggish Heat High Cost Appliances Expensive High Monthly UtilityBills 

# I--------
....**-,;.~"""::i~~f I---------tk-::~l:j;,..:;.**  ----f 

## \\.

Electricity Gas

Image Image Clean Safe Easyto Hookup )..



## .... 

# - Convenient 

### ""'" 

## :§ Good Service C'

from Utility Reliable Fast Heat LowCost Appliances l.I.l ::J 

### ""'" Inexpensive Low Monthly

Utility Bills

The REUC project surveys also included questions designed to elicit participants' images of electricity and gas. Figure 5-5 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 

### Qualityfactor (a composite of the Safety, Convenience, Service and Reliability images) and a 

### Valuefactor (a composite ofthe Appliance Cost, General Expense and Monthly Utility Bill images).

5-8 Dirty Dangerous Hardto Hookup Inconvenient PoorService from Utility Unreliable Sluggish Heat HighCost Appliances Expensive HighMonthly UtilityBills Figure 5-5 REUC Participant Images ofElectric vs. Gas
Clean

Safe Easyto Hookup )..



## .... 

# - I--------
....**-,;.~"""::i~~f**

## :ll;,..i~!ii-.----/ Convenient 

### ""'" 

## :§ GoodService C'

from Utility Reliable Fast Heat LowCost Appliances 1.1.1 ::J 

### ""'" Inexpensive

### ~ LowMonthly

UtilityBills Electricity Gas

Image Image

The REUC project surveys also included questions designed to elicit participants' images of electricity and gas. Figure 5-5 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 

### Qualityfactor (a composite of the Safety, Convenience, Service and Reliability images) and a 

### Valuefactor (a composite ofthe Appliance Cost, General Expense and Monthly Utility Bill images).

5-8Figure S-6 SCE REUC: 

### Quality Factor Segments Winter Weekday Heat Pump

La Electric/Hi GasQuality Hi Electric/La GasQuality

24 21 18 15 12 

# ,\\\

### \\\\

# ,

# --

9 6 

## I I I I 

# , I I .J Load 1.4

In kW 1.2 1.0
0.8 0.6 0.4 0.2
0.0 0 3 Timeof Day (in Hours PST)

Figure 5-6 illustrates the use of the 

### Qualityfactor for customer segmentation. The figure presents average winter weekday load profiles for participants' heat pumps segmented with the electric and gas 

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



### 5-9 Load 1.4

In kW 1.2 1.0
0.8 0.6 0.4 0
.2
0
.0 0 Figure S-6 SCE REUC: QualityFactor Segments Winter Weekday Heat Pump

\\\

3 6 9 12 La Electric/Hi GasQuality Hi Electric/LaGasQuality



# ,

# --

15 18 21 24 TimeofDay (in Hours PST)

Figure 5-6 illustrates the use ofthe 

### Qualityfactorfor customer segmentation. The figure presents average winter weekday load profiles for participants' heat pumps segmented with the electric and gas 

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



### 5-9

### FigureS-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. ValueSeekers

.....**"'~** ..

. ..".......,

.,,,. .. .•. .. :' :; /"'.,rI
.......... 24 21 18 15 12 9 6 3 0.0**+-....,..--r-.....-""'T""""""'T-~-r---r-.--..,.--....-r---.---.,----,---l** 

## o TimeofDay(inHours

### psn

The EPRI Customer Preference and Behavior (CP&B) CLASSIFYTM survey was administered to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure 5-7 presents average summer weekday load profiles for heat pumps segmented by Appearance Conscious, Resource Conserver and Value Seeker needs-based segments.



### 5-10 Figure S-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. ValueSeekers**.~....,.**.........................**~**..**~**.•.**~**••**~** .. :'
......:;............. ................ /"'<J'" ....,.".......
.**,~,~**
. ..".......,**O.O+-~~--~-r~--~~~--~~~--~~--"'-~~** 

## o 3 6 9 12 15 18 21 24 TimeofDay (inHours

### psn

The EPRI Customer Preference and Behavior (CP&B) CLASSIFYTM survey was administered to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure 5-7 presents average summer weekday load profiles for heat pumps segmented by Appearance Conscious, Resource Conserver and Value Seeker needs-based segments.



### 5-10

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

Older Households Younger Households ;\ I' \ I \ I \ I \ I \\\\\

# ,, /'
.... , I ,

, I \



## , / \

\ I \

\ I \ \ 

## ;' \

\. 

### .. 

### ,; \ '- -; \\ ........ -; .....

# _- 0.4 0.6

0.2 Load 0.8**~-------------------------,** in kw

24 21 18 15 12 9 6 3 0.0**+----,r---r----,,----,.---.----,--.---r-...----r--r----r-~_,--r-_;** 

## o Time ofDay (in Hours PST) The household characteristic data from REUC participants were also used in the project's load shape modeling research. End-use load shapes were modeled llsing household characteristics in a two-step regression procedure: (1) harmonic modeling of load shapes, followed by (2) household characteristic regressions on the harmonic model parameters. Figure 5-8 illustrates an application of this approach by comparing derived heat pump water heater load shapes for two hypothetical demographic market segments produced with the load shape modeling methodology. This figure shows how the load shapes may differ between younger and older households. Additional integrated market research results of the REUC project are presented in Section 8.

5-11 

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

Load 0.8**~-------------------------.,** in kw

0.6

0.4

0.2 

### ....... -; .....

# _-

o 3 6 ;'\ I' \ I \ I \ I \ I \

9 \\\\

# ,

12 OlderHouseholds Younger Households

, i"......

" I \ " I \



# , / \

\ I \

\ I \ \ 

## ;,' \

\., ;,f'

....... -;'

15 18 21 TimeofDay (in Hours PST) 24

The household characteristic data from REUC participants were also used in the project's load shape modeling research. End-use load shapes were modeled llsing household characteristics in a two-step regression procedure: (1) harmonic modeling of load shapes, followed by (2) household characteristic regressions on the harmonic model parameters. Figure 5-8 illustrates an application of this approach by comparing derived heat pump water heater load shapes for two hypothetical demographic market segments produced with the load shape modeling methodology
. This figure shows how the load shapes may differ between younger and older households. Additional integrated market research results of the REUC project are presented in Section 8.

S-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 (EPR!). 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 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 laboratory settings or computer simulation approaches in order to assess these end-use technologies· performances. The nature of the 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 captured 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 l usage and response to new electrotechnologies. These advantages offset the reduction in "control" afforded by a IJboratory simulation study.

1-1 

### 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 (EPR!). The REUC project is an innovative demand-side project designed to measure and compare typical energy consumption patterns of energy efficient residential electricand 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 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 laboratory settings orcomputersimulation 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 itaccounts for many ofthe subtle but important determinants of appliance usage not captured in engineering models, including unobservable household characteristics. The advantages of conducting a IIfjeld" experiment are reflected in the value of more comprehen

### sive, realistic assessments of actual customers' usage and response to new electrotechnologies. These advantages offset the reduction in IIcontrol ll afforded by a laboratory simulation study.

1-1Project Objectives 

### The REUC project was designed, in part, to meet several of SCEls needs for assessing new electrotechnologies available to residential customers. These needs have been satisfied by meeting several objectives:

• measure energy usage of electric and gas residential appliances;

• compare usage patterns of similarelectric 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 REUC 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 Project Objectives The REUC project was designed, in part, to meet several of SCEls needs for assessing new electrotechnologies available to residential customers. These needs have been satisfied by meeting several objectives: • measure energy usage of electric and gas residential appliances; • compare usage patterns ofsimilarelectric and gas appliances; • assess customer acceptance of new end-use technologies; and • generalize results to other residential customers. This report will illustrate howthese 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 ofsubsequent data collection and analysis, which was completed in the summer of 1990. The major REUC 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 - Idea Suggested by CPUC and SCE Early 1986 - Discussions with EPRI Summer 1986 - CustomerSample Design; REUC-RSS Survey; Installations Begun Winter 1986 - Mission Viejo Installations Complete Fall 1987 - ProjectAnalysis Plan; First REUC Survey Winter 1987- Irvine Installations Complete Spring 1988 - Second REUC Survey Summer 1989 -Third REUC Survey; Winter 1989 - Final REUC Survey; EqUipment Removal --' I W Figure 1-1 

### REUC PROJECTMILESTONES 

# 1985 1986 1987 1988 1989 1990 

# I I I I I I 

# 4t**lI~.** 

# ••• • .. 4t 11 

# • • •
.. .. <It --' 

### , w 

# 1985 

# I Figure 1-1 

### REUC PROJECTMILE STONES 

# 1986 1987 1988 1989 

# I I I I 

# 4t 1985- Idea Suggested byCPUC and SCE

# ~ Early 1986- Discussionswith EPRI

# ~ Summer 1986- CustomerSample Design; 1990 

# I

REUC-RSS Survey; Installations Begun • Winter 1986- MissionViejo InstallationsComplete • Fall 1987- ProjectAnalysis Plan; First REUC Survey • Winter 1987- Irvine InstallationsComplete

.. Spring 1988- Second REUC Survey

.. Summer 1989-Third REUC Survey; 

# <II Winter 1989- Final REUC Survey; Equipment RemovalProject 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 SCEls residential customer population. In addition, the REUC experimental 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 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 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 staff by offering primary management of the REUC project design and dataanalyses 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.



