EPRl 

Zlectric Power 

3esearch Institute 

Keywords: 

Customer attitudes 

End-use consumption 

Energy efficiency 

Load research 

Residential Energy Usage 

Comparison: Findings 

Prepared by 

Quantum Consulting Inc. 

Berkeley, California 

EPRl CU-7392 

Project 2863-3 

Final Report 

August 1991 

 EE-RM-94-230 COMMENT 61F

EPRI 

:::Iectric Power 

'lesearch Institute 

Keywords: 

Customer attitudes 

End-use consumption 

Energy efficiency 

Load research 

Residential Energy Usage 

Comparison: Fi~dings 

Prepared by 

Quantum Consulting Inc. 

Berkeley, California 

EPRI CU-7392 

Project 2863-3 

Final Report 

August 1991 
DISCLAIMER OF WARRANTIES AND 

LIMITATION OF LiABlLlTlES 

THIS REPOKI WAS PREPARED BY THE ORGANIZAT.ON(S) hAMEO BELOW AS Ah 

ACCOUNT OF WORK SPONSORED OR COSPONSORED BY TrlE ELECTRIC POWER 

RESEARCH INSTITUTE. INC lEPRll NEITHER EPRI. ANY MEMBER OF EPRI. ANY 

COSPONSOR THE ORGANI&~~ON~SI NAME0 BELOW NOR ANY PERSON ACTING 

IN MAKES AhY WARRANTY OR REPRESENWION WtiAlSOEVER. EXPRESS OR 

MPLIED. (I) WITH RESPECT m THE USE OF ANY INFORMAT ON. APPARATUS. 

METHOD. PROCESS OR SIMILAR ITEM 0 SCLOSEO IN Th S REPORT, INCUlDlhG 

MERChANTABI-IN AND FITNESS FOR A PAAnCU4R PURPOSE. OR (11) ThAT SJCH 

USE DOES NOI IhFRlNGE IlN IlR INTERFERE WITH PRrVA1E.Y OWNED RIGhTS. 

INCLUDING ANY PARP('SINTELLE'~UAL PROPERTY.OR(III~ THAT THlS REPORT 

IS SUITABLE TO ANY PARTICULAR USER'S CIRCUMSTANCE; OR 

10) ASSUMES RESPONSBlLlPl FOR AhY DAMAGES OR OTHER LIABILITY 

fiHAnOEVER llNCWDlNG ANY CONSEOUEhTlAL OAMAGES. EVEN IF EPRI OR ANY 

EPRI REPRESEI(TATIVEHASBEE~AD~I~E~OF T~E POSSI~~L~~Y OF SJCH DAMAGES) 

RESULTlhG FROM WUR SELECTION ORUSE OF THS REPORT OR ANY IhFORMAT ON. 

APPARATUS, hIETHO0. PROCESS. OR SIMILAR ITEM DISCLOSED IN Th S REPORT 

ORGANIUFIION(S) THAT PREPARED THIS REPORt 

QUANTUM CONSULTING INC. 

Printed on Recycled Paper 

Residential Energy Usage 

Comparison: Findings 

CU-7392 

Research Project 2863-3 

Final Report, August 1991 

Prepared by 

QUANTUM CONSULTING INC. 

2030 Addison Street 

Berkeley, California 94704 

Principal Investigators 

6. A. Smith 

R. T. Uhlaner 

T. N. Cason 

S. Courteau 

Prepared for 

Southern California Edison Co. 

2244 Walnut Grove Avenue 

Rosemead, California 91770 

Project Manager 

P. Delaney 

Electric Power Research Institute 

3412 Hillview Avenue 

Palo Alto, California 94304 

EPRI Project Managers 

S. Braithwait 

P. Cleary 

P. Meagher 

Demand-Side Management Program 

Customer Systems Division 

 EE-RM-94-230 COMMENT 61F

DISCLAIMER OF WARRANTIES AND 

LIMITATION OF LIABILITIES 

THIS REPORT WPS PREPARED BY THE ORGANIZArION(S) NAMED BELOW PS AN 

ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER 

RESEARCH INSTITUTE, INC. (EPRII. NErTHER EPRI, ANY MEMBER OF EPRI, ANY 

COSPONSOR, THE ORGANIZArION(SI NAMED BElOW. NOR ANY PERSON ACTING 

ON BEHALF OF ANY OF THEM: 

tAl MAKES ANY WARRANi"I OR REPRESENTI!!ION WHATSOEVER, EXPRESS OR 

IMPUED, 0) WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, 

METHOD. PROCESE, OR SIMILAR ITEM DISCLOSED IN THIS REPORT INCLUDING 

MERCHANTABILITY AND ATNESS FOR A PARl1CULAR PURPOSE, OR (II) THAT SUCH 

USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED RIGHTS, 

INCLUDING ANY PARTY'S INTELLECTUAL PROPERTY. OR (III) THAT THIS REPORT 

IS SUITABLE TO ANY PARTICULAR USER'S CIRCUMSTANCE; OR 

te) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY 

WHi!!SOEVER (INCLUDING ANY CONSEQUENTIAL DAMAGES, EVEN IF EPRI OR ANY 

EPRI REPRESENTATIVE HAS BEEN ADViseD OF THE POSSIBILITY OF SUCH DAMAGES) 

RESULTING FROM veUR SELECTION OR USE OF THIS REPORT OR ANY INFORlloATION, 

APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS REPORT 

ORGANIZATION(S) THAT PREPARED THIS REPORT: 

QIIAlilUM CONSUIJING INC. 

* Printed on Recycled Paper 

Residential Energy Usage 

Comparison: Findings 

CU·7392 

Research Project 2863·3 

Final Report, August 1991 

Prepared by 

QUANTUM CONSULTING INC, 

2030 Addison Street 

Berkeley, California 94704 

Principal Investigators 

B, A.Smith 

R. T. Uhlaner 

T. N, Cason 

S. Courteau 

Prepared for 

Southern California Edison Co. 

2244 Walnut Grove Avenue 

Rosemead, California 91770 

Project Manager 

p, Delaney 

Electric Power Research Institute 

3412 Hillview Avenue 

Palo Alto, california 94304 

EPRI Project Managers 

S. Braithwait 

P. Cleary 

P. Meagher 

Demand-Side Management Program 

Customer Systems Division 
Residential Energy Usage Comparison: Findings 

The load shapes of major residential electric and gas appliances were 

compared in an integrated load and market research project in southern California. The energy consumption data provide a benchmark for 

comparing the costs of alternative technologies; the market research, 

data relate customer attitudes with appliance load shapes. 

BACKGROUND Comparisons of the relative energy consumption of gas and 

INTEREST CATEGORIES electric household appliances have tended to rely on laboratory analysis and com- 

~uter simulations. By contrast, this study, cosponsored by Southern California Edison Co. (SCE), looked at 92 similar households and measured the actual energy Demand-side planning consumption of their appliances, either gas or electric. Market assessment 

Lighting and appliances OBJECTIVES To measure the actual energy consumption of com~arable modKEYWORDS ern electric an0 gas appliances; to determinethe factors behind customer appliance oecisions: to determine the relationships between a customer's characteristics and the corresponding load shape and energy usage. Customer attitudes 

End-use consumption 

Energy efficiency APPROACH The project team identified 92 homes in Orange County, California, 

Load research southeast of Los Angeles, to serve as the study sample. The homes were owneroccupied, single-family homes in a high-growth area and were representative of 

typical newer homes in the SCE service area. In each home, new appliances were 

installed for space conditioning, water heating, cooking, and clothes drying. Half 

of the homes received electrical equipment and half gas. Load monitoring equipment was 'first tested in eight out-of-sample homes and then installed on each 

individual appliance in the 92-home sample. Energy data were collected regularly 

over a period of approximately two years. The project team surveyed the households several times to obtain information on their energy needs and their acceptance of the new appliances. 

RESULTS All comparisons of energy consumption were made in terms of site 

energy with 1 kwh = 3.412 kBtu. Average annual per-household energy consumption for cooking was 3.24 MBtu for the gas sites and 1.31 MBtu for the electric 

sites. Clothes drying consumed 4.32 MBtu for gas sites and 3.66 MBtu for electric 

sites. In winter, gas sites consumed an average 20.49 MBtu for.space conditioning, 

and electric sites used an average of 7.50 MBtu. 

Customer surveys showed that electric heat pumps for space heating were well 

accepted. Of the households whose gas furnaces were replaced with heat pumps, 

50% changed their preferences from gas to electricity. The acceptance ratings for 

electric heat pump water heaters were not as high as for gas water heaters. Participants found the heat pumps slow to respond. The acceptance ratings for electric cooking appliances were strong. Of the households retrofitted from gas ranges 

 EE-RM-94-230 COMMENT 61F

INTEREST CATEGORIES 

Demand-side planning 

Market assessment 

Lighting and appliances 

KEYWORDS 

Customer attitudes 

End-use consumption 

Energy efficiency 

Load research 

REP 0 R T SUMMARY 

Residential Energy Usage Comparison: Findings 

The load shapes of major residential electric and gas appliances were 

compared in an integrated load and market research project in southern California. The energy consumption data provide a benchmark for 

comparing the costs of alternative technologies; the market research 

data relate customer attitudes with appliance load shapes. . 

BACKGROUND Comparisons of the relative energy consumption of gas and 

electric household appliances have tended to rely on laboratory analysis and computer simulations. By contrast, this study, cosponsored by Southern California Edison Co. (SCE), looked at 92 similar households and measured the actual energy 

consumption of their appliances, either gas or electric. 

OBJECTIVES To measure the actual energy consumption of comparable modern electric and gas appliances; to determine the factors behind customer appli· 

ance decisions; to determine the relationships between a customer's characteristics and the corresponding load shape and energy usage. 

APPROACH The project team identified 92 homes in Orange County, California, 

southeast of Los Angeles, to serve as the study sample. The homes were owneroccupied, single-family homes in a high-growth area and were representative of 

typical newer homes in the SCE service area. In each home, new appliances were 

installed for space conditioning, water heating, cooking, and clothes drying. Half 

of the homes received electrical equipment and half gas. Load monitoring equipment was 'first tested in eight out-of-sample homes and then installed on each 

individual appliance in the 92-home sample. Energy data were collected regularly 

over a period of approximately two years. The project team surveyed the households several times to obtain information on their energy needs and their acceptance of the new appliances. 

RESULTS All comparisons of energy consumption were made in terms of site 

energy with 1 kWh = 3.412 kBtu. Average annual per-household energy consump· 

tion for cooking was 3.24 MBtu for the gas sites and 1.31 MBtu for the electric 

sites. Clothes drying consumed 4.32 MBtu for gas sites and 3.66 MBtu for electric 

sites. In winter, gas sites consumed an average 20.49 MBtu for -space conditioning, 

and electric sites used an average of 7.50 MBtu. -

Customer surveys showed that electric heat pumps for space heating were well 

accepted. Of the households whose gas furnaces were replaced with heat pumps, 

50% changed their preferences from gas to electricity. The acceptance ratings for 

electric heat pump water heaters were not as high as for gas water heaters. Participants found the heat pumps slow to respond. The acceptance ratings for electric cooking appliances were strong. Of the households retrofitted from gas ranges 
to electric induction stoves, 50°/o expressed a preference for electricity as 

a fuel for cooking. 

~p 

EPRI PERSPECTIVE This project provides new information about how 

comparable gas and electric appliances are used and accepted in customer homes. An overview report on this research was published earlier 

(EPRI report CU-6952). Utilities in similar climate zones can use these 

data on actual energy consumption and load shapes to estimate the costeffectiveness and attractiveness of different appliance options for their 

customers. The project also provides an example of how load shapes 

may differ by market segment, including the energy-needs-based segments developed under EPRl's customer preference and behavior project 

(EPRI report EM-5908). This can help utilities assess the likely impact 

of attracting particular types of customers through targeted marketing 

programs. 

-- - 

PROJECT 

RP2863-3 

Project Managers: Peter Cleary; Steven D. Braithwait; Paul C. Meagher 

Customer Systems Division 

Contractor: Quantum Consulting Inc. 

For further information on EPRI research programs, call 

EPRI Technical Information Specialists (415) 855-2411. 

 EE-RM-94-230 COMMENT 61F

to electric induction stoves, 50% expressed a preference for electricity as 

a fuel for cooking. 

EPRI PERSPECTIVE This project provides new information about how 

comparable gas and electric appliances are used and accepted in customer homes. An overview report on this research was published earlier 

(EPRI report CU-6952). Utilities in similar climate zones qan use these 

data on actual energy consumption and load shapes to estimate the costeffectiveness and attractiveness of different appliance options for their 

customers. The project also provides an example of how load shapes 

may differ by market segment, including the energy-needs-based segments developed under EPRJ's customer preference and behavior project 

(EPRI report EM-590B). This can help utilities assess the likely impact 

of attracting particular types of customers \hrough targeted marketing 

programs. 

PROJECT 

RP2863-3 

Project Managers: Peter Cleary; Steven D. Braithwait; Paul C. Meagher 

Customer Systems Division 

Contractor: Quantum Consulting Inc. 

For further information on EPRI research programs, call 

EPRI Technical Information Specialists (415) 855-2411. 

, 
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 EE-RM-94-230 COMMENT 61F

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ii 
ABSTRACT 

This report presents results from Electric Power Research Institute research 

project 2863-3, the Residential Energy Usage Comparison (REUC) Project. The 

REUC project, a joint effort by Southern California Edison Company (SCE) and 

EPRI, has measured and compared typical energy consumption patterns of 

energy-efficient residential electric and gas appliances for space conditioning, 

water heating, cooking and clothes drying. 

A wealth of appliance (end-use) energy data and customer survey data were 

gathered between February 1988 and December 1989 from the 92 participating 

households located in Orange County California, southeast of Los Angeles, 

Results of analysis regarding the electric and gas appliances' energy usage 

patterns, load profiles and customer acceptance are presented in the report. 

Analysis of the appliances' energy usage at point-of-use (which does not include 

losses incurred in generation, transmission or distribution) indicates that the 

energy-efficient electric appliances used less site energy than gas appliances. 

Electric heat pumps used 36 percent of the energy used by comparable gas 

furnaces during the winter months. Electric induction stoves used 32 percent of 

the energy used by gas stoves at point-of-use during the year. Electric water 

heaters used 40 percent of the site energy used by 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 a friend. 

The REUCproject dbta were also used to develop segmentation approaches that 

portrayed load shapes for different customer groups. Several segmentation 

schemes, including one based on EPRl's CLASSIFYTM code and one based on 

participant's images of electricity and gas, accurately simulated water heater load 

shapes based only on customer attribute information. 

 EE-RM-94-230 COMMENT 61F

 

 

 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 of the appliances' energy usage at point-of-use (which does not include 

losses incurred in generation, transmission or distribution) indicates that the 

energy-efficient electric appliances used less site energy than gas appliances. 

Electric heat pumps used 36 percent of the energy used by comparable gas 

furnaces during the winter months, Electric induction stoves used 32 percent of 

the energy used by gas stoves at point-of-use during the. year, Electric water 

heaters used 40 percent of the site energy used by 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 4S percent. In contrast, electric induction stoves were well accepted; 80 

percent would recommend their induction stoves to a friend. . 

The REUCproject data were also used to deve'lop segmentation approaches that 

portrayed load shapes for different customer groups. Several segmentation 

schemes, including one based on EPRI's CLASSIFYTM code and one based on 

participant's images of electricity and gas, accurately simulated water heater load 

shapes based only on customer attribute information. 

," 

'" 
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 EE-RM-94-230 COMMENT 61F

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iv 
EPRI FOREWORD 

The residential energy usage comparison (REUC) project has been a joint effort by EPRI and 

Southern California Edison Co. to measure and compare typical energy comsurnption patterns 

of energy-efficient, residential electric and gas appliances. This innovative demand-side 

project has produced valuable information about how actual households use electricity and 

gas for space conditioning, water heating, cooking, and clothes drying. This report presents 

the design, analysis methods, and results from the REUC project. 

Steven D. Braithwait; Peter Cleary; Paul C. Meagher, Prbject Managers 

Customer Systems Division 

 EE-RM-94-230 COMMENT 61F

EPRI FOREWORD 

The residential energy usage comparison (REUCl project has been a joint effort by EPRI and 

Southern California Edison Co. to measure and compare typical energy comsumption patterns 

of energy-efficient, residential electric and gas appliances. This innovative demand-side 

project has produced valuable information about how actual households use electricity and 

gas for space conditioning, water heating, cooking, and clothes drying. This report presents 

the design, analysis methods, and results from the REUC project. . 

Steven D. Braithwait; Peter Cleary; Paul C. Meagher, Project Managers 

. Customer Systems Division 

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 EE-RM-94-230 COMMENT 61F

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vi 
ACKNOWLEDGEMENTS 

The Quantum Consulting project staff wishes to acknowledge the assistance and 

- 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 EPRl Project Managers, of Mr. Stephan 

Galanter and Mr. Paul Delaney, our Southern California Edison Project Officers, 

and of Dr. Mark Shirilau. Their support, guidance and insights have been a 

valuable ingredient in our research. 

vii 

 EE-RM-94-230 COMMENT 61F

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 s.ubstantive contributions of Dr. Steven 

Braithwait and Mr. Peter Cleary, our EPRI Project Managers, of Mr. Stephan 

Galanter and Mr. Paul Delaney, our Southern California Edison Project Officers, 

and of .Dr. Mark Shirilau. Their support, guidance and insights have been a 

valuable ingredient in our research. 

vii 
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viii 
Section 

SUMMARY 

1 INTRODUCTION 

CONTENTS 

Overview of the REUC Project 

EPRl Involvement in .the REUC Project 

Report .Organization 

2 REUC PROJECT DESIGN OBJECTIVES 

lntroduction 

REUC Design 

REUC Analysis 

lntroduction 

REUC Project Scope 

History of REUC Data Collection 

? THE REUC CUSTOMER SAMPLE 

lntroduction 

REUC Sample Design 

Demographic Description of the REUC Sample 

Representativeness of the REUC Sample 

5 REUC LOAD DATA ANALYSIS: ELECTRIC AND GAS APPLIANCE 

USAGE 

lntroduction 

Load Data Collection and Analysis 

Average Yearly Total Usage 

Distribution of Average Daily Usage 

Space Conditioning Weather Model 

6 REUC APPLIANCE LOAD PROFILES 

Introduction 6-1 

Load Profiles 6-1 

Using Household Characteristics to Explain Load Shapes 6-1 0 

Total Energy Regressions 6-1 8 

 EE-RM-94-230 COMMENT 61F

 

 

 ix

CONTENTS 

Section 

1 

2 

3 

4 

5 

6 

SUMMARY 

INTRODUCTION 

Overview of the REUC Project 

EPRI Involvement in .the REUC Project 

Report .Organization 

REUC PROJECT DESIGN OBJECTIVES 

Introduction 

REUC Design 

REUC Analysis 

REUC PROJECT SCOPE AND DATA COLLECTION HISTORY 

Introduction 

REUC Project Scope 

History of REUC Data Collection 

THE REUC CUSTOMER SAMPLE 

Introduction 

REUC Sample Design 

Demographic Description 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 of Average Daily Usage 

Space Conditioning Weather Model 

REUC APPLIANCE LOAD PROFILES 

Introduction 

Load Profiles 

Using Household Characteristics to Explain Load Shapes 

Total Energy Regressions 

S-l 

1-1 

1-1 

1-2 

1-5 

2-1 

2-1 

2-1 

2-2 

3-1 

3-1 

3-1 

3-4 

4-1 

·4-1 . 

4-1 

4-2 

4-4 

5-1 

5-1 

5-1 

5-4 

5-11 

5-16 

6-1 

6-1 

6-1 

6-10 

6-18 
CONTENTS, cont. 

Section 

7 CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE 

Introduction 

Fuel Type Image 

Space Heat.ing 

Water heat in^ - 

Cooking 

Appliance Discount Factors 

8 INTEGRATION OF LOAD AND SURVEY ANALYSES 

Introduction 

Load Shapes by REUC Customer Segment 4 

Determination of "Optimal" Stratifying Variables 

Appliance Life Cycle Costs 

APPENDIX A 

Supplementary Energy Usage Information and Load Profiles 

APPENDIX B 

Methodology to Assess the Generalizability of the REUC Customer 

Sample 

APPENDIX C 

Load Data Editing and Validation 

GLOSSARY OF TERMS 

 EE-RM-94-230 COMMENT 61F

CONTENTS, cont. 

Section 

7 

8 

CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE 

Introduction 

Fuel Type Image 

Space Heating 

Water Heating· . 

Cooking 

Appliance Discour:tt Factors 

INTEGRATION OF LOAD AND SURVEY ANALYSES 

Introduction 

Load Shapes by REUC Customer Segment 

Determination of "Optimal" Stratifying Variables 

. Appliance Life Cycle Costs 

APPENDIX A 

Supplementary Energy Usage Information and Load Profiles 

APPENDIX B 

Methodology to Assess the Generalizability of the REUC Customer 

.. Page 

7-1 

7-1 

. 7-2 

7-6 

7-14 

7-18 

7-21 

8-1 

8-1 

8-2 

8-5 

8-7 

A-1 

Sample B-1 

APPENDIX C 

Load Data Editing and Validation C-1 

GLOSSARY OF TERMS G-1 

)( 
FIGURES 

S-1 Annual Average Energy Usage - Space Conditioning and 

Water Heating 

S-2 SCE REUC: Average Load Profiles - Winter Weekday 

Space Conditioning 

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

S-4 Customers' Required Return on an lnvestme'nt in Appliance 

Efficiency 

S-5 REUC Participant Images of Electric vs. Gas 

S-6 SCE REUC: Quality Factor Segments - Winter Weekday 

Heat Pump 

S-7 SCE REUC: CP&B Needs-Based Segments - Summer Weekday 

Heat Pump 

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

1-1 REUC Project Milestones 

3-1 REUC Survey and Load Data Collection History 

5-1 Annual Average Energy Usage Space Conditioning and Water 

Heating 

5-2 Annual Average Energy Usage - Clothes Drying and Cooking 

5-3 Difference in Annual Energy Consumption between Gas 

and Electric Appliances 

5-4 Seasonal Average Weekday Electricity Usage Electric Sample 

End-Uses 

5-5 Whole House: Average Daily Electricity Usage 

5-6 SCE REUC Weekday Average UEC ~istribition - Winter 

Electric Heat Pump: Pooled Cities 

5-7 SCE REUC Weekday Average UEC Distribution - Summer 

Space Conditioning: Pooled Cities 

 EE-RM-94-230 COMMENT 61F

FIGURES 

Figure Page 

S-1 Annual Average Energy Usage - Space Conditioning and S-3 , 

Water Heating 

S-2 SCE REUC: Average Load Profiles - Winter Weekday S-4 

Space Conditioning 

S-3 Customers' Required Return on a Small Riskless Investment 5-6. 

S-4 Customers' Required .Return on an Investme·nt in Appliance 5-7 

Efficiency 

S-5 REUC Participant Images of Electric vs. Gas 5-8 

S-6 SCE REUC: Quality Factor Segments - Winter Weekday 5-9 

Heat Pump 

S-7 SCE REUC: CP&B Needs-Based Segments - Summer Weekday 5-10 

Heat Pump 

S-8 REUC: Heat Pump Water Heater - Demographic Sceriario ·5-11 

1-1 REUC Project Milestones 1 -3 

3-1 REUC Survey and Load DataColiection History 3-4 

5-1 Annual Averag~ Energy Usage Space Conditioning and Water 5-4 

Heating 

5-2 Annual Average Energy Usage - Clothes Drying and Cooking 5-5 

5-3 Difference in Annual Energy Consumption between Gas 5-7 

and Electric Appliances 

5-4 Seasonal Average Weekday Electricity Usage Electric Sample 

End-Uses . 5-10 

5-5 Whole House: Average Daily Electricity Usage 5-11 

5-6 SCE REUC Weekday Average UEC Distribution - Winter 5-13 

Electric Heat Pump: Pooled Cities 

5-7 SCE REUC Weekday Average UEC Distribution - Summer 

Space Conditioning: Pooled Cities 

5-13 

xi 
FIGURES, cont. 

5-8 SCE REUC Weekday Average UEC ~istribution - Winter 

Gas Furnace: Pooled Cities 

5-9 SCE REUC Weekday Average UEC Distribution - Winter 

Electric Water Heating: Pooled Cities 

5-1 0 SCE REUC Weekday Average UEC Distribution - Winter 

Gas Water Heating: pooled Cities 

6-1 SCE REUC winter: Space Conditioning 

6-2 SCE REUC Summer: Space Conditioning 

6-3 SCE REUC Winter: Water Heating 

6-4 SCE REUC Summer: Water Heating 

6-5 SCE REUC: Space Conditioning Electric Sample 

6-6 SCE REUC: Heat Pump Space Conditioning 

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

6-8 SCE REUC Winter Weekday Electricity: Electric Sample 

6-9 SCE REUC.Summer Weekday Electricity: Electric Sample 

6-1 0 SCE REUC Winter: Whole House Electricity 

61 1 SCE REUC Summer: Whole House Electricity 

6-1 2 REUC Heat Pump Water Heater - House 50 - First Step 

Estimated Load vs. Average Load 

6-1 3 REUC Heat Pump Water Heater -.House 86 - First Step 

Estimated Load vs. Average Load 

6-1 4 REUC Heat Pump Water Heater - House 50 - Second Step 

Estimated Load vs. Average Load 

6-1 5 REUC Heat Pump Water Heater - House 86 - Second Step 

Estimated Load vs. Average Load 

6-1 6 , REUC Heat Pump Water Heater Demographic Trend Scenario 

xii 

 EE-RM-94-230 COMMENT 61F

FIGURES, cont. 

Figure 

5-8 SCE REUC Weekday Average UEC Distribution - Winter 5-14 

Gas Furnace: Pooled Cities 

5-9 SCE REUC Weekday Average UEC Distribution - Winter 5-14 

Electric Water Heating: Pooled Cities 

5-10 SCE REUC Weekday Average UEC Distribution - Winter 5-15 

Gas Water Heating: Pooled Cities 

6-1 SCE REUC Winter: Space Conditioning 6-2 

6-2 SCE REUC Summer: Space Conditioning 6-2 

6-3 SCE REUC Winter: Water Heating 6-3 

(J-4 SCE REUC Summer: Water Heating 6-3 

6-5 SCE REUC: Space Conditioning Electric Sample 6-4 

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

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

6-8 SCE REUC Winter Weekday Electricity: Electric Sample 6-8 

6-9 SCE REUC Summer Weekday Electricity: Electric Sample6-a 

6-10 SCE REUC Winter: Whole House Electricity 6-9 

6-11 SCE REUC Summer: Whole House Electricity 6-9 

6-12 REUC Heat Pump Water Heater - House 50 - First Step 6-13 

Estimated Load vs. Average Load 

6-13 REUC Heat Pump Water Heater - ·House 86 - First Step 6-13 

Estimated Load vs. Average Load 

6-14 REUC Heat Pump Water Heater - House 50 - Secqnd Step 6-1 6 

Estimated Load vs. Average Load 

6-1 5 REUC Heat Pump Water Heater - House 86 - Second Step 6-1 6 

Estimated Load vs. Average Load 

6-16' REUC Heat Pump Water Heater Demographic Trend Scenario 6-18 

xii 
FIGURES, cont. 

Effect of Household Size on Average Weekday. Usage: 

REUC Heat Pump Water Heater (Model) 

Effect of Quality Image of Electricity on Average weekday Usage: 

REUC Heat Pump Water Heater (Model) 

Effects of CP&B Classification on Average Weekday Usage; 

REUC Heat Pump Water Heater (Model) 

REUC Participant Images of Electric vs. Gas 

REUC Participants' Comparisons between Old and New Space 

Heaters by Fuel Type 

lmportance 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 New Water 

Heaters by Fuel Type 

~on~itudinal change in Electric Induction Range Subsample - Participants' Fuel Type Preference for Cooking 

REUC Participants' Required Return on a Small Riskless 

lnvestment 

-7 REUC Participants' Required Return on an Investment 7-24 

in Appliance Efficiency 

-1 REUC Summer Weekday Load Shape: Electric Space 8-3 

Conditioning Customer Preference & Behavior Stratification 

-2 .REUCSummer Weekday Load Shape: Electric Space 8-4 

Conditioning Electric ValuelGas Value Stratification 

-3 REUC Winter Weekday Load Shape: Heat Pump Water Heater 8-4 

Electric QualitylGas Value Stratification 

-4 REUC Winter Weekday Load Shape: Heat Pump Water Heater 8-5 

Income by Square Feet Stratification 

xiii 

 EE-RM-94-230 COMMENT 61F

FIGURES, cont. 

igure Page 

-17 Effect of Household Size on Average Weekday- Usage: 6-21 

REUC Heat Pump Water Heater (Model) 

-18 Effect of Quality Image of Electricity on Average Weekday Usage: 

REUC Heat Pump Water Heater (Model) 

6-22 

-19 Effects of CP&B Classification on Average Weekday Usage; 6-23 

REUC Heat Pump Water Heater (Model) 

-1 . REUC Participant Images of Electric vs. Gas 7-2 

-2 REUC Participants' Comparisons between Old and New Space 7-10 

Heaters by Fuel Type 

-3a Importance of Heating System Attributes by Fuel Type Electric 7-1 r 

Sample 

-3b Importance of Heating System Attributes by Fuel Type Gas 7-12 

Sample 

-4 REUC Participants' Comparisons between Old and New Water 7-14 

Heaters by Fuel Type 

-5 Longitudinal Change in Electric Induction Range Subsample -

Participants' Fuel Type Preference for Cooking 

7-19 

-6 REUC Participants' Required Return on a Small Riskless 7-23 

Investment 

-7 REUC Participants' Required Return on an Investment 7-24 

in Appliance Efficiency 

-1 REUC Summer Weekday Load Shape: Electric Space 

Conditioning Customer Preference & Behavior Stratification 

8-3 

-2 REUCSummer Weekday Load Shape: Electric Space 8-4 

Conditioning Electric Value/Gas Value Stratification 

-3 REUC Winter Weekday Load Shape: Heat Pump Water Heater 

Electric Quality/Gas Value Stratification 

8-4 

-4 REUC Winter Weekday Load Shape: Heat Pump Water Heater 8-5 

Income by Square Feet Stratification 

xiii 
FIGURES, cont. 

REUC Participants' Required Payback periods 

REUC Space Conditioning Fuel Choice Preference Regions 

Space Conditioning: Winter Average Daily Usage 

Water Heating: Average Daily Usage 

Cooking Range: Average Daily Usage 

Clothes Drying: Average Daily Usage 

Space Conditioning: Summer Average Daily Usage 

5CE REUC Winter: Clothes Drying 

SCE REUC Summer: Clothes Drying 

SCE REUC Winter: Cooking 

SCE REUC Summer: Cooking 

SCE REUC Winter: Whole House Electricity 

SCE REUC Summer: Whole House Electricity 

sCE REUC Winter: Space Conditioning 

SCE REUC Summer: Space Conditioning 

SCE REUC Winter: Water Heating 

SCE REUC Summer: Water Heating 

SCE REUC Weekday Average Load: Electric Sample Space 

Conditioning 

.;cE REUC Weekday Average Load: Electric Sample Space 

&>nditioning 

SiE REUC Weekday Average Load: Electric Sample Space 

conditioning 

xiv 

 EE-RM-94-230 COMMENT 61F

FIGURES, cont. 

Figure Page 

8-5 REUC Participants' Required Payback Periods 8-9 

8-6 REUC Space Conditioning Fuel Choice Preference Regions 8-10 

A-l Space Conditioning: Winter Average Daily Usage A-2 

A-2 Water Heating: Average Daily Usage A-2 

A-3 Cooking Range: Average Daily Usage A-3 

A-4 Clothes Drying: Average Daily Usage A-3 

A-5 Space Coriditioning: Summer Average Daily Usage A-4 

A-6 SCE REUC Winter: Clothes Drying A-4 

A-7 SCE REUC Summer: Clothes Drying A-S 

A-8 SCE REUC Winter: Cooking A-5 

A-9 SCE REUC Summer: Cooking A-6 

A-10 SCE REUC Winter: Whole House Electricity. A-6 

A-l1' SCE REUC Summer: Whole House Electricity A-7 

A-12 . SCE REUC Winter: Space Conditioning A-7 

A-13 SCE REUC Summer: Space Conditioning A-8 

A-14 SCE REUC Winter: Water Heating A-8 

A-15 SCE REUC Summer: Water Heating A-9 

A-16 SCE REUC Weekday Average Load: Electric Sample Space A-9 

C,)nditioning 

A-17 SeE REUC Weekday Average Load: Electric Sample Space A-10 

CL)nditioning 

A-18 SCE REUC Weekday Average Load: Electric Sample Space A-10 

C,)nditioning 

xiv 
FIGURES, cont. 

SCE REUC Weekday Average Load: Gas Sample Space 

Conditioning 

SCE REUC Weekday Average Load: Gas Sample Space 

Conditioning 

SCE REUC Weekday Average Load: Gas Sample Space 

Conditioning 

SCE REUC Winter Weekday Electricity: Electric Sample 

SCE REUC Summer Weekday, Electricity: Gas Sample 

SCE REUC Summer Weekday Electricity: Electric Sample 

SCE REUC Whole House Electricity: Electric Sample 

SCE REUC Whole House Electricity: Gas Sample 

SCE REUC Space Conditioning: Electric Sample 

SCE REUC Water Heating: Electric Sample 

SCE REUC Cooking: Ele'ctric Sample 

SCE REUC Clothes Drying: Electric Sample 

SCE REUC: Temperature 

REUC Sample Assessment Methodology 

 EE-RM-94-230 COMMENT 61F

FIGURES, cont. 

Figure 

A-19 SCE REUC Weekday Average Load: Gas Sample Space 

Conditioning 

A-20 SCE REUC Weekday Average Load: Gas Sample Space 

Conditioning 

A-21. SCE REUC Weekday Average Load: Gas Sample Space 

Cond ition i ng 

A-22 SCE REUC Winter Weekday Electricity: Electric Sample 

A-23 SCE REUC Summer Weekday Electricity: Gas Sample 

A-24 SCE REUC Summer Weekday Electricity: Electric Sample 

A-25 SCE REUC Whole House Electricity: Electric Sample 

A-26 SCE REUC Whole House Electricity: Gas Sample 

A-27 SCE REUC Space Conditioning: Electric Sample 

A-28 SCE REUC Water Heating: Electric Sample 

A-29 SCE REUC Cooking: Electric Sample 

A-30 SCE REUC Clothes Drying: Electric Sample. 

A-31 SCE REUC: Temperature 

8-1 REUC Sample Assessment Methodology 

.. 

xv 

A-11 

A-11 

A-12 

A-12. 

A-13 

. A-13 

A-14 

A-14 

A-15 

A-15 

A-16 

A-16 

A-17 

8-2 
xvi 

 EE-RM-94-230 COMMENT 61F

This page left 

deliberately blank 

xvi 
TABLES. 

REUC Participant Demographics - Sample Design Stratification 

Variables 

REUC Participants ~emo~ra~hics - Highest Completed 

Education Level Among Key Household Members 

REUC sample Representativeness by Income Stratum. 

REUC sample Representativ6ness by House size Strhtum 

REUC: Table of Month Label to Date Correspondence 

Average Annual and Seasonal Energy Usage - Pooled Cities 

Monthly Average Usage for Pooled Cities 

Estimated Linear Relationship between Average Morning Space 

Heating and Morning Heating Degree-Days: All Households 

Estimated and Simulated Harmonic Model Load Shape 

Parameters 

Differing Average Characteristics of the Households 

Factor Loadings Based on Analysis of Fuel Typelmage Question 

Longitudinal Changes in Electric Sample Participants' Fuel Type 

Preferences for Space Heating Systems 

Longitudinal Changes in Gas Sample Participants' Fuel Type 

Preferences for Space Heating Systems 

REUC Participants' Willingness to Recommend Their Space 

Heating System to a Friend - By Fuel Type 

Longitudinal Changes in Electric Sample Participants' Fuel Type 

Preferences for Water Heaters 

Longitudinal Changes in Gas Sample Participants' Fuel Type 

Preferences for Water Heaters 

xvii 

 EE-RM-94-230 COMMENT 61F

TABLES. 

Table Page 

4-1 REUC Participant Demographics - Sample Design Stratification 

Variables 

4-3 

4-2 REUC Participants Demographics - Highest Completed 4-4 

Education Level Among Key Household Members 

4-3 REUC sample Representativeness by Income Stratum, 4-6 

4-4 REUC sample Representativeness by House Size Stratum 4-7 

5-1 REUC:. Table of Month Label to Date Correspondence 5-3 

5-2 Average Annual and Seasonal Energy Usage - Pooled Cities 5-6 

5-3 Monthly Average Usage for Pooled Cities 5-8 

5-4 Estimated Linear Relationship between Average Morning Space 

Heating and Morning Heating Degree-Days: All Households 

5-17 

6-1 Estimated and Simulated Harmonic Model Load Shape 

Paramete·rs 

6-12 

6-2 Differing Average Characteristics of the 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 

Preferences for Space Heating Systems 

7-7 

7-2b Longitudinal Changes in Gas Sample Participants' Fuel Type 7-8 

Preferences for Space Heating Systems 

7-3 REUC Participants' Willingness to Recommend Their Space 7-13 

Heating System to a Friend - By Fuel Type 

7-4a Longitudinal Changes in Electric Sample Participants' Fuel Type 

Preferences for Water Heaters 

7-16 

7-4b Longitudina! Changes in Gas Sample Participants' Fuel Type 7 -17 

Preferences for Water Heaters 

xvii 
- Table 

7-5 

7-6 

7-7 

7-8 

8-1 

A- 1 

A-2 , 

A-3 

A-4 

8-1 

8-2 

0-3 

8-4 

C-1 

C-2 

C-3 

C-4 

TABLES, cont. 