### 1-4 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 SCEls residential customer population. In addition, the REUC experimental design needed to determine how much and what type ofcustomer 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 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 EA-3286. Proiect 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 lIintegrated market research ll 

### techniques and IIbehavioral engineering statistical techniques (BEST)II 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 staff by offering primary management ofthe REUC project design and dataanalyses 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 projectat numerous industry conferences and meetings.



### 1-4EPORT ORGANIZATION The remainder of this report is organized as follows: Section 2, REUC Project Design Objectives, describes the design of the REUC project. Section 3, REUC Project Scope and Data Collection, describes the history of REUC data collection. Section 4, REUC Customer Sample, provides a summary of the REUC sample design and a demographic description of the REUC sample. Section 5, REUC load Data Analysis: Gas and Electric Appliance Energy Usage, presents results from the analysis of appliance energy data. Section 6, REUC Appliance Load Profiles, provides appliance load profiles forthe REUC electric and gas appliances. Section 7, Customer Preferences and Appliance Acceptance, discusses REUC participants l attitudes toward their new electric and gas appliances. Section 8, Integration of Load and Survey Analyses, presents results of integrated market research analyses, such as participants' fuel images and load profiles by customer segment. Appendix A provides supplementary energy usage information load profiles. Appendix B contains a description of the methodology for generalizing the REUC Customer Sample Results. Appendix C describes the load data editing and validation methods.

1-5 HEPORT ORGANIZATION The remainder of this report is organized as follows: Section 2, REUC Project Design Objectives, describes the design of the REUC project. Section 3, REUC ProjectScope and Data Collection, describes the history of REUC data collection. S

ection 4, REUC Customer Sample, provides a summary of the REUC sample design and a demographic description of the REUC sample. Section 5, REUC l oad Data Analysis: Gas and Electric Appliance Energy Usage, presents results from the analysis of appliance energy data. Section 6, REUC Appliance Load I>rofiles, provides appliance load profiles forthe REUC electric and gas appliances. Section 7, Customer Preferences and Appliance Acceptance, discusses REUC participants l attitudes toward their new electric and gas appliances. Section 8, Integration of Load and Survey Analyses, presents results of integrated market research analyses, such as participants l fuel images and load profiles by customer segment. Appendix A provides supplementary energy usage information load profiles. Appendix Bcontains a description ofthe methodology for generalizing the REUC Customer Sample Results. Appendix C describes the load data editing and validation methods.

1-5Section 2 REUC PROJECT DESIGN OBJECTIVES

INTRODUCTION As mentioned in Section 1, one of the REUC project objectives 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 project1s 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 SCEls entire residential customer class of over 3.5 million customers. Rather, the REUC participants were selected to represent owneroccupied, single-family new homes in SCE's Southern Division. Thus, the project sample represents one specific and important segment of residential customers, those most 

### Iikely 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 ProjecV IEEE Transactions on Power Systems, Vol. 3, No.3, August 1988, pp. 1298-1305.
2-1 Section 2 REUC PROJECT DESIGN OBJECTIVES

INTRODUCTION As mentioned in Section 1, one of the REUC project objectives 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 howthe REUC design and analysis methods can be used byother utilities. Thus, this section provides a response to the question, "Howcan utilities otherthan 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 resources.1 The REUC participant sample was not designed to be a completely random sample norto represent SCE's entire residential customer class ofover3.5 million customers. Rather, the REUC participants were selected to represent owneroccupied, single-family new homes in SCE'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.
2-1

### of residential customers is growing rapidly in SCE's as well 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 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 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 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 of the project activities, many of SCE'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
2-2 ofresidential customers is growing rapidly in SCE's as well as otherutilities' 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, will facilitatethe 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 helpensurethatthe utility'ssizeable 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 ofthe project results. The collection of both end-use load data and customer survey responses has allowed the extension ofload profile results to other residential customers at SCE. As a result ofthe project activities, many of SCE's other load monitoring customers receive residential appliancesaturation 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 ofthe two groups ofcustomers. The project found that the
2-2most natural set of descriptors is customer responses to the demographic/characteristic variables in utility saturation surveys. Furthermore, the kinds of data collected and techniques employed on the REUC project can allow for the successful application of these results to residential customers served by other utiIities. The REUC project administered four different survey instruments, as described in Section 3. The first survey was based on SCE's 1985 Residential Saturation Survey (RSS). With this and the additional survey information, the REUC results were assessed for their applicability to other SCE residential customers. This assessment is described in Section 

### 4. Together with the detailed electric and gas end-use load shapes that have been developed on the project, the survey research results determined customers' attitudes and acceptance of these energy-efficient end-use technologies. This combination of load and survey information can be used to assess the impacts and penetration of utility demand-side programs that emphasize customers' efficient energy use. As discussed in Section 7, the fuel images of the REUC participants appearto be correlated to customers' energy usage behavior and, as a result, their likelihood of program participation. Finally, the REUC project included load shape modeling analyses that used both the end-use load data and customer survey responses. Results of several of these analyses are described in Section 6. The load shape modeling linked customer characteristics to appliance load shapes and usage patterns. End-use load shapes were modeled using customer characteristics with a two-step procedure. The procedure uses customers' demographic, attitudinal and/or needs-based segment characteristics to explain end-use load shapes. These results suggest how other utilities may adapt the REUC load shapes to their service territories.

2-3 most natural set of descriptors is customer responses to the demographic!characteristic variables in utility saturation surveys. Furthermore, the kinds of data collected and techniques employed on the REUC project can allow for the successful application of these results to residential customers served by other utiIities. The REUC project administered four different survey instruments, as described in Section 3. Thefirst survey was based on SCE's 1985 Residential Saturation Survey (RSS)' With this and the additional survey information, the REUC results were assessed fortheir applicabilityto otherSCE residential customers. This assessment is described in Section 

### 4. Together with the detailed electric and gas end-use load shapes that have been developed on the project, the survey research results determined customers' attitudes and acceptance of these energy-efficient end-use technologies. This combination of load and survey information can be used to assess the impacts and penetration of utility demand-side programs that emphasize customers' efficient energy use. As discussed in Section 7, the fuel images ofthe REUC participants appearto be correlated to customers' energy usage behaviorand, as a result, their likelihood ofprogram participation. Finally, the REUC project included load shape modeling analyses that used both the end-use load data and customer survey responses. Results ofseveral ofthese analyses are described in Section 6. The load shape modeling linked customer characteristics to appliance load shapes and usage patterns. End-use load shapes were modeled using customer characteristics with a two-step procedure. The procedure uses customers' demographic, attitudinal and/or needs-based segment characteristics to explain end-use load shapes. These results suggest how other utilities may adaptthe REUC load shapes to their service territories.

2-3

### Section 3 REUC PROJECT SCOPE AND DATA COLLECTION HISTORY
INTRODUCTION The REUC customer sample consists of 100 households. These 100 households are comprised oftwo groups: eight homes where load monitoring equipment was initially tested, the "equipment test sample"; and 92 homes located in Irvine and Mission Viejo, the "REUC sample." This reportfocuses only on datafrom the REUCs 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



### 3-1 INTRODUCTION 

### Section 3 REUC PROJECT SCOPE AND DATA COLLECTION HISTORY

The REUC customer sample consists of 100 households. These 100 households are comprised oftwo groups: eight homes where load monitoringequipmentwas initially tested, the "equipment test sample"; and 92 homes located in Irvine and Mission Viejo, the "REUC sample." This reportfocuses onlyon datafrom the REUCs 92-household sample. Irvineand Mission Viejo are located in Orange County alonga 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 ofthe REUC projectfollowed 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 of approximately 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 heating systems that have output capacities 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 (80 percent efficiency), electric airconditioners (typical SEER of9) 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 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

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 of 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 retrofitted with new appliances, halfwith gas appliances and half with electric or electric heat pump appliances. Second, the participants have
3-2 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 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 heating systems that have output capacities of58 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] of3). Many of the heat pump water heaters had a resistance backup system for water heating; this system could draw loads ofabout 2.5 kW. The gas homes received new gas furnaces (80 percentefficiency), electricairconditioners (typical SEER of9) 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 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

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 newappliances, halfwith gas appliances and halfwith electric or electric heat pump appliances. Second, the participants have
3-2been repeatedly surveyed in order to assess their experiences with their new appliances and the influence these appliances may have had on their image of gas 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 of fuel-switched former gas customers matched with a Ilcontrol groupll 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 of the appliances' energy usage have been made by customer strata that reflect the initial sample design, as well as by appropriate alternative stratifications or segmentations of the sample derived from the analysis of the market research data. These energy usage comparisons account for seasonal and weather effects. Load Profiles The REUC load profiles can aid utilities in assessing the effectiveness of encouraging customers' adoption of energy-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 of appliances 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 attitudes and behavior toward the electric and 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 include other qualities associated with the two fuels? Second, analysis ofcustomer reaction to the performance of their new appliances allows qualitative assessment of non-pecuniary benefits, not just the dollar value of the savings from operating
3-3 been repeatedly surveyed in order to assess their experiences with their new appliances and the influencethese 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 IIcontrol groupll 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 segmentations 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 attitudes and behaviortoward 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 twofuels? 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 and 50 homes in Irvine. Figure 3-1 summarizes the survey and load data collection activities from 5eptember 1986 through December 1989 ofthe REUC project. Figure 3-1

REUC Survey and load Data Collection History 9/86 6/87 3/88 6/89 12/89 Mission 

### I 

### I Viejo 50 51 52 53 54 Irvine I I 50 51 52 53 54 

### I-I 50 51-54 

# =Load data collection
= REUC-R55 survey 

## =REUC Market Research survey
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 5CE 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 customer1s propensity to lIinvestll

3-4 and capital costs. Finally, the integration ofthis 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 and 50 homes in Irvine. Figure 

### 3-1 summarizes the survey and load data collection activities from September 

### 1986 through December 

### 1989 ofthe REUC project.

Mission Viejo Irvine


### I-I SO 

### 51-54 Figure 3-1
REUC**Surve~** and load DataCollection**Histor~** 

### 9/86

SO SO



# =Load data collection = REUC-RSS survey 

### 6/87 I 51
51



## =REUC Market Research survey 

### 3/88 52
I 

### 52 6/89 12/89 

### I 53 54
53 54

Four different survey instruments have been administered to each sample. The first survey, REUC-RSS, was designed to gather specific appliance saturation data in order to select prospective participants. 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 offuel types and end-uses as well as the customer1s propensity to lIinvestll



### 3-4in energy-efficient appliances. The second REUC survey instrument included rdined versions of the first survey's questions, additional questions that measure (ustomer acceptance of the new appliances, and the EPRI CLASSIFYTM questionnaire. In order to perform longitudinal analysis of the participants' acceptance of the new appliances, the second REUC survey was administered twice (March 1988 ;md June 1989). The final survey was a shorterversion ofthe second REUC survey ;md contained portions of SCE'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 and 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 of this 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
3-5 in energy-efficient appliances
. The second REUC survey instrument included Idined versions of the first survey's questions, additional questions that measure (us tomer acceptance of the new appliances, and the EPRI CLASSIFYTM questionIlaire. In order to perform longitudinal analysis ofthe participants' acceptance of !he new appliances, the second REUC survey was administered twice (March 1988 ;md June 1989). The final survey was a shorterversion ofthe second REUC survey ;md contained portions ofSCE's residential attitude survey instrument. In this way, !hefinal survey measures the long-term acceptance of the REUC appliances and provides a linkto similar surveys administered to otherSCE customers. Appliance load data collection continued through December 1989. As is shown, !he data collection history for the Irvine sample differs from the Mission Viejo s

ample. In Phase I, the REUC projectwas limited to the Mission Viejo sample and wasprimarily envisioned as an end-use load monitoring project. Oncethe Mission Viejo sample was selected and retrofitted with new appliances, the scope ofthe project broadened in Phase II to include a second customer sample in Irvine and 10 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 F

ebruary 1988 through November 1989 so that results from both cities can be compared
. 

### load Data Analysis Workstation All ofthe REUC projectdata 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 specificallyforthe 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 afast 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
3-5

### 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. A VGA 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 of any specific project such as the REUC. Batch processing on the LDAW makes it possible to string together these analytic IIbuilding blocksll 

### 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;

• editthese data for possibly invalid measurements;

• estimate average load profiles;

• perform surveyanalysis;

• 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 liThe Load Data Analysis Workstation User's Guide,1I Version O.4RG, May 1990, EPRI RP2342-B.
3-6 

### 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. A VGA 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 asingle function and can be modified to meet the needs of any specific project such as the REUC. Batch processing on the LDAW makes it possible to string together these analytic IIbuilding blocksll 

### to accomplish complexanalysistasks. These programs include routinesto:
• design load research and demand-side program customer samples; • read load data in standard central station tape formats; • editthese data for possibly invalid measurements; • estimate average load profiles; • performsurveyanalysis; • undertake market research analysis; • estimate appliance-connected loads from metered observations; • calculateappliancedutycycles and duty-cycledistributions; • calculate load reductions from direct load control strategies; and
1

### For more information, see liThe Load Data Analysis Workstation User's Guide," Version OARG, May 1990, EPRI RP2342-B.
3-6

### • perform other important program evaluation functions.
The idea of conducting all the analysis tasks for a large-scale end-use load monitoring program such as the REUC on a microcomputer would have been out of the question until recently. Fortunately, continuing advances in microcomputer technology have put sufficient computing power and disk storage into desktop machines to make the Workstation a viable, cost-effective alternative to mainframe computers. In fact, under most circumstances, analysts can expect quicker turnaround on the Workstation even on very large jobs than they would getfrom a mainframe. By combining the ease of use of existing microcomputers with the newly available computer power, the Workstation puts the control of program data analysis directly in the hands of program analysis staff, not remote data processingfacilities.



### 3-7 • perform other important program evaluation functions.
The idea ofconducting all the analysis tasks for a large-scale end-use load monitoring program such as the REUC on a microcomputer would have been out of the question until recently. Fortunately, continuing advances in microcomputer technology have put sufficient computing power and disk storage into desktop machines to make the Workstation a viable, cost-effective alternative to mainframe computers. In fact, under most circumstances, analysts can expect quicker turnaround on the Workstation even on very largejobs than they would getfrom a mainframe. By combining the ease ofuse ofexisting microcomputers with the newly available computer power, the Workstation puts the control of program data analysis directly in the hands of program analysis staff, not remote data processingfacilities.



### 3-7

### Section 4 

### THE REUC CUSTOMER SAMPLE
INTRODUCTION This section describes the REUC project customer sample. The sample design

.procedures and criteria are described, followed by the demographic characteristics ofthe REUC sample. This section concludes by summarizing the results of an analysis of the generalizability of the REUC sample to other customers and other regions of SCE's service territory. 