REUC Participants' Willingness to Recommend Their Water 

Heater System to a Friend.- By Fuel Type 

Electric induction Subsample Participants' Willingness to 

Recommend Their Range to a Friend 

REUC Participants' Rangeloven Comparisons between ~uel 

Types 

Energy Savings Requirements of Higher Efficiency Appliances 

REUC Stratification Scheme Variance Calculations 

Average Annual and Seasonal Energy Usage - Mission Viejo 

Households 

Average Annual and Seasonal Energy Usage - l~ine 

Households 

Monthly Average Usage for Mission Viejo Households 

Monthly Average Usage for lrvine Households 

Appliance Stock lnventory (ASI) Share by SCE Division 

Chi-Square Statistics for Customer Descriptors that Discriminate 

between Appliance Stock Inventories (ASi) and Pool Ownership 

RSS-Based Stratification of Customer Descriptors 

Customer Descriptors that Meaningfully Discriminate Annual 

Usage Profiles by Appliance Stock lnventory (ASI) and SCE 

Division 

"Reasonable" Connected Load Estimates 

"Reasonable" Ranges for Weekly Appliance Use 

ACUPA Error Flag Criteria 

Key to invalid Data Flagged During Data Validation Process 

xviii 

 EE-RM-94-230 COMMENT 61F

TABLES, cont. 

Table Page 

7-5 REUC Participants' Willingness to Recommend Their Water 7-18 

Heater System to a Friend·- 8y Fuel Type 

7-6 Electric Induction Subsample Participants' Willingness to 

Recommend Their Range to a Friend 

7-20 

7-7 REUC Participants' Range/Oven Compa~isons between Fuel 7-21 

Types 

7-8 Energy Savings Requirements of Higher Efficiency Appliances 7-25 

8-1 REUC Stratification Scheme Variance Calculations 8-6 

A-1 Average Annual and Seasonal Energy Usage - Mission Viejo 

Households 

A-18 

A-2 Average Annual and Seasonal Energy Usage - Irvine 

Households 

A-19 

A-3 Monthly Average Usage for Mission Viejo Households . A-20 

A-4 Monthly Average Usage for 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 (ASI) and Pool Ownership 

8-3 RSS-8ased Stratification of Customer Descriptors 8-6 

8-4 Customer Descriptors that Meaningfully Discriminate Annual 

Usage Profiles by Appliance Stock Inventory (AS!) and SCE 

8-7 

Division 

C-1 "Reasonable" Connected Load Estimates C-4 

C-2 "Reasonable" Ranges for Weekly Appliance Use C-4 

C-3 ACUPA Error Flag Criteria C-5 

C-4 Key to Invalid Data Flagged During Data Validation Process C-6 

xviii 
SUMMARY 

This report presents the research methods and results from the Residential Energy 

Usage Comparison (REUC) project, a joint effort by Southern California Edison 

Company (SCE) and the Electric Power Research Institute (EPRI). The REUC 

project design activities began in early 1986. The REUC project is an in)lovative 

demand-side project designed to measure and compare' typical energy 

consumption patterns of energy efficient residential electric and gas appliances. 

REUC 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 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 of two unique facets of the project's design: 

'first, end-use (appliance) load data have been collected for both electric and gas 

appliances; and, second, both customer survey data and end-use data have been 

collected from the participants. End-use data collection began in late 1986 for a 

Ponion of the Orange County participants; survey research data collection began 

in mid-1 987. Both end-use data and survey research data collection concluded in 

 EE-RM-94-230 COMMENT 61F

SUMMARY 

This report presents the research methods and results from the Residential Energy 

Usage Comparison (REUC) project, a joint effort by Southern California Edison 

Company (SCE) and the Electric Power Research Institute (EPRI). Th~ REUC 

project design activities began in early 1986. The REUC project is an innovative 

demand-side project designed to measure and compare - typical energy 

consumption patterns 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 

that have examined the relative energy efficiencies of electric and gas end-use 

technologies have relied On laboratory settings or computer s(mulation models. 

The .nature of the REUC project is to move away from the laboratory setting by 

relying on the energy demands and customer behavior of actual households 

instead of simulated "test bench" usage patterns. The REUC project results have 

thus accounted for many of the subtle but important determinants of appliance 

usage, including household characteristics, that are not captured in engineering 

models. The project design is discussed more fully in Sections 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 of two unique facets of the project's design: 

first, end-use (appliance) load data have been collected for both electric and gas 

appliances; and,_ second, both customer survey data and end-use data have been 

collected from the participants. End-use data collection began in late 1986 for a 

POrtion of the Orange County participants; survey research data collection began 

in mia-1987. Both end-use data and survey research data collection concluded in 

S-l 
December 1989. The project's October 1990 interim report (CU-6952) 

presented results based on one year of data ending in January 1989. This report 

presents analysis results using all the available REUC data. 

A summary of the findings presented in this report is provided below. 

ELECTRIC AND GAS APPLIANCE ENERGY CONSUMPTION 

A principal objective of the REUC project has been to m'easure electric and gas 

appliance energy consumption, at point of use. All energy in this Report is given 

as point-of-use 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 'be 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 about 40 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 of the REUC project has been the collection of both electric 

and gas appliance load data. The unique pairing of electric and gas end-uses 

affords an opportunity to carefully compare the daily usage patterns of appliances 

with different energy sources. 

 EE-RM-94-230 COMMENT 61F

December 1989. The project's October 1990 interim report (CU-6952l 

presented results based on one year of data ending in January 1989. T,his report 

presents analysis results using all the available REUC data. 

A summary of the findings presented in this report is provided below. 

ElECTRIC AND GAS APPLIANCE ENERGY CONSUMPTION 

A principal objective of the REUC project has been to m'easure electric and gas 

appliance energy consumption, at point of use. All energy in this Report is given 

as point-of-use site energy; i.e.,l kWh = 3.412 kBTU.Southern California Edison 

identified four residential energy uses to be considered in the project: space 

conditioning, water heating, cooking and clothes drying. these four end-uses 

were selected because of the direct competition between electricity and natural 

gas and because of the development of new, high-efficiency electric 

technologies. 

Figure 5-1 illustrates the point-of-use annual average energy consumption for the 

REUC space conditioning appliances (electric heat pumps relative to gas 

furnaces/electric air conditioners) and water heating appliances (electric heat 

pump water heaters and gas water heaters). These 'results are based on appliance 

energy data gathered over nearly two years, from February 1988 through 

December 1989. The figure shows that, at point of use, electric heat pumps 

consume less than 45 percent as much energy as gas space conditioning systems. 

During 'l]e winter season, electric heat pumps consume less than 36 percent as 

much site energy as gas furnaces. In addition, electric heat pump water heaters 

consume about 40 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 of the REUC project has been the collection of both electric 

and gas appliance load data. The unique pairing of electric and gas end-uses 

affords an opportunity to carefully compare the daily usage patterns of appliances 

with different energy sources. 

5-2 
. . 

Figure S-1 

Annual Averace Enerw Usaee 

Space Conditioning and Water Heatine . (at ~oint of use) *+ 

R ,Gas Sample u Electric Sample 

! 

. . 

i 

I ! 

$ Additional REUC appliance load ~rofiles developed from end-use data are 3 

j presented in section 6 and in Appendix A. 

s,. 

~. . 

;pace Conditioning 

Water Heating 

0 5 10 15 20 25 30 

MBTU 

Figure 5-2 illustratesthe average winter weekday load profiles for both gas and 

i : :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 

smple. As the figure indicates, gas furnaces have a much sharper morning peak 

(about 11 kBTU per hour) .than electric heat pumps, which have a lower 

diversified load (about 3 kBTU per hour or 1 kW). 

 EE-RM-94-230 COMMENT 61F

I,. _ 

Figure 5-1 

Annual Average Energy Usage 

Space Conditioning and Water Heating (at point of use) 

;pace Conditioning 

Water Heating 

o 5 10 15 

MOTU 

I:l! ,~Sam~le 

iii Electric Sample 

23.4 

20 25 

" 

30 

'Figure 5-2 illustrates ,the average winter weekday load profiles for both gas and 

electric space conditioning appliances (electric heat pumps and gas furnaces). 

,These load profiles are based on appliance energy data gathered for nearly two 

years, from February 1988 through December 1989, for the 92 household 

sample. As the figure indicates, gas furnaces have a much sharper morning peak 

(about 11 kBTU per hour) ,than electric heat pumps, which have a lower 

diversified load (about 3 kBTU per hour or 1 kWl. 

Additional REUC appliance load profiles developed from end-use data are 

presented in Section 6 and in Appendix A. 

5-3 
Figure S-2 

SCE REUC: Avera~e Load Profiles 

Winter Weekday Soace Conditionine: (at ooint of use) 

Elccfric Hcal Pump 

Car Furnace 

CUSTOMER ACCEPTANCE OF APPLIANCES 

Measuring customer acceptance of these energy efficient appliances was another 

objective of the 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. 

kBTU 

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, 

per 12 -7 

Hour 

10- 

8- 

6 - 

('1 

11 

'I 

; ; 

; ; I 

I: 

!I 

'I I I 

I I 

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

0 3 6 9 12 15 18 21 24 

T~mc of Day 

 EE-RM-94-230 COMMENT 61F

Figure 5-2 

SCE REUC: Average load Profiles 

Winter Weekday Space Conditioning (at point of use) 

kBTU 

per 12 

----- Hour Electric Meat Pump (I 

I \ ---_. Gas Furnace 

10- I \ 

I I 

I I 

I I 

I I 

8 I I 

I I 

I I 

I I 

& - I \ 

I I 

I \ I 

I \ 

4 I I 

I \ ./~-.-'""\ 

I \ ;' . \ 

" \ I \ 

"../ \ 

, I • 

2 ,,,-" \ I 

'---/' 

0 • 

0 3 & 9 12 15 18 21 24 

Time of Day 

CUSTOMER ACCEPTANCE OF APPLIANCES 

Measuring customer acceptance of these energy efficient appliances was another 

objective of the 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, 

5-4 
ng systems are more likely to have proponents than heat pump systems. 

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

nts were the perceived quality problems with the units installed jn 1986 .L+ 

ey responses disclosed that participants found their heat pump water 

too slow to respond. As a result, acceptance ratings for the heat pump 

aters were quite low, at 45 percent. In contrast, electric induction stoves 

ell accepted; 80 percent would recommend their induction stoves to a 

':Among the participants retrofitted from gas ranges to electric induction 

50 percent expressed an opinion prefeFring electricity as a fuel for cooking. 

kijonal results on appliance acceptance are provided in Section 7. 

RATED MARKET RESEARCH 

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

er distinctive feature of the REUC project. Integrated market research is the 

ve synthesis of load research information with market research/survey infor- " to provide insight about customers' energy usage behavior patterns. . , 

 EE-RM-94-230 COMMENT 61F

systems are more likely to have proponents than heat pump systems. 

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

were the perceived quality problems with the units installed in 1986 

responses disclosed that participants found their heat pump water 

slow to respond. As a result, acceptance ratings for the heat pump 

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

accepted; 80 percent would recommend their induction stoves to a 

'.' Among the participants retrofitted from gas ranges to electric induction 

, 50 percent expressed an opinion preferring electricity as a fuel for cooking. 

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

TED MARKET RESEARCH 

:~olilection of both end-use load data and participant survey data is yet 

distinctive feature of the REUC project. Integrated market research is the 

synthesis of load research information with market research/survey inforto provide insight about customers' energy usage behavior patterns. 

s-s 
Figure S-3 

Customers' Reauired Return on a Small Riskless Investment 

% 

of so 

Participanls 

40 

30 

20 

10 

0 

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

Required Return 

- 

Income: SS0;OOl -SnO,OOO 

IncomE $30,000-$50,000 

The REUC project surveys included questions designed to estimate participants' 

appliance investment payback requirements based on their required rates of 

return. The higher an individual customer's required rate of return for an 

appliance investment, the shorter the required payback period for such 

investments. Participants were also asked to state their required return on a small 

riskless investment, such as a bank certificate of deposit. As illustrated by Figures 

5-3 and S-4, the REUC participants' required rate of return on appliance 

efiiciency investments is much higher than that of their riskless rate of return. 

These high required rates of return imply very short payback periods; as a result, 

expensive appliances must demonstrate large efficiencies to compete with 

cheaper alternatives. 

 EE-RM-94-230 COMMENT 61F

Figure S-3 

Customers' Required Return on a Small Riskless Investment 

% 

of 50,-------------------------~--------_, 

Participants 

40 

30 

20 

10 

o 

-

G%-8'% 90/0-100/0 

Required Return 

rn Income: >$80,000 

Illl Income: $50;001·$80,000 

~ Income·: $30,000-$50,000 

11%-13% 

The REUC project surveys included questions designed to estimate participants' 

appliance investment payback requirements based on their required rates of 

return. The higher an individual customer's required rate of return for an 

appliance investment, the shorter the required payback period for such 

investments. Participants were also asked to state their required return on a small 

riskless investment, such as a bank certificate of deposit. As illustrated by Figures 

5-3 and 5-4, the REUC participants' required rate of return on appliance 

efiiciency investments is much higher than that of their riskless rate of return. 

These high required rates of return imply very short payback periods; as a result, 

expensive appliances must demonstrate large efficiencies to compete with 

cheaper alternatives. 

5-6 
Figure 5-4 

Customerst Required Return on an Investment in Aapliance Efficiency 

% 

of 40 

Participanls 

30 

20 

10 

0 

45% 15%-59% >59% 

Required Relurn 

Income: $50,001-$80,000 

5 Income: $30,000-150,000 

 EE-RM-94-230 COMMENT 61F

Figure 5-4 

Customers' Required Return on an Investment in Appliance Efficiency 

% 

of 40~----------------------------------' Participants 

Required Return 

fa lncome: >$80,000 

Iii! I""om.: $50,001.$80,000 

E!1I Income: $30,000·$50,000 
Figure S-5 

REUC Participant lma~es of Electric vs. Gas 

Inconvenient 

Poor Service 

Low Monl 

The REUC project surveys also included questions designed to elicit participants' 

images of electricity and gas. Figure S-5 portrays the REUC participants' fuel 

images for electricity and gas. Fuel images were found to be useful for defining 

customer segments. Factor analysis of the REUC participants' fuel images resulted 

in the derivation of a Quality factor (a composite of the Safety, Convenience, 

Service and Reliability images) and a Value factor (a composite of the Appliance 

Cost, General Expense and Monthly Utility Bill images). 

 EE-RM-94-230 COMMENT 61F

Figure 5-5 

REUC Participant Images of Electric V5. Gas 

Dirly '-" " Clean -,- , 

J 

Dangerous !Jj Safe tf I Hard to Hookup Easy 10 Hookup ~ :;:.:' 

~\ :.. 

IInconvenient Convenient -.... ~ :3 ~ 0- Poor Service t Good Service 

(rom Utility if from Utility 

" !J 

Unrcli<lblc }f Reliable dfi/'" 

Sluggish Heal '~-:;: .. Fasl Heal-I- l/ ~(, 

High Cost .,:t \ low Cost 

Appliances Appliances .... , ::J 

.... Expensive ,£ Inexpensive ~ 

High Monthly ~t \ Low Monlhly t:! Utility Bills UlililyBills_~ 

Electricity Gas 

Image Image 

The REUC project surveys also included questions designed to elicit participants' 

images of electricity and gas. Figure S-5 portrays the REUC participants' fuel 

images for electricity and gas. Fuel images were found to be useful for defining 

customer segments. Factor analysis of the REUC participants' fuel images resulted 

in the derivation of a Quality factor (a composite of the Safety, Convenience, 

Service arid Reliability images) and a Value factor (a composite of the Appliance 

Cost, General Expense and Monthly Utility Bill images). 

S-8 
Figure S-6 

SCE REUC: Ouality Factor Seements 

Winter Weekday Heat Pumo 

Load 1.4 - 

in 

kw 1.2- Hi ElcclridLo Gas Qualily 

0.8- 

0.G0.0 ,'I.,.,.,.,. 

' 0 3 6 9 12 15 18 21 24 

Timc of Day (in Hours Pm 

Figure S-6 illustrates the use of the Quality factor for customer segmentation. The 

figure presents average winter weekday load profiles for participants' heat pumps 

segmented with the electric and gas Quality factors. This figure shows the distinct 

load shapes for these customer segments. 

 EE-RM-94-230 COMMENT 61F

Figure S-6 

SCE REUC: Quality Factor Segments 

Winter Weekday Heat Pump 

La Electric/Hi Gas Quality 

Hi ElcctridLo Gas Quality 

6 9 12 1S 18 21 24 

Time of Day (in Hours pm 

Figure 5-6 illustrates the use of the Quality factor for customer segmentation. The 

figure presents average winter weekday load profiles for participants' heat pumps 

segmented with the electric and gas Quality factors. This figure shows the distinct 

load shapes for these customer segments. 

5-9 
Figure S-7 

SCE REUC: CP&B Needs-Based Seaments 

Summer Weekday Heat Pump 

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

----- Valuc Seekers 

Load 1 .Oin kW 

0.8- 

0.4- 

0.2- 

0.01 .,..,~,.,.,.,. 

0 3 6 9 12 15 18 21 24 

Timc of Day (in Hours PST) 

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

administered to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure S-7 presents average summer weekday 

load profiles for heat pumps segmented by Appearance Conscious, Resource 

Conserver and Value Seeker needs-based segments. 

 EE-RM-94-230 COMMENT 61F

Figure 5-7 

SCE REUC: CP&B Needs-Based Segments 

Summer Weekday Heat Pump 

Load 1.0,.----------------------...., 

inkW 

0.8 

0.6 

0.4 

0.2 

•.••••• , ........ "''"'< .... . 

Appear. Cons. 

Res. Consrv . 

Value Seekers 

O.O+-~-,-~-.--.....-.... --.----.-~.,-~-r---r-.--....---l 

o 3 6 9 12 15 18 21 24 

Time of Day (in Hours PST) 

The EPRI Customer Preference and Behavior (CP&B) CLASSIFY'" survey was 

administered- to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure S-7 presents average summer weekday 

load profiles for heat pumps segmented by Appearance Conscious, Resource 

Conserver and Value Seeker needs-based segments. 

S-10 
Figure S-8 

REUC: Heat Pump Water He* 

Demopraphic Scenario 

ousehold characteristic data from REUC participants were also used in the 

:t's load shape modeling research. End-use load shapes yere modeled 

household characteristics in a two-step regression'procedure: (1) harmonic 

ling of load shapes, followed by (2) household characteristic regressions on 

armonic model parameters. Figure S-8 illustrates an application of this 

~ach by comparing derived heat pump water heater load shapes for two 

:hetical demographic market segments produced with the load shape 

,ling methodology. This figure shows how the load shapes may differ 

:en younger and older households. 

.d 0.8 

. . 

iorial integrated market research results of the REUC project are presented 

:tion 8. 

W 

0.6 - 

c \ Older ~ouseholds 

I \ ----- Younger Households 

I \ 

I \ 

I \ 

0.4 - 

0.2 - 

0.0 I.I.,','I.. 

0 3 6 9 12 . 15 18 21 24 

Time of Day (in Hours PST) 

 EE-RM-94-230 COMMENT 61F

Figure S-8 

REUC: Heat Pump Water Heater 

DemographiC Scenario 

.~ 0.8 J---------::~-r_~~~~~~~~;==jl ~ \ Older Households t' \ 

0.6 

0.4 

0.2 .." ....... _-

o 3 6 

I \ - - - - - Younger Households 

I \ 

I \ 

I \ 

\ 

9 

\ 

\ 

\ 

\. 

, 

" 

12 

, 

\ 

\ 

\ 

15 

Time of Day (in Hours psn 

18 21 24 

ousehold characteristic data from REUC participants were also used in the 

:t's load shape modeling research. End-use load shapes ~ere modeled 

household characteristics in a two-step regression· procedure: . (1) harmonic 

ling of load shapes, followed by (2) household characteristic regressions on 

armonic model parameters. Figure 5-8 illustrates an appli.cation of this 

,ach by compar.ing derived heat pump water heater load shapes for two 

:hetical demographic market segments produced with the load shape 

·Iing methodology. This figure shows how the load shapes may differ 

~en younger and older households. 

ional integrated market research results of the REUC project are presented 

:tion 8. 

5-11 

. .' 
Section 1 

INTRODUCTION 

This report presents the research activities and findings from the Residential 

Energy Usage Comparison (REUC) project, a joint effort by Southern California 

Edison Company (SCE) and the Electric Power Research institute (EPRI). The 

REUC project is an innovative demand-side project designed to measure and 

compare typical energy consumption patterns of energy efficient residential 

electric and gas appliances. 

OVERVIEW OF THE REUC PROJECT 

The REUC project has been 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 of these 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 of this approach is that it accounts for many of the 

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' usage and response to new electrotechnologies. These advantages.offset the reduction in "control" afforded by a 

laboratory simulation study. 

 EE-RM-94-230 COMMENT 61F

Section 1 

INTRODUCTION 

This report presents the research activities and findings from the Residential 

Energy Usage Comparison (REUC) project, a joint effort by Southern California 

Edison Company (SCE) and the Electric Power Research Institute (EPRI). The 

REUC project is an innovative demand-side project designed to measure and 

compare typical energy consumption patterns of energy efficient residential 

electric and gas appliances. 

OVERVIEW OF THE REUC PROJECT 

The REUC project has been designed to provide information necessary for assessing the energy performance, the relative effectiveness and the customer acceptance 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 of these 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 of this approach is that it accounts for many of the 

subtle but important determinants of appliance usage not capt\lred in engineering models, including unobservable household characteristics. The advantages of 

conducting a "field" experiment are reflected in the value of more comprehensive, realistic assessments of actual customers' usage and response to new electrotechnologies. These advantages- offset the reduction in "control" afforded by a 

laboratory simulation study. 

1.1 
Proiect Obiectives 

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

assessing new electrotechnologies available to residential customers. ,These 

needs have been satisfied by meeting several objectives: 

measure energy usage of electric and gas residential appliances; 

compare usage patterns of similar electric and gas appliances; 

assess customer acceptance of new end-use technologies; 

and 

generalize results to other residential customers. 

This report will illustrate how these objectives have been met. 

Proiect 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 Ill has consisted of subsequent data 

collection and analysis, which was completed in the summer of 1990. The major 

REU,C project milestones are illustrated in Figure 1-1. 

EPRl INVOLVEMENT IN THE REUC PROJECT 

EPRl has contributed to the REUC project in three.principal areas: 

project design and planning; 

project analysis; and 

project management. 

 EE-RM-94-230 COMMENT 61F

Project Objectives 

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

<;lssessing new electrotechnologies available to residential customers. 'These 

needs have been satisfied by meeting several objectives: 

• measure energy usage of electric and gas residential appliances; 

• compare usage patterns of similar electric and gas appliances; 

• assess customer acceptance of new end-use technologies; 

and 

• generalize results to other residential customers. 

This report will illustrate how these objectives have been met. 

'Project Milestones 

Since 1985, when it was first suggested by SCE and the California Public Utilities, 

Commission, the REUC project has involved three phases: Phase I, from early 

1986 through late 1987, included the experimental design, analysis plan 'and 

instrumentation installations; Phase II, which consisted of initial data collection 

and analysis, ran through early 1989. Phase III has consisted of subsequent data 

collection and analYSiS, which was completed in the summer of 1990. The major 

REU,C project milestOnes are illustrated in Figure 1-1. 

EPRI INVOlVEMENT IN THE REUC PROJECT 

EPRI has contributed to the REUC project in three,principal areas: 

• project design and planning; 

• project analysis; and 

• project management. 

1-2 
1985 - Idea Suggested by CPUC and SCE 

q/ Early 1986 - Discussions with EPRl 

9 Summer 1986 - Customer Sample Design; 

REUC-RSS Survey; lnstallations Begun 

qf Winter 1986 - Mission Viejo Installations Complete 

/ Fall 1987 - Project Analysis Plan; First REUC Survey 

q/ Winter 1987 - lrvine lnstallations Complete 

q/ Spring 1988 - Second REUC Survey 

Summer 1989 - Third REUC Survey; 

Winter 1989 - Final REUC Survey; Equipment Removal 

 EE-RM-94-230 COMMENT 61F

1985 

1 

-

00 

1986 1987 1988 1989 

I· 1 1 1 

..q 1985 - Idea Suggested by CPUC and SCE 

~ Early 1986 - Discussions with EPRI 

~ Summer 1986 - Customer Sample Design; 

1990 

I 

REUC-RSS Survey; Installations Begun 

-<fI Winter 1986 - Mission Viejo Installations Complete 

4t Fall 1987 - Project Analysis Plan; First REUC Survey 

~ Winter 1987 - Irvine Installations Complete 

~ Spring 1988 - Second REUC Survey 

11 Summer 1989 - Third REUC Survey; 

-«f Winter 1989 - Final REUC Survey; Equipment Removal. 
Proiect Desien 

EPRI first became involved with the REUC project during its experimental design 

and planning activities. EPRl project staff emphasized the need to focus on'a a> 

clear set of objectives of the project and then to carefully and systematically 

design the REUC household sample accordingly. The participants would need to 

accurately represent important segments of SCE's residential customer population. In addition, the REUC ex6erimental design needed to determine how 

much and what type of customer data should 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 £A-3286. 

Proiect Analvsis 

The REUC project has pioneered the integration of load research/load shape 

analysis with market research/customer behavior analysis. The project team has 

developed "integrated market research" techniques and "behavioral engineering 

statistical techniques (BEST)" to meet the project objectives. These techniques 

have leveraged upon research results from several other EPRI projects, including 

the Customer Preference and Behavior (CP&B) Project (~~2671) and the Load 

Data Analysis Workstation project (RP23421. : 

Proiect Mana~ement 

EPRI has assisted SCE staff by offering primary management of the REUC project 

design and data analyses tasks. 

SCE staff provided effective management of the customer contact, equipment 

installation, and data collection efforts. Members of the project team, from EPRI, 

SCE and the Contractor, have presented preliminary results from the REUC 

project at numerous industry conferences and meetings. 

 EE-RM-94-230 COMMENT 61F

Project Design 

EPRI first became involved with the REUC project during its experimental design 

and planning activities. EPRI project staff emphasized the need to focus on'a 

,. clear set of objectives of the project and then to carefully and systematically 

design the REUC household sample accordingly. The participants would need to 

accurately represent important segments of SCE's residential customer population. In addition, the REUC experimentai design needed to determine how 

much and what type of customer data should be collected in order to ensure the 

precision of subsequent REUC analyses. Finally, the design needed to include a 

comprehensive data analysis plan, so that the collected REUC data would satisfy 

the project objectives. 

The REUC experimental design activities took advantage of previous EPRI-

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

Control Experiment (EPRI EM-4628l and the model-based sample design 

techniques in EPRl EA-3286. 

Project Analysis 

The REUC project has pioneered the integration of load research/load shape 

analysis with market research/customer behavior analysis. The project team has 

developed "integrated market research" techniques and "behavioral engineering 

statistical techniques (BEST)" to meet the project objectives. These techniques 

have leveraged upon research results from several other EPRI projects, including 

the Customer Preference and Behavior (CP&B) Project (RP2671) and the Load 

Data Analysis Workstation Project (RP2342). 

Project Management· 

EPRI has assisted SCE staff by offering primary management of the REUC project 

design and data analyses tasks. 

SCE staff provided effective management of the customer contact, eqUipment 

installation, and data collection efforts. Members of the project team, from EPRI, 

SCE and the Contractor, have presented preliminary results from the REUC 

project at numerous industry conferences and meetings. 

1-4 
is organized as follows: Section 2, REUC Project 

s the design of the REUC project. Section 3, REUC 

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

REUC Customer Sample, provides a summary of the REUC sample 

description of the REUC sample. Section 5, REUC 

d Electric Appliance Energy Usage, presents results 

ce energy data. Section 6, REUC Appliance Load 

ides appliance load profiles for the REUC electric and gas appliances. 

ustomer Preferences and Appliance Acceptance, discusses REUC 

attitudes toward their new electric and gas appliances. Section 8, 

n of Load and Survey Analyses, presents results of'integrated market 

rticipants' fuel images and load profiles b.y customer 

pendix A provides supplementary energy usage information .load 

Appendix B contains a description of the kethodology for generalizing 

c Customer Sample Results. Appendix C describes the load data editing 

 EE-RM-94-230 COMMENT 61F

rATION 

of this report is organized as follows: Section 2, REUC Project 

5bj,ectives, describes the design of the REUC project. Section 3, REUC 

and Data Collection, describes the history of REUC data collection. 

REUC Customer Sample, provides a summary of the REUC sample 

a demographic description of the REUC sample. Section S, REUC 

'Analysis: Gas and Electric Appliance Energy Usage, presents results 

lysis of appliance energy data. Section 6, REUC Appliance load 

IrO\lidE~S appliance load profiles for the REUC electric and gas appliances. 

Customer Preferences and Appliance Acceptance, discusses REUC 

attitudes toward their new electric and gas appliances. Section 8, 

of Load and Survey Analyses, presents results ofintegrated market 

an;l.IY!ieS, such as participants' fuel images and load profiles by customer 

Appendix A provides supplementary energy usage information ,load 

'Appendix B contains a description of the methodology for generalizing 

Customer Sample Results. Appendix C describes the load data editing 

1il1i"""'i;,,n methods. 

1-5 
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 how the REUC design .and analysis methods can be used by other 

utilities. Thus, this section provides a response to the question, "How can utilities 

other than Southern California Edison utilize the REUC project's research results?" 

These research results include both the analysis methods themselves as well as 

the analysis findings. 

REUC DESIGN 

The REUC project's experimental design developed a consolidated customer 

sample design, data collection plan and analysis plan. Project resources limited 

the sample to no more than 100 households. A model-based sample design was 

employed in order to produce an efficient customer sample that was consistent 

with the available project reso~rces.~ 

The REUC participant sample was not designed to be a completely random 

sample nor to represent SCE's 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 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," lEEE Transactions on Power Systems, Vol. 3, No. 3, August 1988, pp. 

1298-1 305. L. 

 EE-RM-94-230 COMMENT 61F

Section 2 

REUC PROJECT DESIGN OBJECTIVES 

INTRODUCTION 

As mentioned in Section 1, one of the REUC project 'objeciives has been to 

generalize the results obtained from the REUC sample itself to other residential 

customers. In order to satisfy this objective, the project experimental design, 

project data and analysis methods were developed in an integrated fashion. This 

section suggests how the REUC design ,and analysis methods can be used by other 

utilities. Thus, this section provides a response to the question, "How can utilities 

other than Southern California Edison utilize the REUC project's research results?" 

These research results include both the analysis methods themselves as well as 

the analysis findings. 

REUC DESIGN 

The REUC project's experimental design developed a consolidated customer 

sample design, data collection plan and analysis plan. Project resources limited 

the. sample to no more than 100 households. A model-based sample design was 

employed in order to produce an efficient customer sample that was consistent 

with the available project resources.1 

The REUC participant sample was not designed to be a completely random 

sample nor to represent SCE's entire residential customer class of over 3.5 million 

customers. Rather, the REUC participants were selected to represent owneroccupied, single-family new homes in SeE's Southern Division. Thus, the project 

sample represents one specific and important segment of residential customers, 

those most likely to purchase new, energy-efficient electric end-use technologies 

for space conditioning, water heating, cooking and clothes drying. This segment 

1J.T. Powers, M.S. Shirilau, R.T. Uhlaner, B.A. Smith, "Design of a Utility 

Competitive Assessment Experiment: The Residential Energy Usage Comparison 

Project," IEEE Transactions on Power Systems, Vol. 3, No.3, August 1988, pp. 

1298-1305. 

2-1 
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 that the 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 

 EE-RM-94-230 COMMENT 61F

of residential customers is growing rapidly in SCE's as well as other utilities' servi'ce 

territories. 

The demographics of the REUC sample are described in Section 4. This 

segment-specific design, based on information widely available about residential 

customers -- household income and dwelling size, will facilitate the application of 

the project results to a larger portion of SCE's customers as well as to other 

utilities. 

As mentioned above, the REUC project's experimental deSign linked the sample 

requirements to the planned data analysis methods using a model-based sample 

design. Such integrated design is recommended for two reasons on utility 

projects that assess energy-efficient end-use technologies such as the REUC 

project. , First, such designs specify the type and number 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 that the 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 
t-of descriptors is customer responses to the demographic/charles in utility saturation surveys. Furthermore, the kinds of data 

techniques employed on the REUC project can allow for the . 

lication of these results to residential customers served by other * 

tered four different survey instruments, as described in 

was based on SCE's 1985 Residential Saturation Survey 

additional survey information, the REUC results were 

lity to other SCE residential customers. This assessment 

. . 

fwith the detailed electric and gas end-use load shapes that have been 

on the project, the survey research results determined customers' 

nd acceptance of these energy-efficient end-use technologies. this 

of load and survey information can be used to assess the impacts and 

of utility demand-side programs that emphasize customers' efficient 

;e. As discussed in Section 7, the fie1 images of the REUC participants 

e correlated to customers' energy usage behavior and, as a result, their 

program participation. 

I 

the REUC project included load shape modeling analyses that used both 

-use load data and customer survey responses. Results of,several of these 

ses are described in Section 6. The load shape modeling linked customer 

Cteristics to appliance load shapes and usage patterns. End-use load shapes 

e modeled using customer characteristics with a two-step procedure. The 

edure uses customers' demographic, attitudinal and/or needs-based segment 

racteristics to explain end-use load shapes. These results suggest how other F+"$S 

.A -. . . :lJtllltles may adapt the REUC load shapes to their service territories. 

 EE-RM-94-230 COMMENT 61F

set"of descriptors is customer responses to the demographic/charriatlles in utility saturation surveys. Furthermore, the kinds of data 

techniques employed on the REUC project can allow for the 

'appli.:atllon of these results to residential customers served by other 

project administered four different survey instruments, as described in 

" first survey was based on SCE's 1985 Residential Saturation Survey 

this and the additional survey information, the REUC results were 

" their applicability to other SCE residential customers. This assessment 

in Section 4. 

with the detailed electric and gas end-use load shapes that have been 

- on the project, the survey research results determined customers' 

acceptance of these energy-efficient end-use technologies. "This 

of load and survey information can be used to assess the impacts and 

of utility demand-side programs that emphasize customers' efficient 

As discussed in Section 7, the fuel images of the REUC participants 

be correlated to customers' energy usage behavior and, as a result, their 

""-'.',"VV of program participation. 

the REUC project included load shape modeling analyses that used both 

~n(l-w;e load data and customer survey responses. Results of -several of these 

are described in Section 6. The load shape modeling linked cUstomer 

irae:t",i<t;,-< to appliance load shapes and usage patterns. End-use load shapes 

modeled using customer characteristics with a two-step procedure. The 

U\.~UUI"P uses customers' demographic, attitudinal and/or needs-based segment 

to explain end-use load shapes. These results suggest how other 

may adapt the 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 of two groups: eight homes where load monitoring equipment was 

initially tested, the "equipment test sample"; and 92 homes located in ,lrvine and 

Mission Viejo, the "REUC sample." 

This report focuses only on data from the REUC's 92-household sample. lrvine 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 of the SCE service area and of the 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 

 EE-RM-94-230 COMMENT 61F

INTRODUCTION 

Section 3 

REUC PROJECT SCOPE AND 

DATA COLLECTION HISTORY 

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

are comprised of two groups: eight homes where load monitoring equipment was 

initially t~sted, the "equipment test sample"; and 92 homes located in .Irvine and 

Mission Viejo, the "REUC sample." 

This report focuses only on data from the REUC's 92-household sample. Irvine and 

Mission Viejo are located in Orange County along a high-growth corridor between 

San Diego and Los Angeles. Over the past 20 years, this area has undergone a 

transition from a lightly populated agriculturally based economy to a metropolitan 

area consisting of high-technology research and manufacturing industries. The 

population is largely new to the area and represents a diverse cross-section of 

other parts of the SCE service area and of the 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 
strategy reduced variation induced by building envelope characteristics and 

weather. 

The heat pump space conditioning systems have connected loads &f 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 11 7 kBTU/hr and electric fans 

that have loads under 800 watts. As mentioned above, the electric homes 

received new electric heat pumps for space conditioning (typical Seasonal Energy 

Efficiency Ratio [SEER] of 9, or Heating Season Performance Factor [HSPF] of 7) 

and heat pump water heating (Coefficient of Performance [COP] of 3). Many of 

the heat pump water heaters had a resistance backup system for water heating; 

this system could draw loads of about 2.5 kW. The gas homes received new gas 

furnaces (80 percent efficiency), electric air conditioners (typical SEER of 9) and gas 

water heaters (75 percent recovery efficiency). 

The cooking appliance experiment relies on the existing gas and electric resistance ranges, except in the Mission Viejo electric test sites, where new electric 

induction ranges were installed. The gas ranges included an electronic ignition 

system. The installed induction cooktops consisted of one 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, half with gas appliances and 

half with electric or electric heat pump appliances. Second, the participants have 

 EE-RM-94-230 COMMENT 61F

strategy reduced variation induced by building envelope characteristics and 

weather. 