### REUC SAMPLE DESIGN In order to identify a potential sample of residential customers, SCE administered an initial survey, called the REUC-RSS, to 2,380 potential participants. This survey was based on SCE's 1985 Residential Saturation Survey (RSS). From the 756 survey responses, a subset of potential participants was visited by SCE project staff to gather additional information on specific appliances in the household, household characteristics, and occupancy patterns. As described elsewhere,' extensive pre..;experimental research was undertaken to identify a sample of customers that could provide the most pertinent and valuable information for the study, namely electric and gas end-use load shapes and customer characteristics. The goal of the REUC sample design was to determine the optimal allocation of the available 92 sets of sub-metering equipment and appliances so that the difference in energy usage between electric and gas appliances could be accurately measured. This allocation was achieved by identifying observable household characteristics that best predict overall household electric load shape and then stratifying the sample according to those characteristics. Required sample sizes were calculated using estimated variances '"Design of a Utility Competitive Assessment Experiment: The Residential Energy Usage Comparison Project," J.T. Powers, M.S. Shirilau, R.T. Uhlaner and B.A. Smith, 

### IEEE Transactions on Power Systems (August 1988), Vol. 3, No.3, pp. 1298-1305.



### 4-1 Section 4 

### THE REUC CUSTOMER SAMPLE
INTRODUCTION This section describes the REUC project customer sample. The sample design procedures and criteria are described, followed by the demographic characteristics ofthe REUC sample. This section concludes bysummarizingthe results of an analysis of the generalizability of the REUC sample to other customers and other regions ofSCE's service territory. 

### REUC SAMPLE DESIGN In order to identify a potential sample of residential customers, SCE administered an initial survey, called the REUC-RSS, to 2,380 potential participants. This survey was based on SCE's 1985 Residential Saturation Survey (RSS)' From the 756 survey responses, a subset ofpotential participants was visited by SCE project staff to gather additional information on specific appliances in the household, household characteristics, and occupancy patterns. As described elsewhere,' extensive pre..;experimental research was undertaken to identify a sample ofcustomers that could provide the most pertinent and valuable information for the study, namely electric and gas end-use load shapes and customer characteristics. The goal ofthe REUC sample design was to determine the optimal allocation of the available 92 sets of sub-metering equipment and appliances so that the difference in energy usage between electric and gas appliances could be accurately measured. This allocation was achieved by identifying observable household characteristics that best predict overall household electric load shape and then stratifying the sample according to those characteristics. Required sample sizes were calculated using estimated variances '"Design ofa Utility Competitive Assessment Experiment: The Residential Energy Usage Comparison Project," J.T. Powers, M.S. Shirilau, R.T. Uhlaner and B.A. Smith, 

### IEEE Transactions on Power Systems (August 1988), Vol. 3, No.3, pp. 1298-1305.



### 4-1from the pre-experimental study (as a function 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 appliance. 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-occupied, single-family homes in SCE's Southern Division. Household income and square feet were selected as the key stratification variables. In orderto control for the effects of other household characteristics, 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 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 inventories 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 changed. Table 

### 4-2 illustrates the distribution of the highest



### 4-2 from the pre-experimental study (as a function 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 appliance. In order to develop the customer sample, analysis was performed on key demographic variables used as stratification variables in load research projects
. Ultimate selection ofthe stratification variables reflected the project objectives ofselecting 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. In orderto control for the effects ofother household characteristics, 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 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 inventories were collected forthe 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



### 4-2education 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 lime as SCE's 1987 RSS was administered. As described below, the 1987 RSS sample of single-family homes is used to assess the representativeness ofthe REUC sample. 

### Table 4-1 REUC Participant Demographics Sample Design StratificationVariables ElectricSample Number ofHouseholds House Size Medium House 1,250 - 1,749 sq ft Large House 1,750 sq ft or more Total 

### Mission Income Irvine Viejo Total

< $50K 12 3 15 $50K or more 2 6 8


### < $50K 2 

### 4 6 $50K or more 9 8 

## 11 46 

### Gas SampleNumber ofHouseholds Mission House Size Income Irvine Viejo Total Medium House 

### < $50K 2 3 5 1,250 -1,749 sq ft $50K or more 12 6 18 Large House 

### < $50K 1 4 5 1,750 sq ft or more $50K or more 10 8 

## JJ2 Total 46

2lbid., p. 1302.

4-3 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 lime 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 1,750sq ft or more Total 

### Table 4-1 REUC Participant Demographics Sample Design StratificationVariables ElectricSample Number ofHouseholds Mission Income Irvine Viejo

< $50K 12 3 $50K or more 2 6


### < $50K 2 4 $50K or more 9 8


### Gas Sample Total 15

8 6 

## 11 46 

### Number ofHouseholds Mission Income Irvine Viejo Total

< $50K 2 3 5 $50K or more 12 6 18



### < $50K 1 4 5 $50K or more 10 8 

## JJ2 46

2lbid.,

### p. 1302.

4-3

### Table 4-2 REUC Participants Demographics Highest Completed Education Level Among Key Household Members ElectricSample Level Completed High School 2-Year Associate Bachelor Masters Doctorate

Level Completed High School 2-Year Associate Bachelor Masters Doctorate 

### Gas Sample Number of Households Irvine Mission Viejo 

### 2 3 3 4 14 3



### 5 6 1 2
Number of Households

Irvine Mission Viejo 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 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 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-YearAssociate Bachelor Masters Doctorate ElectricSample

Gas Sample

REPRESENTATIVENESS OF THE REUC SAMPLE Number of Households Irvine MissionViejo 2 

### 3 3 4 14 

### 3

5 6 1 2
Numberof Households

Irvine MissionViejo 1 2 

### 5 3 

### 8 8 10 2 2

To perform the assessment of the representativeness of the REUC customer sample, Integrated Market Research (lMR) 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

4-4validity of the original model-based sample design. The second objective is met 

### hy determining the share of SCE's single-family homes for which the REUC results 

### ;ne applicable. This share represents a quantitative measure of the direct 'lpplicability 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 percent of SCE's single-family homes using the income variable, and over 55 percent of SCE's single-family homes when using the square feet 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 (AS!). This segmentation by ASI allows the energy usage characteristics ofthe two samples to be directly linked. Seventy-eight percent of the RSS 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/NOACL See Appendix 

### Bfor details ofthe ASI definitions. In 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 singlefamily subsample, the ASI shares for each stratum are reported. For the stratum corresponding to the REUC sample, the "REUC Share of ASj,U is computed. For example, in the case of the Income variable for the GSH/CAC 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 ASI are represented by the REUC sample using income as the basis for comparison. The "ASI Share of SCE 3See Appendix 

### Bfor a definition of each AS/.

4-5 validity ofthe original model-based sample design
. The second objective is met 

### hy determining the share ofSCE's single-family homes for which the REUC results 

### ; ne applicable
. This share represents a quantitative measure of the direct ;1pplicabilityofthe REUC results to SCE's residential customers
. Comparison of REUC and RSS Samples in assessing the representativeness ofthe REUC sample, the original REUC sample s

tratification variables, income and square feet, were used. The principal finding isthat 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 squarefeet variable. A key concept underlying the comparison ofthe REUC and RSS customers rests on segmenting these two customer samples by their appliance stock inventories (AS!). This segmentation byASI allowsthe energy usage characteristics ofthe two samples to be directly linked. Seventy-eight percent of the RSS Single-family subsample fell into one ofthree 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 heatingand noair-conditioning (GSH/NOAC)' See Appendix 

### Bfor details ofthe ASI definitions. In 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 singlefamily subsample, the ASI shares for each stratum are reported. For the stratum corresponding to the REUC sample, the "REUC Share ofASI,u is computed. For example, in the case of the Income variable for the GSH/CAC 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 ASI are represented by the REUC sample using income as the basisfor comparison. The "ASI Share of SCE 3See Appendix 

### Bfor a definition ofeach AS!.

4-5Single Familyll is taken from the IITotal SCEII column ofTable B-1. Multiplying the IIREUC Share ofASIII by the liASI Share of SCE Single Familyll produces the IIREUC Share ofSCE Single Family by ASI.II Finally, the IIREUC Total Share of SCE Single Familyll is the sum across each ASI ofthe REUC Share of 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 Representativeness by Income Stratum RSS Single-Family Subsample REUC sample REUC Income Stratum ELE/GAS Size RSS Income Stratum Appliance Stock Inventory (AS!) GSH/CAC GSH!WWAC GSH/NOAC

REUC Total Share ofSCE Single Family REUC Share of SCE Single Family byASI 15.4 REUC Share ofASI 67.0 ASI Share ofSCE Single Family 23.0 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 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%



### 4-6 Single Family" is taken from the "Total SCE" column ofTable B-1. Multiplyingthe "REUC Share ofASI" bythe liASI Share ofSCE Single Family" produces the "REUC Share ofSCE Single Family by ASI." 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 40 percent of SCE1s single-family households, by income strata 

### (15.4 

### + 6.0 

### + 18.6), representing over 

### 550,000 households throughoutSCE1s service territory. Table 4-3 REUC Sample Representativeness by Income Stratum RSS Single-Family Subsample REUC sample REUC Income Stratum ELE/GAS Size RSS Income Stratum Appliance Stock Inventory (AS!) GSH/CAC GSH!WWAC 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 byASI 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 GSH/CAC ASI is very high, 86.4 percent. Following the calculations discussed above for the income strata, the IIREUC Total Share of SCE Single Familyll for the GSH/CAC ASI is 55.3 percent, representing over 750,000 households. Table 4-4 REUC Sample Representativeness 

### by House Size Stratum REUC sample REUC SqFt Stratum RSS Single-Family Subsample ELE/GAS RSS SqFt Appliance Stock Inventory (ASI) Size Stratum GSH/CAC GSH!WWAC GSH/NOAC

REUC Share of SCE Single Family byASI 19.9 REUC Share ofASI 86.4 ASI Share ofSCE Single Family 23.0

REUC Total Share ofSCE Single Family < 1,250 SqFt 1,250-1,749 SqFt 1,750+ SqFt 0/0 23/23 23/23 < 1,250 SqFt 1,250-1,749 SqFt 1,750+ SqFt 13.6% 38.2 48.2 45.2% 31.1% 37.1 34.1 17.7 34.8
54.8 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. 4-7 Table 4-4 provides similar results of the REUC Sample1s 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 ELE/GAS RSS SqFt Appliance Stock Inventory (AS!) REUC sample REUC SqFt Stratum Size Stratum GSH/CAC GSH!WWAC GSH/NOAC < 1,250 SqFt 1,250-1,749 SqFt 1,750+ SqFt 0/0 23/23 23/23 < 1,250 SqFt 1,250-1,749 SqFt 1,750+ SqFt 13.6% 38.2 48.2 REUC Share ofASI 86.4 ASI Share ofSCE Single Family 23.0 REUC Share ofSCE Single Family byASI 19.9 REUC Total Share ofSCE Single Family 45.2% 31.1% 37.1 34.1 17.7 34.8
54.8 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 SCEls single-family homes by income strata and almost 20 percent of SCEls 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 conditioningalongwith decidingto install a heat pump. 4-7

### 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 residential customers. This analysis also confirms that the REUC sample design, based on income and square feet, was avalid sampling framework. Finally, the IMR techniques applied to this representativeness assessment combined both quantitative and qualitative assessments of how survey-based data can be efficiently distilled into a useful set of customer descriptors. The two-step framework of first 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.

4-8 

### 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 residential customers. This analysis also confirms that the REUC sample design, based on incomeand squarefeet, was avalid samplingframework. Finally, the IMR techniques applied to this representativeness assessment combined both quantitative and qualitative assessments of howsurvey-based data can be efficiently distilled into a useful set ofcustomer descriptors. The two-step framework offirst developing simplified appliance stock inventories followed by analyzingthe relationship ofcustomer characteristics to howcustomer 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.

4-8

### Section 5 

### REUC LOAD DATA ANALYSIS: ELECTRIC AND GAS APPLIANCE ENERGY USAGE
INTRODUCTION This section presents the results from the analysis of nearly two years of load data collected from the REUC sample. The project monitored both electric and gas appliances in these households. As mentioned before, all energy information presented here is given as pOint-of-use or site energy; i.e., 1 kWh

## =3.412 kBTU. This section includes a description of how the end-uses were monitored, how the data were collected, and the analysis methodology. For each end-use, monthly and yearly average total usage tables and charts indicate the average appliance energy usage. These results are followed by comparison of the distribution of average daily UECs for space conditioning and water heating. Finally, results of a weather model of space conditioning usage are presented. 

### LOAD DATA COLLECTION AND ANALYSIS End-use load data were gathered from the 92 households participating in the REUC project with load monitoring devices, each equipped with four load measurement channels that record usage readings in five-minute intervals. Each household provided data on from eight to twelve channels. Total household electric usage was collected from each site, providing an important cross-check for validating the end-use data. Each load monitoring device downloaded its data via modem to the central load data computer approximately once per week. Simultaneously monitoring both electric and gas appliances from a single household including heat pump technologies posed new data collection and validation challenges. Moreover, this was the first SCE load monitoring project conducted entirely with microcomputers rather than mainframe computers -- from initial data collection to final data analysis. This has allowed rapid
5-1 

### Section 5 

### REUC LOAD DATA ANALYSIS: ELECTRIC AND GAS APPLIANCE ENERGY USAGE
INTRODUCTION This section presents the results from the analysis of nearly twoyears of load data collected from the REUC sample. The project monitored both electric and gas appliances in these households. As mentioned before, all energy information presented here is given as pOint-of-use orsite energy; i.e., 1 kWh

## =3.412 kBTU. This section includes adescription of howthe end-uses were monitored, howthe data were collected, and the analysis methodology. For each end-use, monthly and yearly average total usage tables and charts indicate the average appliance energy usage. These results are followed by comparison of the distribution of average daily UECs for space conditioning and water heating. Finally, results ofa weather model of space conditioning usage are presented. 

### LOAD DATA COLLECTION AND ANALYSIS End-use load data were gathered from the 92 households participating in the REUC project with load monitoring devices, each equipped with four load measurement channels that record usage readings in five-minute intervals. Each household provided data on from eightto twelve channels. Total household electric usage was collected from each site, providing an important cross-check for validating the end-use data. Each load monitoring device downloaded its data via modem to the central load data computer approximately once per week. Simultaneously monitoring both electric and gas appliances from a single household including heat pump technologies posed new data collection and validation challenges. Moreover, this was the first SCE load monitoring project conducted entirely with microcomputers rather than mainframe computers -- from initial data collection to final data analysis. This has allowed rapid
5-1

### turnaround of initial data validation so that malfunctioning monitors and appliances can be quickly identified.' The nearly two years of usage 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 by summing up all valid usage readings. Average usage figures were calculated by normalizing each channel by the number of valid days of data, giving the usage of typical appliances for the entire year. Details of all these validation, editing and analysis procedures can be found in Appendix 

### C.

1 For 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 1988L 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.
.

5-2 

### turnaround of initial data validation so that malfunctioning monitors and appliances can be quickly identified.' The nearly two years of usage data presented in this report consist ofthe period February 5, 1988 through November 30, 1989. Therefore, the complete 1988 and 1989 summer seasons and nearlytwo 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 exactlywhich 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 by summing up all valid usage readings. Average usage figures were calculated by normalizingeach channel by the number of valid days ofdata, giving the usage of typical appliances for the entire year. Details ofall these validation, editingand 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 a31-day month, and the April estimate is based on a30-day month, etc.
.

5-2Table 

### 5-1 REUC: Table of Month Label to Date Correspondence Month Label January February March April May June July August September October November December Actual Dates Dec 30, 1988- Feb 2, 1989 Feb 5- Mar 3, 1988 Feb 3- Mar2, 1989 Mar4-Mar 31, 1988 Mar 3-Mar 30, 1989 Apr 1- Apr 28, 1988 Mar 31- Apr 27,1989 Apr 29- Jun 2, 1988 Apr 28- Jun 1, 1989 Jun 3- Jun 30, 1988 Jun 2- Jun 29, 1989 Jul 1-Jul 28, 1988 Jun 30- Aug 3, 1989 Jul 29- 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 Days 35 28 28 28 28a 28 28 28 35 28 28 28 35a 28 28 35 28 28 35 35 28 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.