The heat pump space conditioning systems have connected loads of approximately 6 kW,or 20.5 kBTU/hour. The heat pump systems nave a resistance· 

backup system designed to ·provide supplemental heat on cold days; this system 

can draw loads up to 16 kW. The gas homes are equipped with efficient gas 

heating systems that have output capacities of 58 to 117 kBTU/hr and electric fans 

that have loads under 800 watts. As mentioned above, the electric homes 

received new electric heat pumps for space conditioning (typical Seasonal Energy 

Efficiency Ratio [SEER] of 9, or Heating Season Performance Factor [HSPF] of 7) 

and heat pump water heating (Coefficient of Performance [COP] of 3). Many of 

the heat pump water heaters had a resistance backup system for water heating; 

this system could draw loads of about 2.5 kW. The gas homes received new gas 

furnaces (80 percent efficiency), electric air conditioners (typical SEER o(9) and gas 

water heaters (75 percent recovery efficiency). 

The cooking appliance experiment relies on the existing gas and electric resistance ranges, except in the Mission Viejo electric test sites, where new electric 

induction ranges were installed. The gas ranges included an electronic ignition 

system. The installed induction cooktops consisted of one induction two-burner 

"cartridge" and one resistance two-burner cartridge. The induction cooktop uses a 

high-frequency, electronically controlled electromagnet to produce heat in 

ferromagnetic cookware. Induction stoves feature the same qJJiCk responsiveness 

as gas ranges because of their low thermal capacity. Also, they are safer than 

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

relatively cool. Though no new technologies have been applied to clothes drying, 

new gas and electric clothes dryers were installed in the test homes to evaluate 

customer attitudes and collect end-use consumption information. 

In addition to energy usage comparisons, the REUC project data can support 

assessment 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, half with gas appliances and 

half with electric or electric heat pump appliances. Second, the participants have 

3-2 
been repeatedly surveyed in order to assess their experiences with their new 

appliances and the influence these appliances may have had an 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 "control group" of 

comparably equipped gas customers. 

The REUC project analyses cover three main topic areas: realized appliance 

energy usage comparisons; load profile analysis; and the integration of these topics 

with market research data. Each of these areas is summarized below. 

Enerev Usaee 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 researchlattitudinal 

data in an attempt to understand the factors influencing the load shapes. The 

pairing of gas and electric homes affords an opportunity to carefully compare the 

usage patterns of appliances with different energy sources, since gas end-use data 

are rarely collected. 

Market Research Analvsis 

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 of customer 

reaction to the periormance of their new appliances allows qualitative assessment 

of non-pecuniary benefits, not just the dollar value of the savings from operating 

 EE-RM-94-230 COMMENT 61F

been 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 "control group" of 

comparably equipped gas customers. 

The REUC project analyses cover three main topic areas: realized appliance 

energy usage comparisons; load profile analysis; and the integration of these topics 

with market research data. Each of these 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 segmentaiions 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 appl iances 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 of customer 

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 
and capital costs. Finally, the integration of this expanded set of customer data 

with the traditionally collected set of household characteristics enhanced the 

capability to model, or explain, customer energy usage by different market 

segments. 

HISTORY OF REUC DATA COLLECTION 

The REUC data collection activities have consisted of load and survey data collected from two samples: 42 homes in Mission Viejo aod 50 homes in lrvine. 

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

Figure 3-1 

REUC Survey and 

Load Data Collection History 

9/86 618 7 3/88 6/89 12/89 

Mission I I 

Viejo SO S 1 S 2 S3 S4 

lrvine I I 

S 0 S 1 S 2 S3 S 4 

1-1 = Load data collection 

SO = REUC-RSS survey 

51-54 = REUC Market Research survey 

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 of fuel types and end-uses as well as the customer's propensity to "invest" 

 EE-RM-94-230 COMMENT 61F

and capital costs. Finally, the integration of this expanded set of customer data 

with the traditionally collected set of household characteristics enhanced the 

capability to model, or explain, customer energy usage by different market 

segments. 

HISTORY OF REUC DATA COLLECTION 

The REUC data collection activities have consisted of load and survey data collected from two samples: 42 homes in Mission Viejo al;ld 50 homes in Irvine . 

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

Mission 

Viejo 

Irvine 

I-I 

SO 

51-54 

Figure 3-1 

REUC Survey and 

Load Data Collection History 

. 9/86 

SO 

SO 

= Load data collection 

= REUC-R55 survey 

6/87 

I 

51 

51 

= REUC Market Research survey 

3/88 

52 

I 

52 

6/89 12/89 

I 

53 54 

I 

53 54 

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

first survey, REUC-R55, was designed to gather specific appliance saturation data in 

order to select prospective participants. The REUC-R55 survey instrument is based 

on the SCE 1985 R55 survey but augmented by several attitudinal and demographic questions. The first REUC market research survey, administered in june 

1987, contained all new questions that focus on customer attitudes towards 

attributes of fuel types and end-uses as well as the customer's propensity to "invest" 

3-4 
. . iin energy-effi~ient appliances. The second REUC survey instrument included ! 

Irefined versions of the firsf survey's questions, additional questions that measure 

acceptance of the new appliances, and the EPRl 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 

.and June 1989). The final survey was a shorter version of the second REUC survey 

!and contained portions of SCE's residentia! attitude survey instrument. In this way; 

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

provides a link to similar surveys administered to other SCE cusiomers. 

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

the data collection history for the lrvine sample differs from the Mission Viejo 

sample. In Phase I, the REUC 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 lrvine and 

to undertake a comprehensive market research study. The load data analysis 

results contained in Sections 5 and 6 are based on the 22-month period from 

February 1988 through November 1989 so that results from both cities can be 

compared. 

Load Data Analysis Workstation 

All of the 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 EPRl under research 

project 2342-8. The Workstation is a hardwarelsoftware system developed 

specifically for the design, analysis and evaluation of demand-side programs. With 

the support oi SCE, the REUC project has provided another opportunity to 

demonstrate the usefulness of this PC-based Workstation. 

The REUC Workstation is based on the EPRl LDAW and uses an 80386 PC with a 

floating point (math) co-processor. To accommodate the very large data sets 

generated in end-use load monitoring experiments, the machine includes a fast 

internal 600-megabyte hard disk and an external 6250 bpi 9-track tape drive. 

The internal hard disk is large enough to store on-line the data from an entire 

 EE-RM-94-230 COMMENT 61F

ri energy-efficient appliances. The second REUC survey instrument included 

refined versions of the first survey's questions, additional questions that measure 

'customer acceptance of the new appliances, and the EPRI CLASSIFYTM question- . . 

naire. In order to perform longitudinal analysis of the participants' acceptance of' 

,the new appliances, the second REUC survey was administered twice (March 1988 

and June 1989l. The final survey was a shorter version of the second REUC survey 

;and 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 link to similar surveys administered to other SCE customers. 

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

the data collection history for the Irvine sample differs from the Mission Viejo 

sample. In Phase I, the REUC project was limited to the Mission Viejo sample arid 

was primarily envisioned as an end-use load monitoring project. Once the Mission 

Viejo sample was selected and retrofitted with new appliances, the scope of the 

project broadened in Phase II to include a second customer sample in Irvine and 

to undertake a comprehensive market research study. The load data analysis 

results contained in Sections 5 and 6 are based on the 22-month period from 

February 1988 through November 1989 so that results from both cities can be 

compared. 

Load Data Analysis Workstation 

All of the 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 

specifically for the design, analysis and evaluation of demand-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 
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. ~ommunic~tions are 

handled through a 2400-baud internal modem. A VGA graphics board and highresolution color monitor allow the software to display results in the form of 

intuitive, presentation-quality graphics.' 

The REUC Workstation also has taken advantage of the LDAW software, which is 

specifically tailored for large-scale end-use load research and demand-side 

projects. All the LDAW's analysis routines are written in SAS-PC. The use of this 

statistics package ensures that analysts already familiar with SAS on a mainframe 

can work productively on the Workstation from the first day. Similar to 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 "building blocks" to 

accomplish complex analysis tasks. These programs include routines to: 

design load research and demand-side program customer 

samples; 

read load data in standard central station tape formats; 

edit these data for possibly invalid measurements; 

estimate average load profiles; 

perform survey analysis; 

undertake market research analysis; 

estimate appliance-connected loads from metered observations; 

calculate appliance duty cycles and duty-cycle distributions; 

calculate load reductions from direct load control strategies; 

and 

1For more information, see "The Load Data Analysis 'workstation Userls 

Guide," Version 0.4RG, May 1990, EPRl RP2342-8. 

 EE-RM-94-230 COMMENT 61F

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 of the 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 of this 

statistics package ensures that analysts already familiar with SAS on a mainframe 

can work productively on the Workstation from the first day. Similar to 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 "building blocks" to 

accomplish complex analysis tasks. These programs include routines to: 

• design load research and demand-side program customer 

samples; 

• read load data in standard central station tape formats; 

• edit these data for possibly invalid measurements; 

• estimate average load profiles; 

• perform survey analysis; . 

• undertake market research analysis; 

• estimate appliance-connected loads from metered observations; 

• calculate appliance duty cycles and duty-cycle distributions; 

• calculate load reductions from direct load control strategies; 

and 

, For more information, see "The Load Data Analysis Workstation User's 

Guide," Version O.4RG, May 1990, EPRI RP2342-8. 

3-6 
Section 4 

THE REUC CUSTOMER SAMPLE 

ibes the REUC project customer sample. The sample design 

and criteria are described, followed by the demographic characthe REUC sample. This section concludes by summarizing the results of 

gerieralizability of the REUC sample to other customers and 

&f SCE's service territory. 

identify a potential sample of residential customers, SCE administered 

survey, called the REUC-RSS, to 2,380 potential participants. This survey 

85 Residential Saturation Survey (RSS). From the 756 

t of potential participants was visited by SCE project staff 

dditional information on specific appliances in the household, 

ld characteristics, and occupancy patterns. 

elsewhere,' extensive pre-experimental research was undertaken to 

of customers that could provide the most pertinent and valuable 

the study, namely electric and gas end-use load shapes and 

The goal of the REUC sample design was to determine 

92 sets of sub-metering equipment and 

usage between electric and gas 

measured. This allocation was achieved by 

characteristics that best predict overall 

stratifying the sample according to those 

[+a!acteristics. Required sample sizes were calculated using estimated variances 

. - 

''Design of a Utility Competitive Assessment Experiment: The Residential 

lmerg~ Usage Comparison Project," J.T. Powers, M.S. Shirilau, R.T. ~hlaner and 

@Smith, IEEE Transactions on Power Systems (August 1988), Vol. 3, NO. 3, [m. 1298-1305. 

 EE-RM-94-230 COMMENT 61F

Section 4 

, THE REUC CUSTOMER SAMPLE 

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

and criteria are described, followed by the demographic charac-

, REUC sample. This section concludes by summarizing the results of 

the gerieralizability of the REUC sample to other customers and 

of SCE's servi,ce territory. 

'M'CL~ DESIGN 

ide~tify a potential sample of residential customers, SCE administered 

sun, .. v. called the REUC-RSS, to 2,380 potential participants. This survey 

on SCE's 1985 Residential Saturation Survey (RSS)' From the 756 

esp<lnSE!S, a subset of potential participants was visited by set project staff 

additional information on specific appliances in the household, 

characteristics, and occupancy patterns. 

r.\S··iI~!;cril,,,rl elsewhere,l extensive pre-experimental research was undertaken to 

a sample of customers that could proVide the most pertinent and valuable 

for the study, namely electric and gas end-use load shapes and 

characteristics. The goal of the REUC sample design was to determine 

allocation of the available 92 sets of sub-metering equipment arid 

Jnanr,', so that the differenoe in energy usage between electric and gas 

i!l?1llia,ncles could be accurately measured. This allocation was achieved by 

observable household characteristics that best predict overall 

electric load shape and then stratifying the sample according to those 

[qJ;ara<:teristi,-s. Required sample sizes were calculated using estimated variances 

l

til '"Design of a Utility Competitive Assessment Experiment: The Residential .Blrgl ~sage Comparison Project," J.T. Powers, M.S. Shirilau, R.T. Uhlaner and 

F.IID'" l'2m1th, IEEE Transactions on Power Systems (August 1988), Vol. 3, No.3, 

!'. 98-1305. 
from the pre-experimental study (as a function of household characteristics) and 

the criterion that a 95 percent confidence interval around the estimated 

difference in energy usage between gas and electric appliances be no wider than 

5 percent of total 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 order to 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 

 EE-RM-94-230 COMMENT 61F

from the pre-experimental study (as a functi.on of household characteristics) and 

the criterion that a 95 percent confidence interval around the estimated 

difference in energy usage between gas and electric appliances be no wider than 

5 percent of total load for each appl.iance. 

In order to develop the customer sample, analysis was performed on key demographic variables used as stratification variables in load research projects; Ultimate 

selection of the stratification variables reflected the project objeCtives of selecting 

a sample that was statistically supportable and that represented key customer 

segments of interest to SCE. As described in Section 2, this sample represents 

customers. in the new, owner-oc;:cupied, single-family homes in SCE's Southern 

Division. 

Household income and square feet were selected as the key stratification 

variables. In order to control for the effects 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 hOl.lseholds with between 

two and five members. Age and education level of the head of household were 

not controlled for in the sample design. 

DEMOGRAPHIC DESCRIPTION OF THE REUC SAMPLE 

Analysis of utility demand-side programs typically can involve relating customer 

energy usage, appliance choice, and program acceptance to key demographic 

variables such as household income, age of head of household, and family size, 

and dwelling characteristics such as square feet, number of bedrooms, and type. 

These data describing household characteristics and appliance jnventories were 

collected for the REUC by surveying the REUC participants. 

Table 4-1 tabulates the key demographic data for the REUC sample based on the 

initial stratification variables. This tabulation is based on the March 1988 REUC 

customer survey. Since the sample was selected in 1986, the household income 

distribution has c.hanged. Table 4-2 illustrates the distribution of the highest 

4-2 
education level completed among the heads of household. Education was identified as an important potential stratification variable.2 As shown, almost threequarters of the REUC Electric and Gas Sample participants have a Bachelor or 

graduate degrees. The above results describe the REUC sample at about the same ' 

time as SCE's 1987 RSS was administered. As described below, the 1987 RSS 

sample of single-family homes is used to assess the representativeness of the REUC 

sample. 

Table 4-1 

REUC Partici~ant Demoeraphics 

Samole Desien Stratification Variables 

House Size 

Medium House 

1,250 - 1,749 sq ft 

Large House 

1,750 sq ft or more 

Total 

House Size 

Medium House 

1,250 -1,749 sq ft 

Large House 

1,750 sq ft or more 

Electric Sample 

Number of Households 

Mission 

Income & Vieio Total 

< $5OK 12 3 15 

$50K or more 2 6 8 

< $SOK 2 4' 6 

$50K or more 9 8 - 17 

46 

Gas Sample 

Number of Households 

Mission 

Income & Vieio Total 

< $50K 2 3 5 

$50K or more 12 6 18 

$50K or more 10 8 - 18 

Total 4 6 

 EE-RM-94-230 COMMENT 61F

education level completed among the heads of household. Education was identified as an important potential stratification variable.2 As, shown, almost threequarters of the REUC Electric and Gas Sample participants have a Bachelor 'or 

graduate degrees. The above results describe the REUC sample at about the same' 

time as SCE's 1987 RSS was administered. As described below, the 1987 RSS 

sample of single-family homes is used to assess the representativeness of the REUC 

sample. 

House Size 

Medium House 

1,250 - 1,749 sq ft 

Large House 

1,750 sq ft or more 

Total 

House Size 

Medium House 

1,250 -1 ,749 sq ft 

Large House 

1,750 sq ft or more 

Total 

2Ibid., p. 1302. 

Table 4-1 

REUC Participant Demographics 

Sample Design Stratification Variables 

Electric Sample 

Number of Households 

Mission 

Income Irvine Viejo, Total 

< $50K 12 3 15 

, $50K or more 2 6 8 

< $50K 2 4 6 

$50K or more 9 8 lZ 

46 

Gas Sample 

Number of Households 

Mission 

Income Irvine Viejo Total' 

< $50K 2 3 5 

$50K or more 12 6 18 

< $50K 1 4 5 

$50K or more 10 8 l§. 

46 

<\-3 
Table 4-2 

REUC Particioants Demoeraohics 

Hiehest Comoleted Education level Amone Key Household Members 

Level Comoleted 

High School 

2-Year ~ssociate 

Bachelor 

Masters 

Doctorate 

Level Comoleted 

High School 

2-Year Associate 

Bachelor 

Masters 

Doctorate 

Electric Sample 

Number of Households 

lwine Mission Vieio 

2 3 

3 4 

14 3 

5 6 

1 2 

Gas Sample 

Number of Households 

lrvine Mission Vieio 

1 2 

5 3 

8 8 

10 2 

- 2 

REPRESENTATIVENESS OF THE REUC SAMPLE 

To perform the assessment of the representativeness of the REUC customer 

sample, Integrated Market Research (IMR) techniques were applied to analyze 

the relationships between energy usage and household characteristics. Customer 

data from both the REUC and 1987 RSS surveys were transformed into customer 

descriptors and summaries of household appliance holdings. Then, IMR 

techniques were used to identify those customer descriptors that segment 

customers into groups that use electricity similarly. Appendix .B describes the 

methodology 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 toexplain customer energy usage. This re-examination confirms the 

 EE-RM-94-230 COMMENT 61F

Table 4-2 

REUC Participants Demographics 

Highest Completed Education Level Among Key Household Members 

Level Completed 

High School 

2-Year Associate 

Bachelor 

Masters 

Doctorate 

Level Completed 

High School 

2-Year Associate 

Bachelor 

Masters 

Doctorate 

Electric Sample 

Gas Sample 

REPRESENTATIVENESS OF THE REUC SAMPLE 

Number of Households 

Irvine Mission Viejo 

2' 3 

3 4 

14 3 

5 6 

1 2 

Number of Households 

Irvine Mission Viejo 

2 

5 3 

8 8 

10 2 

2 

To perform the assessment of the representativeness of the REUC customer 

sample, Integrated Market Research (iMR) techniques were applied to analyze 

the relationships between energy usage and household characteristics. Customer 

data from both the REUC and 1987 RSS surveys were transformed, into customer 

descriptors and summaries of household appliance holdings. Then, IMR 

techniques were used to identify those customer descriptors that segment 

customers into groups that use electricity similarly. Appendix ,B describes the 

methodology developed to asses the REUC sample's representativeness. 

Two objectives are met by the results presented below. First, the customer 

descriptors originally used to design the REUC sample are re-examined in terms of 

their ability to explain customer energy usage. This re-examination confirms the 

4-4 
.' !validity of the ~ri~inal'model-based sample design. The second objective is met 

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

;are applicable. This share represents a quantitative measure of the direct .. . 

.applicability of the REUC results to SCE's residential customers. 

comparison of REUC and RSS Samples 

In assessing the representativeness of the 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 

(ASI). This segmentation by AS1 allows the energy usage characteristics of the two 

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

subsample fell into one of three AS1 definitions: 

(1) gas space heating and electric central air-conditioning 

(GSHICAC); 

(2) gas space heating and electric wail/window air-conditioning 

(GSHIWWAC); and 

(3) gas space heating and no air-conditioning (GSHINOAC). 

See Appendix B for details of the AS1 definitions. 

In Table 4-3, the Electric and Gas (ELVGAS) Sample sizes for each stratum are 

shown for the REUC participants using the lncome variable. For the RSS singlefamily subsample, the AS1 shares for each stratum are reported. For the stratum 

corresponding to the REUC sample, the "REUC Share of ASI," is computed. For 

example, in the case of the lncome variable for the GSHICAC ASI, this share 

equals 67 percent (25.0 + 42.0).3 Only two RSS income strata are used in this 

summation since there were no REUC customers in the lower two RSS income 

strata. Thus, 67 percent of the households in this AS1 are represented by the 

REUC sample using income as the basis for comparison. The "AS1 Share of SCE 

3~ee Appendix B for a definition of each ASI. 

 EE-RM-94-230 COMMENT 61F

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

:by determining the share of SCE's single-family homes for which the REUC results 

:are applicable. This share represents a quantitative measure of the direct 

,applicability of the REUC results to SCE's residential customers. 

Comparison of REUC and RSS Samples 

In,assessing the representativeness of the 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 

(ASi). This segmentation by AS! allows the energy usage characteristics of the two 

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

subsample fell into one of three AS! definitions: 

(1) gas space heating and electric central air-conditioning 

(GSH/CAC); 

(2) gas space heating and electric wall/window air-conditioning 

(GSH/WWACl; and 

(3) gas space heating and no air-conditioning (GSH/NOAC). 

See Appendix B for details of the AS! definitions. 

!n Table 4-3, the Electric and Gas (ELE/GAS) Sample sizes for each stratum are 

" shown for the REUC participants using the Income variable. For the RSS single~ 

family subsample, the AS! shares for each stratum are reported. For' the stratum 

corresponding to the REUC sample, the "REUC Share of AS/," is computed. For 

example, in the case of the Income variable for the GSH/CAC AS!, this share 

equals 67 percent (25.0 + 42.0).3 Only two RSS income strata are used in this 

summation since there were no REUC customers in the lower two RSS income 

strata. Thus, 67 percent of the households in this AS! are represented by the 

REUC sample using income as the basis for comparison. The "ASI Share of SCE 

3See Appendix B for a definition of each ASI. 

4-5 
Single Family" is taken from the "Total SCE" column of Table B-1. Multiplying the 

"REUC Share of ASI" by the "AS1 Share of SCE Single Family" produces the "REUC 

Share of SCE Single Family by ASI." Finally, the "REUC Total Share of SCE Single 

Family" is the sum across each AS1 of the 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 Reoresentativeness by Income Stratum 

REUC sample RSS Single-Family Subsarnple 

REUC Income ELEIGAS RSS Income Appliance Stock Inventory (ASI) 

- Size Stratum CSH/CAC GSHMIWAC GSHINOAC 

REUC Share of AS1 

AS1 Share of SCE Single Family 

REUC Share of SCE Single Family by AS1 15.4 6.0 18.6 

REUC Total Share of 5CE Single Family 40.0% 

 EE-RM-94-230 COMMENT 61F

Single Family" is taken from the "Total SCE" column of Table 8-1. Multiplying the 

"REUC Share of ASI" by the "ASI Share of SCE Single Family" produces the "REUC 

Share of SCE Single Family by AS!." Finally, the "REUC Total Share of SCE Single 

Family" is the sum across each ASI of the 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 

REUC sample RSS Single-Family Subsample 

REUC Income 

Stratum 

ELE/GAS RSS Income 

Size Stratum 

Appliance Stock Inventory (ASI) 

GSHiCAC GSHfWWAC GSH/NOAC 

NA 

<$30,000 

$30,000-$49,999 

$50,000+ 

NA 

0/0. 

21/10 

25/36 

<$25,000 

$25,000·$34,999 

$35,000·$49,999 

$50,000+ 

19.5% 

13.5 

25.0 

42.0 

REUC Share of ASI 67.0 

ASI Share of SCE Single Family 23.0 

REUC Share of SCE Single Family by ASI 15.4 

REUC Total Share of SCE Single Family 

4-6 

48.8% 31.3% 

17.8 18.5 

17.4 21.5 

16.0 28.7 

33.4 50.2 

18.0 37.0 

6.0 18.6 

40.0% 
Table 4-4 provides similar results of the REUC Sample's representativeness 

according to the house size (SqFt) strata. From the table, the REUC share of the . 

GSHICAC AS1 is very high, 86.4 percent. Following the calculations discussed . 

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

GSHICAC AS1 is 55.3 percent, representing over 750,000 households. 

Table 4-4 

REUC ~amnle' Representativeness by House Size Stratum 

REUC sample RSS Single-Family Subsample 

REUC SqFt ELE/GAS RSS SqFt Appliance Stock Inventory (AS11 

Stratum - Size CSHICAC CSHIWWAC GSHINOAC 

< 1,250 SqFt 010 < 1,250 SqFt 13.6% 45.2% 31.1% 

1,250-1,749 SqFt 23/23 1,250-1,749 SqFt 38.2 37.1 34.1 

1,7504 SqFt 23/23 1,750+ SqFt 48.2 . 17.7 34.8 

REUC Share of AS1 86.4 54.8 68.9 

AS1 Share of SCE Single Family 23.0 18.0 37.0 

REUC Share of SCE Single Family by AS1 19.9 9.9 25.5 

REUC Total Share of SCE Single Family 55.3% 

perhaps the most relevant result presented in Tables 4-3 and 4-4 is the REUC 

share of the CSHICAC ASI, customers with gas space heat and central airconditioning. This is because these customers would most likely purchase heat 

pumps to replace their existing space' conditioning appliances.4 For this 

important single ASI, the REUC 92-household sample represents over 15 percent 

of SCE's single-family homes by income strata and almost 20 percentof SCE1s 

single-family homes by square feet, or over 200,000 and 2S0,000 households, by 

income and square feet strata, respectively. 

4~ustomers in the other two ASls would have to decide to use central' air 

conditioning along with deciding to install a heat pump. 

 EE-RM-94-230 COMMENT 61F

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 "REUC Total Share of SCE 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 

REUC sample RSS Single-Family Subsample 

REUC SqFt 

Stratum 

ELE/GAS RSS SqFt 

Size Stratum 

Appli;lnce Stock Inventory (ASU 

GSH/CAC GSHIWWAC GSH/NOAC 

< 1,250 SqFt 

1,250-1,749 SqFt 

1,750+ SqFt 

0/0 

23/23 

23/23 

REUC Share of ASI 

< 1,250 SqFt 

1,250-1,749 SqFt 

1,750+ SqFt 

ASI Share of SCE Single Family 

REUC Share of SCE Single Family by ASI 

REUC Total Share of SCE Single Family 

13.6% 

38.2 

48.2 

86.4 

23.0 

19.9 

45.2% 31.1% 

37.1 34.1 

17.7 34.8 

S4.8 68.9 

18.0 37.0 

9.9 25.5 

55.3% 

Perhaps the most relevant result presented in Tables 4-3 and 4-4 is the REUC 

share of the GSH/CAC ASI, customers with gas space heat and central airconditioning. This is because these customers would most likely purchase heat 

pumps to replace their existing space' conditioning appliances.4 For this 

important single ASI, the REUC 92-household sample represents over 15 percent 

of SCE's single-family homes by income strata and almost 20 percent,of SCE's 

single-family homes by square feet, or over 200,000 and 250,000 households, by 

income and square feet strata, respectively. 

4Customers in the other two ASls would have to decide to use central air 

conditioning along with deciding to install a heat pump. 

4-7 
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 ;hat the REUC 

sample design, based on income and square feet, was a valid 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 of this type. 

 EE-RM-94-230 COMMENT 61F

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 inc:ome and square feet, was a valid 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 of customer 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 of this type. 

4-8 
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 {he 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. 

'For more information, see John T. Powers and Bruce A. Smith, "Load 

Data Analysis Workstation: DOS-Based Load and Customer Data Analysis 

System," /FEE Computer Applications in Power, Vol. 1, No. 4 (October 1988). 

2~onthly 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. 

 EE-RM-94-230 COMMENT 61F

turnaround of initial data validation so that malfunctioning monitors and 

appliances can be quickly identified.1 

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 Computer Applications in Power, Vol. 1, No.4 (October 1988). 

2Monthly usage estimates were computed by normalizing the daily usage 

estimates by the "average" number of days in each month. Thus, the March 

estimate is based on a 31-day month, and the April estimate is based on a 30-day 

month, etc. . 

5-2 
Table 5-1 

REUC: Table of Month Label to Date Correspondence 

;Month - Label 

Ijanuary 

! 

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- Mar 2, 1989 

Mar CMar 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 

JuI 1- JuI 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 

Oct 28- Dec 1, 1989 

NOV 3- NOV 30, 1989 

Dec 2- Dec 29, 1988 

Number of Davs 

3 5 

2 8 

28 

2 8 

2Ba 

28 

28 

2 8 

3 5 

28 

28 

2 8 

35a 

2 8 

28 

35 

28 

2 8 

3 5 

3 5 

28 

28 

aThe following two weeks are not included in the Mission Viejo sample because 

of data quality problems: March 24 - March 30, 1989 and July 7 -July 13, 1989. 

 EE-RM-94-230 COMMENT 61F

iMonth Label 

r: 

lJ~nuary 

ffebruary 

,. 

'March 

April 

May 

june 

July 

. August 

. September 

October 

November 

December 

Table 5-1 

REUC: Table of Month Label to Date Correspondence 

Actual Dates Number of Da~s 

Dec 30, 1988- Feb 2, 1989 35 

Feb 5- Mar 3, 1988 28 

Feb 3- Mar 2, 1989 28· 

Mar 4-Mar 31, 1988 28 

Mar 3-Mar 30, 1989 28a 

Apr 1- Apr 28, 1988 28 

Mar 31- Apr 27,1989 28 

Apr 29- jun 2, 1988 28 

Apr 28- jun 1, 1989 35 

jun 3- jun 30, 1988 28 

jun 2- jun 29, 1989 28 

jul 1- jul 28, 1988 28 

jun 30- Aug 3, 1989 35a 

jul 29- Aug 25, 1988 28 

Aug 4- Aug 31, 1989 28 

Aug 26- Sep 29, 1988 35 

Sep 1 -5ep 28, 1989 28 

5ep 30- Oct 27, 1988 28 

5ep 29- Nov 2, 1989 35 

Oct 28- Dec 1, 1989 35 

Nov 3- Nov 30, 1989 28 

Dec 2- Dec 29, 1988 28 

aThe following two weeks are not included in the Mission Viejo sample because 

of data quality problems: March 24 - March 30, 1989 and july 7 - july 13, 1989. 

5-3 
AVERAGE YEARLY TOTAL USAGE 

The average yearly total usage for space conditioning, water heating, cooking and 

clothes drying are presented in Figures 5-1 and 5-2, respectively.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 therrns) 

and are broken down by season in Table 5-2.4 

Figure 5-1 

Annual Averaee Enerw Usage 

Soace Conditionine and Water Heatinga 

a At Paint of Use MBTU 

3~ll the energy comparisons made in this report are at the point of use; 

therefore, the energy losses from generation and transmission are not reflected in 

these numbers. 

4~or the two appliances that overlap with SCE's Residential Appliance 

End-Use Study (RAEUS), the. REUC usages exceed the RAEUS usages (Cooking: 

385 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. 

 EE-RM-94-230 COMMENT 61F

AVERAGE YEARLY TOTAL USAGE 

The average yearly total usage for space conditioning, water heating, cooking and 

clothes drying are presented in Figures 5-1 and 5-2, respectively) Thesevalues 

are given in MBTUs for direct comparison between the gas and electric' samples. 

Also, these average usage figures are given in their natural units (kWh and therms) 

and are broken down by season in Table 5-2.4 

Figure 5-1 

Annual Average Energy Usage 

Space Conditioning and Water Heatinga 

Space Conditioning 

Wate-r Heating 

o 5 

a AI Point of Use 

10 1S 

MBlU 

20 

~ Gas Sample 

.. Eledri<: Sample 

23.4 

2S 30 

3 All the energy comparisons made in this report are at the point of use; 

therefore, the energy losses from generation and transmission are not reflected in 

these numbers. 

4For the two appliances that overlap with SCE's Residential Appliance 

End-Use Study (RAEUS), the REUC usages exceed the RAEUS usages (Cooking: 

385 kWh vs. 224 kWh; Clothes Drying: 1070 kWh VS. 749 kWh). The usage 

estimates are further broken down by city, month and day type (weekend vs. 

weekday) in Appendix A. 

5-4 
Figure 5-2 

Annual. Averaee Enerw Usage 

Clothes Dryine and Cookinga 

a At Point of Use ' MBTU 

The REUC load data confirm that, with the exception of clothes drying, households with gas appliances consume considerably 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. 

 EE-RM-94-230 COMMENT 61F

Clothes Drying 

Cooking 

o 

Figure 5-2 

Annual. Average Energy Usage 

Clothes Drying and Cooking" 

1 2 4 

a At Point of Use 

3 

MBTU 

eJ Gas Sample 

II Electric Sample 

4.7 

5 6 

The REUC load data confirm that, with the exception of clothes drying, households with gas appliances consume considerably more .energy at point of use than 

households with electric appliances. As illustrated in Figure 5~ 1, the. Gas Si3mple 

consumes two to three times the total energy consumed by the Electric Sample 

for space conditioning. Figure 5-1 also indicates that gas water heaters consume 

two-to three times the energy consumed by electric heat pump water heaters at 

point of use. Gas ranges, as can be seen by Figure 5-2, consume more energy at 

point of use than electric ranges by a similar factor. Clothes drying, in contrast, 

does not appear to differ as much in energy usage across fuel types as the other 

appliances. As shown in Figure 5-2, gas dryers use ·about 25 percent more energy 

at point of use than eleCtric dryers, which average 3.7 MBTU per year. 

5-5 
Table 5-2 

Average Annual and Seasonal Ener~v Usa~ea. 

Pooled Cities 

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 N A 7540 25.70 

Space Conditioning 2950 10.1 0 19.20 11 70 3.99 

Water Heating 281 0 9.60 23.40 N A N A 

Clothes Dryer 1070 3.66 4.32 110 0.38 

Rangeloven 385 1.31 3.24 N A N A 

~interb Average Household Usage (8 months) 

Whole House 9230 31.50 N A 4720 16.1 0 

Space Conditioning 21 90 7.50 1 9.00 437 1.49 

Water Heating 2080 7.10 17.00 N A N A 

Clothes Dryer 760 2.60 2.97 7 5 0.26 

Rangeloven 274 0.94 2.24 N A N A 

SummerC Average Household Usage (4 months) 

Whole House 3770 12.90 N A 2820 9.60 

Space Conditioning 760 2.60 0.20 733 2.50 

Water Heating 730 2.50 6.40 N A N A 

Clothes Dryer 310 1.06 1.35 3 5 0.1 2 

Rangeloven 11 1 0.37 1 .OO N A N A 

a~t point of use. 

b~anuary through May and October through December. 

=June 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 over the assumed 15-year appliance lifetime. 

 EE-RM-94-230 COMMENT 61F

Table 5-2 

Average Annual and Seasonal Energy Usage>' 

Pooled Cities 

Appliance 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Range/Oven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Range/Oven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Range/Oven 

aAt point of use. 

Electric Sample 

kWh MBTU' 

Gas Sample 

(Gas components) 

MBTU 

Gas Sample 

(Eledric Components) 

kWh MBTU' 

Annual Average Household Usage (12 months) 

13000 44.40 NA 7540 25.70 

2950 10.10 19.20 1170 3.99 

2810 9.60 23.40 NA NA 

1070 3.66 4.32 110 0.38 

385 1.31 3.24 NA NA 

Winterb Average Household Usage (8 months) 

9230 31.50 NA 4720 16.10 

2190 7.50 19.00 437 1.49 

2080 7.10 17.00 NA NA 

760 2.60 2.97 75 0.26 

274 0.94 2.24 NA NA 

SummerC Average Household Usage (4 months) 

3770 12.90 NA 2820 9.60 

760 2.60 0.20 733 2.50 

730 2,50 6.40 NA NA 

310 1.06 1.35 35 0.12 

111 0.37 1.00 NA NA 

b]anuary through May and October through December. 

clune through September. 

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

illustrated in Figure 5-3. This figure indicates that an all-electric end-use home 

can use nearly 30 MBTUs less per year relative to an all-gas end-use home, a 

difference of almost 450 MBTUs over the assumed 15-year appliance lifetime. 

5-6 
Figure 5-3 

Difference in Annual Enerw Consumotion 

between Gas and Electric ADDliancesa 

Space Conditioning ' 

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. ihis table is disaggregated by city in Appendix A so that the 

reader can make inter-city comparisons. . . 

 EE-RM-94-230 COMMENT 61F

Figure 5-3 

Difference in Annual Energy Consumption 

between Gas and Electric Appliancesa 

Space Conditioning' 

Water Heating 

Clothes Drying 

Cooking 

o 5 10 15 

a Point of Use Annual Difference (MBTU) 

20 

To show which time of year has the highest and lowest relative energy 

consumption for the end-uses, Table 5-3 provides daily and monthly average 

usage by month. This table is dis aggregated by city in Appendix A so that the 

reader can make inter-city comparisons. 

5-7 
Table 5-3 

Monthlv Average Usage for Pooled Cities 

Electric Sample Gas Sample Gas Sample. . 

(Gas Components) (Electric Components) 

kwh Therms kwh 

Weekday Monthly Weekday Monthly Weekday Monthly 

Averape Averape w-AveraQe 

January 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

February 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Range/Oven 

1.6 

'NA 

March 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Range/Oven 

April 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer ' 

RangeIOven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

June 

Whole House 27.3 825 NA NA 18.5 558 

Space Cond. 2.6 74 0.04 1.6 2.5 7 1 

Water Heating 6.7 204 0.58 17.7 NA N A 

Clothes Dryer 2.5 80 0.1 1 3.6 0.3 9 

Rangeloven 0.9 29 0.08 2.5 N A NA 

 EE-RM-94-230 COMMENT 61F

Table 5-3 

Monthly Average Usage for Pooled Gties 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electri c Components) 

kWh Therms kwh 

Armliancea Weekday Monthly Weekday Monthly Weekday Monthly 

Average Average Average Average Average Average 

January 

Whole House 50.4 1625 NA NA 19.0 598 

Space Condo 19.8 644 1.84 60.2 2.0 65 

Water Heating 10.0 314 0.78 24.4 NA NA 

Clothes Dryer 3.3 108 0.13 4.2 0.3 11 Range/Oven 1.2 38 0.09 2.8 NA NA 

February 

Whole House 41.1 1177 NA NA 18.2 517 

Space Condo 12.3 344 1.22 34.2 1.6 45 

Water Heating 9.2 261 0.77 21.9 'NA NA 

Clothes Dryer 2.9 92 0.12 . 3.5 0.3 9. 