5-3 Table 

### 5-1 REUC: Table ofMonth Label to DateCorrespondence 

### Month Label Actual Dates Numberof**Da~s** 

### January Dec 30, 1988- Feb 2, 1989 35 February Feb 5- Mar3, 1988 28 Feb 3- Mar2, 1989 28 March Mar4-Mar31, 1988 28 Mar3-Mar 30, 1989 28a April Apr 1- Apr 28, 1988 28 Mar31- Apr 27,1989 28 May Apr 29- Jun 2, 1988 28 Apr 28- Jun 1, 1989 35 June Jun 3- Jun 30, 1988 28 Jun 2- Jun 29, 1989 28 July Jul 1-Jul 28, 1988 28 Jun 30- Aug 3, 1989 35a August Jul 29- Aug 25, 1988 28 Aug 4- Aug 31, 1989 28 September Aug 26- Sep 29, 1988 35 Sep 1 -Sep 28, 1989 28 October Sep 30- Oct 27, 1988 28 Sep 29- Nov 2, 1989 35 November Oct28- Dec 1, 1989 35 Nov3- Nov30, 1989 28 December Dec 2- Dec 29, 1988 28 aThe followingtwoweeks 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 average yearly total usage for space conditioning, water heating, cooking and clothes drying are presented in Figures 5-1 and 5-2, respectively.3 These values 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 Space Conditioning and Water Heatinga

WaterHeating 23.4

o a At PointofUse 5 10 15 MBTU 20 25 30

Ea Gas Sample
• Electric Sample
3AII 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.
5-4 AVERAGE YEARLY TOTAL USAGE The average yearlytotal usage for space conditioning, water heating, cooking and clothes drying are presented in Figures 5-1 and 5-2, respectively) These values are given in MBTUsfor 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 byseason in Table 5-2.4 Figure 5-1 Annual Average Energy Usage Space Conditioningand Water Heatinga

SpaceConditioning

WaterHeating

o 5 a At PointofUse 10 15 MBTU**~** GasSample • ElectricSample20 23.4

25 30

3AII 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
. 5-4

### figure 5-2 Annual Average Energy Usage Clothes Drying and Cookinga

Clothes Drying

Cooking 4.7

o a At PointofUse 2 3 MBTU Ea Gas Sample
• Electric Sample 4 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 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 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 of use than electric dryers, which average 3.7 MBTU per year.

5-5 ClothesDrying

Cooking

o figure 

### 5-2 Annual Average Energy Usage Clothes Dryingand Cookinga

2 4 a At PointofUse 3 MBTU Ea GasSample • ElectricSample 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 pointofuse 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 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 similarfactor. 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 pointofuse than electric dryers, which average 3.7 MBTU peryear.

5-5

### Table 5-2 Average Annual and Seasonal Energy Usagea 

### Pooled Cities
Appliance Electric Sample kWh MBTUa 

### Gas Sample Gas Sample (Gas Components) (Electric Components) 

### 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 NA Clothes Dryer 1070 3.66 4.32 110 0.38 Range/Oven 385 1.31 3.24 NA NA Winterb

### Average Household Usage (8 

### months) Whole House 9230 31.50 NA 4720 16.10 Space Conditioning 2190 7.50 19.00 437 1.49 Water Heating 2080 7.10 17.00 NA NA Clothes Dryer 760 2.60 2.97 75 0.26 Range/Oven 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 35 0.12 Range/Oven 111 0.37 1.00 NA NA aAt point of use. bJanuary through May and Octoberthrough December. CJune 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 of almost 450 MBTUs overthe assumed 15-year appliance lifetime.
5-6 Table 5-2 Average Annual and Seasonal Energy Usagea 

### PooledCities Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) 

### 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 NA Clothes Dryer 1070 3.66 4.32 110 0.38 Range/Oven 385 1.31 3.24 NA NA WinterbAverage Household Usage (8 months) Whole House 9230 31.50 NA 4720 16.10 Space Conditioning 2190 7.50 19.00 437 1.49 Water Heating 2080 7.10 17.00 NA NA Clothes Dryer 760 2.60 2.97 75 0.26 Range/Oven 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 35 0.12 Range/Oven 111 0.37 1.00 NA NA aAt pointof use. bJanuary through May and Octoberthrough December. CJune 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 overthe assumed 15-year appliance lifetime.
5-6Figure 5-3 Difference in Annual Energy Consumption between Gas and Electric Appliancesa
Space Conditioning·

Water Heating

Clothes Drying

Cooking



## o 5 10 15 20 a At Point of Use Annual Energy Difference (MBTU)



### 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.

5-7 

### Figure 5-3 Differencein Annual Energy Consumption betweenGas and ElectricAppliancesa
SpaceConditioning '

WaterHeating

ClothesDrying

Cooking



## o 5 10 15 a At PointofUse AnnualE 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 Monthly Average Usage for Pooled Cities Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) kWh Therms kWh Appliancea 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

5-8 Table 5-3 MonthlyAverage Usagefor Pooled Cities Electric Sample Gas Sample Gas Sample (Gas Components) (Electric Components) kWh Therms kWh ARRliancea 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 1 1 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

5-8

### Table 5-3, Cont. Electric Sample Gas Sample Gas Sample (Gas Components) (Eledric 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 Water 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 28.7 912 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

5-9 

### T

able 5-3, Cont. Electric Sample Gas Sample Gas Sample (Gas Components) (Eledric 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 Water 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 28.7 912 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

5-9

### 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 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 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 SeasonalAverageWeekdayElectricity Usage Electric Sample End-Uses

WaterHeating

SpaceConditioning

Cooking

Clothes Drying 

### t:a WinterWeekday II SummerWeekday 8.8



### o 1 2 3 4 5 6 7 8 9 10

5-10 kWh 

### 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 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 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 SeasonalAverageWeekdayElectricity Usage Electric Sample End-Uses

WaterHeating

SpaceConditioning**~~~~~~~~**
Cooking

ClothesDrying 

### t:a WinterWeekday II SummerWeekday 8.8



### o 1 2 3 4 5 6 7 8 9 10 kWh

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 it decreases during the summer for 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 aseason oryear is lost. The distribution of average daily usage reveals, for example, that space conditioning seasonal averages do not represent usage average for extreme days. 

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

WinterWeekend

WinterWeekday

SummerWeekend

SummerWeekday

o 10 20 kWh 30 40 50
E3 GasSample
• ElectricSample Figures 5-6 through 5-10 plot the distributions ofthe sample averaged daily usage for the space conditioning and water heating end-uses. These figures are divided
5-11 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 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 variabilityofaverage usage across differentdays in aseason oryear is lost. The distribution of average daily usage reveals, for example, that space conditioning seasonal averages do not represent usage average for extreme days. Figure 5-5 Whole House: Average Daily Electricity Usage

WinterWeekend

WinterWeekday

SummerWeekend

SummerWeekday

o 10 20 30 40 50 kWh 

### E:a GasSample • ElectricSample 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 type and season. They illustrate the modal usage level fortypical days in a season and the degree of skewness 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 weathersensitivity.



### 5-12 byfuel type and season. They illustratethe modal usage level fortypical days in a season and the degree of skewness 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 ofthe mean, while the range for space conditioning is about five times the mean. Both ofthese observations regarding the difference in the two end-uses' distributions (skewness and range) reflect their different degrees of weathersensitivity.



### 5-12Figure 5-6 SCE REUC Weekday Average UEC Distribution Winter Electric Heat Pump: Pooled Cities
Percent ofTotal 40 35 30 25 20 15 10 5 o <2 4 8 12 16 20 24 28 32 36 40 44 Daily kWh Midpoints Figure 5-7 SCE REUC Weekday Average UEC Distribution

Summer Space Conditioning: Pooled Cities
PercentofTotal 40 35 30 25 20 15 10 5 o <2 4 8 12 16 20 24 28 32 36 40 Daily kWh Midpoints 

### 5-13 Figure 5-6 SCE REUC WeekdayAverage UEC Distribution WinterElectric HeatPump: PooledCities
PercentofTotal 40 35 30 25 20 15 10 5 o <2 4 8 12 16 20 24 28 32 36 40 44 DailykWhMidpoints 

### Figure 5-7 SCE REUC WeekdayAverage UEC Distribution

SummerSpaceConditioning: PooledCities
PercentofTotal 40 35 30 25 20 15 10 5 o <2 4 8 12 16 20 24 28 32 36 40 DailykWhMidpoints 

### 5-13

### figure 5-8 SCE REVC WeekdayAverage VEC Distribution Winter Gas furnace: PooledCities
PercentofTotal 50 45 40 35 30 25 20 15 10 5 0 <2 50 100 150 200 250 300 350 400 450 DailykBTU Midpoints 

### figure 5-9 SCE REVC WeekdayAverage VEC Distribution WinterElectricWaterHeating: PooledCities

PercentofTotal 20 18 16 14 12 10 8 6 4 2 

# 0-----"- 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.511 11.5 12 12.5 13 13.5 DailykWhMidpoints


### 5-14 

### figure 5-8 SCE REUC WeekdayAverage UEC Distribution WinterGas furnace: PooledCities
PercentofTotal 50 45 40 35 30 25 20 15 10 5 0 <2 50 100 150 200 250 300 350 400 450 DailykBTU Midpoints 

### figure 5-9 SCE REUC WeekdayAverage UEC Distribution WinterElectricWaterHeating: PooledCities

PercentofTotal 20 18 16 14 12 10 8 6 4 2 

# 0-----"- 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.511 11.5 12 12.5 13 13.5 DailykWhMidpoints


### 5-14

### Figure 5-10 SCE REUCWeekday Average UEC Distribution WinterGas Water Heating: PooledCities
PercentofT olal 20 18 16 14 12 10 8 6 4 2 0 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 DailykBTU Midpoints



### 5-15 PercentofTotal 

### Figure 5-10 SCE REUC WeekdayAverageUEC Distribution WinterGasWaterHeating: PooledCities
20 18 16 14 12 10 8 6 4 2 0 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 DailykBTU Midpoints



### 5-15SPACE 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 exteriortemperature. 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 UftiS the morning usage in kBTU between 6:00 and 10:00 a.m. for k = gas or electric, j indexes cities, and t ranges over all days of the winter. Hjt is the number of heating degree days base 68°F in cityj on day 1. 

### Heating degree days are defined asfollows: 120 

# [ I max (O,68-temptjm)]

m=73 (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; tjt is a random disturbance term. Thus, two

# i3r 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.

5-16 SPACE CONDITIONING WEATHER MODEL 

### It is evidentfrom 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 oftemperature. The amount of energy required to heat a home on a given day depends on a numberoffactors includingthe exteriortemperature. The load shapes in Section 7 show thatthe usage between 6:00 and 10:00a.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 UftiSthe morning usage in kBTU between 6:00 and 10:00a.m. for k = gas or electric, 

### j indexes cities, and t ranges over all days ofthe winter. Hjt is the numberof heating degree days base 68°F in city

### j on day t. 

### Heating degree days are defined asfollows: 120 

# [ I max (O,68-temptjm)]

m=73 (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; tjt is a random disturbanceterm. Thus, two

# i3r 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.



### 5-16

### Table 5-4 Estimated linear Relationship betweenAverage Morning Space

Heatingand Morning Heating Degree-Days: All Households
Fuel 

# a k Std. Error N R2 Gas 26.5a 0.45 841 0.81 Electricity 7.5a 0.14 841 0.76 aOenotes 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 orderterms 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.

5-17 

### Table 5-4 Estimated Linear Relationship betweenAverage Morning Space

Heatingand Morning Heating Degree-Days: All Households
Fuel 

# a k Std. Error N R2 Gas 26.5a 0.45 841 0.81 Electricity 7.5a 0.14 841 0.76 aOenotes 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 heatingdegree day. This relationship is linear; higherorderterms do not significantly increase the explanatory power ofthe model. Furthermore, no significant usage difference between weekends and weekdays is detected, so both daytypes are pooled in the regressions.



### 5-17

### Section 6 REUC APPLIANCE LOAD PROFILES
INTRODUCTION This 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 andgas residential 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 the key comparisons made for the monitored appliances. Appendix A contains 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 of electric 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 appearquite 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.



### 6-1 

### Section 6 REUC APPLIANCE LOAD PROFILES
INTRODUCTION This 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 andgas residential 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 the key comparisons made for the monitored appliances. Appendix A contains a comprehensive set 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 ofelectric and gas appliances. Cookingand clothes drying usage profiles are provided in Appendix A. The load profile results mirrorthe ' seasonal and annual energy usage presented in the previous section. The load shapes forelectric and gas appliances appearquite 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 AppendixA and generally supportthis observation.



### 6-1

### Figure 6-1 SCE REUC Winter: SpaceConditioning (atpoint ofuse)
kBTU per Hour 12 Elec. Weekdays Elec. Weekends GasWeekdays GasWeekends

4 " i , 

### ii\ ! I , !I i' !, i' !, i' ! Ii' !/ i\ I • , 

# ! ;\ 

# , .

# , , 

# r ,
. 

# , I \ \**-\~** ,,_\ I',
..**?..-.--~-** 

### -'.\ h \ \ .'" '.\**~/** 

# "',
..\---I \.
2**,~'-** ','., \ ., 

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

# _--_..."

........

### _-----,- 6 8 10

24 21 18 15 12 9 63 O-+-...-..-
......."""T"-r...-T"""1
.......**--r-r.....-...-,......,r"""'T""T"""'T""~T""'lr"""'T--,**

o TimeofDay 

### Figure 6-2 SCE REUC Summer: Space Conditioning (at point ofuse)
k8TU per Hour 3.0
2.5
2.0
1.5
1.0
0.5 Elec. Weekdays Elec. Weekends GasWeekdays GasWeekends

24 21 18 15 12 963 0.0

### +-............,--r.....,.-'T'-
............,"""T"""T""'T'-
............,-,..""T"",.-
........,.....,-,.......,.-..., o TimeofDay

6-2 

### Figure 6-1 SCE REUC Winter: SpaceConditioning(atpointofuse)
kBTU per Hour 12
10

8
6
4

o 3 6 9 12 TimeofDay 

### Figure 6-2 15 18 Elec.Weekdays Elec
.Weekends GasWeekdays GasWeekends

21 24



### SCE REUC Summer: SpaceConditioning(at pointofuse)
kBTU per Hour 3
.0
2.5
2
.0

1.5
1.0
0
.5 Elec.Weekdays Elec
.Weekends GasWeekdays GasWeekends**O.O;-~ro~-r,-~rolT-r,-~ro~-r,-~ro~-r~** o 3 6 9 12 15 18 21 24 TimeofDay

6-2

### Figure 6-3 SCE REUC Winter: Water Heating (atpointofuse)
kBTU perHour 8 

## " . ' Bee.Weekdays I' 7 i i Bee.Weekends i " GasWeekdays , ' I 

## \1\ GasWeekends 6 I " 

### \ 

## , ), I \ S 

## , Ii \ 

### ,--\ 

### • 

# ,

# , \ 

# , , 

# .""' \ I I 

### 't 4 

### • 

# , ;. \ /! ' I 

# , 

# " \ II t., 

### • 

# , \, 

# "--1/ 

# ,\ 3 

### • 

# , , \"-. I 

# , 

# " .' 

## \, I 

### ...

## -... , ,1 

### , " , 2 

### "

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

## ,,

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

24 21 18 lS 12 963 O+-'-'--T"'""--r-.....,,.....--r--"'"'T--T"'""--'

o TIme ofDay 

### Figure 6-4 SCE REUC Summer: Water Heating (at pointofuse)

Elec.Weekdays Elec. Weekends GasWeekdays GasWeekends i'. ! '. 

# ., J ," "'/\**~I** \ t \ 

## 1\ \ " , \ ':A I ", \ 

### .r**~,** 

# , \ \**~'~** 

# , 

# .. \ 

# ,..., , " '-./i \
... 

# , \**~,** \

......, 

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

# ,,

## '
.......

## _-.._,;' kUTUperHour 6
S

4 I I I 3 I I 

### • 2

24 21 18 lS 12 9 63 0+--
...........-....--...........-....--.....--...--.....----.

o Time ofDay



### 6-3 

### Figure 6-3 SCE REUC Winter: WaterHeating(atpointofuse)
kBTU perHour 8 

### ,1



## .. Bee.Weekdays 

### , , 7 i i Bee.Weekends i**.~** GasWeekdays 

## \1\ GasWeekends 6 

# ., \ 

### , ), \ S 

## , 

# " \ 

### ,-\ 

### • 

# ,

# , \ 

# , , 

# ''''' \ I \ I 

### 't 4 

### , 

# , ;. 

## , 

# , "' \ /! t. 

# , /' 

## . I \ 

# "-.-J..' ''. 3 

### • 

# , \ I \
... 

# , \. '**~** 

## . \

..._, , ,1 

## .. , , 2 

### "--...

### _-, 

## ..

## ,

## ..........._-"**Oi----,----T_---r--~~--~--~----T_--,** o 3 6 9 12 lS 18 21 24 TImeofDay 

### Figure 6-4 SCE REUC Summer: WaterHeating(at pointofuse)

o 3 6 9 12 lS 18 21 24 TimeofDay



### 6-3

### For example, considerthe 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 of time. Thus, not only do the total usage amounts differ 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 of 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 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. Figure 6-5 SCE REUC: Space Conditioning Electric Sample