Range/Oven 1.1 32 0.08 2.3 NA NA 

March 

Whole House 32.3 1048 NA NA 17.6 559 

Space Condo 5.0 176 0.47 16.3 1.4 51 

Water Heating 8.7 272 0.74 23.4 NA NA 

Clothes Dryer 2.8 98 0.12 3.8 0.3 9 

Range/Oven 1.0 33 0.08 2.5 NA NA 

April 

Whole House 31.2 955 NA NA 19.4 579 

Space Condo 4.5 131 0.24 7.3 2.8 78 

Water Heating 8.3 251 0.67 20.2 NA NA 

Clothes Dryer . 2.7 92 0.11 3.5 0.3 9 

Range/Oven 1.0 32 0.09 2.8 NA NA 

May 

Whole House 28.0 888 NA NA 18.2 569 

Space Condo 2.4 79 0.13 4.6 1.6 50 

Water Heating 7.8 245 0.64 20.0 NA NA 

Clothes Dryer 2.7 91 0.11 3.7 0.3 9 

Range/Oven 1.0 30 0.08 2.6 NA NA 

June 

Whole House 27.3 825 NA NA 18.5 558 

Space Condo 2.6 74 0.04 1.6 2.5 71 

Water Heating 6.7 204 0.58 17.7 NA NA 

Clothes Dryer 2.5 80 0.11 3.6 0.3 9 

Range/Oven 0.9 29 0.08 2.5 . NA NA 

5-8 
Table 5-3, Cont. 

Gas Sample 

(Electric Components) 

kwh 

Electric Sample Gas Sample 

(Gas Components) 

kwh Therms 

Weekday Monthly Weekday Monthly Weekday Monthly 

~~oliance Averaae AveraQe AveraBe Averaqe 

whole House 32.8 1022 NA NA 

space Cond. 8.3 257 0.00 0.1 

Water Heating 5.7 177 0.51 15.8 

clothes Dryer 2.5 82 0.1 1 3.4 

~ange/Oven 0.9 2 7 0.08 2.5 

August 

Whole House 29.6 934 NA NA . 

Space Cond. 5.9 191 0.00 0.1 

Water Heating 5.5 173 0.50 15.3 

Clothes Dryer 2.2 75 0.1 0 3.3 

Rangeloven 0.9 27 0.08 2.5 

September 

999 NA 

244 0.01 

173 0.51 

75 0.10 

28 0.08 

October 

91 2 NA 

74 0.05 

204 0.56 

86 0.1 0 

31 0.09 

November 

1059 NA 

184 0.55 

239 0.66 

Whole House 32.3 

Space Cond. 7.4 

Water Heating 5.7 

Clothes Dryer 2.3 

Rangeloven 0.9 

Whole House 28.7 

Space Cond. 2.1 

Water Heating 6.6 

Clothes Dryer 2.5 

Rangeloven 1 .O 

Whole House 34.8 

Space Cond. 6.2 

Water Heating 7.9 

Clothes Dryer 2.7 

Rangeloven 1.2 

December 

Whole House 49.9 

Space Cond. 18.2 

Water Heating 9.4 

Clothes Dryer 3.2 

Rangeloven 1.3 

 EE-RM-94-230 COMMENT 61F

Table 5-3, Cont. 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

kWh Therms kWh .' 

Weekday Monthly Weekday Monthly Weekday Monthly 

Appliance Average Average Average· Average Average Average 

July 

Whole House 32.8 1022 NA NA 26.1 803 

Space Condo 8.3 257 0.00 0.1 8.9 272 

Water Heating 5.7 177 0.51 15.8 NA NA 

Clothes Dryer 2.5 82 0.11 3.4 0.3 9 

Range/Oven 0.9 27 0.08 2.5 NA NA 

August , 

Whole House 29.6 934 NA NA. 22.8 719 

Space Condo 5.9 191 0.00 0.1 5.4 177 

Wat~r Heating 5.5 173 0.50 15.3 NA NA 

Clothes Dryer 2.2 75 0.10 3.3 0.3 9 

Range/Oven 0.9 27 0.08 2.5 NA NA 

September 

Whole House 32.3 999 NA NA 24.5 748 

Space Condo 7.4 244 0.01 0.4 6.6 211 

Water Heating 5.7 173 0.51 15.2 NA NA 

Clothes Dryer 2.3 75 0.10 3.2 0.3 8 

Range/Oven 0.9 28 0,08 2.6 NA NA 

October 

Whole House 2B.7 912 NA NA 19.3 607 

Space Condo 2.1 74 0.05 1.7 1.5 53 

Water Heating 6.6 204 0.56 17.2 NA NA 

Clothes Dryer 2.5 86 0.10 3.4 0.3 9 

Range/Oven 1.0 31 0.09 2.7 NA NA 

November 

. Whole House 34.8 1059 NA NA 19.3 582 

Space Condo 6.2 184 0.55 16.4 1.2 37 

Water Heating 7.9 239 0.66 19.6 NA NA, 

Clothes Dryer 2.7 90 0.11 3.6 0.3 9 

Range/Oven 1.2 35 0.10 2.9 NA NA 

December 

Whole House 49.9 1564 NA NA 22.2 704 

Space Condo 18.2 556 1.59 49.3 1.9 58 

Water Heating 9.4 293 0.74 23.2 NA NA 

Clothes Dryer 3.2 104 0.13 4.1 0.3 11 

Range/Oven 1.3 42 0.11 3.7 NA NA 

5-9 
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 

Seasonal Averaee Weekdav Electricitv Usaee 

Electric Samole End-Uses . - 

Space Conditioning 

 EE-RM-94-230 COMMENT 61F

Not surprisingly, the relative energy used by gas furnaces as compared to electric 

heat pumps increases in the colder months. This occurs because during the 

sUlilmer heat pumps operate similarly to the air conditioners installed in"the Gas 

Sample. The usage pattern of the other end-uses is stable throughout the year, 

although water heating requires somewhat more energy in the colder months. 

This is further illustrated by Figure 5-4, which presents average weekday usage 

separately by season for the four electric end-uses. Comparisons between the 

Electric and Gas· Samples' average daily energy use by .season are included in 

Figure 5-4 

Seasonal Average Weekday Electricity Usage 

Electric sample End-Uses 

Water Healing 

Space Conditioning 

Cooking 

Clothes Drying. 

f2l Winter Weekday 

II Summ ... Weekday 

8.8 

o 1 2 3 4 5 6 7· 8 9 10 

h 

5-10 
Appendix A. Finally, Figure 5-5 presents weekday and weekend whole house 

average usage in winter and summer for the Electric and Gas Samples. Usage 

increases during the summer for the Gas Sample because of electric air 

conditioning, while it decreases during the 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 of average usage across different days in a season or year 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 Electricitv Usage 

Winter Weekend 

Summer Weekend 

kwh 

Figures 5-6 through 5-10 plot the distributions of the sample averaged daily usage 

for the space conditioning and water heating end-uses. These figures are divided 

 EE-RM-94-230 COMMENT 61F

Appendix A. Finally, Figure 5-5 presents weekday and weekend whole house 

average usage in winter and summer for the Electric and Gas Samples. Usage 

increases during the summer for the Gas Sample because of electric air 

conditioning, while it decreases during the 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 of average usage across different days in a season or year is 

lost. The distribution of average daily usage reveals, for example, that space 

condition'ing seasonal averages do not represent usage average for extreme days. 

Figure 5-5 

Whole House: Average Daily Electricity Usage 

Winter Weekend 

Winter Weekday 

Summer Weekend 

Summer Weekday 

o 10 20 30 40 so 

kWh 

Gas Sample 

II Electric Sample 

Figures 5-6 through 5-10 plot the distributions of the sample averaged daily usage 

for the space conditioning and water heating end-uses. These figures are divided 

5-11 
by fuel type and season. They illustrate the modal usage level for typical 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 

weather sensitivity. 

 EE-RM-94-230 COMMENT 61F

by fuel type and season. They illustrate the modal usage level for typical 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 

weather sensitivity. 

5-12 
Figure 5-6 

SCE REUC Weekdav Averaae UEC Distribution 

Winter Electric Heat Puma: Pooled Cities 

Pcrcenl of Total 40 

35 

30 

25 

20 

15 

10 

5 

0 

<2 4 8 12 16 20 24 28 32 36 40 44 

Daily kwh Midpoinlr 

Figure 5-7 

SCE REUC Weekdav Averaee UEC Distribution 

Summer Space Conditionine: Pooled Cities 

Percml ofTotd 40 

35 

30 

25 

20 

15 

10 

5 

0 

<Z 4 8 12 16 20 24 28 3'1 36 40 

Ddly kwh Midpoints 

 EE-RM-94-230 COMMENT 61F

Figure 5-6 

SCE REUC Weekday Average UEC Distribution 

Winter Electric Heat Pump: Pooled Cities· 

PffUflt of Tob:l 40 

35 

<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 

PercenlorTotai 40 

<2 4 8 12 16 20 24 28 3~ 36 40 

Daily kWh Midpoinu 

5-13 
Figure 5-8 

SCE REUC Weekdav Averace UEC Distribution 

Winter Gas Furnace: Pooled Cities 

Daily kBTU Midpointr I 

Figure 5-9 

SCE REUC Weekday Averaae UEC Distribution 

Winter Electric Water Heatina: Pooled Cities 

 EE-RM-94-230 COMMENT 61F

Figure 5-8 

SCE REUC Weekday Average UEC Distribution 

Winter Gas Furnace: Pooled Cities 

Percent of T olal SO 

Percent of T olal 

45 

<2 50 100 150 200 250 300 350 400 450 

Daily kBTU Midpoints 

Figure 5-9 

SCE REUC Weekday Average UEC Distribution 

Winter Electric Water Heating: Pooled Cities 

20 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

4.5 5 5.5 6 6.5 7 7.S 8 8.5 9 9.5 1010.511 11.512 12.513 13.5 

Daily kWh Midpoints 

5-14 
Figure 5-1 0 

SCE REUC Weekday Averacre UEC Distribution 

Winter Gas Water Heating: Pooled Cities 

Pmcenl ofTobl 20 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

30 35 40 45 50 55 60 65 70 75 BO 85 90 95 100 105 110 

Daily kBTU Midpoints 

 EE-RM-94-230 COMMENT 61F

PercenlorTolal 

Figure 5-10 

SCE REUC Weekday Average UEe Distribution 

Winter Gas Water Heating: Pooled Cities 

20 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

30 3S 40 45 50 5S 60 65 70 7S 80 85 90 95 100 105110 

Daily kBTU Midpoints 

5-15 
SPACE CONDITIONING WEATHER MODEL 

It is evident from the usage tables that heat pumps require less energy at point of 

use than gas furnaces. This section explains the usage of each of the space 

heating appliances as a function of temperature. 

The amount of energy required to heat a home on a given day depends on a 

number of factors including the exterior temperature. The load shapes in Section 

7 show that the usage between 6:00 and 10:OO 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: 

where ~k is the morning usage in kBTU between 6:00 and 10:OO a.m. for k = gas It 

or electric, j indexes cities, and t ranges over all days of the winter. Hjt is the 

number of heating degree days base 68°F in city j on day t. Heating degree days 

are defined as follows: 

120 

[ max (0,68-tempti,)] 

m=73 Hjt = 288 (5.2) 

where m=73 to 120 are the five-minute time periods between 6:00 and 10:OO 

a.m. Both usage and heating degree days are subtracted from their means for the 

estimation so that no intercept is required in the model; Ejt is a random 

"k disturbance term. Thus, two PI are estimated, one for each fuel type. Estimation 

is carried out after first averaging across all of the households. The estimated 

parameters for Equation 5.1 are given in Table 5-4 for each fuel type, electricity 

and gas. 

 EE-RM-94-230 COMMENT 61F

" ,I 

SPACE CONDITIONING WEATHER MODEL 

It is evident from the usage tables that heat pumps require less energy at point of 

use than gas furnaces. This section explains the usage of each of the space 

heating appliances as a function of temperature. 

The amount of energy required to heat a home on a given day depends on a 

number of factors including the exterior temperature. The load shapes in Section 

7 show that the usage between 6:00 and 10:00 a.m. is the most important period 

for space heating. A model is estimated below that uses the morning heating 

degree days to explain the morning heating 'energy usage. 

The model that is estimated has the following general form: 

(5.1 ) 

where uft is the morning usage in kBTU between 6:00 and 10:00 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 city j on day t. Heating degree days 

are defined as follows: 

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

m=73 

Hjt = 288 (5.2) 

where m=73 to 120 are the five-minute time periods between 6:00 and 10:00 

a.m. Both usage and heating degree days are subtracted from their means for the 

estimation so that no intercept is required in the model; Ejt is a randoni 

disturbance term. Thus, two Bf are estimated, one for each fuel type. Estimation 

is carried out after first averaging across all of the households. The estimated 

parameters for Equation 5.1 are given in Table 5-4 for each fuel type, electricity 

and gas. 

5-16 
Table 5-4 

Estimated Linear Relationshiv between Average Mornine. Svace 

Heatine. and morn in^ heat in^ Degree-Davs: All Households 

.# 

&I fik Std. Error - N - R2 

:Gas 26.5a 0.45 841 .0.81 

'Electricity 7.Sa 0.1 4 841 0.76 

aDenotes significance at the one percent level. 

For gas furnaces, the morning usage increases about 25 kBTU for every heating 

degree day and, for electric heat pumps, the morning usage increases about 7 

kBTU for every heating degree day. This relationship is linear; higher order terms 

do not significantly increase the explanatory power of the model. Furthermore, 

no significant usage difference between weekends and weekdays is detected, so 

both day types are pooled in the regressions. 

 EE-RM-94-230 COMMENT 61F

Table 5-4 

Estimated Linear Relationship between Average Morning Space 

Heating and Morning Heating Degree-Days: All Households 

, ak ifiJel Std, Error N g2 

'Gas 26.Sa 0.45 841 0.81 

. Electricity 7.5a 0.14 841 0.76 

aDenotes significance at the one percent level. 

For gas furnaces, the morning usage increases about 25 kBTU for every heating 

degree day and, for electric heat pumps, the morning usage increases about 7 

kBTU for every heating degree day. This relationship is linear; higher order terms 

do not significantly increase the explanatory power of the model. Furthermore, 

no significant usage difference between weekends and weekdays is detected, so 

both day types are pooled in the regressions. 

5-17 
Section 6 

REUC APPLIANCE LOAD PROFILES 

.!@NTRoDUCTION 

i .. 

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

!if data collected from the REUC sample. The REUC appliance load data are 

/;nique in that daily energy usage patterns of both electric and gas residential 

/appliances have been calculated and compared from field data. he data were 

(collected at five-minute intervals and subsequently aggregated over half-hour 

i ,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 appear quite 

similar, differing primarily in the amount of energy under the respective curves. 

Load profiles expressed as a percentage of total daily usage are provided in 

Appendix A and generally support this observation. 

 EE-RM-94-230 COMMENT 61F

Section 6 

REUC APPLIANCE LOAD PROFILES 

:INTRODUCTION ' ' 

1, .• -. 

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

~of data collected from the REUC sample. The REUC appliance load data are 

!unique in that daily energy usage patterns of both electric and gas residential t··- . 

(appliances have been calculated and compared from field data. The data were 

!collected at five-minute intervals and subsequently aggregated over half-hour 

'periods for graphical presentation. 

This section presents load profiles highlighting th'e key comparisons made for the 

'monitored appliances. Appendix 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 appear quite 

Similar, differing primarily in the amount of energy under the respective curves, 

Load profiles expressed as a percentage of total daily usage are provided in 

Appendix A and generally support this observation. 

6-1 
Figure 6-1 

SCE REUC Winter: Space Conditionine: (at point of use) 

kBTU pr Hour 12 - 

Elec. Wekdryr ?? ------ Elec. Weekends 

10- -.-.-.- Gas Weekdryr 

Gig Weekends 

8- 

6- 

E 

I '. I 

4- 

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

0 3 6 9 12 15 18 21 24 

Tim of Day 

Figure 6-2 

SCE REUC Summer: Space Conditionine: (at point of use) 

kBTU pr Hour 3.0- 

Gas Weeken& 

2.0 - 

15- 

1.0- 

o.o,..I..I..I..I=.I-.I.".'I 

0 3 6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

Figure 6-1 

SCE REUC Winter: Space Conditioning (at point of use) 

kBTU per Hour 12 

10 

8 

6 

4 

o 3 6 9 12 

lime o(Oay 

Figure 6-2 

15 18 

Elec. Weekdays 

Elec. Weekends 

Gas Weekdays 

Gas Weekends 

21 24 

SCE REUC Summer: Space Conditioning (at point of use) 

kBTU per Hour 3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

Elec. Weekdays 

Elec. Weekends 

Cas Weekdays 

Cas Weekends 

.~~ .:;>' '¥, 111::: .... _ ...... - _ 

0.0 -I-r--r-r-~ro"'-~~"'-r-T~-r-"""'-r-~-r~,..., 

o 3 6 9 12 15 18 21 24 

Time of Day 

6-2 
Figure 6-3 

SCE REUC Winter: Water heat in^ (at point of use) 

r- kBTU pr Hour 8 - !! 

! ! 

7 - ! ! Ecc. Wehn* 

! ! Ga W-kdayr 

! !A Ga Wrrkedl 

6 - ! !I \ 

i I \. 

0 7 I 

0 3 ' 6 ' 9 12 IS 18 21 14 

Tim of Dq 

Figure 6-4 

SCE REUC Summer: Water Heatine (at ooint of use) 

k8TU pr Hour 6 - 

Ekc. Weekendr 

5 - 

3 - 

0 -, I 

0 3 6 9 12 15 18 21 24 

Time of Day - 

 EE-RM-94-230 COMMENT 61F

Figure 6-3 

SCE REUC Winter: Water Heating (at point of use) 

kBTU per Hour 8 

7 

• 

5 

4 

1 

,. 

i i 

i i 

i " 

F ! f\ 

• • ; .j' \ 

i , \ 

8ee. Weekdays 

Bte. Weekrnds 

Gas Weekdays 

Gas Weekends 

i " \ iii \ i"'\ 

i I ; ;",\ 

i I '. \ r ~ 

,

;,' '., '\ ,,~. 

, I' " ; I " ' •. _, .• .Ii ~~ i I _\, .' '\, 

2 f

\. .. ' ~.... ...-.. ... -... " ... ' 

: I I ..... .. 

1 ~ ':0. ,/ ',/ ',----...,-

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

Time of D~ 

Figure 6-4 

SCE REUC Summer: Water Heating (at point of use) 

. ,'. , 

i "' ; i 

i i ""/', 

I \1 \ 

Elec. Weekd;;ays 

Elec. Weekends 

Gas Weekdays 

Gas Weekends 

i ,\ \ ~ i \ , ... • ,. \ /A, 

i I"', \ tl' ',\ j J '; \ ., l\~ 

! I \ \ /...-f ''..;~\. I' .. '-. ; i I '. '-J .. ; .......... ' .. , , ..... .. I ',"'-'" I I , 

j I ,J " ~,- -....... :._~.' I ----,' ""'- .' 

• • 12 15 ,. 21 24 

Time of Day 

6-3 
For example, consider the water heating load profiles in Figures 6-3 and 6-4. Gas 

water heaters have a high peak consumption level. Electric water heaters, on the 

other hand, have a lower connected load although they sustain this load over 

longer periods of time. Thus, not only do the total usage amounts diffeiby fuel 

type, but the load shape differs as well. Interesting comparisons can be made 

across seasons. By comparing Figure 6-1 with Figure 6-2, the 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: hate Conditionina Electric Sample 

January Weekends 

-.-.-. *.- July Weekdays 

----- July Weekends 

kW Load 2.0 - 

1.8 - 

1.6 - 

1.4 - 

0.2 -%-, 

. / 

0.0 ..I.-I.sI..I.-I..I"l"i 

0 3 6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

For example, consider the water heating load profiles in Figures 6-3 and 6-4. Gas 

water heaters have a high peak consumption level. Electric water heaters, on the 

other hand, have a lower connected load although they sustain this load over 

longer periods of time. Thus, not only do the total usage amounts diffe; by fuel 

type, but the load shape differs as well. Interesting comparisons can be made 

across seasons. By comparing Figure 6-1 with Figure. 6-2, the magnitude 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. 

kW load 2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

Figure 6-5 

SCE REUC: Space Conditioning Electric Sample· 

• • • • • , • 

/ 

\ ,/ 

~~' • • '-... ~ 

.~-c- ... 

... .:-.."::".:. 

0 3 6 

,.../1 

9 

II 

// 

I"~ 

12 

Time of Day 

6-4 

15 18 

January Weekdays 

January Weekends 

July Weekdays 

July Weekends 

21 24 
""re3 6-6 and 6-7 present the average load profiles across all twelve months of 2 

year for heat pump and gas space conditioning, respectively. These threemensional figures trace,the changing height of the average load profile peak in 

,ch month. 

11 four electric end-uses' load profiles are presented in one plot in Figures 6-8 

,d 6-9 in order to illustrate the different magnitudes of average appliance load. 

Figures 6-10 and 6-1 1 present whole house load shapes for ihe Electric 

,d Gas Samples. Space conditioning and whole house load are overlaid in 

;gure A-22 in Appendix A. 

hese load shapes and average load profiles are consistent with other residential . . 

lad metering studies. For example, load profiles produced from the SCE 

esidential Appliance End-Use Survey (RAEUS) project are consistent with the 

EUC load profiles where comparisons can be made. The RAEUS project, while 

letering only electric appliances, has a larger number of end-use pr&files.l 

he profiles provided in Appendix A include: (1 load profiles for cooking and 

lothes drying; (2) load profiles for the electric appliances in kW rather than 

:BTU; (3) two-dimensional space conditioning average load profiles for each 

nonth; (4) load profiles.for weekdays expressed as a percentage of total daily 

sage; (5) whole house, space conditioning and water heating load profiles on a 

:ommon graph; and (6) a three-dimensional daily temperature profile by month. 

'For more information about the RAEUS load profiles, see .southern 

California Edison, "Residential Appliance End-Use Survey: Sample I Final Report," 

February 1989. 

 EE-RM-94-230 COMMENT 61F

oures 6-6 and 6-7 present the average load profiles across all twelve months of o 

e year for heat pump and gas space conditioning, respectively. These thteemensional figures trace ·the changing height of the average load profile peak in 

lch month. 

II four electric end-uses' load profiles are presented in one plot in Figures 6-8 

ld 6-9 in order to illustrate the different magnitudes of average appliance load. 

nally, Figures 6-10 and 6-11 present whole house load shapes for the Electric 

,d Gas Samples. Space conditioning afld whole house load are overlaid in 

'gure A-22 in Appendix A. 

hese load shapes and average load profiles are consistent with other residential 

lad metering studies. For example, load profiles produced from the seE 

esidential Appliance End-Use Survey (RAEUS) project are consistent with the 

EUC load profiles where comparisons can be made. The RAEUS project, while 

letering only electric appliances, has a larger number of end-use profiles.' 

he profiles provided in Appendix A include: (1) load profiles for cooking and 

lathes drying; (2) load profiles for the electric appliances in kW rather than 

:BTU; (3) two-dimensional space cbnditioning average load profiles for each 

nanth; (4) load profiles ·for weekdays expressed as a percentage of total daily 

:sage; (5) whole house, space conditioning and water heating load profiles on a 

:ammon graph; and (6) a three-dimensional daily temperature profile by month. 

, For more information about the RAEUS load profiles, see .Southern 

California Edison, "Residential Appliance End-Use Survey: Sample I Final Report," 

February 1989. 

6-5 
Figure 6-6 

SCE REUC: Heat Pumo S~ace Conditioning 

 EE-RM-94-230 COMMENT 61F

Figure 6-6 

SCE REUC: Heat Pump Space Conditioning 

kWLood 

z.o 

L6 

1.0 

OJ; 

21.1>0 

18:00 

'11m. (PST) 

6-6 
Figure 6-7 

SCE REUC: CasIElectric Space Conditioning 

 EE-RM-94-230 COMMENT 61F

Figure 6-7 

SCE REUC: Gas/Electric Space Conditioning 

6-7 
Figure 6-8 

SCE REUC Winter Weekdav Electricitv: Electric Sam~le 

kWLord 1.0- 

0.9- 

0.8- 

0.7- 

0 3 6 9 12 15 18 21 24 

limeof Day 

Figure 6-9 

SCE REUC Summer Weekdav Electricitv: ~lectric Sample 

kWLwd 1.0 - 

Spice Conditioning 

Water Hialing 

Cmking 0.8 Cloths Drying 

0.7 * 

0.6 - 

0.5 - 

0.4 - 

0.3 - 

0 3 6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

Figure 6-8 

SCE REUC Winter Weekday Electricity: Electric Sample 

kW Load 1.0 

D.' 

0.8 

0.7 

0.6 . 

05 

D.' 

0.3 

0.2 

0.1 

0.0 

0 

(' 

• 

, •

• • • • • • • • • • • • • • ' ... _-,' 

'" \ 

\ , 

\ 

, 

, 

", 

, 

" 

Space Conditioning 

W~ter Heating 

Cooking 

Clothes Drying 

! 

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

3 6 9 12 15 18 21 2. 

TIme of Day 

Figure 6-9 

SCE REUC Summer Weekday Electricity: Electric Sample 

kW Load 1.0 

D.' 

0.8 

0.7 

0.6 

0.5 

D.' 

0.3 

0.2 

Space Conditioning 

Water He"aling 

Cooking 

Clothes Drying 

. 

/\ , . I \ , : ..... , • • .. \ ' ,. . , , , . , \ I '.. ,..,/ 

\, _ ... ___ / 

I /.J / ~ 

I' \ 

0.0 -F='-.,;:...,:::::~~;~·:-:-~-;:-:-;.-:;-:.·------:-;:.--::;-:.·-·-.. ,....--·T'-'---::-:;~ 0.1 

o 3 • 9 12 15 18 21 2. 

Time of Day 

6-8 
Figure 6-1 0 

SCE REUC Winter: Whole House Electricity 

- kW Load 3.0- 

2.8 - 

2.6 - 

2.2 - 

1.8- 

0 3 6 9 12 IS 18 21 24 

Time of Day 

Figure 6-1 1 

SCE REUC Summer: Whole House Electricity 

kWLoad 2.07 

1.8- 

0.4 - 

0.2 - 

0.0- ..,..I..,..,.' IIII 

0 3 6 9 12 15 18 21 24 

Time of Day 

Elec. Sample Weekdays 

------- Elec..Sample Weekends 

Gas Sample Weekdays 

Gas Sample Weekends 

 EE-RM-94-230 COMMENT 61F

kW load 3.0 

2.8 

2.6 

lA 

2.2 

2.0 

1.8 

1.6 

1.' 

1.2 

1.0 

0.8 

0.6 

0.' 

0.2 

0.0 

0 

kWLoad 

Figure 6-10 

SCE REUC Winter: Whole House Electricity 

3 6 • 

,-

, 

I 

, 

, 

I " ,/ . ..... _, 

12 15 18 

Time of DOIlY 

Figure 6-11 

21 

Elec. S ... mple Weekdays 

Elec. Sample Weekends 

G.as Sample Weekdays 

Gas Sample Weekends 

2. 

SCE REUC Summer: Whole House Electricity 

2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 3 6 9 

6-9 

r"\ 

/...-'-'~~ 

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

12 lS 

Time of Day 

flee. Sample Weekdoys 

Elec . .sample Weekends 

Cas Sample Weekdays 

Gas Sample Weekend.~ 

18 21 24 
USING HOUSEHOLD CHARACTERISTICS TO EXPLAIN LOAD SHAPES 

Traditionally, most load shape modeling approaches have concentrated on 

describing premise load characteristics as a function of demographics, household 

appliance inventories, and time. The REUC project has shown thai' these 

approaches can be enhanced through the inclusion of other measurable 

variables, such as a customer's image of electricity. The REUC project has 

extended these approaches by successfully including these additional variables 

into appliance (end-use) load shape modeling. 

A goal of this research is to show that a relationship exists between the load shape 

of an appliance and the characteristics of the household that uses the appliance. 

A two-step regression procedure is used to ascertain this relationship: (1) 

harmonic modeling of load shapes, followed by (2) household characteristic 

regressions on the harmonic model parameters. The first step explains the load 

shape of an appliance with a harmonic specification based on time of day as the 

independent variable. For each appliance monitored from each REUC household, a set of parameters was estimated that summarized the information in the 

load data. That is, these parameters are said to "summarize" the appliance's load 

shape. In the second step of the procedure, these estimated parameters were 

modeled using the REUC household characteristics as the independent variables. 

Using the parameter estimates from this model, household characteristics can be 

used to generate "simulated" load shapes. 

The results presented here are based on electric and gas water heaters from the 

REUC sample. Water heaters were chosen because they consume a large 

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

weather-sensitivity, so the behavioral aspects of usage may be easier to identify. 

Stea 1: Harmonic Load Shaae Reeressions 

Using a technique developed in earlier EPRI research for premise (whole house) 

data,2 appliance load shapes wece here modeled using harmonic functions (i.e., 

2"Residential End-Use Load-Shape Estimation, Volume 1: Methodology 

and Results of Statistical Disaggregation from Whole-House Metered Loads" (EPRI 

EM-4525), May 1986. 

 EE-RM-94-230 COMMENT 61F

USING HOUSEHOLD CHARACTERISTICS TO EXPLAIN LOAD SHAPES 

Traditionally, most load shape modeling approaches have concentrated on 

describing premise load characteristics as a function of demographics, household 

appliance inventories, and time. The REUC project has shown that' these 

. approaches can be enhanced through the inclusion of other measurable 

variables, such as a customer's image of electricity. The REUC project has 

extended these approaches by successfully including these additional va·riables 

into appliance (end-use) load shape modeling. 

A goal of this research is to show that a relationship exists between the load shape 

of an appliance and the characteristics of the household that uses the appliance. 

A two-step regression procedure. is used to ascertain this relationship: (n 

harmonic modeling of load shapes, followed by (2) household characteristic 

regressions on the harmonic model parameters. The first step explains the load 

shape of an appliance with a harmonic specification based on time of day as the 

independe.nt variable. For each appliance monitored from each REUC household, a set of parameters was estimated that summarized the information in the 

load data. That is, these parameters are said to "summarize" the appliance's load 

shape. In the second step of the procedure, these estimated parameters were 

modeled using the REUC household characteristics as the independent variables. 

Using the parameter estimates from this model, household characteristics can be 

used to generate "~imulated" load shapes. 

The results presented here are based on electric and gas water heaters from the 

REUC sample. Water heaters were chosen because they consume a large 

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

weather-sensitivity, so the behavioral aspects of usage may be easier to identify. 

Step 1: Harmonic Load Shape Regressions 

Using a technique developed in earlier EPRI research for premise (whole house) 

data,2 appliance load shapes were here modeled using harmonic functions (i.e., 

2"Residential End-Use Load-Shape Estimation, Volume 1: Methodology 

and Results of Statistical Disaggregation from Whole-House Metered Loads" (EPRI 

EM-4525), May 1986. 

6-10 
sine and cosine). The first-step regression produces a set of parameters that 

describe the daily load shape of an appliance with respect to time. 

The first step is accomplished by running regressions of appliance load shapes on 

' 

water heaters for each home in the lwine and Mission Viejo samples of the REUC 

database. Using harmonic functions of various periodicities, the daily load shape 

for both electric and gas water heaters is described as a function of time. This 

method of using harmonic functions to model whole house load is described in 

detail elsewhere.3 The REUC project's innovation is to apply these techniques to 

end-use data. 

The end-use harmonic model specifications are given as follows: 

where 

t = time of day in hours ranging from 1 to 24, and 

LOAD, = appliance load in Watts at time t. 

Results of the harmonic regressions for the heat pump water heaters in two 

households are presented in Table 6-1 under the column heading "Estimated." 

Figures 6-12 and 6-13 illustrate these results by comparing the estimated load 

shape to the actual daily load shape for the same two homes. The close fit of the 

regression model to the actual daily load shape illustrates the effectiveness of the 

harmonic load shape modeling technique. 

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

 EE-RM-94-230 COMMENT 61F

sine and cosine). The first-step regression produces a set of parameters that 

describe the daily load shape of an appliance with respect to time. 

The first step is accomplished by running regressions of appliance load shapes on 

water heaters for each home in the Irvine and Mission Viejo samples of the REUC 

database. Using harmonic functions of various periodicities, the daily load shape 

·.for both electric and gas water heaters is described as a function of time. This 

method of using harmonic functions to model whole house load is described in 

detail elsewhere.3 The REUC project's innovation is to apply these techniques to 

end-use data. 

The end-use harmonic model specifications are given as follows: 

where 

(2ltt ) (2ltt ) . (2ltt ) (2ltt ) LOADt = Ao + A1sin 24 + A2cos 24 + A3sin U + A4COS U + 

t= 

LOADt = 

(2ltt) (2ltt) (2ltt) (2ltt) + Assin 8 + A6COS 8 + A7S in 6 + Agcos 6 + 

time of day in hours ranging from 1 to 24, and 

appliance load in Watts at time t. 

(Eq.6-1) 

Results of the harmonic regressions for the heat pump water heaters in two 

households are presented in Table 6-1 under the column heading "Estimated." 

Figures 6-12 and 6-13 illustrate these results by comparing the estimated load 

shape to the actual daily load shape for the same two homes. The close fit of the 

regression model to the actual daily load shape illustrates the effectiveness of the 

harmonic load shape modeling technique. 

3"Residential End-Use Load Shape Estimation," op cit. 

6-11 
Table 6-1 

Estimated and Simulated ~armonic Model 

Load Shaoe Parameters 

HOUSE 50 

Estimatedb Simulated 

367.32 41 4.62 

(8.47) 

34.28 39.56 

(1 1.98) 

-1 28.01 -76.53 

(1 1.99) 

-1 67.75 -235.92 

(1 1.98) 

3.28 17.04 

(1 1.99) 

30.28 50.61 

(1 1.98) 

-35.41 0.30 

(1 1.99) 

43.50 49.62 

(1 1.98) 

2.88 7.1 5 

(1 1.99) 

-40.56 -49.73 

(1 1.98) 

54.84 46.02 

(1 1.99) 

aAo-Ale are defined in quat ti on 6-1. 

HOUSE 86 

Estirnatedb Simulated 

350.08 385.29 

(7.37) 

-59.53 -41.60 

(1 0.42) 

-80.02 -1 62.76 

(1 0.42) 

-1 64.53 -155.10 

(1 0.42) 

95.97 166.89 

(1 0.42) 

58.97 79.00 

(1 0.42) 

16.87 -14.96 

(1 0.42) 

-9.89 -72.30 

(1 0.42) 

-45.47 -54.32 

(1 0.42) 

19.27 23.67 

(1 0.42) 

11.89 -3.70 

(1 0.42) 

b~tandard errors are given in parentheses. 

 EE-RM-94-230 COMMENT 61F

Table 6-1 

Estimated and Simulated Harmonic Model 

load Shape Parameters 

HOUSE 50 HOUSE 86 

Variablea Estimatedb Simulated Estimatedb Simulated 

Ao 367.32 414.62 350.08 385.29 

(8.47) (7.37) 

Al 34.28 39.56 -59.53 -41.60 

(11.98) (10.42) 

A2 -128.01 -76.53 -80.02 -162.76 

(11.99) (10.4:2) 

A3 -167.75 -235.92 -164.53 -155.10 

(11.98) (10.42) 

A4 3.28 17.04 95.97 166.89 

(11.99) (10.42) 

As 30.28 50.61 58.97 79.00 

(11.98) (10.42) 

A6 -35.41 0.30 16.87 -14.96 

(11.99) (10.42) 

A7 43.50 49.62 -9.89 -72.30 

(11.98) (10.42) 

A8 2.88 7.15 -45.47 -54.32 

(11.99) (10.42) 

A9 -40.56 -49.73 19.27 23.67 

(11.98) (10.42) 

AIO 54.84 46.02 11.89 -3.70 

(11.99) (10.42) 

aAo-AIO are defined in Equation 6-1. 

bStandard errors are given in parentheses. 

6-12 
Figure 6-1 2 

REUC Heat Pump Water Heater - House 50 

First Steo Estimated Load vs. Actual Load 

Figure 6-1 3 

. 

REUC Heat Pump Water Heater - House 86 

First Step Estimated Load vs. Actual Load 

0 8- 

0 6- 

036 g 12 15 18 21 24 

rime ol nay (in Hours PST1 

0.6' 

0.4- 

. . 036 g 12 15 i8 21 24 . 

~ime 01 Day (in Hours PST1 

 EE-RM-94-230 COMMENT 61F

Figure 6-12 

REUC Heat Pump Water Heater - House 50 

First Step Estimated load vs. Actual load 

kWLoad "o,----------~------_, 

0.6 

o. 

I Estlmaled L1 ---, Actualload_ 

-

0.0+--....--.............. --..... - ..... - ..... - ..... --1 

a 3 6 9 12 15 16 21 24 

Time of Day (In Hours PST) 

Figure 6-13 

REUC Heat Pump Water Heater - House 86 

First Step Estimated load vs. Actual load 

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

0.6 

0.4 

0.2 

o.o.J--.,....-.,....-.,....-.---.---,....~.---I 

a 3 6 9 12 15 16 21 24 

Time 01 Day (in Hours PST) 

6-13 
Step 2: Characteristic Regressions 

The second step is conducted on the estimated parameters of the water heater 

load shapes, from step one. The purpose of this analysis is to assess the ability of 

customer characteristics to explain the step 1 parameters, the parameters that 

describe periodicities in the appliance load shapes. Estimated load shape 

parameters were used as the dependent variables in a model where customer 

characteristics, including the CP&B and REUC attitudinal variables, were used as 

the explanatory variables. The development of the attitudinal variables is 

explained in Section 7. 

The end-use characteristics model specifications are given as follows: 

Ai = Bo + BI * MAXHHAGE + B2 * SQFT + B3 * MAXEDLEV + 

+ B4 * HHSIZE + B5 * HHlNC + B6 * CASVAL + B7 * GASQUA t 

+ Bg * ELECVAL + Bg * ELECQUA + B10 * QUALGUY + 

where 

Ai = 

MAXHHAGE = 

SQFT = 

MAXEDLEV = 

HHSIZE = 

HHlNC = 

GASVAL = 

ELECVAL = 

one of the eleven parameters estimated by the 

harmonic model (i.e., A. - Alo in Eq. 6-I), 

categorical variable representing the age of the oldest 

member of the household, 

area measure (in square feet) of the home,' 

categorical variable representing the highest level of 

education attained by members of the household, 

number of occupants in the household, and .. 

categorical variable representing the combined income 

of members of the household, , 

variable representing a customer's image of gas as a high 

-value fuel, 

variable representing a customer's image of gas as a high 

quality fuel, 

variable representing a customer's image of electricity as 

a high value fuel, 

 EE-RM-94-230 COMMENT 61F

Step 2: Characteristic Regressions 

The second step is conducted on the estimated parameters of the water heater 

load shapes, from step one. The purpose of this analysis is to assess the ability of 

customer characteristics to explain the step 1 parameters, the parameters that 

describe periodicities in the appliance load shapes. Estimated load shape 

parameters were used as the dependent variables in a model where customer 

characteristics, including the CP&B and REUC attitudinal variables, were used as 

the explanatory variables. The development of the attitudinal variables is 

explained in Section 7. 

The end-use characteristics model specifications are given as follows: 

where 

Ai'" Bo + BI * MAXHHAGE + Bz * SQFT + B3 * MAXEDLEV + 

+ B4 * HHSIZE + B5 * HHINC + B6 * GASVAL + B7 • GASQUA + 

+ B8 * ELECVAL + B9 • ELECQUA + BIO * QUALGUY + 

. + BII • CPBN02 + BIZ * CPBN03 + BI3 * CPBN04 + 

(Eq.6-2) 

one of the eleven parameters estimated by the 

harmonic model (i.e., An - AIO in Eq. 6-1), 

MAXHHAGE '" categorical variable representing the age of the oldest 

member of the household, 

SQFT = area measure (in square feet) of the home; 

MAXEDLEV = categorical variable representing the highest level of 

education attained by members of the household, 

HHSIZE = number of occupants in the household, and 

HHINC = categorical variable representing the combined income 

of members of the household, / 

GASVAL = variable representing a customer's image of gas as a high 

value fuel, . 

GASQUA = variable representing a customer's image of gas as a high 

quality fuel, 

ELECVAL = variable representing a customer's image of electricity as 

a high value fuel, 

6-14 
ELECQUA = variable representing a customer's image of electricity as 

a high quality fuel, and 

., QuALGUY = indicator variable representing whether a customer's 

ELECQUA score is higher than the customer's ELECVAL 

score, 

CPBN02 = indicator variable representing, a customer being 

classified as "Appearance Conscious," 

CPBN03 = indicator variable representing a customer being 

classified as "Resource Conserver," 

. CPBN04 = indicator variable representing a customer being 

classified as "Lifestyle Simplifier," 

CPBN06 = indicator variable representing a customer being 

classified as "Value Seeker." 

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

Table 6-1 under the column heading "Simulated." These parameters were 

produced by substituting the actual customer characteristics of each household 

into the end-use characteristic model (Equation 6-21 estimated using the entire 

REUC sample of heat pump water heaters. Note the similarity between these 

simulated parameters, generated from the characteristic model, and the estimated parameters, developed from the harmonic model. 

These results are illustrated in Figures 6-14 and 6-15 for the same two homes. For 

these two homes, simulated load shapes are generated in two steps: 

Steo 1: Use characteristics regression results and actual 

household characteristics to estimate harmonic model 

parameters; and 

Step 2: Use estimated harmonic model parameters to 

simulate a load shape for each home's water heater. 

In other words, the simulated load shapes are generated using only household 

characteristic information collected in the surveys. Also plotted on this figure are 

actual average load shapes for the two homes. The relatively close fit of these two 

shapes demonstrates the potential of this load shape modeling approach. 

 EE-RM-94-230 COMMENT 61F

ELECQUA = 

.' QUALGUY = 

CPBN02 = 

CPBN03 = 

.CPBN04 = 

CPBN06 = 

variable representing a customer's image of electricity as 

a high quality fuel, and 

indicator variable representing whether a customer's 

ELECQUA score is higher than the customer's ElECVAl 

score, 

indicator variable representing. a customer being 

classified as "Appearance Conscious," 

indicator variable· representing a customer being 

classified as "Resource Conserver," 

indicator variable representing a customer being 

classified as "Lifestyle Simplifier," 

indicator variable representing a customer being 

classified as "Value Seeker." 

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

Table 6-1 under the column heading "Simulated." These parameters were 

produced by substituting the actual customer characteristics of each household 

into the end-use characteristic model (Equation 6-2) estimated using the entire 

REUC sample of heat pump water heaters. Note the similarity between these 

simulated parameters, generated from the characteristic model, and the estimated parameters, developed from the harmonic l1)odel. 

These results are illustrated in Figures 6-14 and 6-15 for the same two homes. For 

these two homes, simulated load shapes are generated in two steps: 

Step 1: Use characteristics regression results and actual 

household characteristics to estimate harmonic model 

parameters; and 

Step 2: Use estimated harmonic model parameters to 

simulate a load shape for each home's water heater. 

In other words, the simulated load shapes are generated using only household 

characteristic information collected in the suryeys. Also plotted on this figure are 

actual average load shapes for the two homes. The relatively close fit of these two. 

shapes demonstrates the potential of this load shape modeling approach. 

6-15 
Figure 6-1 4 

REUC Heat Pump Water Heater - House 50 

Second Step Simulated Load vs. Actual Load 

0.6- 

0 3 6 9 12 15 18 21 24 

Time of Csy (in Hours PST) 

Figure 6-15 

REUC Heat Pump Water Heater - House 86 

Second Step Simulated Load vs. Actual Load 

0.b 

O.C 

0 3 6 9 12 15 18 21 24 

Time of Day (in Hwrs PSTJ 

 EE-RM-94-230 COMMENT 61F

Figure 6-14 

REUC Heat Pump Water Heater - House 50 

Second Step Simulated load vs. Actual load 

kWl~ 1..1r--------------------;.;;;;:;;;;;;~1 

o. 

o. 

o. \ 

'-... 

o 3 6 9 12 15 ,. 21 24 

Time of Day (in Hours PST) 

Figure 6-15 

REUC Heat Pump Water Heater - House 86 

Second Step Simulated Load vs. Actual Load 

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

o. 

o. 

o. 

o 3 6 9 12 15 

Time of Day (in Hours PST) 

Simulated Loa 

Acrual Load 

,. 21 24 
he REUC pro;- :4:z in extensive demographic, aititudinal and load data, is 

p. . . ;$ifiicularly \VE i:.:22 to evaluate how changes in customer population . iharacteristics E: :?<l;ence utility load-shzpes. The end-use characteristic model p 4' 

is,effective for eizazring the effects oi changing demographics on appliance load 

Cipes. 

'Figure 6-16 illu:~=ss -ow an "aging" residential customer population can effect k 

heater loac ihax~. In this example, customers are divided 'into two groups. 

that the xetz:e household in each group differs only by those variables 

[given in Table 0-2. :i~ average age oi the head of the household, the number oi . . 

persons in the lousehold, and the square feet'of the household. All other 

thousehold charzeris:io are the same between the two groups. Load shapes are 

igiven in Figure 6-1 6 kr !ounger and older households, "sing the characteristic f 

land load shape 5armznic models estimated with REUC data. As is shown, the 

I ;load shape of the olcer. smaller households have somewhat earlier and lower 

!,peaks. This exar~le illustrates both how differences in customer characteristics 

:can influence end-are load shapes and how utility planning may benefit from load 

I. 

I shape models. 

Table 6-2 

Diherine Averaee Characteristics of the Households 

Younger. Households Older Households 

Average Age of Head oi 

Household 

Persons in Household 3.7 2.3 

Square Feet 1900 1550 

Note: All other household characteristics are assumed equal. 

 EE-RM-94-230 COMMENT 61F

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

~articularly we s~::e-:i to evaluate how changes in customer population 

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

I

!seffective for e\'2;ua::ng the eiie~ts of changing demographics on appliance "ioad 

shapes, . ' 

;Figure 6-16 illu;:c;es ~ow an "aging" residential customer population can effect 

l

iwater heater loac shaDeS. In this example, customers are divided 'into two groups. 

Assume that the 2';erc~e household in each group differs only by those variables 

(gi~en in Table 6-2. the al'erage age of the head of the household, the number of 

ipersons in the ~ousenold. and the square feet' of the household. All other 

! household charac:erislic; are the same between the two groups, Load shapes are 

!given in Figure c-i 6 :or \ounger and older households. using the characteristic 

r: 

land load shape "2rrro~ic models estimated with REUC data. As is shown, the 

: load shape of the oleer. smaller households have somewhat earl ier and lower 

[,peaks, This exarple iilustrates both how differences in customer characteristics 

ican influence ene-use load shapes and how utility pranning may benefit from load 

.' shape models. 

Table 6-2 

Differing Average Characteristics of the Households 

Younger, Households Older Households 

Average Age of Head of 39 61 . Household 

Persons in Household 3.7 2.3 

Square Feet 1900 1550 

Note: All other household characteristics are assumed equal. 

f:,-t'" 
. Figure 6-16 

REUC Heat Pumo Water Heater Demoeraohic Trend Scenario 

-- 

TOTAL ENERGY REGRESSIONS 

Exploratory modeling was carried out on the total amount of energy used by heat 

pump water heaters on an average day in the pooled lrvine and Mission Viejo 

samples of the REUC households. Three types of analysis took place: (1) modeling 

of the energy statistic based on the traditional household demographic variables 

(i.e., square footage, number of occupants); (2) modeling of the energy statistic 

based on the REUC attitudinal variables (a set of five variables designed to 

measure customers' image of different fuels); and (3) modeling of the energy 

statistic based on the customer's Customer Preference and Behavior (CP&B) 

classification. Each is described below. 

Load 0.8 

In kW 

0.6 - 

,\ Older Households 

I \ ----- Younger Households 

I \ 

I \ 

I ! 

", 

I ! 

0.4 - 

0.2 - 

0.0 I.,-b. 

0 3 6 9 12 15 18 21 24 

T~me of Day (~n Hours PST) 

 EE-RM-94-230 COMMENT 61F

Figure 6-16 

. REUC Heat Pump Water Heater Demographic Trend Scenario 

load 

inkW 0.8r----~r.~~~~~11 ~ \ Older Households r \ 

0.6 

0.4 

0.2 

o 3 6 

TOTAL ENERGY REGRESSIONS 

I \ 

I \ 

I \ 

I \ .............. \ 

l \ 

\ 

\ 

Younger Households 

I 

I 

I 

I 

" 1', 

" I \ 

I 

9 12 

, I \ 

\ I \ 

\ I \ 

\ I \ 

\. "I \ 

, ~'" ...... -, 

15 18 21 

Time of Da (in Hours PST) 

24 

Explora\ory modeling was carried out on the total amount of energy used by heat 

pump water heaters on an average day in the pooled Irvine and Mission Viejo 

samples of the REUC households. Three types of analysis took place: (1) modeling 

of the energy statistic based on the traditional household demographic variables 

(i.e., square footage, number of occupants); (2) modeling of the energy statistic 

based on the REUC attitudinal variables (a set of five variables designed to 

measure customers' image of different fuels); and (3) modeling of the energy 

statistic based on the customer's Customer Preference and Behavior (CP&B) 

classification. Each is described below. 

6-18 
heat pump water heater demographic model specifications for the daily UEC ?,.A 

the winter season are given as follows: 

AVEUSE = 29.83 - 3.72 * MAXHHAGE + 

(1 2.60) (1.39) ( 

- 2.28 * MAXEDLEV + 2.05 * HHSIZE + 0.54 * HHINC 

(1.03) (0.95) (2.07) 

- 

R2 = 0.24 SSE = 1437.65 n = 38 

where 

AVEUSE = average energy usage on a winter weekday; 

MAXHHAGE = categorical variable representing the age of the oldest 

member of the household, 

SQFT = area measure (in square feet) of the home, 

MAXEDLEV= categorical variable representing the highest level of 

education attained by members of the household, 

HHSIZE = number of occupants in the household, and 

HHlNC = categorical variable representing the combined income 

of members of the household, 

standard errors or given in ( ). 

The R2 statistic presented for Eq. 6-3 (as well as each of the other regression 

models discussed in this report) measures the "total fit" of the specification. It is 

derived from equally weighting all observations, therefore providing valuable 

information on total fit. For evaluating load shape regressions, it may be more 

relevant to weight peak and near-peak observations more heavily and to 

graphically compare the estimated (synthetic) load shape with the actual 

appliance load shape, see below. 

The heat pump water heater attitudinal model specifications for the daily UEC in 

the winter season are given as follows: 

AVEUSE = 28.62 - 4.10 ' GASVAL + 0.98 * GASQUA 

(1.78) (1.96) (1.84) (Eq. 6-4) 

- 2.93 * ELECVAL + 4.04 * ELECQUA + 2.94 * QUALGUY 

(1.43) , (1.44) (2.48) 

- 

R2 ~0.22 SSE = 201 6.79 - n = 40 

 EE-RM-94-230 COMMENT 61F

iThe heat pump water heater demographic model specifications for the daily UEC 

rIn the winter season are given as follows: 

where 

AVEUSE = 29.83 - 3.72 • MAXHHAGE + 0.007 • SQFT 

(12.60) (1.39) (0.004) 

(Eq. 6-3) 

- 2.28 • MAXEDLEV + 2.05 • HHSIZE + 0.54 • HHINC 

(1.03) (0.95) (2.07) 

R2 = 0.24 

AVEUSE = 

MAXHHAGE = 

SQFT= 

MAXEDLEV = 

SSE= 1437.65 n = 38 

. average energy usage on a winter weekday; 

categorical variable representing the age of the oldest 

member of the household, 

area measure (in square feet) of the home" 

categorical variable representing the highest level of 

education attained by members of the household, 

HHSIZE= 

. HHINC= 

number of occupants in the household, and 

categorical variable representing the combined income 

of members of the household, 

standard errors or given in ( ). 

The R2 statistic presented for Eq. 6-3 (as well as each of the other regression 

models discussed in this report)" measures -the "total fit" of the specification. It is 

derived from equally weighting all observations, therefore providing valuable 

information on total fit. For evaluating load shape regressions, it may be more· 

relevant to weight peak and neaf-peak observations more heavily. and to 

graphically compare the estimated (synthetic) load shape with the actual 

• 

appliance load shape, see below .. 

The heat pump water heater attitudinal model specifications for the daily UEC in 

the winter season are given as follows: 

AVEUSE = 28.62 - 4.10 • GASVAL + 0.98 • GASQUA 

(1.78) (1.96) (1.84) (Eq. 6-4) 

- 2.93 * ELECVAL + 4.04 • ELECQUA + 2.94 * QUALGUY 

(1.43) . (1.44) (2.48) 

R2 =0.22 SSE = 2016.79· n = 40 

6-19 

.,.' 
where 

AVEUSE = average energy usage on a winter weekday, 

GASVAL = variable representing a customer's image of gas as a high 

value fuel, ., , . . ~. 

GASQUA = variable representing a customer's image of .ga's as a 

quality fuel, 

ELECVAL = variable representing a customer's image of electricity as 

a high value fuel, 

ELECQUA = variable representing a customer's image of electricity as 

a quality fuel, and 

QUALGUY = indicator variable representing whether a customer's 

ELECQUA score is higher than the customer's ELECVAL 

score, 

standard errors are given in ( ). 

The heat pump water heater Customer Preference & Behavior model specifications for the daily UEC in the winter season are given as follows: 

AVEUSE = 24.02 - 3.97 * CPBN02 + 4.56 * CPBN03 

(2.57) (4.60) (5.56) 

(Eq. 6-51 

+ 5.16 * CPBN04 + 8.42 * CPBN06 

(3.39) (3.73) 

where 

AVEUSE = . average energy usage on a winter weekday, 

CPBN02 = indicator variable representing a customer being 

classified as "Appearance Conscious," 

CPBN03 = indicator variable representing a customer being 

classified as "Resource Conserver," 

CPBN04 = indicator variable representing a customer being 

classified as "Lifestyle Simplifier," 

CPBN06 = indicator variable representing a customer being 

classified as "Value Seeker," 

standard errors are given in ( 1. 

 EE-RM-94-230 COMMENT 61F

. where 

AVEUSE '" 

GASVAL '" 

GASQUA '" 

ELECVAL '" 

ELECQUA", 

QUALGUY= 

average energy usage on. a winter weekday, 

variable representing a customer's image of gas as a high. 

value fuel, .. . . . .. . 

variable representing a customer's image of g~s as a 

quality fuel, 

variable representing a customer's image of electricity as 

a high value fuel, 

variable representing a customer's image of electricity as 

a quality fuel, and 

indicator variable representing whether a customer's 

ELECQUA score is higher than the customer's ELECVAL 

score, 

standard errors are given in ( ). 

The heat pump water heater Customer Preference & Behavi()r model specifications for the daily UEC in the winter season are given as follows: 

·where 

AVEUSE = 24.02 - 3.97 • CPBN02 + 4.56 • CPBN03 

(2.57) (4.60) (5.56) 

+ 5.16 * CPBN04 + 8.42 * CPBN06 

(3.39) (3.73) 

R2 =0.03 SSE", 2841.12 n = 43 

AVEUSE = 

CPBN02 = 

average energy usage on a winter weekday, 

indicator variable representing a customer being 

classified as "Appearance Conscious," 

CPBN03 = indicator variable representing a customer being 

classified as "Resource Conserver," 

CPBN04 = indicator variable representing a customer being 

classified as "Lifestyle Simplifier," 

CPBN06 '" ·indicator variable representing a customer being 

classified as "Value Seeker," 

standard errors are given in ( ). 

6-20 

(Eq. 6-5) 
nergy use models described above are displayed in Figures 6-17 

-19. Figure 6-1 7 shows the marginal effect on energy use of increasing 

of household occupants from two to four to six with the household 

cs model. As intuition would suggest, the modeled average daily 

s' 

usage for the heat pump water heater increases when the number of 

ccupants increases. 

Figure 6-1 7 

Effect of Household Size on Averace Weekdav Usage: 

REUC Heat Pump Water Heater (Model) 

 EE-RM-94-230 COMMENT 61F

energy use models described above are displayed in Figures 6-17 

"6-19. Figure 6·17 shows the marginal effect on energy use of increasing 

of household occupants from two to four to six with the household 

~raplhi,'CS model. As intuition would suggest, the modeled average daily 

usage for the heat pump water heater increases when the number of 

occupants increases. 

o 

figure 6,-17 

Effect of Household Size on Average Weekday Usage: 

REUC Heat Pump Water Heater (Model) 

10 20 30 

6-21 

Summer 

Winter 

31 

40 
Figure 6-18 shows the effects on energy usage brought about by an increase in 

customers' perceptions of electricity as a quality fuel with the attitudinal model. 

This figure modeled estimated energy usage in both winter and summer for three 

types of customers: (1) customers with a below average image of electricity as a 

Quality fuel; (2) customers with an average image of electricity as a Quality fuel; 

and (3) customers with an above average Quality image for electricity. As the 

figure illustrates, a negative relationship exists between energy usage and 

electricity quality image. That is, as quality image increases, energy use decreases. 

Additional research on other appliances will help identify the source of this and 

other image relationships. 

. . . .. .. 

Figure 6-18 . . 

Effect of Quality lmaee of Electricity on Average Weekday Usage: 

REUC Heat Pump Water Heater (Model) 

1 S.D. Below Mean 

1 S.D. Above Mean 

 EE-RM-94-230 COMMENT 61F

Figure 6-18 shows the effects on energy usage brought about by an increase in 

customers' perceptions of electricity as a quality fuel with the attitudinal model. 

This figure modeled estimated energy usage in both winter and summer for three 

types of customers: (1) customers with a below average image of electrjcity as a 

Quality fuel; (2) customers with an average image of electricity as a Quality fuel; 

and (3) customers with an above average Quality image for electricity. As the 

figure illustrates, a negative relationship exists between energy usage and 

electricity quality image. That is, as quality image increases, energy use decreases. 

Additiona"1 research on other appliances will help identify the source of this and 

other image relationships. 

Figure 6-18 

Effect of Quality Image of Electricity on Average Weekday Usage: 

REUC Heat Pump Water Heater (Moden 

1 S.D. Below Mean 

Mean 

1 S.D. Above Mean 

o 10 20 30 

6-22 

Summer 

Winter 

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

mp water heaters under the CP&B 'regression model. This figure presents 

$mated usage in both winter and summer for customers classified as . 

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

ustomers consume more energy than Appearance Conscious customers. 

Figure 6-19 

Effects of CP&B Classification on Average Weekdav Usape: 

REUC Heat Puma Water Heater (Model) , . 

 EE-RM-94-230 COMMENT 61F

1

8ure 6-19 shows the effects of the CP&B variable on energy usage for heat 

':.U.~~p water heate.rs under t~e CP&B regression model. This figure ~~esents 

estimated usage In both winter and summer for customers classified as 

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

~~stomers consume more energy than Appearance Conscious customers. 

Figure 6-19 

Effects of CP&B Classification on Average Weekday Usage: 

Appear. 

Value Seeker 

o 

REUC Heat Pump Water Heater (Model) 

18 

24 

10 20 

6-23 

30 

r2I Summer 

l1li Winter 

32 

kBTU 40 
Section 7 

CUSTOMER PREFERENCES AND 

APPLIANCE ACCEPTANCE 

INTRODUCTION 

The design of the REUC project allowed for research directed at understanding 

customer preferences for and acceptance of new energy-efficient appliances. As 

part of the survey research component of the project, participants were asked to 

periodically rate their new appliances along various attributes as well as to 

respond to questions that assess appliance performance and value. In particular, 

emphasis was placed on identifying the relationship between appliance preference and fuel type (i.e., electric or gas). See Figure 3-1 for the REUC survey 

research timeline. 

This section discusses the evolution of customers' attitudes toward their electric 

and gas appliances. The lrvine participants answered two surveys before being 

retrofitted with either new gas or new electric appliances and three surveys after 

receiving new appliances, whereas the Mission Viejo participants answered only 

one survey before and four after their appliance retrofit. Many of the survey questions related to the discussion in this section are found in Surveys 2, 3, and 4. In 

these cases, only lrvine participants provide before and after retrofit data. Consequently, only lrvine data are presented in many instances. Some participants 

moved in the middle of the study and, consequently, longitudinal data exist both 

for the original participants (i.e., Surveys 1-3) and for the home's new residents 

(i.e., Surveys 3-5). As a result, data will also sometimes be presented with more 

observations than retrofitted houses. 

This section describes the longitudinal changes in customers' image of electricity 

and gas and in their preferences in space heating, water heating, and cooking, 

respectively. The section conc!udes with analysis of the effect of customers' 

discount rates on their perceptions of conservation and efficiency as investment 

Opportunities. 

 EE-RM-94-230 COMMENT 61F

INTRODUCTION 

Section 7 

CUSTOMER PREFERENCES AND 

APPLIANCE ACCEPTANCE 

The design of the REUC project allowed for research directed at understanding 

customer preferences for and acceptance of new energy-efficient appliances. As 

part of the survey research component of the project, participants were asked to 

periodically rate their new appliances along various attributes as well as to 

respond to questions that assess appliance performance and value. In particular, 

emphasis was placed on identifying the relationship between appliance preference and fuel· type (i.e., electric or gas). See Figure 3-1 for the REUC survey 

research timeline. 

This section discusses the evolution of customers' attitudes toward their electric 

and gas appliances. The Irvine participants answered two surveys before being 

retrofitted with either new gas or new electric appliances and three surveys after 

receiving new appliances, whereas the Mission Viejo participants answered only 

one survey before and four after their appliance retrofit. Many of the survey questions related to the discussion in this section are found in Surveys 2, 3, and 4. In 

these cases, only Irvine participants provide before and after retrofit data. Consequently, only Irvine data are presented in many instances. Some participants 

moved in the middle of the study and, consequently, longitudinal data exist both 

for the original participants (i.e., Surveys 1-3) and for the home's new residents 

(i.e., Surveys 3-5). As a result, data will also sometimes be presented with more 

observations than retrofitted houses. 

This section describes the longitudinal changes in customers' image of electricity 

and gas and in their preferences in space heating, water heating, and cooking, 

respectively. The section concludes with analysis of the effect of customers' 

discount rates on their perceptions of conservation and efficiency as investment 

opportunities. 

7-1 
FUEL NPE IMAGE 

REUC participants were asked to rate on a scale of one to seven which of a pair of 

antonyms best describes electricity and gas. Substan:ial differences appear 

between images of electricity and gas; the means are plotted along the scales in 

Figure 7-1. Here, the "hour-glass" shape of the electricitylgas graphs in this chart 

illustrate the different images these fuels project to utility customers. For 

example, participants on average considered electricity a "cleaner" and "safer" 

fuel, and they perceived gas as a less expensive fuel. 

Figure 7-1 

REUC Participant lmazes of Electric vs. Gas 

Dirty Clean - 

Dangerous Safe 

Hard to Hookup Easy to Hookup 

Inconvenient Convenient 

Poor Service Good Service 

from Utility from Utility 

Unreliable Reliable 

Sluggish Heat Fast Heat-- 

High Cost Low Cost 

Appliances Appliances 

Expensive Inexpensive 

High Monthly Low Monthly 

Utility Bills Utility BillsL 

k 

4 

g 3 

0. 

2 

2 L 

Electricity Gas 

Image Image 

 EE-RM-94-230 COMMENT 61F

FUEL TYPE IMAGE 

REUC participants were asked to rate on a scale of one to seven which of a pair of 

antonyms best describes electricity and gas. Substantial differenj:es appear 

between images of electricity and gas; the means are plotted along the scales in 

Figure 7-1. Here, the "hour-glass" shape of the electricity/gas graphs in this chart 

illustrate the different images these fuels project to utility customers. For 

example, participants on average considered electricity a "!:leaner" and "safer" 

fuel, and they perceived gas asa less expensive fuel. 

Dirty 

Dangerous 

Hard to HooKup 

Inconvenient 

- Poor Service 

from Util ity 

Unreliable 

Sluggish Heat 

High Cost 

Appliances 

Expensive 

High Monthly 

Utility Bills 

Figure 7-1 

REUC Participant Images of Electric vs. Gas 

, I 

ir I. 

~ ~k i 

~ # iiff J 

Clean 

Safe 

Easy to Hookup 

:.. 

~ 

Convenient -.... 

:§ 

Good Service 0'-

from Utility 

Reliable 

~,~/ 

"/ 11 

11 

t~ ~ 

/ \ ' -

¥ , \ , 

Fast Heat 

Low Cost 

Appliances ..... 

:::J 

.... Inexpensive ~ 

La:" Monthly 

Utility Bills 

Electricity Gas 

Image Image 

7-2-
research analysis also included factor analysis to determine how 

of gas and electricity along various specific attributes (e.g.,, 

safety) can be reduced to a more . . parsimonious set of "factors." Before 

the REUC project's analyses, a brief overview of factor 

is presented.. 

L.. 

:Factor analysis is a statistical technique for analyzing the interdependence among I 

/a set of observed variables.' Factor analysis is based on the assumption that an 

iunobserved, reduced set of dimensions (or factors) captures most of the process ! (that generates the larger set of observed variables. In the case of a data set 

]consisting of responses to a survey, the dimensions are assumed to be the same for 

:all respondents, but each respondent will be positioned differently along the 

,dimensions. 

For example, it may be desirable to analyze a sample of responses to questions 

that required individuals to rank the quality of a product based on its performance 

across various attributes. Factor analysis is applied to reduce the extensive list of 

attributes into a smaller number of composite attributes that still largely preserves 

the overall image each respondent has of the quality of the product. In general, 

the objective is to identify a smaller, manageable set of factors that captures a 

significant percentage of the information contained in the larger set of survey 

responses. 

The method of factor analysis consists of identifying factors that capture a large 

percentage of this data set's variability. Each factor produced is a linear transformation in which each variable is weighted by a value called the "factor loading." 

The factor loading is the correlation between the variable and the factor. In order. 

to form interpretable factors, the researcher transforms or "rotates" the factor 

loadings so that each variable significantly loads onto only one factor. Variables 

that load significantly onto a factor, i.e., have a loading > 0.5, are considered to be 

that subset of the original data set "captured" by that factor. The researcher 

'For more details about factor analysis, see "Market Segmentation 

Research, Volumes 1 and 2," prepared for the Electric Power Research Institute 

by Dipankar Chakravarti, Philip. E. Hendrix, and William L. Wilkie, EPRl Research 

Proj'ect 1537, 1987. 

 EE-RM-94-230 COMMENT 61F

'~Ctor Anal sis 

,- "e REUC market research analysis also included factor analysis to determine how 

~krticipants' assessment of gas and electricity al~ng ~arious specific attributes (e.g., ' 

~ost, safety) can be reduced to a more parsimonious set of "factors.", Before 

~'isCussing the results of the REUC project's analyses, a brief overview of factor 

r~nalysis is presented. ' , ~"r • • 

[Factor analysis is a statistical technique for analyzing the interdependence among ia set of observed variables.1 Factor analysis is based on the assumption that an 

iunobserved, reduced set of dimensions (or factors) capfures most of the process , 

!that generates the larger set of ob'served variables. In the case of a data set 

Iconsisting of responses to a survey, the dimensions are assumed to be the same for 

;all respondents, but each respondent will be positioned differently along the 

,dimensions. 

For example, it may be desirable to analyze a sample of responses to' questions 

that required individuals to rank the quality ,of a product based on its performance 

across various attributes. Factor analysis is applied to reduce the extensive list of 

attributes into a smaller number of composite attributes that still largely preserves 

the overall image each respondent has of the quality of the product. In general, 

the objective is to identify a smalier, manageable set of factors that captures a 

significant percentage of the information contained in the larger set of survey 

responses. 

The method of factor analysis consists of identifyi ng factors that capture a large 

percentage of this data set's variability. Each factor produced is a linear transformation in which each variable is weighted by a value called the "factor loading." 

The factor loading is the correlation between the variable and the factor. In order. 

to form interpretable factors, the researcher transforms or "rotates" the factor 

loadings so that each variable significantly loads onto only one factor. Variables 

that load significantly onto a factor, i.e., have a loading> 0.5, are considered to be 

that subset of the original data set "captu'red" by that factor. The researcher 

1 For more details about factor analysis, see "Market Segmentation 

Research, Volumes 1 and 2," prepared for the Electric Power Research Institute 

by Dipankar Chakravarti, Philip. E. Hendrix, and William L. Wilkie, EPRI Research 

Project 1537,1987. ' 
applies a qualitative assessment of the commonality of this subset of variables in 

order to "name" the factor. : . ~. ~ 

. ..~. . i 

Rotation methods are essential in creating interpretable or "nameable" factors. A 

rotation is a linear transformation of the matrix of factor loadings. Orthogonal 

rotations do not change the total explanatory power of the original factors.2 

Rotation methods could be thought of as "moving" information from one factor 

into another in order that each factor has a particular identity. 

The application of a factor analysis often follows a two-stage process. The first 

stage consists of performing factor analysis in order to determine a manageable 

set of factors as described above. The second stage involves applying the factors to 

classify respondents into groups which are "meaningful." "Meaningful" groups in 

the context of market research tend. to be customer segments that respond to 

attributes of a particular class of products in a similar way. 

For example, in the case of automobiles, factor analysis may reveal that a car has 

two basic composite attributes, economy and performance. Some respondents 

may value economy highly, resulting in a high factor score for these respondents 

along that dimension. Other respondents may value performance and have correspondingly high factor scores along that dimension. Next, these factors would 

be used to classify these respondents into "economy-minded" and "performanceoriented" groups. It would then be important to determine if in fact these groups 

conform to behavior that is consistent with their classification. That is, whether 

the "economy-minded" group would be observed to purchase economical 

smaller cars (e.g., Ford Escort), while the "performance-oriented" group would 

tend to purchase high performance cars (e.g., Porsche 91 1 ). 

20ne of the most commonly used rotation methods is the Varimax 

rotation. This is the rotation used in the REUC analysis. 

 EE-RM-94-230 COMMENT 61F

applies a qualitative assessment of the commonality of this subset of variables in 

order to "name" the factor. 

, , 

Rotation methods are essential in creating interpretable or "nameable" factors. A 

rotation is a linear tr<:tnsformation of the matrix of factor loadings. Orthogonal 

rotations do not change the total explanatory power of the original factors.2 

Rotation methods could be thought of as "moving" information from one factor 

into another in order that each factor has a particular identity. 

The application of a factor analysis often follows a two-stage process. The first 

stage consists of performing factor analysis in order to determine a manageable 

, set of factors as described above. The second stage involves applying the factors to 

classify respondents into groups which are "meaningful." "Meaningful" groups in 

the context of market research tend, to be customer segments that respond to 

attributes of a particular class of products in a similar way. 

For example, in the case of automobiles, factor analysis may reveal that a car has 

two basic composite attributes, economy and performance. Some respon'dents 

may value economy highly, resulting in a high factor score for these respondents 

along that dimension. Other respondents may value performance and have correspondingly high factor scores along that dimension. Next, these factors would 

be used to classify these respondents into "economy-minded" and "performanceoriented" groups. It would then be important to determine if in fact these groups 

conform to behavior that is consistent with their classification. That is, whether 

the "economy-minded" group would be observed to purchase economical 

smaller cars (e.g., Ford Escort), while the "performance-oriented" group would 

tend to purchase high performance cars (e.g., Porsche 911). 

20ne of the most commonly used rotation methods is the Varimax 

rotation. This is the rotation used in the REUC analysis. 
Gel image Results 

In the REUC project, for each fuel type, factor analysis was used to identify the 

between several variables. Table 7-1 repo'rts the factor loadings 

of the two significant factors derived from the factor analysis of each fuel. 

significant factor loadings are asterisked. Each column in this tabk reports the 

factor loading of the survey question item onto the factor named in the column 

heading. That is, the results of performing factor analysis of the REUC participants' 

ratings of their image of electricity, based on various qualit'ies, resulted in the 

derivation of a "Quality" factor which is a composite of the Safety, Convenience, , , 

service, and Reliability survey items. The' other electricity factor is "Value," a 

composite of the ratings along the Appliance Cost, General Expense, and Monthly 

Utility Bill qualities. Each fuel has a Quality and Value dimension which 

combined capture about 45 percent of the variation in the responses to the ten 

survey items for fuel. In the case of gas, the "Value" and "Quality" factors do not 

work out as well. Safety loads onto the "Value" factor while one of the cost 

attributes, level of monthly bills, loads onto "Quality." Nevertheless, the other key 

attributes load onto the appropriate factor. 

The Quality and Value factor scores are "normalized" so that their distribution is 

described by the standard normal distribution. This distribution is symmetric with 

a standard deviation of 1.0, and approximately two-thirds of its weight is within 

one standard deviation of the mean. For example, a Quality factor score of 0.0 is 

at the fiftieth percentile; i.e., exactly one-half of the sample has a lower Quality 

factor score: A Gas Quality factor score of -1.0 is one standard deviation below 

the mean score, indicating that this participant gave lower ratings for gas on the 

quality attributes'of cleanliness, service and reliability relative to other REUC 

participants. Section 8 uses these factor scores to segment customers for the 

purpose of explaining energy usage. 

 EE-RM-94-230 COMMENT 61F

fuel image Results 

In the REUC project, for each fuel type, factor analysis was used to identify the 

interdependence between several variables. Table 7-1 repo'rts the factor loadings 

of the two significant factors derived from the factor analysis of each fuel. 

Significant factor loadings are asterisked. Each column in this tab"le'reports the 

factor loading of the survey question item onto the factor named in the column 

heading. That is, the results of performing factor analysis of the REUC participants' 

ratings of their image of electricity, based on various qualiti'es, resulted in the 

derivation of a "Quality" factor which is a composite of the Safety, Convenience, 

Service, and Reliability survey items. The' other electricity factor is "Value," a 

composite of the ratings along the Appliance Cost, General Expense, and Monthly 

Utility Bill qualities. Each fuel has a Quality and Value dimension which 

combined capture about 4S percent of the variation in the responses to the ten 

survey items for fuel. In the case of gas, the "Value" and "Quality" factors do not 

, work out as well. Safety loads onto the "Value" factor while one of the cost 

attributes, level of monthly bills, loads onto "Quality." Nevertheless, the other key 

attributes load onto the appropriate factor. 

The Quality and Value factor scores are "normalized" so that their distribution is 

described by the standard normal distribution. This distribution is symmetriC with 

a standard deviation of 1.0, and approximately two-thirds of its weight is within 

one standard deviation of the mean. For example, a Quality factor score of 0.0 is 

at the fiftieth percentile; i.e., exactly one-half of the sample has a lower Quality 

, factor score.' A Gas Quality factor score of -1.0 is one standard deviation below 

the, mean score, indicating that this participant gave lower ratings for gas on the 

quality attributes of cleanliness, service and reliability relative to other REUC 

participants. Section 8 uses these factor scores to segment customers for the 

purpose of explaining energy usage. 