24 21 JanuaryWeekdays JanuaryWeekends JulyWeekdays JulyWeekends

18 15 12 96 

### I" /.i I" 

# ----,-/' 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 TimeofDay



### 6-4 For example, considerthe 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 differ 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 of 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 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.

kwload 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: SpaceConditioningElectricSample**.~~**

### ...
....**~~.:.** 0 3 

### , 

### , 

# , \

### , 

### , , \ \ \

### , \ \

# , \

### , , 

### , \

### , , 

### , \

/" 

### /.i 

# ,., ----,-// 6 9 12 TimeofDay



### 6-4 15 18 JanuaryWeekdays JanuaryWeekends JulyWeekdays JulyWeekends

21 24

### Figures 6-6 and 6-7 present the average load profiles across all lwclw ,l)ol1lh c, ill 

### the year for heat pump and gas space conditioning, respectively. 

### Th(,,,l' II1IlT 

### dimensional figures trace the changing height ofthe average load profile peJk iJ1 

### each month. All four electric end-uses I 

### load profiles are presented in one plot in Figures 6-8 and 6-9 in order to illustrate the different magnitudes of average appliance load. Finally, Figures 6-10 and 6-11 present whole house load shapes for the Electric and Gas Samples. Space conditioning and whole house load are overlaid in Figure A-22 in Appendix A. These load shapes and average load profiles are consistent with other residential load metering studies. For example, load profiles produced from the SCE Residential Appliance End-Use Survey (RAEUS) project are consistent with the REUC load profiles where comparisons can be made. The RAEUS project, while metering only electric appliances, has a larger number ofend-use profiles.' The profiles provided in Appendix A include: (1) 

### load profiles for cooking and clothes drying; (2) load profiles for the electric appliances in kW rather than KBTU; (3) two-dimensional space conditioning average load profiles for each month; (4) 

### load profiles for weekdays expressed as a percentage of total daily usage; (5) whole house, space conditioning and water heating load profiles on a common graph; and (6) athree-dimensional daily temperature profile by month.

1 

### For more information about the RAEUS load profiles, see Southern California Edison, "Residential Appliance End-Use Survey: Sample I Final Report," February 1989.


### 6-5 Figures6-6 and 6-7 present the average load profilesacross all lvvclw 111011 Ih c, i)1 

### the year for heat pump and gas space conditioning, respectively. 

### Thcn ,(, I

### hr('(' dimensional figures trace the changing height ofthe average load profile peJk if1 

### each month
. All four electric end-uses I 

### load profiles are presented in one plot in Figures 6-8 and 6-9 in order to illustrate the different magnitudes ofaverage appliance load. Finally, Figures 6-10 and 6-11 present whole house load shapes for the Electric and Gas Samples. Space conditioning and whole house load are overlaid in Figure A-22 in Appendix A
. These load shapes and average load profiles are consistent with other residential load metering studies. For example, load profiles produced from the SCE Residential Appliance End-Use Survey (RAEUS) project are consistent with the REUC load profiles where comparisons can be made. The RAEUS project, while metering only electric appliances, has a larger numberofend-use profiles.' The profiles provided in Appendix A include: (1) load profiles for cooking and clothes drying; (2) load profiles for the electric appliances in kW rather than KBTU; (3) two-dimensional space conditioning average load profiles for each month; (4) 

### load profiles for weekdays expressed as a percentage of total daily usage; (5) whole house, space conditioning and water heating load profiles on a common graph; and (6) athree-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.


### 6-5

### Figure 6-6 SCE REUC: Heat Pump Space Conditioning

6-6 figure 6-6 SCE REUC: HeatPumpSpaceConditioning

kW Load**~O**

Ui

LO

OJ)

'nme (PST)



### 6-6kBTU per Hour 26

20

lli

10

6 Figure 6-7 SCE REUC: Gas/Electric Space Conditioning

I

## "''''''''-7'- May 

# M:r February January Deamber

### ::>7s..::;:<:;~(c'"~ september August July June 0:00

6-7 kBTU perHour

20

10

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

---'-""""."'- May 

# M;r FI!bruary January Detember Nlm!mber October september August July June 0:00

6-7

### Figure 6-8 SCE REUC WinterWeekday Electricity: Electric Sample
SpaceConditioning WaterHeating Cooking Clothes Drying kW Load 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 3 6 9 12 1S 18 21 24 TimeofDay 

### Figure 6-9 SCE REUC SummerWeekday Electricity: Electric Sample

24 21 18 15 12 9 6 3 SpaceConditioning WaterHeating Cooking Clothes Drying 

### /\ 

### , .. 

# , \ I , I , I
.. I '
.. I \ I I I I 

# ,

.. I , I



## -.._---, ;' ;,' 

## , /

".. ,/I' I
.. \ 

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

### -..

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

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

### - 0.0**-F.,..,.--l~---T""""~_r_-......,.--r_""'-"'T""-;.:,** o kW Load 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Time ofDay

6-8 

### Figure6-8 SCE REUC WinterWeekdayElectricity: ElectricSample
kW Load 1.0 0.9 0
.8 0.7 0.6 0.5 0.4 0
.3 0.2 0.1 0.0 0 3 6 9 12 TimeofDay 

### Figure 6-9 

### , 

## -.-,' 15 SpaceConditioning WaterHeating Cooking ClothesDrying

!
18 21 24



### SCE REUC SummerWeekdayElectricity: ElectricSample
kW Load 1
.0 0
.9 0.8 0.7 0.6 o.s 0.4 0
.3 0.2 0.1 SpaceConditioning WaterHeating Cooking ClothesDrying
/\ I , 

# , \ 

### , 

# , : " , 

## '. ;' ;,' 

## , / 

## , \ 

### , , 

### , , 

# , '..... /I' " 

# "**~** I , \ 

# -

...

# _---' .'

.,**~** ...,

### ...

## -....

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

## _-_..." 

### '-

...

# --- 0.0**..p.~..,.--.,::.;-""T"-.....,---r--'""'r--T'"""'~;.:,** o 3 6 9 12 1S 18 21 24 TimeofDay

6-8

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

Elec. SampleWeekdays Elec. SampleWeekends Gas Sample Weekdays Gas SampleWeekends 24 

# '\


# .

21 1815 II 

## , I I I

" ,1

.....

# - ..

12 96 3 0.4 0.2 0.0**t.-~T"""""'~T"""""'"-..-,.~**

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

## ..............--"':::;:::::::::;---------' o kW load 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0

TimeofDay 

### Figure 6-11 SCE REUC Summer: Whole House Electricity

24 21 18 Elec. SampleWeekdays Elec. SampleWeekends Gas SampleWeekdays Gas SampleWeekends

..
15 ,"

# ..'\ I ,

### ......,., 

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

12 9 6 3 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 TimeofDay

6-9 kW load 

### Figure 6-10 SCE REUC Winter: WholeHouseElectricity

3.0 2.8 2.6 2.4 2
.2 2.0 1.8 1.6 1.4 1.2 1.0 

## ,"'-
...**~**

0.8 ,. 

### I'...
.. ...**~~** ,/,/ 

# -----,'-- 0.6**.,._~** 0.4 0.2 

### , 

### , 

## , , , I I 

# - I


# '\


# . Elec. SampleWeekdays Elec. SampleWeekends Gas SampleWeekdays GasSampleWeekends 0.0**i-.~T"""'"~r-r-..........,.~"'--"~,.....,..**

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

## '"'""T""""":::;:::::::::::;---------' o 3 6 9 12 15 18 21 24 TimeofDay



### Figure 6-11 SCEREUC Summer: Whole House Electricity
kW Load 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.00 3 6 9

6-9 

### ,.'" , 

# , I ,........., 

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

12 15 TimeofDay Elec. SampleWeekdays Elec. SampleWeekends GasSampleWeekdays Gas SampleWeekends

..**~** .
18 21 24