1-5 
Table 7-1 

Factor Loadin~s Based on Analvsis of Fuel Tvpe lmage Ouestiona 

Fuel Oualities 

Electricity Image Gas Image 

Quality Value Quality Value 

Factor Factor Factor Factor 

Hard to hookup1Easy to hookup .37 .12 .I 8 .67* 

Poor service from utility1 .76* -.I 5 .75* .04 

Good service from utility 

Sluggish heat/Fast heat .10 .49 .76* .002 

High cost appliances1 .02 .56* .03 .59* 

Low cost appliances 

High monthly utility bills1 .13 .78* .64* .17 

Low monthly utility bills 

% Total Variance Explained 43% 46% 

aseparate factor analyses were applied to each fuel type. In both cases, two 

significant factors were found, Quality and Value, These results are based on 

Survey 53. 

*Significant factor loadings at the 95 percent level. 

SPACE HEATING 

Space heating appliances, viewed as a product, are interesting because they 

.provide a service valued by all customers and are often perceived as a necessity. 

Three questions are explored in this subsection. First, how did the retrofit fuel 

type affect REUC participants' fuel type preferences? Second, how did fuel type 

 EE-RM-94-230 COMMENT 61F

Table 7-1 

Factor Loadings Based on Analysis of Fuel Type Image Questiona 

Electricity Image Gas Image 

Quality Value Qualify Value 

Fuel Qualities Factor Factor Factor Factor 

Dirty/Clean .54' -.27 .57" .32 

Dangerous/Safe .52- -.01 .12 .76-

Hard to hookup/Easy to hookup .37 .12 .18 .67* 

Inconvenient/Convenient .62* .26 .25 .18 

Poor service from utility/ .76* -.15 .75* .04 

Good service from utility 

Unreliable/Reliable .81 * .13 .87* -.06 

Sluggish heat/Fast heat .10 .49 .76* .002 

High cost appl iances/ 

Low cost appliances 

.02 .56* .03 .59* 

Expensive/Inexpensive -.21 .76* .01 .59* 

High monthly utility bills/ 

low monthly utility bills 

.13 .78' .64* .17 

% Total Variance Explained 43% 46% 

aSeparate factor analyses were applied to each fuel type. In both cases, two 

significant factors were found, Qualjty and Value. These results are based on 

Survey S3. 

'Significant factor loadings at the 95 percent level. 

SPACE HEATING 

Space heating appliances, viewed as a product, are interesting because they 

. provide a service valued by all .customers and are often perceived as a necessity. 

Three questions are explored in this subsection. First, how did the retrofit fuel 

type affect REUC participants' fuel type preferences? Second, how did fuel type 
&&ct customers' perceptions of their new appliances? And, third, how did these 

i'berceptioris affect the importance to custorndrs of various characteristics of space 

i heating appliances? ,.., 

*+ 

Tables 7-2a and 7-2b summarize the effect upon participants' preferences for fuel 

type of retrofitting them with either gas or heat pump space heating appliances. 

Table 7-2a 

Lonzitudinal Chan~es in Electric Samole Participants' Fuel Type 

Preferences for Space Heatina Systems 

First Survey After Retrofit 

Before Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 5 1 7 13 

Prefer Electricity 1 4 3 8 

Ambivalent 6 2 8 16 

Total 12 7 18 3 7 

Second Survey Afier Retrofit 

First Survey 

After Retrofit Prefer Gas Fi+fer Electricitv Ambivalent Total 

Prefer Gas 7 0 5 12 

Prefer Electricity 0 4 3 7 

Ambivalent 2 2 14 18 

Total 9 6 2 2 3 7 

Third Survey After Retrofit (Mission Viejo only)a 

Second Survey 

After Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 5 0 0 5. 

Prefer Electricity 0 3 0 3 

Ambivalent 3 1 7 11 

Total 8 4 7 19 

aOnly Mission Viejo participants were surveyed a third time after receiving their 

new appliances. Consequently, the third survey is based on a much smaller 

sample than the first two surveys, above. 

 EE-RM-94-230 COMMENT 61F

~ffect customers' perceptions of their new appliances? And, third, how did these 

;perceptiOriS affect the importance to customers of various characteristics of space 

'heating appliances? 

Tables 7-2a and 7-2b summarize the effect upon participants' preferences for fuel 

type of retrofitting them with either gas or heat pump space heating appliances. 

Table 7-2a 

longitudinal Changes in Electric Samllie Particillants' Fuel T),lle 

Preferences for Sllace Heating S),stems . 

First Survey After Retrofit 

Before Retrofit Prefer Gas· Prefer Electricity Ambivalent Total 

Prefer Gas 5 1 7 13 

Prefer Electricity 1 4 3 8 

Ambivalent 6 2 8 16 

Total 12 7 18 37 

Second Survey After Retrofit 

Fi rst Su rvey 

After Retrofit Prefer Gas "PrHer Electricity Ambivalent Total 

Prefer Gas 7 0 5 12 

Prefer Electricity 0 4 3 7 

Ambivalent 2 2 14 18 

Total 9 6 22 37 

Third Survey After Retrofit (Mission Viejo only)a 

Second Survey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent Total 

Prefer Gas 5 0 0 5 

Prefer Electricity 0 3 0 3 

Ambivalent 3 7 11 

Total 8 4 7 19 

aOnly Mission Viejo participants were surveyed a third time after receiving their 

new appliances. Consequently, the third survey is based on a much smaller 

sample than the first two surveys, above. 

7-7 
Table 7-2b 

Longitudinal Changes in as Sample Participants' Fuel Tvoe 

Preferences for Space Heating Systems 

First Survey After Retrofit 

Before Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 15 1 2 18 

Prefer Electricity 0 1 0, 1 

Ambivalent 13 1 7 2 1 

Total 28 3 9 40 

Second Survey After Retrofits 

First Survey 

After Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 2 0 1 7 28 

Prefer Electricity 2 0 2 4 

Ambivalent 5 0 4 9 

Total 27 1 13 4 1 

Third Survey After Retrofit (Mission Viejo only)b 

Second Survey 

After Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 3 1 2 6 

Prefer Electricity 1 0 0 1 

Ambivalent 2 1 3 6 

Total 6 2 5 13 

aThe second survey contains one more survey response than the first survey, 

above. Thus, the number of participants who strqngly preferred electricity is one 

higher. 

Mission Viejo participants were surveyed a third time after receiving their 

new appliances. Consequently, the third survey is based on a much smaller 

sample than the first two surveys, above. 

 EE-RM-94-230 COMMENT 61F

Table 7-2b 

longitudinal Changes in Gas Sample Participants' Fuel Type 

Preferences for Space Heating Systems ., 

First Survey After Retrofit 

Before Retrofit Prefer Gas Prefer Electricity Ambivalent Total 

Prefer Gas 15 1 2 18 

Prefer Electricity 0 0 

Ambivalent 13 7 21 

Total 28 3 9 40 

Second Survey After Retrofita 

First Survey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent Total 

Prefer Gas 20 1 7 28 

Prefer Electricity 2 0 2 4 

Ambivalent 5 0 4 9 

Total 27 1 13 41 

Third Survey After Retrofit (Mission Viejo only)b 

Second Survey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent Total 

Prefer Gas 3 1 2 6 

Prefer Electricity . 1 0 0 1 

Ambivalent 2 1 3 6 

Total 6 2 5 13 

aThe second survey contains one more survey response than the first survey, 

above. Thus, the number of participants who strongly preferred electricity is one 

higher. 

bOn!y Mission Viejo participa~ts were surveyed a third time after receiving their· 

new appliances. Consequently, the third survey is based on a much smaller 

sample than the first two surveys, above. 

7-8 
(he Electric Sample (Table 7-2a), seven of the 13 participants who originally 

istrongly preferred gas became ambivalent immediately after retrofit: Over the 

lentire time period,3 a strong preference for . gas , .-... dropped .. by 31 percent and 

ambivalence rose by 38 percent. By comparison, two-thirds of those who were ' 

briginally ambivalent came to strongly prefer gas immediately after retrofit in the 

Gas Sample (Table 7-2b). After another year, a strong preference for gas had 

increased by 50 percent over its original figure and the number of ambivalent 

prticipants had dripped by 38 per;ent. Thus, participants who were retrofitted 

with heat pumps came to prefer gas less, but did not necessarily prefer electricity 

more. Those retrofitted with gas systems preferred gas even more strongly than at 

first. 

In another set of questions, participants were asked to compare their new space 

heaters to their old space heaters on five attributes. Figure 7-2 summarizes these 

results. Gas Sample customers agreed that their new systems heated more 

quickly, heated more evenly, were less noisy, and turned on and off less frequently 

than their old heaters. Conversely, Electric Sample customers agreed that their 

new heat pumps heated less quickly, were more noisy, and turned on and off 

more frequently than their old heaters. 

Not surprisingly, the type of fuel with which participants were retrofitted affected 

the importance they gave to various attributes of a heating system. Figures 7-3a 

and 7-3b summarize these effects as reflected in participants' longitudinal 

responses. The Electric Sample (Figure 7-3a) did not believe fuel type to be 

important before retrofit (Survey 2). After retrofit with heat pumps, however, they 

found it to be increasingly important (Surveys 3 and 4). In contrast, the Gas 

Sample (Figure 7-3b) accorded somewhat less importance to fuel type after 

retrofit. 

3See data collection timeline in Figure 3-1. 

 EE-RM-94-230 COMMENT 61F

!In the Electric Sample (Table 7-2a), seven of the 13 participants who originally 

tstrongly preferred gas became ambivalent immediately after retrofit. Over the 

:entire time period,3 a strong preference for gas_.df()pped by 31 percent and 

ambivalence rose by 38 percent. By comparison, two-thirds of those who were 

'originally ambivalent came to strongly prefer gas immediately after retrofit in the 

Gas Sample (Table 7 -2b). After another year, a strong preference for gas had 

increased by 50 percent over its original figure and the number of ambivalent 

participants had dropped by 38 per~ent. Thus, participants who were retrofitted 

with heat pumps came to prefer gas less, but did not necessarily prefer !electricity 

more. Those retrofitted with gas systems preferred gas even more strongly than at 

first. 

In another set of questions, participants were asked to compare their new space 

heaters to their old space heaters on five attributes. Figure 7-2 summarizes these 

results. Gas Sample customers agreed that their new systems heated more 

quickly, heated more evenly, were less noisy, and turned on and off less frequently' 

than their old heaters. Conversely, Electric Sample customers agreed that their 

new heat pumps heated less quickly, were more noisy, and turned on and off 

more frequently than their old heaters. 

Not surprisingly, the type of fuel with which participants were retrofitted affected 

the importance they gave to various attributes of a heating system. Figures 7-3a 

and 7-3b summarize these effects as reflected in participants' longitudinal 

responses. The Electric Sample (Figure 7-3a) did not believe fuel type to be 

important before retrofit (Survey 2). After retrofit with heat pumps, however, they 

found it to be increasingly important (Surveys 3 and 4). In contrast, the Gas 

Sample (Figure 7-3b) accorded somewhat less importance to fuel type after 

retrofit. 

3See data collection timeline in Figure 3·1. 

7-9 
Figure 7-2 

REUC Participants' Comoarisons between Old and New 

Space eaters bv Fuel Tvoe 

Other attributes were also affected. Operating costs and monthly utility bills, 

although still very important, were seen as less important over time in both 

samples. On the other hand, only response time became less important for Gas 

Sample participants. Similarly, accuracy of heat control became much more 

important to Electric Sample participants while remaining stable for those in the 

Gas Sample. 

Slmngly , 

- 

Disagree 

6 - 

/ -4 

4 - --. 

-__--- 

3 - 

2 - 

Strongly 

Heat Pump 

Agree , Heats Produces Makes Turns On/Off Heats 

Less Less Less Less More 

Quickly Odor Noise Frequently Evenly 

 EE-RM-94-230 COMMENT 61F

figure 7-2 

REUC Participants' Comparisons between Old and New 

Space Heaters by fuel Type 

Strongly 7 Disagree 

6-

" , ...... 5 -

, ... , ...... , ... , 

, ........... , ...... , ...... 4 -

... _----J 

3 

2 

Gas Furnace 

---_. Heat Pump 

Strongly 1 Agree • Heats Produces Makes Turns On/Off Heats 

Less Less Less Less More 

Quickly Odor Noise Frequently Evenly 

Other attributes were also affected. Operating costs and monthly utility bills, 

although still very important,. were seen as less important over time in both 

samples. On the other hand, only response time became less important for Gas 

Sample participants. Similarly, accuracy of heat control became much more 

important to Electric Sample participants while remaining stable for those in the 

Gas Sample. 

7-10 
ants were also asked to indicate their willingness to recommend their new 

heater to a friend. AS shown in Table 7-3, 80 percent of the Gas Sample 

&pants said they would recommend their heating system, and 75.6 percent .. 

'they would recommend it strongly. Not a single participant declined to + 

mmend their gas heating system, and over time participants became less 

ivalent and more likely to recommend their gas heating systems. ApproxiFigure 7-3a 

Importance of Heating System Attributes bv Fuel Tvoe 

Electric Sample 

Very 

Unimportant 4 - 

= 7 

3 - 

2 - 

Very 

- . 

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

-----._------_-_-__-------- 

Important 1 - , Survey 2 Survey 3 Survey 4 

Response Time 

Accuracy of Temperature Control 

----- Type of Fuel 

 EE-RM-94-230 COMMENT 61F

cip,lnts were also asked to indicate their willingness to recommend their new 

heater to a friend. As shown in Table 7-3, 80 percent of the Gas Sample 

cip,mts said they would recommend their heating system, and 75.6 percent 

. they would recommend it strongly. Not a single participant declined to .' 

. their gas heating system, and over time participants became less 

and more likely to recommend their gas heating systems. ApproxiFigure 7-3a 

Importance of Heating System Attributes by Fuel Type 

Electric Sample 

Very 

Unimportant 4 

=7 , , , 

............... , , 

'" " , , , 3 '. , " , ". " " ............ " , , ............. " ... ... ........... '. 

~.-.-.-.-.-.-.-.-.-.-.-

2 -----~------------------------- ---. 

- Very 1 Important , , , 

Survey 2 Survey 3 Survey 4 

Operating Costs/Monthly Bills 

.. ------. Response Time 

.............. Accuracy of Temperature Control 

----... Type of Fuel 

7-11 
Figure 7-3b 

Importance of Heatiner System Attributes bv Fuel Tv~e 

Gas Sam~le 

Very Unimportant 4 - = 7 

3 - 

2 - 

Very 

Important 1 

, 

. , 

, '

. . 4' 

----__ . ,' -----4 

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

I 

Survey 2 Survey 3 Survey 4 

Response Time 

Accuracy of Temperature Control 

 EE-RM-94-230 COMMENT 61F

Figure 7-3b 

Importance of Heating System Attributes by Fuel Type 

Gas Sample " 

Very 

Unimportant 4,----------------------, 

=7 

Very 

Important 

3 

2 

/ ........................... ...-

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

.' .' .............. -.' ............ -_ .. -

.' .. ' .' .' .. ' ." 

,L-----.-----------r-----------.----J 

Survey 2 Survey 3 Survey 4 

Operating Costs/Monthly Bills 

Response Time 

Accuracy of Temperature Control 

Type ofFuel 

7-12 
Table 7-3 

participants1 Willingness to Recommend Their Space heat in^ System - TO a Friend - By Fuel Tvpe 

No. of Participants (%) 

,Recommend Don't Recommend 

Very Very 

=am& Strongly Weaklv Weaklv Stronnlv 

?mately 40 percent of the Electric Sample participants responded that they would i" 

'recommend their heating system, with the number of respondents who would :.; 

istrongly recommend it increasing over time from 21.7 percent to 30.8 percent. i. .However, the number of respondents who' declined to recommend their heat 

i, 

pump heating system also increased from 19.6.percent to 33.3 percent. 

Three findings can be summarized from this analysis. First, as customers gain 

experience with more than one fuel type, fuel type increases in importance. 

Second, gas systems are more likely to have proponents than heat pump systems. 

It appears that the causes of the weaker reception of heat pumps in this case 

were the quality problems that the Electric Sample participants perceived. Third, 

experience wit'h an appliance is required in order to make strong 

recommendations, as demonstrated by the consistently higher number of 

respondents expressing opinions in Survey 4. 

 EE-RM-94-230 COMMENT 61F

Table 7·3 

i!!Jc' Particillants' Willingness to Recommend Their Sllace Heating System 

- . To a Friend - By Fuel TYlle .' 

No. of Participants ("!o) 

.Recommend Don't Recommend 

Very Very 

rm'(Sampie Strongly Weakly Unsure Weakly Strongly 

. -·T~~, 

1Il~) 3 

34 . 2 9 0 0 

(75.6) (4.4) (20.0) (0.0) . (0.0) 

fF1J4 

.22 8 4 0 0 

(64.7) (23.5) (11.8) (0.0) (0.0) .. ,;~ 

• 

j£Teciric Sample 

lfR.rvey 3 10 8 19 1 8 

";46) . (21.7) (17.4) (41.3) (2.2) (17.4) 

lsurvey 4 12 5 9 3 TO 

[(nb:39) (30.8) (12.8) (23.1 ) (7.7) (25.6) 

[mately 40 percent of the Electric Sample participants responded that they would ~ . 

;recommend their heating system, with the number of respondents who would ~ . . 

[strongly recommend it increasing over time from 21.7 percent to 30.8 percent. 

I_However, the number of respondents who" declined to recommend their heat . ' . 

p\Jmp heating system also increased from 19.6·percent to 33.3 percent. 

Three findings can be summarized from this analysis. First, as customers gain 

experience with more than one fuel tYPe, fuel type increases in importance. 

Second, gas systems are more likely to have proponents than heat pump systems. 

It appears that the causes of the weaker reception of heat pumps in this case 

were the quality proplems that the Electric Sample participants perceived. Third, 

experience with an appliance is required in order to make strong 

recommendations, as demonstrated by the consistently higher number of 

respondents expressing opinions in Survey 4. 

7-13 
WATER HEATING 

This section examines customers' reactions to their new water heaters. As with 

the space heaters, the REUC participants were retrofitted with either new heat 

pump or gas water heaters. The results from this section indicate that the heat 

pump water heaters were not received well by most customers. As with the 

space heaters, their poor reception was attributable not only to their cost 

performance but also to their inability to provide quality service. 

Figure 7-4 summarizes participants1 comparisons of their new water heaters with 

their old water heaters. The Gas Sample participants agreed that their new gas 

water heaters heated water more quickly and cost less to operate. In contrast, 

the Electric Sample participants gave lower ratings for their new heat pump water ' 

heaters on both of these quality attributes. 

Figure 7-4 

REUC Participants' Comparisons between Old and New 

.water Heaters bv Fuel Tvpe 

Strongly , - Disagree 

6 - 

5 - 

' 4- 

3 - 

2 - 

Strongly , 

----- Heat Pump Water Heater 

-_------------__ -----___ 

Agree 4 I 

Runs Out Heats Costs 

of Water Water More Less To 

Slower Quickly Operate - 

 EE-RM-94-230 COMMENT 61F

WATER HEATING 

This section examines customers' reactions to their new water heaters. As with 

the space heaters, the REUC participants were retrofitted with either new heat 

pump or gas water heaters. The results from this section indicate that the heat 

pump water heaters were noi received well by most customers. As with the 

space' heaters, their poor reception was attributable not only to their cost 

performance but also to their inability to provide quality service. 

Figure 7-4 summarizes participants' comparisons of their new water heaters with 

their old water heaters. The Gas Sample participants agreed that their new gas 

water heaters heated water more quickly and cost less to operate. In contrast, 

the Electric Sample participants gave lower ratings Tor their new heat pump water ; 

heaters on both of these quality attributes. 

Figure 7-4 

REUC Participants' Comparisons between Old and New 

. Water Heaters by Fuel Type 

Strongly 7 Disagree 

Gas Water Heater 

6 - ----- Heat Pump Water Heater 

5 

------------------------

4 

3 

2 

Strongly 1 Agree Runs Out Heats Costs 

of Water Water More Less To 

Slower Quickly Operate 

7-14 
Tables 7-4a and 7-4b detail longitudinal changes in participant fuel type prefer- 

:ences for water heaters. Immediately after being retrofitted, ambivalence toward 

/fuel type doubled from 10 to 20 among Electric Sample participants (~ablk.7-4a), .. . .. 

I /while the number strongly preferring electricity dropped by over one-fdurth and ' 

'the number strongly preferring gas plummeted by one-half. within the next year, 

!participants1 ambivalence remained fairly stable, the number of participants 

.strongly preferring electricity dropped yet further, and the number of participants. ' 

'strongly preferring gas rose, but not back to its original level. In the Gas Sample I 

(Table 7-4b), the number of ambivalent participants dropped slightly, yet, over 

time, a full 44 percent of the Gas Sample participants remained ambivalent. 

'preferences for both gas and electricity rose slightly, but no significant changes 

.occurred. The ambivalence manifested in both the Gas and Electric Samples , 

'resulted in some participants (30 percent to 45 percent) strongly recommending 

their heat pumps, while an equivalent number (40 percent,-to 45 percent) 

strongly declined to recommend them, as shown in Table 7-5. 

This section has shown that water heater quality attributes strongly affected fuel 

type preferences. Overall, Gas Sample customers were more satisfied with their 

new water heaters than Electric Sample customers; however, both groups were 

primarily ambivalent. There may have been some factors affecting the Electric 

Sample participants' reactions that should be mentioned. First, the heat pump 

recipients may have been unaware of any cost savings attributed to their water 

heaters, since they were probably unable to separate this cost out of their electric 

bills, which had increaied while their gas bills decreased. With regard to appliance quality, participants were probably aware of such noticeable performance 

attributes of water heaters as the quickness of heating water; however, it might 

have been possible to sidestep these negative perceptions of performance quality 

by using larger-capacity heat pump water heaters. Although participants may 

have been unaware. of the cost-effectiveness of their new electric heat pumps, 

they experienced their quality. It should also be pointed out that the heat pump 

water heaters installed for this study were among the first commercially available, 

and recent quality improvements may have overcome many of the performance 

problems perceived by the Electric Sample. 

 EE-RM-94-230 COMMENT 61F

;Tables 1-4a and 7-4b detail longitudinal changes in participant fuel type prefer-

:ences for water heaters. Immediately after being retrofitted, ambivalence toward 

[fuel type doubled from 10 to 20 among Electric Sample participants (Table 7 -4a), 

!while th~ number strongly preferring electricity dropped by over one-fourth and' 

the number strongly preferring gas plummeted by one-half. Within the next year, 

!participants' ambivalence remained fairly stable, the number of participants 

'strongly preferring electricity dropped yet further, and the number of participants' 

strongly preferring gas rose, but not back to its original level. In the Gas Sample I . 

(Table 7 -4b), the number of ambivalent participants dropped slightly, yet, over 

,time, a full 44 percent of the Gas Sample participants remained ambivalent. 

'Preferences for both gas and electricity rose slightly, but no significant changes 

·occurred. The ambivalence manifested in both the Gas and Electric Samples , . . ' resulted in some participants (30 percent to 4S percent) strongly recommending 

. their heat pumps, while an equivalent number (40 percent.·to 45 percent) 

strongly declined to recommend them, as shown in Table 7-5. 

This section has shown that water heater quality attributes strongly affected fuel 

type preferences. Overall, Gas Sample customers were more satisfied with their 

new water heaters than Electric Sample customers; however, both groups were 

primarily ambivalent. There may have been some factors affecting the Electric 

Sample participants' reactions that should be mentioned. First, the heat pump 

recipients m.ay have been unaware of any cost savings attributed to t~eir water 

heaters, since they were probably unable to separate this cost out of their electric 

bills, which had increased while their gas bills decreased. With regard to appliance quality, participants were probably aware of such noticeable performance 

attributes of water heaters as the quickness of heating water; however, it might 

have been possible to sidestep these negative perceptions of performance quality 

by using larger-capacity heat pump water heaters. Although participants may 

have been unaware. of the cost-effectiveness of their new electric heat pumps, 

they experienced their quality. It should also be pointed out that the heat pump 

water heaters installed for this study were among the first commercially available, 

and recent quality improvements may have overcome many of the performance 

problems perceived by the Electric Sample. 

7-15 
Table 7-4a 

Longitudinal Changes in Electric Sarnole Particioants' Fuel Tyoe 

Preferences for Water Heaters 

First Survey After Retrofit 

Before Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 3 3. 6 12 

Prefer Electricity 3 2 10 15 

Ambivalent 0 6 4 10 I 

Total 6 11 20 3 7 

Second Survey After Retrofit 

First Survey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent Total 

. . Prefer Gas 4 0 2 6 

Prefer Electricity 2 5 4 11 

~mbivalent 4 4 12 2 0 

Total 10 9 18 3 7 

Third Survey After Retrofit (Mission Viejo on1y)a 

Second Survey 

After Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 5 0 2 7 

Prefer Electricity 0 3 1 4 

Ambivalent 4 0 4 8 

Total 9 3 7 19 

aOnly Mission Viejo participants were surveyed a third time after receiving their 

new appliances. Consequently, the third survey is based on a much smaller 

sample than the first two, above. 

 EE-RM-94-230 COMMENT 61F

Table 7-4a 

Longitudinal Changes in Electric Sample Participants' Fuel Type 

Preferences for Water Heaters 

First Survey After Retrofit 

Before Retrofit Prefer Gas Prefer Electricity Ambivalent 

Prefer Gas 3 3, 6 

Prefer Electricity 3 2 10 

Ambivalent 0 6 4 

Total 6 11 20 

Second Survey After Retrofit 

Fi rst Su rvey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent 

Prefer Gas 4 0 2 

Prefer Electricity 2 5 4 

. Ambivalent 4 4 12 

Total 10 9 18 

Third Survey After Retrofit (Mission Viejo only)a 

Second Survey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent 

Prefer Gas· 5 0 2 

Prefer Electricity 0 3 

Ambivalent 4 0 4 

Total 9 3 7 

Total 

12 

15 

10 

37 

Total 

6 

11 

20 

37 

Total 

7 

4 

8 

19 

aOnly Mission Viejo participants were surveyed a third time after receiving their 

new appliances, Consequently, the third survey is based on a much smaller 

sample than the first two, above. 

7-16 
Lon~itudinal Chances in Gk Sample Participants' Fuel Type 

Preferences for Water Heaters + 

First Survey 'After Retrofit 

Before Retrofit Prefer Gas Prefer Electricity Ambivalent Total 

Prefer Gas 11 1 4 16 

Prefer Electricity 1 0 0 1 

Ambivalent 13 1 9 23 

Total 2 5 2 13 40 

Second Survey After ~etrhfita 

First Survey 

After Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 16 2 7 25 

Prefer Electricity 0 0 3 3 

Ambivalent 4 1 8 13 

Total 2 0 3 18 4 1 

Third Survey After Retrofit (Mission Viejo only)b 

Second Survey 

After Retrofit Prefer Gas Prefer Electricitv Ambivalent Total 

Prefer Gas 2 1 3 6 

Prefer Electricity 2 0 0 2 

Ambivalent 3 1 1 5 

Total 7 2 4 .' 13 

aSurvey contains one more response than the first survey, above; thus, the 

number of participants who strongly preferred electricity is one higher. 

bOnly Mission Viejo participants were surveyed a third time after receiving their 

new appliances. Consequently, this survey is based on a much smaller sample 

than the first two, above. 

 EE-RM-94-230 COMMENT 61F

Table 7-4b 

Longitudinal Changes in Gas Sample Participants' Fuel Type 

Preferences for Water Heaters 

First Survey After Retrofit 

Before Retrofit Prefer Gas Prefer Electricity Ambivalent 

Prefer Gas 11 1 4 

Prefer Electricity 1 0 0 

Ambivalent 13 1 9 

Total 25 2 13. 

Second Survey After Retrofita 

First Survey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent 

Prefer Gas 16 2 7 

Prefer Electricity 0 0 3 

Ambivalent A 1 8 

Total 20 3 18 

Third Survey After Retrofit {Mission Viejo only)b 

Second Survey 

After Retrofit Prefer Gas Prefer Electricity Ambivalent 

Prefer Gas 2 1 3 

Prefer Electricity 2 0 0 

Ambivalent 3 1 . 1 

Total 7 2 4 

aSurvey contains one more response than the first survey, above; thus, the 

number of participants who strongly preferred electricity is one higher. 

Total 

16. 

1 

23 

40 

Total 

25 

3 

13 

41 

Total 

6 

2 

5 

13 

bOnly Mission Viejo participants were surveyed a third time after receiving their 

new appliances. Consequently, this survey is based on a much smaller sample 

than the first two, above. 
Table 7-5 

REUC Particinants' Willingness to Recommend Their Water Heater 

-- - - - To a Friend - BY Fuel Tv~e 

No. of Participants (%) 

Recommend Don't Recommend 

Very Very 

Gas Samole Stron~lv Weaklv Unsure Weaklv Strongly 

Survey 3 2 2 10 12 0 2 

(n=46) (47.8) (21.7) (26.1 ) (0.0) (4.4) 

Survey 4 2 1 4 5 4 1 

(n=35) (60.0) (1 1.4) (14.3) (1 1.4) (2.9): 

Electric Samole 

Survey 3 12 2 13 1 18 

(n=46) (26.1) (4.3) (28.31, (2.2) (39.2) 

Survey 4 14 4 4 3 15 

(n=40) (35.0) (1 0.0) (1 0.0) (7.5) (37.5) 

COOKING 

This section discusses customer reactions to the cooking appliances. The experimental group for this appliance is different from the other appliances. Here, only 

the 22 households in the Mission Viejo Electric Sample received new electric 

induction ranges. Customers retrofitted with electric induction ranges were very 

pleased with their performance. 

Figure 7-5 shows that one-third of the Mission Viejo Electric Sample customers 

who originally strongly preferred gas ranges switched to strongly preferring electric 

ranges, allowing the number who preferred electric ranges to quadruple. Before 

retrofit, over 75 percent of the participants strongly preferred gas and approximately 10 percent strongly strongly preferred electricity. However, immediately 

after retrofit, the number of participants preferring gas plummeted to 

approximately 25 percent. At the same time, those strongly preferring electricity 

rose to approximately 25 percent. Thus, exposure to electric ranges resulted in 

strong changes in fuel type preferences favoring electricity. 

 EE-RM-94-230 COMMENT 61F

Table 7-5 

REUC Particillants' Willingness to Recommend Their Water Heater 

To a Friend By Fuel TYlle .. "'-.:... 

No. of Participants (%) 

Recommend Don't Recommend 

Very Very 

Gas Sample Strongly Weakly Unsure Weakly Strongly 

Survey 3 22 10 12 0 2 

(n=46) (47.8) (21.7) (26.1 ) (0.0) (4.4) 

Survey 4 21 4 5 4 1 

(n=35) (60.0) (11.4) (14.3) (11.4) (2.9)' 

Electric Sample 

Survey 3 12 2 13 1 18 

(n=46) (26.1 ) (4.3) (28.3) (2.2) (39.2) 

Survey 4 14 4 4 3 15 

(n=40) (35.0) (10.0) (10.0) (7.5) (37.5) 

COOKING 

This section discusses customer reactions to the cooking appliances. The experimental group for this appliance is different from the other appliances. Here, only 

the 22 households in the Mission Viejo Electric Sample received new electric 

induction ranges. Customers retrofitted with electric induction ranges were'very 

pleased with their performance. 

Figure 7-5 shows that one-third of the Mission Viejo Electric Sample customers 

who originally strongly preferred gas ranges switched to strongly preferring electric 

ranges, allowing the number who preferred electric ranges to quadruple. Before 

retrofit, over 75 percent of the participants strongly preferred gas and approximately 10 percent strongly strongly preferred electricity. However, immediately 

after retrofit, the number of participants preferring gas plummeted to 

approximately 25 percent. At the same time, those strongly preferring electricity 

rose to approximately 25 percent. Thus, exposure to electric ranges resulted in 

strong changes in fuel type preferences favoring electricity. 

7-18 
Figure 7-5 

Loneitudinal Chanze in Electric Induction Ranee Subsamole 

Particioants' Fuel Tvoe Preference for Cooking 

% 100 Preferring 

-------. 

80 - 

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

20 ,.-- -- 

*.*' 

. 

0 I 

Before Fir3 Survey Second Survey Third Suwey 

Retrofit After Retroffi After Retrofit After Retrofit 

Additionally, as shown in Table 7-6, Mission Viejo Electric Sample participants 

were likely to recommend their new ranges to friends. Over time, however, the 

number who would recommend the ranges decreased from 80 percent to 68.4 

percent, and.the number who declined to recommend them increased from 10 

percent to 21.1 percent. But the number of participants who would recommend 

their range very strongly remained stable at approximately 60 percent. 

Table 7-7 compares participants"attitudes toward electric and gas ranges 

between the Mission Viejo Electric Sample (new electric induction ranges) and 

the lrvine Sample (no new ranges or new information).4 Significant differences 

4This comparison is not being used as a beforelafter temporal comparison 

of one test group. Rather, it is a comparison between one "fully informed group 

and another group with less information. The lrvine sample contains 44 gas 

ranges and six conventional electric ranges. 

 EE-RM-94-230 COMMENT 61F

Figure 7-5 

longitudinal Change in Electric Induction Range Subsample 

Participants' fuel Type ·Preference for Cooking 

'Yo 100 Preferring 

Strongly Prefer Gas --'""---.. ~ Strongly Prefer Electricity 80 

60 • 

40 • 

.' ----~~-----~--p--------.~ 

" 20 - ,.' 

.. -" 

.-" 

0 

Before First Survey Second Survey Third Survey 

Retrofit After Retrofit . After Retrofit After Retrofit 

Additionally, as shown in Table 7-6, Mission Viejo Electric Sample participants 

were .likely to recommend their new ranges to friends. Over time, however, the 

number who would recommend the ranges decreased from 80 percent to 68.4 

percent, and·the number who declined to recommend them increased from 10 

percent to 21.1 percent. But the number of participants who would recommend 

their range very strongly remained stable at approximately 60 percent. 

Table 7-7 compares participants'· attitudes toward electric and gas ranges 

between the Mission Viejo Electric Sample (new electric induction ranges) and 

the Irvine Sample (no new ranges or new informationl.4 Signifrcant differences 

4This comparison is not being used as a before/after temporal comparison 

of one test group. Rather, it is a comparison between one "fully informed" group 

and another group with less information. The Irvine sample contains 44 gas 

ranges and six conventional electric ranges. 

71fl 
occurred for only two of the nine attributes. The lrvine Sample disagreed that gas 

ranges/ovens had an unpleasant smell, whereas the Electric lnduction Sample was 

unsure. Conversely, the Electric lnduction Subsample agreed that electric ranges - - - and ovens are safer, while the lrvine Sample was unsure. 

In both samples, customers agreed that they do not know what it costs to operate 

a range or an oven. This is an indication that customers in general are unaware of 

the cost efficiencies of their cooking appliances. It implies that their valuations of 

these appliances are driven by quality, not operating cost. 

Table 7-6 

Electric lnduction Subsamole Participants' Willineness to Recommend 

Their Ranve To a Friend 

No. of Participants (%) 

Recommend Don't Recomniend 

Very Very 

Strongly Weaklv Unsure Weaklv Strongly 

Survey 3 

(n=20) 

Survey 4 

(n=19) 

 EE-RM-94-230 COMMENT 61F

occurred for only two of the nine attributes. The Irvine Sample disagreed that gas 

ranges/ovens had an unpleasant smell, whereas the Electric Induction Sample was 

unsure. Conversely, the Electric Induction Subsample agreed that electric ranges 

and.ovensare safer, while the Irvine Sample was unsure. ' 

In both samples, customers agreed that they do not know what it costs to operate 

a range or an oven. This is an indication that customers in general are unaware of 

the c9st efficiencies of their cooking appliances. It implies that their valuations of 

these appliances are driven by quality, not operating cost. 

Table 7-6 

Electric Induction Subsample Participants' Willingness to Recommend 

Their Range To a Friend 

No. of Participants ('Yo) 

Recommend Don't Recommend 

Very Very 

Strongly Weakly Unsure Weakly Strongly 

Survey 3 12 4 2 1 1 

(n=20) (60.0) (20.0) (10.0) (5.0) (5.0) 

Survey 4 11 2 2 1 3 

(n=19) (57.9) (10.5) (10.5) (5.3) (15.8) 

7-20 
Table 7-7 

REUC Participants' Rangeloven Comparisons 

between Fuel Tvpes 

Means (standard errors) 

Electric 

(1). . . . . . . . . . . . . . . . . . . . . (7) lrvine Samole Induction Subsam~le 

strongly Strongly (n=50) (n=2S) 

Agree Disagree 

Gas rangeloven has unpleasant 5.19* (.25) 3.92 (.35) 

smell 

Electric rangeloven less expensive 4.91 (.22) ' 4.78 (.24) 

to operate 

Electric ovens cook more evenly 4.30 (.29) 4.08 (.31) 

Electric ovens heat up kitchen less 4.28 (.25) 4.04 (.34) 

Electric rangeloven safer 4.12* (.24) . 2.88 (.36) 

Gas rangeloven less expensive 3.77 (.21) 4.05 (.28) 

to buy 

Electric ovens easier to clean 3.56 (.27) 3.09 (.33) 

No idea of cost to operate 2.86 (.29) 3.42 (.42) 

Easier to control temperature on 2.74 (.30) 3.25 (.43) 

a gas range 

*Difference is significant at at least the 5% level. 

APPLIANCE DISCOUNT FACTORS 

An important consideration in the planning of a demand-side program or development of a new end-use technology is an assessment of the market participants' 

financial expectations from energy-related investments. The customer acceptance of a utility demand-side program may be disappointing because the rate at 

which individuals would like to be paid back for their conservation or efficiency 

investments is underestimated. The higher an individual customer's discount 

rate, or required annualized return, the shorter the required payback period for a 

fixed investment. 

 EE-RM-94-230 COMMENT 61F

Table 7-7 

REUC Participants' Range/Oven Comparisons 

between Fuel Types 

Means (standard errors) 

Electric 

(1) ..•....•..•..•....... (7) Irvine Sam(:lle Induction Subsamf;lle 

Strongly Strongly (n=50) (n=25) 

Agree Disagree 

> Gas range/oven has unpleasant 5.19* (.25) 3.92 (.35) 

smell 

Electric range/oven less expensive 4.91 (.22) 4.78 (.24) 

to operate 

Electric ovens cook more evenly 4.30 (.29) 4.08 (.31) 

Electric ovens heat up kitchen less 4.28 (.25) 4.04 (.34) 

Electric range/oven safer 4.12* (.24) . 2.88 (.36) 

Gas range/oven less expensive 3.77 (.21 ) 4.05 (.28) 

to buy 

Electric ovens easier to clean 3.56 (.27) 3.09 (.33) 

No idea of cost to operate 2.86 1.29) 3.42 (.42) 

Easier to control temperature on 2.74 (.30) 3.25 (.43) 

a gas range 

'Difference is significant at at least the 5% level. 

APPLIANCE DISCOUNT FACTORS 

An important consideration in the planning of a demand-side program or development of a new end-use technology is an assessment of the market participants' 

financial expectations from energy-related investments. The customer acceptance of a utility demand-side program may be disappointing because the rate at 

which individuals would like to be paid back for their conservation or efficiency 

investments is underestimated. The higher an individual customer's discount 

rate, or reqUired annualized return, the shorter the required payback period for a 

fixed investment. 

7-21 
The REUC project analyses have included the development and administration of 

survey questions designed to calculate the participants' implied discount rates. 

When this economic information about the participants is combined with analysis 

of the project's load data, results can be drawn regarding the potential penetration of the REUC's end-use technologies such as high efficiency heat pumps. 

Thus, if the REUC project data analysis leads to the determination that space 

heating with such heat pumps is substantially more energy efficient than 

comparable gas space heating systems, calculation of the participants' discount 

rates should provide some revealing answers about how individuals may choose to 

make such appliance efficiency investments. 

An individual's appliance choice is often modeled as a tradeoff between the 

initial capital investment and ongoing operating costs. The principle is that more 

energy-efficient appliances are typically based on more capital-intensive, and 

hence more expensive, technologies. Formally, Life Cycle Cost (LCC) models' of 

appliance choice compute for various appliance options the discounted stream of 

capital and operating cost expenditures. An important assumption in these calculations is the choice of discount rate. An individual's required return on an 

investment increases as the uncertainty of the stream of cash flows, or savings, 

associated with the investment increases. Money in the bank earning a guaranteed rate of return is a far less risky prospect than investing in higher efficiency 

appliances that may reduce monthly energy bills. Therefore, the discount rate 

applied to energy investments should be high enough to compensate for the 

uncertainty of the savings in monthly energy costs. Surveys 2 and 3 included 

several questions that permit estimation of respondents' discount rates. 

5End-use forecasting models such as REEPS and COMMEND rely on LCCtype calculations for forecasting appliance choice. See, "Household Appliance 

Choice: Revision of REEPS Behavioral Models," EPRl EA2512, February 1984; and 

"COMMEND Planning System: National and Regional Data and Analysis," 

prepared by Georgia Institute of Technology for.the Electric Power Research 

Institute, 1986. 

 EE-RM-94-230 COMMENT 61F

The REUC project analyses have included the development and administration of 

survey questions designed to ca!culate the participants' implied discount rates. 

When this economic information about th.e participants is combined wiifl analysis 

of the project's load data, results can be drawn regarding the potential penetra-. 

tion of the REUC's end-use technologies such as high efficiency heat pumps. 

Thus, if the REUC project da.ta analysis leads to the determination that space 

heating with such heat pumps is substantially more energy efficient than 

comparable gas space heating systems, calculation of the participants' discount 

rates should provide some revealing answers about how individuals may choose to 

make such appliance efficiency investments. 

An individual's appliance choice is often modeled as a tradeoff between .the 

initial capital investment and ongoing operating costs. the principle is that more 

energy-efficient appliances are typically based on more capital-intensive, and 

hence more expensive, technologies. Formally, life Cycle Cost (LCc) models5 of 

appliance choice compute for various appliance options the discounted stream of 

capital and operating cost expenditures. An important assumption in these calculations is the choice of discount rate. An individual's required return on an 

investment increases as the uncertainty of the stream of cash flows, or savings, 

associated with the investment increases. Money in the bank earning a guaranteed rate of return is a far less risky prospect than investing in higher efficiency 

appliances that may reduce monthly energy bills. Therefore, the discount rate 

applied to energy investments should be high enough t?· compensate for the 

uncertainty of the savings in monthly energy costs. Surveys 2 and 3 included 

several questions that permit estimation of respondents' discount rates. 

SEnd-use forecasting models such as REEPS and COMMEND rely on LCCtype calculations for forecasting appliance choice. See, "Household Appliance 

Choice: Revision of REEPS Behavioral Models," EPRI EA2512, February 1984; and 

"COMMEND Planning System: National and Regional Data and Analysis," 

prepared by Georgia Institute of Technology for' the Electric Power Research 

Institute, 1986. . 
the majority of the REUC sample, the relatively small, long-term required 

m on a riskless investment is, estimated to be around the expected 9 to 10 

ent, as illustrated by Figure 7-6. In contrast, Figure 7-7 demonstrates that the 

ired.return on an energy investment that yields an expected monthly savings . 

0 over a ten-year period ranged between 15and 60 percent. The "Implied 

lscount Rate" calculated in this table is the Internal Rate of Return of the initial 

gative cash flow. of the "Maximum Capital Investment" followed by 120 months 

$10 positive cash flows derived from energy savings. 

Figure 7-6 i 

REUC Participants' Reauired Return on a 

Small Riskless Investment 

Required Return 

Income: $50,001-$80,000 

 EE-RM-94-230 COMMENT 61F

majority of the REUC sample, the relatively small, long-term required 

on a riskless investment is estimated to be around the expected 9 to 10 

as illustrated by Figure 7-6. In contrast, Figure 7-7 demonstrates that the 

UII,:u.""urn on an energy investment that yields an expected monthly savings 

$10 over a ten-year period ranged between 15 and 60 percent. The "Implied 

Rate" calculated in this table is the Internal Rate of Return of the initial 

cash flow of the "Maximum Capital Investment" followed by 120 months 

.' $1 0 positive cash flows derived from energy savings. 

% 

Figure 7-6 

REUC Participants' Required Return on a 

Small Riskless Investment 

of 50~------------------------------------~ 

Participants 

40 

30 

20 

10 

o 

6%-8% 

Required Return 

rn Income: >$80,000 

I!III Income: $50,001-$80,000 

II Income: $30,000-$50,000 

11%-13% 
Figure 7-7 

REUC Participants' Rewired Return on an 

Investment in Appliance Efficiency 

"lo 

of 

Participants 

15%-59% 

Required Return 

& Income: $50,001-$80,000 

At discount rates as high as 60 percent, expensive appliances must demonstrate 

large efficiencies before they can compete with cheaper alternatives. For 

example, if the household discounts cash savings from energy investments at the 

rate of 60 percent, then in order to equate the Net Present Value of real cash 

savings to the investment, the improved appliance must use 84 percent less 

energy than the standard appliance. Table 7-8 illustrates an example that calculates the required efficiency of an appliance costing $500 more than the 

"standard" alternative. The efficiency is calculated based on assuming an annual 

real energy cost of $360 per year for operating the standard appliance. For 

 EE-RM-94-230 COMMENT 61F

% 

Figure 7-7 

REUC Participants' Required Return on an 

Investment in Appliance Efficiency 

of 40,--------------------------------------, Participants 

30 

20 

10 

o 

<15% 15%·59% 

Required Return 

~ Income: >$80,000 

III Income: $50,001·$80,000 

III Income: $30,000·$50,000 

>59% 

At discount rates as· high as 60 percent, expensive appliances must demonstrate 

large efficiencies before ihey can· compete with cheaper alternatives. For 

example, if the household discounts cash .savings from energy investments at the 

rate of 60 percent, then in order to equate the Net Present Value of real cash 

savings to the investment, the improved appliance must use 84 percent less 

energy than the standard appliance. Table 7-8 illustrates an example that calculates the required effiCiency of an appliance costing $500 more than the 

"standard" alternative. The efficiency is calculated based on assuming an annual 

real energy cost of $360 per year for operating the standard appliance. For 

7~? tl 
xample, the 28 percent figure in the middle column indicates that an individual 

[ith a 15 percent discount rate will be indifferent between the standard 

ppliance and an appliance that is 28 percent more efficient. This is based on a 

.n-year appliance life; for longer-lived appliances, the required efficiency level is 

ower (see the 15-year column on the far right). The table illustrates that 

:ustomers with higher discount rates (i.e., shorter required payback periods) need 

yeater efficiency levels to make investments in high-efficiency appliances. This 

2fficiency payback is discussed further in the next section. 

Table 7-8 

Enerav Savinxs Requirements of 

Higher Efficiency Appliances 

Marginal Capital Cost of Higher Efficiency Appliance: $500 

Real Annual Energy Cost for Standard Appliance: $360 

Energy Investment 

Discount Rate 

Required Energy Reduction Relative 

to Standard Appliance 

10-Year Life 15-Year Life 

 EE-RM-94-230 COMMENT 61F

xample, the 28 percent figure in the middle column indicates that an individual 

lith a 15 percent discount rate will be indifferent between the standard 

ppliance and an appliance that is 28 percent more efficient. This is based on a 

::I)-year appliance life; for longer-lived appliances, the reqL!ired effidency level is 

ower (see the 15-year column on the far right). The table illustrates that 

;ustomers with higher discount rates (i.e., shorter required payback periods) need 

$reater efficiency levels'to make investments in highcefficiency appliances. This 

"fficiency payback is discussed further in the next section. 

Assumptions: 

Table 7-8 

Energy Savings Requirements of 

Higher Efficiency Appliances 

• Marginal Capital Cost of Higher Efficiency Appliance: 

Real Annual Energy Cost for Standard Appliance: 

$500 

• $360 

Energy Investment 

Required Energy Reduction Relative 

to Standard Appliance 

Discount Rate lO-Year life 1 5-Year life 

15% . 28% 24% 

20% 33% 30% 

40% 58% 56% 

60% 84·% 83% 

7-25 
Section 8 

INTEGRATION OF LOAD AND SURVEY ANALYSES 

This section discusses the integration of the REUC's market-research based customer data with the REUC participants' end-use load data. The integrated market 

'research (IMR) techniques employed in the analysis of the REUC data have 

provided the following benefits: 

They allow identification of more homogeneous customer 

types than classifications based only on demographic data. In 

doing so, the statistical estimation of load shapes is made 

more precise and hence more useful for utility planning. 

They provide knowledge of the load shapes for different 

market-research based customer segments. This gives utility 

market planners the capability to assess the potential load 

and energy impacts of targeting demand-side programs to 

that segment. 

The results shown here establish the value of developing databases for demandside programs consisting of both behavioral (survey) and engineering (load) data. 

These techniques use existing utility customer data as a foundation to systematically address both "traditional" as well as emerging demand-side planning issues, 

producing enriched characterizations of customer segments. 

The survey variables and classification schemes used for the market research 

dimension of the REUC are explained in the previous section. In this section, average load shapes are calculated across separate customer subgroups of the sample. 

Three different types of variables are employed in the stratification schemes 

below: the Customer Preference and Behavior (CP&B) CLASSIFYTM method,' the 

factor analysis results from Section 7, and the traditional income and square feet 

variables often used to define residential customer segments. 

1 Residential Customer Preference and Behavior: Market Segmentation 

Using "CLASSIFYTM," EPRl EM-5908, March 1989. 

 EE-RM-94-230 COMMENT 61F

Section 8 

INTEGRATION OF LOAD AND SURVEY ANALYSES 

INTRODUCTION 

This section discusses the integration of the REUC's market-research based customer data with the REUC participants' end-use load data. The integrated market 

research (iMR) techniques employed in the analysis of the REUC data have 

provided the following benefits: 

• They allow i,dentification of more homogeneous customer 

types than classifications based only on demographic data. In 

doing so, the statistical estimation of load shapes is' made 

more precise and hence more useful for utility planning. ' 

• ,They provide knowledge of the load shapes for different 

market-research based customer segments. This gives utility 

market planners the ,capability to assess the potential load 

and energy impacts of targeting demand-side programs to 

that segment. 

The results shown here establish the value of developing databases for demandside programs consisting of both behavioral (survey) and engineering (load) data. 

These techniques use existing utility customer data as a foundation to systematically address both "traditional" as well as emerging demand-side planning issues, 

, producing enriched characterizations of customer segments. 

The survey variables and classification schemes used for the market research 

dimension of the REUC are explained in the previous section. In this section, average load shapes are calculated across separate customer subgroups of the sample. 

Three different types of variables are employed in the stratification schemes 

below: the Customer Preference and Behavior (CP&B) CLASSIFYTM method,l the 

factor analysis results from Section 7, and the traditional income and square feet 

variables often used to define resideiltial customer segments. 

1 Residential Customer Preference and Behavior: Market Segmentation 

Using "CLASSIFYTM," EPRI EM-59GB, March 19B9. 
LOAD SHAPES BY REUC CUSTOMER SEGMENT 

A number of different characteristics were used for ex stratification of the 

REUC sample, generating a large number of load profiles for different customer 

segments and end-uses. Four of the stratification schemes are presented for two 

electric end-uses, space conditioning and water heating, in this section. The 

CP&B stratification has four valid customer segments.2 The other three 

stratification schemes (fuel Quality factor score, fuel Value factor score, and 

income-by-square feet) are two-by-two stratifications, each defining four customer 

segments. 

The fuel image questions as discussed in Section 7 indicate that customers have 

distinct images of electricity and gas. The various image responses were successfully reduced to two dimensions (or factors):. fuel qual lit^ (a composite of the . . 

Safety, Convenience, Service and Reliability survey items) and fuel "Value" (a 

composite of the Appliance Cost, Expense, and Monthly Utility Bill items). For 

each REUC respondent, the factor score representing the image of gas and electricity is calculated for both Quality and Value. As illustrated below, these scores 

are an excellent method for stratifying the participant's load shapes of their appliances. 

Summer weekday average electric space conditioning load shapes are shown in 

Figures 8-1 and 8-2. The CP&B stratification is presented in Figure 8-1. As 

expected, the "Resource Conservers" consume the least amount of energy for air 

conditioning. The Value factor score stratification is presented in Figure 8-2. In 

this two-by-two stratification, the customers are grouped by their electric and gas 

Value scores (high versus low), generating a total of four strata. The average load 

shapes across these subgroups are clearly distinct. For example, a customer in the 

"Low ElecILow Gas Value" segment believes both fuels are expensive relative to a 

customer in the "High EledHigh Gas Value" segment. Those customers who 

perceive electricity as low Value consistently use more electricity. Perhaps high 

 TWO CP&B needs-based classification groups were placed into the 

"unclassifiable" category (Pleasure Seekers and Hassle Avoiders) because not 

enough REUC households were classified into these categories to make valid 

subgroups. 

 EE-RM-94-230 COMMENT 61F

LOAD SHAPES BY REUC CUSTOMER SEGMENT 

A number of different characteristics were used for ex post stratification of the 

REUC sample, generating a large number of load profiles for different customer 

segments and end-uses. Four of the stratification schemes are presented for two 

electric end-uses, space conditioning and water heating, in this section. The 

CP&B stratification has four valid customer segments. 2 The ,other three 

stratification schemes (fuel Quality factor score, fuel Value factor score, and 

income-by-square feet) are two-by-two stratifications, each defining four customer 

segments. 

The fuel image questions as discussed in Section 7 indicate that customers have 

distinct images of electricity and gas. The various image responses were succes'sfully reduced to two dimensions (or factors):' fuel "Quality" (a composite of the 

Safety, Convenience, Service and Reliability survey item's) and fuel "Value" (a 

composite of the Appliance Cost, Expense, and Monthly Utility Bill items). For 

each REUC respondent, the factor score representing the image of gas and electricity is calculated for both Quality and Value. As illustrated below, these scores 

are an excellent method for stratifying the participant's load shapes of their appli-

, ances. 

Summer weekday average electric space conditioning load shapes are shown in 

Figures 8-1 and 8-2. The CP&B stratification is presented in Figure 8-1. As 

expected, the "Resource Conservers" consume the least amount of energy for air 

conditioning. The Value factor score stratification is presented in, Figure 8-2. In 

this two-by-two stratification, the customers are grouped by their electric and gas 

Value scores (high versus low), generating a total of four strata. The average load 

shapes across these subgroups are clearly distinct. For example, a customer in the 

"Low Elec/Low Gas Value"segment believes both fuels are expensive relative to a 

customer in the "High Elec/High Gas Value" segment. Those customers who 

perceive electricity as low Value consistently use more electricity. Perhaps high 

2Two CP&B needs-based classification groups were placed into the 

"unclassifiable" category (Pleasure Seekers and Hassle Avoiders) because not 

enough REUC households were classified into these categories to make valid 

subgroups. 

8-2 
F1ls affect customer image more than their image affects usage. Though this conllusion may be obvious in this context, this result is contrary to what one would 

Lpect for standard household products that cbmpete in the market. 

t... i. ' 

! 

.winter weekday average electric water heating load shapes are shown in Figures ~. 

18-3 and 8-4. The Quality factor score stratification is presented in Figure 8-3. This 

!. 

'aimension of fuel image also groups customers into distinct load shapes. As 

:: 

;before, the same result is observed; customers who perceive electricity as low . lquality use more energy. A more traditional stratification scheme, income-bysquare feet, is presented in Figure 8-4. The estimated load shapes by these 

:;ustomer segments, as defined in Section 4, also have different shapes, but, as 

'shown below, this traditional stratification is generally inferior to the factor score 

based stratification schemes. 

Figure 8-1 

REUC Summer Weekday Load Shape: Electric S~ace Conditioning 

Customer Preference & Behavior Stratification 

0.5 - 

0.4- 

0.2 - 

0 3 6 9 12 15 I8 21 24 

Time 01 Day (in Hours PSTI 

 EE-RM-94-230 COMMENT 61F

~iJls affect customer image more than their image affects usage. Though this conclusion may be obvious in this context, this result is contrary to what one would' 

~pect for standard household products that c~mpete in the market. 

, 

r","· 

p 

Winter weekday average electric water heating load shapes are shown in Figures 

l8-3 and 8-4. The Quality factor score stratification is presented in Figure 8-3. This I. 

,'dimension of fuel image also groups customers into distinct load shapes. As 

(before, the same resu It is observed; customers who perceive electricity as low 

'.quality use more energy. A more traditional stratification scheme, income-by-

.square feet, is presented in Figure 8-4. The estimated load shapes by these 

:customer segments, as defined in Section 4, also have different shapes, but, as 

'shown below, this traditional stratification is generally inferior to the factor score 

based stratification schemes. 

Figure 8-1 

REUC Summer Weekday load Shape: Electric Space Conditioning 

Customer Preference & Behavior Stratification 

kW Load 1.0 ~--------------------, 

0.8 

0.6 

0.4 

0.2 

o 3 

Appeer. Con . 

Res, Consry. 

V.1ue Seeker 

6 9 12 15 

Time of Dey (in Hours PST) 

B-3 

18 21 24 
Figure 8-2 

REUC Summer Weekdav Load Shave: Electric Soace Conditioning 

Electric ValuelGas Value Stratification 

0.6 - 

0.4 - 

0 3 6 9 12 15 18 21 24 

Tlms or Day (innoun PST) 

Figure 8-3 

REUC Winter Weekday Load Shaoe: Heat Pumv Water Heater 

Electric Oua/itv/Gas Oualitv Stratification 

LW Load 1.0 

0.8- Hi EleclLo~ G059UPIit 

0.6- 

0.4- 

0.0- , 0 3 6 g 12 15 18 21 24 

Time of Day (in Hours PST) 

 EE-RM-94-230 COMMENT 61F

Figure 8-2 

REUC Summer Weekday load Shape: Electric Space Conditioning 

Electric Value/Gas Value Stratification 

t:.WI.Oe4 10..--------------------. 

•• 

0 .• 

0.4 

•. 2 

l.OYf EleclLow GbS Val " 

Low Elec1Hl GO$ volu 

HI E;locll..ow GOi Volu 

HI EJec1H1 Gus lJell.lO 

• 02 

figure 8-3 

IS 16 21 24 

REUC Winter Weekday load Shape: Heat Pump Water Heater 

Electric Quality/Gas Quality Stratification 

5:.'" I.o~d 1.0'T---------;::========::::;;-, low EleclLow GO$ Qual t 

low Elee/H. G(I~ Quallt 

0.0 Hj Elee/low Gas Qualil 

Hi Elee/Hi Gas Quality 

e.' 

, . 

0.1 " 

o.o~ __ -....,,....-.,..-...,..-.... --,....-.,..-"" 

o 6 9 12 Ie 21 24 

Tima or Dey (in Hours PST) 

.8-4 
Figure 8-4 

REUC Winter Weekdav Load Shaoe: Heat Pump Water Heater 

Income bv Sauare Feet Stratification 

kW Load 10 

LOW Sqft/HI lncom 

08- Hi Sqft/Low lncom 

06- 

04- 

00 -,.,,,.I. I.,. I.,. 

0 3 6 9 12 15 18 21 24 

Ttme of Day dn Hours PST) 

DETERMINATION OF "OPTIMAL" STRATIFYING VARIABLES 

Stratifying a sample into groups of households with similar characteristics can 

produce more homogeneous load shapes for each group, thereby decreasing the 

total variance of the average load shape across the households in each group. This 

is the general principle used to determine "optimal" stratification in traditional 

load research analysis when, for example, a customer sample is divided into small 

and large kwh usage strata. 

Important customer characteristics that determine energy usage may ,not be 

limited to variables such as kwh usage and household demographics, which are 

typically used for stratifying load data. Customer needs and attitudes are measurable customer characteristics that may serve as better determinants of energy 

usage. This presents an interesting question: How does one determine the "best" 

 EE-RM-94-230 COMMENT 61F

Figure 8-4 

REUC Winter Weekday Load Shape:' Heat Pump Water Heater 

I ncome by Square Feet Stratification 

kW LoM 1.0..--------------------. 

0.8 

0.4 

0.2 

low SQftllow Inco e Low SqftlHI Incom 

HI SqftlLow Incom 

HI SQft/HI Income 

0.0 -!-....--,-...,........,--._..--.--,--...-.-...,..-,.-...-..,..... ......... -I 

o 9 12 15 Ie 21 24 

Tim. of D.~ On Hours PST) 

DETERMINATION OF "OPTIMAL" STRATIFYING VARIABLES 

Stratifying a sample into groups of households with similar characteristics can 

produce more homogeneous load shapes for each group, thereby decreasing the 

total variance of the average load shape across the households in each group. This 

is the general principle used to de.termine "optimal" stratification in traditional 

load research analySiS when, for example, a customer sample is divided into small 

and large kWh usage strata. 

Important customer characteristics that determine energy usage may .not be 

limited to variables such as kWh usage and household demographics, which are 

typically used for stratifying load data. Customer needs and attitudes are measurable customer characteristics that may serve as better determinants' of energy 

usage. This presents an interesting question: How does one determine the "best" 
characteristics ex post by which to stratify the sample data? The method applied 

to the REUC data for determining the optimal stratifying variables is a "least variance" criterion. That is, the best grouping of sample data is the one with the least 

total variance for all of the average load shapes for each customer stratum. 

Test results,for the water heating and space conditioning end-uses, as well as 

whole house electricity usage, are presented in Table 8-1. The sample was 

separated by season and pooled across the two Orange County cities for these 

tests. Only weekday average load profiles were used. 

Table 8-1 

REUC Stratification Scheme Variance Calculations 

Appliance Income-byLoad Sha~e~ CP&B Souare Feet Oual,tv 

Whole Houseb 78.1 6* 84.60 84.82 86.44 

Space Conditioningc 27.30 26.63 25.1 4 24.71 * 

Water ~eatingd 9.34 7.24* 8.48 8.38 

Summer 

Whole House 71.28 71.09 68.12* 68.43 

Space Conditioning 21.45 20.67 18.39 18.12* 

Water Heating 11.47 8.29* 10.51 10.45 

*Indicates the stratification variable that results in the least total variance. 

aWhole House, Space Conditioning and Water Heating are from the REUC 

electric sample households. 

b~he Whole House is the electricity usage for the entire home. 

CSpace Conditioning is a heat pump. 

d~ater Heating is a heat pump water heater. 

 EE-RM-94-230 COMMENT 61F

characteristics ex post by which to stratify the sample data? The method applied 

to the REUC data for determining the optimal stratifying variables is a "least variance" criterion. That is, the best grouping of sample data is the one with the least , total variance for all of the average load shapes for. each customer stratum. 

Test results for the water heating and space conditioning end-uses, as well as 

whole house electricity usage, are presented in Table 8-1. The sample was 

separated by season and pooled across the two Orange County cities for these 

tests. Only weekday average load profiles were used. 

Table 8-1 

REUC Stratification Scheme Variance Calculations 

Appliance 

Load Shape' CP&B 

Whole Houseb 78.16* 

Space ConditioningC 27.30 

Water Heatingd 9.34 

Whole House 71.28 

Space Conditioning 21.45 

Water Heating 11.47 

Income-bySquare Feet 

Winter 

84.60 

26.63 

7.24* 

Summer 

71.09 

20.67 

8.29* 

Quality 

84.82 

25.14 

8.48 

68.12* 

18.39 

10.51 

86.44 

24.71* 

8.38 

68.43 

18.12 * 

10.45 

*Indicates the stratification variable that results in the least total variance. 

'Whole House, Space Conditioning and Water Heating are from the REUC 

electric sample households. 

bThe Whole House is the electricity u~age for the entire home. 

CSpace Conditioning is a heat pump. 

dWater Heating is a heat pump water heater. 
/four stratification schemes were considered for these end-uses and whole-house 

CP&B, Value image, Quality image and income-by-square feet. The 

measure shown in.Table . 8-1 is interpreted as follows: lower values 

:indicate lower overall variance. Thus, the lowest value by appliance indicates the 

most effective stratification variable. As Table 8-1 indicates, the factor analysis 

'methods generally work best, as the Quality and Value stratifications have the 

lowest overall variance in three of the six load shape types. The Value stratification 

works well, producing estimates with the least variance in two cases. he 

traditional income-by-Square Feet stratification performs about as well as the 

Value stratification in this ranking. The CP&B stratification performs best for the 

winter weekday whole-house load shape type. 

The results of these limited tests show promise in the development of new, nontraditional, stratification schemes. While additional research is needed to develop 

procedures to test the significance of these stratification schemes more broadly, 

the techniques developed here illustrate how optimal stratification variables can 

be identified for end-use load shapes. 

APPLIANCE LIFE-CYCLE COSTS 

One of the principal objectives of the REUC project was to collect sufficient 

information to permit-a comparison of the ,range of overall customer costs of 

.operating comparable sets of electric and gas .appliances.. Another application of 

the integrated market research data has been comparison of electric and gas 

appliance life-cycle-costs. The following discussion summarizes several aspects of 

these analyses. The cost comparison approach developed for these 'analyses, 

termed the Cost Payback Approach, combines appliance purchase. price, energy 

consumption, fuel price and customer survey data collected on the project to 

compare the life-cycle costs of the monitored appliances. The results for space 

conditioning appliances are summarized here. Water heating, drying, cooking 

and whole house life-cycle costs also were analyzed, though these results are not ', 

presented' in this report. 

The cost comparison method is termed the Cost Payback Approach because it is 

based on calculating a range of life-cycle costs for electric and gas appliances 

based on a range of customer payback periods. The approach accounts for the 

 EE-RM-94-230 COMMENT 61F

iFour stratification schemes were considered for these end-uses and whole-house 

Iload: CP&B, Value image, Quality image and income-by-square feet. The 

;'variance measure shown il).Ja?l~ B-1 is interpreted as follows: lower values 

!indicate lower overall variance. Thus, the lowest value by appliance indicates the 

most effective stratification variable. As Table B-1 indicates, the factor analysis 

. methods generally work best, as the Quality and Value stratifications have the 

lowest overall variance in three of the six load shape types. The Value stratification 

works well, producing estimates with the least variance in two cases. The 

traditional Income-by-Square Feet stratification performs about as well as the 

Value stratification in this ranking. The CP&B stratification performs best for the 

winter weekday whole-house load shape type. 

The results of these limited tests show promise in the development of new, nontraditional, stratification schemes. While additional research is needed to develop 

procedures to test the significance of these stratification schemes more broadly, 

the techniques developed here illustrate how .optimal stratification variables can 

. be identified for end-use load shapes. 

APPLIANCE LIfE-CYCLE COSTS 

One of the principal objectives of the REUC project was to collect sufficient 

information to permit·a comparison of the .range of overall customer costs of 

·operating comparable sets of electric and gas .appliances .. Another application of 

the integrated market research data has been comparison of electric and gas 

appliance life-cycle.costs. The following discussion summarizes several aspects of 

these analyses. The cost comparison approach developed for these analyses, 

termed the Cost Payback Approach, combines appliance purchase. price, energy 

consumption, fuel price and customer survey data collected on the project to 

compare the life-cycle costs of the monitored appliances. The results for space 

conditioning appliances are summarized here. Water heating, drying, cooking 

and whole house life-cycle costs also were analyzed, though these results are not 

presented in this report. 

The .cost comparison method is ter.med the Cost Payback Approach because it is 

based on calculating a range of life-cycle costs for electric and gas appliances 

based .on a range of customer payback periods. The approach accounts for the 

8-7 
three components of a customer's appliance investment decision: (1) appliance , ' 

operatingcosts; (2) the appliance's capital costs; ,and (3) the customer's required. 

payback period. The cost information has been derived from the REUC end-use 

load data and other sources. The customer required payback period'; have been 

derived from ~~~REUC survey responses. 

As used here, customer payback periods represent the amount of time needed 

before an initial energy investment is recovered through reduced operation costs. 

Required payback period represents the maximum length of payback period that 

a customer will accept in deciding upon an energy investment. Required payback period is closely related to the concept of implicit discount rate.3 Higher 

consumer implicit discount rates imply shorter required payback periods. 

Analyses of.survey responses to a series of questions on possible energy investment 

decisions indicate that the required payback period on an energy investment for 

almost three-quarters of the REUC customers is less than five years. The distribution of these customers' required payback periods is illustrated in Figure 8-5, 

ranging from less than two years to 20 years. 

In many areas, natural gas for residential customers costs less per BTU than 

electricity. Thus, gas systems (gas furnacelelectric air conditioner) generally cost 

less to operate than standard electric (resistance heatingair conditioning) systems. 

However, the improved energy efficiency of heat pumps over standard electric 

systems closes the gas-electric cost gap considerably. For customers with a short 

payback period (implying less importance on future operating cost savings), a small 

capital cost advantage for heat pumps over a gas system will result in lower lifecycle costs for the heat pump system. That is, the somewhat lower operating cost 

of the gas system can be offset by a lower initial cost heat pump system to result in 

lower life-cycle costs. 

3~ee "Implicit Discount Rates in Residential Customer Choices," February 

1988, EPRl EM-5587, for a survey of estimates. 

8-8 

 EE-RM-94-230 COMMENT 61F

three components of a customer's appliance investment decision: (1) appliance 

operating costs; (2) the appliance's capital costs; and (3) the customer's required 

payback period. The cost information has been derived from the REUC end-use 

load data and other sources. The customer required payback period; have been 

derived from the REUC survey responses. 

As used here, customer payback periods represent the amount of time needed 

before' an initial energy investment is recovered through reduced operation costs. 

Required payback period represents the maximum length of payback period that 

a customer will accept in deciding upon an energy investment. Required payback period is closely related to the concept of implicit discount rate.3 Higher, 

consumer implicit discount rates imply shorter required payback periods. 

Analyses of. survey responses to a series of questions on possible energy investment 

decisions indicate that the required payback period on an energy investment for 

almost three-quarters of the REUC customers is less than five years. The distribution of these customers' required payback periods is illustrated in Figure 8-5, 

ranging from less than two years to 20 years. 

In many areas, natural gas for residential customers costs less per BTU than 

electricity. Thus, gas systems (gas furnace/electric air conditioner) generally cost 

less to operate than standard electric (resistance heating/air conditioning) systems. 

However, the improved energy efficiency of heat pumps over standard electric 

systems closes the gas-electric cost gap considerably. For customers with a short 

payback Period (implying less importance on future operating cost savings), a small 

capital cost advantage for heat pumps over a gas system will result in lower lifecycle costs for the heat" pump system. That is, the somewhat lower operating cost 

of the gas system can be offset by a lower initial cost heat pump system to result in 

lower life-cycle costs. ' 

3See "Implicit Discount Rates in Residential Customer Choices," February 

1988, EPRI EM-SS87, for a survey of estimates. 

8-8 
Figure 8-5 

REUC Particioants' Rewired Payback Periods 

- . .... Percent oi 52 

panicipanrs I 

<2 " - --3 6-1 2 13-20 

Pavbru Period (Yrs.) 

The Cost Payback Approach results illusrrate this finding. Figure 8-6 indicates the 

combinations of appliance capital cost differences and payback periods for which 

electric heat pumps have a lower life-wcle cost than comparable gas systems. 

Each point on the curve has been derived by combining the REUC appliance 

energy data averages and payback information. It should be noted that the estimates oi electricity.and gas consumption exhibit a range around an average value. 

The curve shown in Figure 8-6 represents the fuel choice "indifference curve" 

based on these average values. The shaded region on the figure indicates the 

combinations oi capital cost differences and payback periods for which the electric heat pump has a lower life-cycle cost than the gas system. 

 EE-RM-94-230 COMMENT 61F

Figure 8-5 

REUC Participants' Required Payback Periods 

Percent or 00 

Particip.ants 

<2 6-12 

Period (Yrs.) 

13-20 

The Cost Payback Approach results illustrate this finding. Figure 8-6 indicates the 

combinations oi appliance capital cost difierences and payback periods for which 

electric heat pumps have a lower liie-cvcle cost than comparable gas systems. 

Each point on the curve has been derived by combining the REUC appliance 

energy data averages and payback iniormation. It should be noted that the estimates oi electricity. and gas consumption exhibit a range around an average value. 

The curve shown in Figure 8-6 represents the fuel choice "indifference curve" 

based on these average values. The shaded region on the figure indicates the 

combinations oi capital cost differences and payback periods for which the electric heat pump has a lower life-cycle cost than the gas system. 

8-'1 
Figure 8-6 

REUC Space Conditionine Fuel Choice Preference Regions. 

Payback Discount Capital Con 

(Yean) 

s447 

For example, a customer who requires a payback period of two years would see a 

lower electric heat pump life-cycle cost if the capital costs for the gas system 

exceeded those of the heat pump by more than $142. The capital cost difference between the gas system and the heat pump system has been estimated to 

be approximately $130. Customers with longer payback periods would require 

the heat pump to have lower capital costs relative to the gas system (e.g., the gas 

vs. electric cost difference would be greater). By using such an approach, a wide 

range of customer payback periods can be considered in appliance life-cycle cost 

comparisons so that utility program planners can target those customers most 

likely to prefer high-efficiency electric appliances. 

 EE-RM-94-230 COMMENT 61F

Figure 8-6 

REUC Space Conditioning Fuel Choice Preference Regions. 

Required 15 

payback 14 

in Payback Discount Capital Cost 

Years 13 (Yean:) Rate Difference 

12 2 s.% $142 

11 5 2.% $:l., 

10 ,. ,.% 5+47 

9 

8 

7 

6 

5 

4 

3 

2 

0 100 200 300 400 500 

Required Capital. COS! Difference in 1987$ (Gas - Electric) 

Ell Electric Preferred Region 

For example, a customer who requires a payback period of two years would see a 

lower electric heat pump life-cycle cost if the capital costs for the gas system 

exceeded those of the heat pump by more than $142. The capital cost difference between the gas system and the heat pump system has been estimated to 

be approximately $130. Customers with longer payback periods would require 

the heat pump to have lower capital costs relative to the gas system (e.g., the gas 

vs. electric cost difference would be greater>. By using such an approach, a wide 

range of customer payback periods can be considered in appliance life-cycle cost 

comparisons so that utility program planners can target those customers most 

likely to prefer high-efficiency electric appliances. 

8-10 
er operating costs ot higher efficiency electric appliances relative to stan- . . 

(b.g., resistance in the case of space conditioning) electric appliances narrows 

ap between the operating costs of electric and gas appliances. Using the. 

e customer operating costs and distribution of required paybacks, the Cost 

k Approach analyzes the life cycle costs of the newer electric appliances 

ve to gas appliances. This approach combines information measured directly . . 

e REUC project -- energy usage and required paybacks -- and leaves assump- 

';about relative capital costs as an unknown that can be filled in by the reader 

bn examining Figure 8-6. 

important to remember that under1ying.a life cycle cost analysis are various 

about customer appliance utilization, required paybacks, and the 

installation costs of the appliances themselves. The results of this 

individual customers will vary. Each customer utilizes appliances 

@ifferently aid has a different forecast of energy prices and overall assessment of k., 

;future uncertainty that affects its required payback. r 

;Furthermore, life cycle costs are only one of many appliance attributes that may 

$e considered by a customer selecting an appliance. The fuel image results 

discussed before support the conclusion that customers value non-cost 

'characteristics of fuels that are the energy source for appliances. These'non-cost 

' attributes can be given a dollar value and can be used to adjust the "Required 

Capital Cost Difference." For example, if electricity is perceived to be more 

reliable than gas, a dollar premium for this attribute can be used to lower the 

required capital cost difference to equalize the life cycle costs, since customers 

would be willing to pay more for electric technologies. 

 EE-RM-94-230 COMMENT 61F

operatmg costs ot higher efficiency electric appliances relative to s.tanresistance in the case of space conditioning) electric appliances narrows 

between the operating costs of electric and gas appliances. Using the. 

customer operating costs and distribution of required paybacks, the Cost 

Approach analyzes the life cycle costs of the newer electric appliances 

to gas appliances. This approach combines information measured directly 

RfUC project -- energy usage and required paybacks -- and leaves assumpabout relative capital costs as an unknown that can be filled in by the reader 

examining Figure 8-6. 

~s imp~rtant to remember that u~derlyin~ .. a li~e cycle c.ost analysis are various 

~ssumptlons about customer appliance utilization, required paybacks, and the 

rgpital and installation costs of the appliances themselves. The results of this 

~pproach for individual customers will vary. Each customer utilizes appl iances 

(differently a~d has a different forecast of energy prices and overall assessment of 

r. ~ture uncertainty that affects its required payback. . . 

iFurthermore, life cycle costs are only one of many appliance attributes that may 

,be considered by a customer selecting an appliance. The fuel image results 

'discussed bef~re support the conclusion that customers value non-cost 

'characteristics of fuels that are the energy source for appliances. These· non-cost 

attributes can be given a dollar valu.e and can be used to adjust the "Required 

Capital Cost Difference." For example, if electricity is perceived to be more 

reliable than gas, a dollar premium for this attribute can be used to lower the 

. required capital cost difference to equalize the life cycle costs, since customers 

would be Willing to pay more for electric technologies. 
APPENDIX A 

SUPPLEMENTARY ENERGY USAGE INFORMATION 

AND LOAD PROFILES 

 EE-RM-94-230 COMMENT 61F

APPENDIX A 

SUPPLEMENTARY ENERGY USAGE INFORMATION 

AND LOAD PROFILES 
Figure A-1 

Space Conditioning: Winter Averaee Dailv Usage . (at point of use) 

... . ". 

Winter Wcrlrend 

kBTU 

Figure A-2 

Water Heating: Averaee Dailv Usage (at ~oint of use) 

Wintcr Weekend 

Summa Weekend 

kBTU 

 EE-RM-94-230 COMMENT 61F

Figure A-l 

Space Conditioning: Winter Average Daily Usage (at point of use) 

89.9 

Winter Weekend 

Winter W~"day 

o 10 20 30 40 SO 60 70 80 90 100 

k8TU 

122 Cas S ... m~e 

II Electric Sample 

Figure A-2 

Water Heating: Average Daily Usage (at point of use) 

Winter Weekend 

Winter Wee"-d;ay 

Sommer Weekend 

Summer Weekday 

0 10 20 30 40 SO 60 70 80 

kBTU 

r:a CuSample 

• Electric S;ample 

A-7 
This page left 

deliberately blank 

 EE-RM-94-230 COMMENT 61F

This page left 

deliberately blank 

A-3 
Figure A-5 . . 

Soace Conditioning: Summer Average Dailv Usage (at point of use) 

Summer Weekend 

bSTU 

Figure A-6 

SCE REUC Winter: Clothes Drvine (at ooint of use) 

 EE-RM-94-230 COMMENT 61F

Figure A·S 

Space Conditioning: Summer Average Daily Usage (at point of use) 

Summer Wil~kend 

• 5 10 

e 

• 

IS 

idlTlI 

Figure A·6 

23.7 

2. 25 3 • 

SCE REUC Winter: Clothes Drying (at point of use) 

kBTU p« HOUf 2.0 

- Elec. w"'da),> 

1.8 .... _-_ .. EIee. W .. """'" ""._ ....... Gas w .. ",",),> 

1.6 ---- GasW ..... ds 

1.4 1';1 . 1.2 I"~ " .... ", 

1,0 ¥ \ 'V"'" i'P, ........ _, ... " ....... ,.<':< .. , \ 

O,S" ill .., "'''' ... 1 -'~~ ;1' \.!-

0.6 

1/ !/ '\ 

0.4 l . 0.2 "-- • I 

,// :;-;, 

0.0 

0 3 6 9 12 15 18 21 24 

TifMofDay 

A-4 
Figure A-7 

Figure A-8 

SCE REUC Summer: Clothes Drying (at ~oint of use) 

Elu. Week&+ 

------ Elu. Weeken& 

Gas Weekdays 

Gz Weekmds 

k8TU per Hour 2.0- 

1.8- 

1.6- 

1.4- 

1.2- 

Elec. Wnkendr 

-.-.-.- Gas Weekday3 

Gas Weken& 

A 

I' 

I\ 

1.0- 

0.8 - 

0.6 - 

0.4- 

0 3 6 9 12 15 18 21 24 

Tim of Day 

SCE REUC Winter: cook in^ (at ~oint of use) 

kBTU per Hour 2.0- 

1.8- 

1.6- 

1.4- I 

1.2- 

?! 

; ! 

; ! 

i ;, b\ , I \\ 

i 11. 

1.0- 

0.8- 

0.6- 

0.4 - 

0 3 6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

Figure 1>.-7 

S.CE REUC Summer: Clothes Drying (at point of use) 

kBiU pn- Hour' '1..0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8' 

0.6 

0.4 

0,2 

£IK.W .. ~ 

£lee.Week .. d. 

CasW .. kdal'" 

Gas Weekends 

o.o.j........:s~-~:;.... ...... .....,,..-......,-....,.-....,.~.., 

o 3 12 15 18 21 

Time ofOay 

Figure A·8 

SCE REUC Winter: Cooking (at point of use) 

UTU per Hour 2.0 

1.8 

I.G 

1.4 

1.2 

1.0 

0.8 

0 •• 

0.4 

0.2, 

0,0 

0 3 

£lee. WeekdaY, 

Bee. Weekends 

GasW .. kdays 

CasW~kmds 

9 12 

Time of Day 

A-S 

15 1. 21 

24 

14 
Figure A-9 

Ek Weekdays ------ Eke. Weekd 

-.-.-.-, Gu Weekdays 

Cu Week& 

Tim of Day 

SCE REUC Summer: Cooking (at point of usel 

k8TU per Hour 1.5- 

Figure A-1 0 

1.2- 

0.9 - 

SCE REUC Winter: Whole House Electricity (at ~oint of use) 

Pncenl of Total 

:. , '. p, \ 

; \i 1 I! 1, 

Tim of ~ay 

0.6 - 

0.3 - 

 EE-RM-94-230 COMMENT 61F

Figure A-9 

SCE REUC Summer: Cooking (at point of yse) 

kBTU pet Hour 1.5 

0.9 

0.3 

Eh!c. W .. lo!ays 

El<c.W .. k_ 

G .. W..lo!a)s 

Gas Weektruls 

., 

. \ 

!,' ~ " I • I • 

I \\ 

1 \ i I I, t I! 

, \\ 

J

' , \ ... "" J /~ \ / ""'" ;""-.;...1/ , \ /'"'. .; I I \ ,I -..... .. ... -............ I \. 

i"' .... _--<","'. " ...... :::.. "'" ~ -....... _.,... --.... , ...... 

o.o..f::=:;=;=:¢'.:.:....,r--......,-....,.-....,..-....,..-..::; 

o 3 6 12· 1$ 18 21 24 

Timeoftny 

Figure A-10 

SCE REUC Winter: Whole House Electricity (at point of use) 

Pet'Cenl of Total 

Daily £nergy USf! 8.0 

7.5 

7.0 

6.5 

6.0 

5.5 

5.0 

4.5 

4.0 

3,5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

flee. Sample W~kday$ 

Gas. $.amp'e Weekdays 

0.0 -h-.....,...., ....... ..,..., ........ ,..., ........ -r-,....,..-.-....... -r-,.....-r~., 

o 3 6 9 12 IS 18 21 2. 

A-6 
Figure A-1 1 

XE REUC Summer: Whole House Electricity (at point of use) 

.~ ... .~< 

r Pcrccnt of Total 

wly Energy use 7.0 : 

5.0 - 

4.5 - 

1.5 - 

1.0- 

0.5 - 

0.07. . 8 I 

0 3 6 9 12 15 18 21 24 

lime of Day 

Figure A-1 2 

SCE REUC Winter: Space Conditioning (at point of use) 

, 

Percent of Total 

Daily Enray U* l4 

13- 

12 - 

11 - 

10 - 

('1 

I \ :: 

I 

I1 

I I 

9- 

8- 

7- 

6 - 

5- 

4- 

3 - 

2 - 

1 - 

Ol..I..L..1III. 18 

0 3 6 9 12 15 18 21 24 

Tim ofDay 

 EE-RM-94-230 COMMENT 61F

Figure A-ll 

SCE REUC Summer: Whole House Electricity (at point of use) 

f1f:tcent of Tolill 

~Iy Energy Use 7.0 

6.5 

6.0 

5.5 

5.0 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

0 3 

Elee. Sample Weekdays 

Gas $ample Weelatt)1l 

6 9 12 

lime of Oar 

Figure A-12 

I 

I 

15 

/ 

i~,.-,~ .. I 

18 21 24 

SCE REUC Winter: Space Conditioning (at point of use) 

Percent of Total 

Diljly Energy Use 14 

(' 13 

, ' ,. , 12 

, \ Elee. Weekdays 

11 , ' CuWHkdays , I 

10 I I 

I I 

9 , 

, \ 

8 , 

7 

I 

, 

, • I 

I 

5 I 

, 

4 I 

I 

3 L. -' 

2 

o 3 6 9 12 15 18 21 

Time orOay 

A-7 

24 
Figure A-1 3 

SCE REUC Summer: Space Conditioninp; (at ooint of use) 

Percml of Tohl 

Dily Energy Use 

11 - 

10 - 

9 - 

8 - 

7 - 

6 - 

5 - 

4 - 

3 - 

2 - 

1 - 

0 1 

0 3 6 9 12 15 18 21 24 

I 

Tim of Day 

Figure A-1 4 

SCE REUC Winter: Water Heatine (at ooint of use) 

Percent ofTolrl 

Daily Enqy UV~ 12 - 

11 - 

10 - 

9- 

r\ 

I I 

1 I 

I\ 

I' ; 

8- I I 

7 - 

6 - 

5- 

0 7 

0 3 6 I 

9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

Figure A-13 

SCE REUC Summer: Space Conditioning (at point of use) 

Percent or Total 

Daily 12 Energy Use 

11 

·1. 1----

9 

8 

7 

6 

5 

4 

3 

2 

1 

• 3 

EI«. Weekdays 

~Weekdays 

6 9 12 

Timeo(Oay 

Figure A-14 

15 18 21 

SCE REUC Winter: Water Heating (at point of use) 

Percent oflotal 

Daily Energy Use 12 

11 

,. 

• 

8 

7 

6 

5 

4 

3 \ 

, 

2 \ 

\., I 

----' 

,.., 

I , 

I , 

I \ 

I 

I 

I 

I 

J 

I 

J 

J \ 

\ 

\ 

\ 

, 

\ 

"-

Elee. Weekdays 

Ga.s WHkda)'S 

24 

.+---'---~~-T--~--~r---r---T-~ 

• 3 6 • 12 15 18 21 24 

Time of Dar 

A-8 
Figure A-1 5 

SCE REUC Summer: Water Heating (at point of use) 

Percenl oiiolrl 

Daily Enagy Use 12 : 

11 - 

10 - fi 

r 

8 - 

7 - 

5- 

0 I 

0 3 6 9 12 15 18 21 24 

Tim of Day 

Februay 

- - - - - -. 

-.-.-.-., April 

----. 

i L. 

Figure A-1 6 

! 

SCE REUC Weekday Average Load: Electric Sample Space Conditioning i 

', 

I 

, 

! 

. 

. 

, 

. kW Load 1.4- 

1.2- 

1.0- 

0.8 - 

. 

0.0 ..*~~I.~,~~a~.~~~#~~

. i; 0 3 6 9 12 15 18 21 24 . 

C Time of Day 

A9 

 EE-RM-94-230 COMMENT 61F

i ' .. 

Figure A-15 

SCE REUC Summer: Water Heating (at point of use) 

Percent of Tolal 

Daily Energy Use 12 

11 

10 

9 

8 

7 

6 

5 

4 

3 

2 

o 3 6 9 

1== 

\ 

1..., 

\ 

\ 

12 

" 

',,<: 

15 

Time of Day 

Figure A-16 

Sec. Weekdays 

Gas Weekdays 

18 21 24 

SCE REUC Weekday Average Load: Electric Sample Space Conditioning 

kW Load 1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

o 3 6 9 12 15 

Time of Day 

February 

March 

April 

May 

18 21 24 
Figure A-1 7 

- . - . - . - . 

September 

Figure A-1 8 

SCE REUC Weekday Average Load: Electric Sample Soace Conditionin~ 

LNV Load 2.0 - 

1.8 - 

1.6 - 

0 3 6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

Figure A-17 

SCE REUC Weekday Average load: Electric Sample Space Conditioning 

kWL .. d 1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

June 

July 

August 

Seplember 

,", 

, , , , I '" \ 'I' \ ' 

/ I \ \ I I ... , ' /1 .' \' \ ',,' ,\ , , . '\ ' / I.' \\ \ 

I I ' ~\ \ " .' " , " l \, \ :/ i \\\ " I \\, 

I i ,\\ t,' ! i'\ 

,'/ ! .I .' . "' "', " \' 

II.' ''','': 

~ . 1/.' ' 

"~'~"'~;l;~:~'/ . 

o 3 6 9 12 15 18 21 24 

Time of Day 

Figure A-18 

.' 

SCE REUC Weekday Average load: Electric Sample Space Conditioning 

kW lo.ad 2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 • • • 0 3 6 9 

A-l0 

• 

12 15 

Time of Day 

• 

Odober 

November 

December 

January 

18 21 24 

I 
Figure A-19 

SCE REUC Weekday Averave Load: Gas Sample Space Conditioning 

kBTU per Hwr 20 - 

18 - 

12 - 

0 3 6 9 12 15 18 21 24 

Time of Day 

Figure A-20 

SCE REUC Weekdav Average Load: Gas Sample Soace Conditioning 

kBTU per Hour 4.0- 

3.5 - 

3.0 - 

/-I - - - - - -. -.-.-.-.. . : ,-; 

---- ; ', September / ', 

2.5 - 

2.0 - 

1.5 - 

1.0- 

0.0 , .1~~1"~'~~"~"'"'"~ 

0 3 6 9 12 15 18 2j 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

 A-11

Figure A-19 

SCE REUC Weekday Average load: Gas Sample Space Conditioning 

kBTU per Hour 20 

18 

16 

14 

12 

10 

8 

6 

4 

2 

o 

o 3 6 9 12 

Time of Day 

Figure A-20 

lS 18 

February 

March 

April 

May 

21 24 

SCE REUC Weekday Average load: Gas Sample Space Conditioning 

kBTU per Hour 4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

o 

June 

July 

August 

September 

~, 

. , . , 

: " \ \ , . , 

, . . , . • • • • I -""~ , 

: r \ \ . / . / ."'. \ \ //;"\' 

/ I,. \ \ 

:f' , I '" ' ' \\ ,,' ,\ " " / " ; ,. " '/ .,' ,,\ '\ 

/ I '. \. ' ''I' , .... ,' 

/' I ,/ '''' ... ~\ ~ .. 01' .' . , ,'.\ , -' -, , '-_~~-__ "'c:: __ , 

3 6 9 12 15 18 2} 24 

Time of Day 
Figure A-21 

SCE REUC Weekday Averaee Load: Gas Sample Space Conditioning 

------ November 

-.-.-.- Dtecmber 

janurry 

Figure A-22 

SCE REUC Winter Weekday Electricity: Electric Sample 

kWLmd 3.0- 

Whdc House 

2.8 - Spm Conditioning 

2.6 - Water Heating 

2.4 - 

2.2 - 

2.0 - 

0.04. .........,.r,..,..,~n, 

0 3 6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

Figure A-21 

SCE REUC Weekdav Average Load: Gas Sample Space Conditioning 

kBru per Hour 27 

24 

21 

18 

15. 

12 

• 

• 

·3 

0 

0 

.-

3 • • 12 15 

Time of Day 

Figure A-22 

18 

October 

November 

December 

January 

21 24 

SCE REUC Winter Weekday Electricity: Electric Sample 

kW Load 3.0 

2.8 

2 •• 

2.4 

2.2 

2.0 

1.8 

1.. 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

a 

, .--" . 

--- Whole House 

Space Conditioning 

WOller Heating 

, ' 

/f~ 

-~ if '" ...... '~--- -' . ... -~.... .../' -...... -... -

3 • • 12 15 18 21 24 

Time of Day 

A-12 
Figure A-23 

SCEREUC Summer Weekday Electricitv: Gas Sample 

Figure A-24 

SCE REUC Summer Weekdav Electricity ~lectric Sample 

- .. 

kW Load 2.0- 

1.8- 

1.6- 

0.4 - 

o.o-..,..,..,..,.-I..,.,i 

0 3 6 9 12 15 18 21 24 

Time of Day 

Whole How 

- . - . - . - . Space Conditioning 

Water Healing 

kW Load 2.0- 

1.8- 

1.6- 

1.4- 

1.2- 

1.0- 

0.8 - 

0.6 - 

0.4 - 

0.2 - 

_ 0.0 I 

C 

.- .-., ---. 0' ----______-- - - . i I 

0 3 6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

 A-13

Figure A-23 

SCE REUC Summer Weekday Electricity: Gas Sample 

kW load 2.0 

1.8 

1.6 Whole House 

Space Conditioning 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

,-. , ... , "' .... ,. ' .. ". " 

;-' " .' " 

0.2 

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

0.0 

0 3 6 9 12 15 18 21 24 

Time ofOay 

Figure A-24 

SCE REUC Summer Weekday Electricity: Electric Sample 

kW load 2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 3 6 

A-H 

9 12 15 

Time of Day 

Whole House 

Space Conditioning 

Waler Healing 

18 21 24 
Figure A-25 

Winler Weekdap 

------. Winter Weekends 

-.-.-.-. Summer Weekdays 

Summer Weekends 

Time of Day I 

Figure A-26 

SCE REUC Whole House Electricitv: Gas Sample 

------- Winter Weekends 

-.-.-.-.. Summer Weekdays 

kW Load 2.6- 

2.4 - 

2.2 - 

2.0- 

1.8- 

1.6- 

1.4- 

1.2- 

0.2 - 

0.0 , . . , , , , . , , , , , . , . 0 3 8 

6 9 12 15 18 21 24 

Time of Day 

 EE-RM-94-230 COMMENT 61F

FigureA-;!5 

SCE REUC Whole House Electricity: Electric Sample 

kW Load 2.6 

2.4 

2.2 

2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 

, 

I 

, 

, 

, 

, 

, 

: I I I ~ __ l, ..... ,. .. "- ,,~,. I 

'! / 

,.' / 

\ i I .... , I, '~; 

3 6 9 12 

Time of Day 

Figure A-26 

lS 

Winter Weekda)'S 

Winter Weekends 

Summer Weekdays 

SUmmer Weekends 

18 21 24 

SCE REUC Whole House Electricity: Gas Sample 

kWload 2.6 

2.4 

2.2 

2.0 

1.8 

1.6 

1.4 . 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 3 

Winter Weekdays 

Winter Weekends 

Summer Weekdays 

Summer Weekends 

6 9 12 

TIme of Day 

A-14 

lS 18 21 24 
Figure A-27 

SCE REUC Svace Conditionin~: Electric Samvle 

-. - 

kW Load 1.0 - 

0.9 - Summer Weekdays 

0.8 - Summer Weekends 

0.7 - 

0.5 - 

0.0- '. I'.I'I'.I"I'II1 

0 3 6 9 12 15 18 21 24 

Time of Day 

Figure A-28 

SCE REUC Water Heating: Electric Sample 

kW Load 0.70 - 

Winter Weekends 

0.60- Summer Weekdays 

0.55- 

0.50 - 

0.45- 

0.40 - 

0.05 - 

O.WI.-I~.II.I..I~.I..B--~ 

0 3 6 9 12 15 18 21 24 

~kne ofDay 

 EE-RM-94-230 COMMENT 61F

 

 

 A-15

Figure A-27 

SCE REUC Space Conditioning: Electric Sample 

kW Load 1.0 Winter Weekdays 

Winter W~ken~s 

Summer Weekdays 

Summer Weekends 

kW Load 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

\ ,,-_ .. -" --

•• : \ 

I • 

. . .... I \ "I '- , \ /; \ 

" \ I! \ " \ I.' 1 

" 1/ " I! 

" ,.1 \// 

'1 " , 'f ... _ ..,"'-' 

0.2 I; 

. " . ',' 0.1 ...... ~~, ~' 

4' 

O.O+-~-r~TO~~~~-r~~~~~~'-~-, 

o 

0.70 

0.65 

0.60 

0.55 

0.50 

0.45 

0.40 

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

0 

3 6 9 12 15 18 21 24 

Time of Day 

Figure A-28 

SCE REUC Water Heating: Electric Sample 

r', 

. 

• • \ 

• 

· \ • • • • 

· \ " f .. 

. \ ' \ .' .~' I .. .' , I \ 

f ,'\' .. j , 1, \ . 'I', .... 

! : I .... " 

Winter Weekdays 

Winter Weekends 

Summer Weekdays 

Summer Weekends 

! I I '. .. .. ,-, 

! ;1 '. .......... ! 'I '. ' ,-!",':, I I .. I' '\ 

i ," \" 1/ ', .. . ,/ " , . . \ :"., I ,'1 "".1 

\\ '.,.", \. 

,_ 'j'J ,. " I I ";~ ......... ,. 

... ~, -; / 

'-~~"V' 

3 6 9 12 15 18 21 24 

Time of Day 

'C· 
Figure A-29 

SCE REUC Cookine: Electric Sample 

kW Laad 0.30 - 

0.20 - 

0.15 - 

0.10- 

0.05 - 

0 3 6 9 12 15 18 21 24 

Time of Day 

Figure A-30 

SCE REUC Clothes Drvine: Electric Samole 

A-VC 

kW Load 0.40- 

Winter Weekends 

0.35 - Summa Weekdays 

Summer Weekends 

0.30- 

0.25 - 

0.20 - 

0.15 - 

0.10-, 

0 3 6 9 12 15 18 21 24 

rime of Day . 

 EE-RM-94-230 COMMENT 61F

 

 

 A-16

Figure A-29 

SCE REUC Cooking: Electric Sample 

kW Load 0.30 

kWload 0.40 

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

0.25 

0.20 

0.15 

0.10 

0.05 

Winter Weekdays 

Winter Weekends 

Summer Weekdays 

Summer Weekends 

0.00 +=~""," .... <F4::;:::~r"T"""""""r"T"""""'''''''''''''''''''''''''''''''''''' o 3 6 9 12 15 18 21 24 

Time of Day 

Figure A-30 

SCE REUC Clothes Drying: Electric Sample 

0 3 6 9 

~ 

•• • • f \. 

• • • • • • • • 

, • .... ....." • ... I I \ ,..... ~'\ :' \ ' ... \ 

" \ \ 

Winter Weekdays 

Winter Weekends 

Summer Weekdays 

. Summer Weekends 

, .... \ i" , r..... \. . \ ' .. I \ . . \ ... , . I I '. I 'I \ , 

,. \. \. . . . \. .... \ 

12 15 

Time of Day 

'-.--....... , ./ 

" .. ,,' 

18 21 24 
Figure A-31 

SCE REUC: Temoerature 

 EE-RM-94-230 COMMENT 61F

 

 

 A-17

BO 

75 

70 

60 

2H)() 1&00 

16:00 

Figure A-31 

SCE REUC: Temperature. 

12.~ 

'lime (PST) 9<lO S<lO 

!.lay i.I.r 

Febnwy 

J",=>, 

Decemb ... Nbvemw 

Odobor 

September 

August 

JnJy 3;00 June 

O<lO 
Appliance 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

RangelOven 

Table A-1 

Average Annual and Seasonal Enerw Usace 

Mission Vieio Households 

Whole House 

Space Conditioning 

Water Heating . 

Clothes Dryer 

RangelOven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Rangeloven 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

kwh MBTUa MBTU kwh MBTUa 

Annual Average Household Usage (12 months) 

Winter Average Household Usage (8 months) 

Summer Average Household Usage (4 months) 

a At point of use. 

 EE-RM-94-230 COMMENT 61F

 

 

 A-18

Table A-1 

Average Annual and Seasonal Energy Usage 

Mission Viejo Households 

Appliance 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Range/Oven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Range/Oven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Drier 

Range/Oven 

a At point of use. 

Electric Sample 

kWh MBTUa 

Gas Sample 

(Gas Components) 

MBTU 

Gas Sample 

(Electric Components) 

kWh MBTUa 

Annual Average Household Usage (12 months) 

12240 41.80 .NA 8050 27.50 

2770 9.50 19.30 1110 3.78 

2870 9.80 25.50 NA NA 

913 3.12 4.71 1 1 1 0.38 

350 1.20 3.70 NA NA 

Winter Average Household Usage (8 months) 

8690 29.70 'NA 5000 17.10 

2050 7.05. 19.00 390 1.33 

2110 7.20 18.51 NA NA 

646 2.21 .3.26 77 0.26 

247 0.85 2.56 NA NA 

Summer Average Household Usage (4 months) 

3550 12.10 NA 3050 10.40 

720 2.45 0.30 720 2.45 

·760 2.60 6.99 NA NA 

267 0.91 1.45 34 0.12 

103 0.35 1.14 NA NA 
Table A-2 

Appliance 

Averaee Annual and Seasonal Energy Usage 

lrvine Households 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Conditioning 

'Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Rangeloven 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

kwh MBTUa MBTU kwh MBTUa 

Annual Average Household Usage (12 months) 

Winter Average Household Usage (8 months) 

Summer Average ~ousehoid Usage (4 months) 

-- 

a At point of use. 

 EE-RM-94-230 COMMENT 61F

 

 

 A-19

Table A-2 

Average Annual and Seasonal Energy Usage 

Irvine Households 

Appliance 

Whole House 

Space Conditioning 

. Water Heating 

Clothes Dryer 

Range/Oven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Range/Oven 

Whole House 

Space Conditioning 

Water Heating 

Clothes Dryer 

Range/Oven 

a At point of use. 

Electric Sample 

kWh MBTUa 

Gas Sample 

(Gas Components) 

MBTU 

Gas Sample 

(Elearic Components) 

kWh MBTUa 

Annual Average Household Usage (12 months) 

13660 

3110 

2750 

1210 

414 

46.60 

10.60 

9.40 

4.12 

1.41 

NA 

19.20 

·21.60 

4.00 

2.86 

7120 

1220 

NA 

110 

NA 

24.30 

4.16 

NA 

0.37 

NA 

Winter Average Household Usage (8 months) 

9680 33.00 NA 4480 15.30 

2300 7.85 19.05 478 1.63 

2060 7.03 15.70 NA NA 

860 2.92 2.73 74 0.25 

297 1.01 1.97 . NA NA 

Summer Average Househoid Usage (4 months) 

3980 13.60 NA 2640 9.00 

810 2.75 0.15 742 2.53 

690 2.37 S.90 NA NA 

350 1.20 . 1.27 36 0.12 

117 0.40 0.89 NA NA 

A-19 
Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

Table A-3 

Monthlv Averaae Usaae for Mission Vieio Households 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

Electric Sample 

Weekday 

M Monthly Averaee 

Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

Therrns kwh 

Weekday - &nlhly Weekday Monthly -.- 

February 

March 

April 

&Y 

NA 

0.1 5 

0.73 

0.1 3 

0.10 

lune 

NA 

0.05 

0.64 

0.12 

0.09 

 EE-RM-94-230 COMMENT 61F

Table A-3 

Monthly Average Usage for Mission Viejo Households 

Electric Sample - Gas Sample Gas Sample 

kWh 

(Gas Components) (Electric Components) 

Therms kWh 

AImliancea Weekday Monthly Weekday Monthly Weekday Monthly 

Average Average Average Average Average Average 

January 

Whole House 44.6 1461 NA NA 19.8 621 

Space Condo 16.8 551 1.81 58.9 1.8 57 

Water Heating 10.3 329 0.83 25.7 NA NA Clothes Dryer 2.4 87 0.14 4.5 0.3 11 Range/Oven 1.0 33 0.11 3.4 NA NA 

February. 

Whole House 39.2 1136 NA NA 19.0 543 

Space Condo 12.3 345 1.27 34.9 1.4 39 

Water Heating 8.8 255 0.84 23.8 NA NA 

Clothes Dryer 2.3 78 0.13 3.9 0.3 9 

Range/Oven 0.9 28 0.10 2.8 NA NA 

March 

Whole House 31.0 1 011 NA NA 18.5 590 

Space Condo 5.3 187 0.52 17.3 1 .1 41 

Water Heating 8.5 270 0.82 25.9 NA NA 

Clothes Dryer 2.4 85 0.12 4.2 0.3 10 

Range/Oven 0.9 30 0.09 3.0 NA NA 

April 

Whole House 29.6 922 NA NA 20.5 614 

Space Condo 4.9 142 0.28 8.3 2.7 71 

Water Heating 8.0 248 0.73 22.0 NA NA 

Clothes Dryer 2.1 76 0.12 3.9 0.3 9 

Range/Oven 0.9 31 0.10 2.9 NA NA 

May 

Whole House 26.6 854 NA NA 19.7 621 

Space Condo 2.5 83 0.15 5.5 1.6 50 

Water Heating 7.7 244 0.73 22.7 NA NA 

Clothes Dryer 2~3 80 0.13 4.2 0.3 9 

Range/Oven 0.9 28 0.10 3.1 NA NA 

lune 

Whole House 25.1 777 NA NA 19.5 590 

Space Condo 2.1 61 0.05 2.3 2.5 71 

Water Heating 6.8 209 0.64 19.4 NA NA 

Clothes Dryer 2.0 71 0.12 4.1 0.3 9 

Range/Oven 0.9 28 0.09 2.9 NA NA 

A,20 
Table A-3, Cont. 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

kwh Therms kwh 

Weekday Monthly Weekday Monthly Weekday Monthly AveraeemwAveraaeAveraQem 

August 

September 

October 

NA 

0.09 

0.62 

0.1 1 

0.10 

November 

NA 

0.58 

0.72 

0.1 2 

0.1 1 

December 

NA 

1.52 

0.78 

0.1 4 

0.12 

 EE-RM-94-230 COMMENT 61F

 

 

 A-21

Table A-3, Cont. 

Electric Sample Gas Sample Gas Sample 

kwh 

(Gas Components) 

Therms 

(Electric Components) 

kWh 

Weekday Monthly Weekday Monthly Weekday Monthly 

APgliance Average Average Average Average Average Average 

July 

Whole House 30.2 956 NA NA 27.7 860 

Space Condo 7.6 241 0.00 0.1 8.6 263 

Water Heating 6.1 194 0.55 17.0 NA NA 

Clothes Dryer 2.1 70 0.11 3.5 0.3 8 

Range/Oven 0.8 26 0.09 2.9 NA NA 

August 

Whole House 26.6 857 NA NA 24.7 778 

Space Condo 5.1 168 0.00 0.1 5.3 170 

Water Heating 5.7 179 0.54 16.8 NA NA 

Clothes Dryer 1.7 63 0.11 3.6 0.3 9 

Range/Oven 0.7 24 0.09 2.9 NA NA 

September 

Whole House 30.4 956 NA NA 26.9 .824 

Space Condo 7.5 247 0.02 0.6 6.7 214 

Water Heating 6.0 184 0.56 1&.7 NA NA 

Clothes Dryer 1.8 &2 0.10 3.4 0.2 8 

Range/Oven 0.7 25 0.09 2.8 NA NA 

October 

Whole House 26.3 859 NA NA 20.5 &47 

Space Condo 2.0 71 0.09 2.8 1.4 49 . Water Heating &.7 210 0.&2 19.0 NA NA 

Clothes Dryer 1.9 73 0.11 3.& 0.3 8 

Range/Oven 0.9 29 0.10 3.2 NA NA 

November 

Whole House 32.6 996 NA NA 20.4 616 

Space Condo 6.1 178 0.58 16.8 1.0 32 

Water Heating 8.0 244 0.72 21.4 NA NA 

Clothes Dryer n 77 0.12 3.9 0.3 9 

Range/Oven 1.1 32 0.11 3.1 NA NA 

December 

Whole House 45.8 1452 NA NA 23.4 74& 

Space Corid. 16.4 495 1.52 45.4 1.7 50 

Water Heating 9.7 308 0.78 24.5 NA NA 

Clothes Dryer 2.6 90 0.14 4.4 0.3 11 

Range/Oven 1.1 37 0.12 4.1 NA NA 
Table A-4 

Monthly Average Usage for lwine Households 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

Therms kwh 

Weekday Monthly Weekday Monthly Weekday Monthly Averanem'-- 

January 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

February 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

March 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

April 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

Rangeloven 

 EE-RM-94-230 COMMENT 61F

Table A·4 

Monthly Average Usage for Irvine Households 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

kWh Therms kWh 

AQQliancea Weekday Monthly Weekday Monthly Weekday Monthly 

Average Average Average Average . Average Average 

January 

Whole House 55.3 1764 NA NA 18.2 579 Space Condo 22.2 722 1.86 61.3 2.2 71 Water Heating 9.6 301 0.74 23.3 NA NA Clothes Dryer 4.0 125 0.12 4.0 0.3 10 Range/Oven 1.3 42 0.08 2.3 NA NA 

February 

Whole House 42.6 1212 NA NA 17.4 495 

Space Condo 12.3 343 1.18 33.7 1.8 51 

Water Heating 9.5 266 0.71 20.3 NA NA 

Clothes Dryer 3.4 104 0.11 3.2 0.3 9 

Range/Oven 1.3 36 0.06 1.8 NA NA 

March 

Whole House 33.5 1078 NA NA 16.9 533 

Space Condo 4.8 168 0.43 15.4 1.7 58 

Water Heating 8.B 274 0.68 21.3 NA NA 

Clothes Dryer 3.2 109 0.12 3.5 0.3 9 

Range/Oven 1.2 37 0.07 2.2 NA NA 

April 

Whole House 32.6 983 NA NA 18.5 550 

Space Condo 4.2 122 0.21 6.5 3.0 84 

Water Heating 8.6 255 0.62 18.7 NA NA 

Clothes Dryer 3.3 105 0.10 3.1 0.3 9 

Range/Oven 1.1 34 0.09 2.7 NA NA 

May 

Whole House 29.2 916 NA NA 16.9 526 

Space Condo 3.1 76 0.11 3.8 1.6 50 

Water Heating 6.6 246 0.57 17.7 NA NA 

Clothes Dryer 3.0 101 0.10 3.2 0.3 9 

Range/Oven 1.0 32 0.07 2.3 NA NA 

June 

Whole House 29.2 865 NA NA 17.7 530 

Space Condo 3.1 85 0.04 1.1 2.6 71 

Water Heating 6.6 200 0.53 16.2 NA NA 

Clothes Dryer 3.0 87 0.10 3.2 0.3 9 

Range/Oven 1.0 30 0.07 2.1 NA NA 

A-22 
Table A-4, Cont. 

Electric Sample Gas sample Gas Sample 

(Gas Components) (Electric Components) ' 

kwh Therms kwh 

Weekday Monlhly Weekday Monthly Weekday Monthly WWWw-Averaae 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

August 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

September 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

October 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

November 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

December 

Whole House 

Space Cond. 

Water Heating 

Clothes Dryer 

RangelOven 

 EE-RM-94-230 COMMENT 61F

Table A-4, Cont. 

Electric Sample Gas Sample . Gas Sample 

(Gas Components) (Electric Components) 

kWh Therms kWh 

Weekday Monthly Weekday Monthly Weekday Monthly 

Appliance Average Average Average Average Average Average 

July 

Whole House . 35.0 1077 NA NA 24.7 755 

Space Condo 8.9 270 0.01 0.2 9.2 279 

Water Heating 5.3 164 0.48 14.8 NA NA 

Clothes Dryer 2.9 92 0.11 3.2 0.3 9 

Range/Oven 0.9 27 0.07 2.1 NA NA 

August 

Whole House 32.1 999 NA NA 21.3 669 

Space Condo 6.7 211 0.00 0.1 5.5 183 

Water Heating . 5.4 167 0.46 14.1 NA NA 

Clothes Dryer 2.6 85 0.10 3.1 0.3 9 

Range/Oven 1.0 30 0.07 2.2 NA NA 

Sep~ember 

Whole House 33.8 1034 NA NA 22.5 685 

Space Condo 6.7 241 0.01 0.2 6.6 209 

Water Heating 5.4 164 0.47 13.9 NA NA 

Clothes Dryer 2.6 86 0.11 3.1 0.3 9 

Range/Oven 1.0 30 0.08 2.4 NA NA 

October 

Whole House 30.6 956 NA NA· 18.3 573· 

Space Condo 2.2 77 0.02 0.7 1.6 56 

Water Heating 6.5 200 0.51 15.7 NA NA 

Clothes Dryer 3.0 96 0.10 3.2 0.3 9 

Range/Oven 1.1 33 0.07 2.4 NA NA 

November 

Whole House 36.6 1111 NA NA 18.5 553 

Space Condo 6.2 190 0.53 16.0 1.4 41 

Water Heating 7.9 235 0.61 18.1 NA NA 

Clothes Dryer 3.2 101 0.11 3.3 0.3 9 

Range/Oven 1.3 37 0.09 2.8 NA NA 

December 

Whole House 53.4 1658 NA NA 21.2 667 

Space Condo 19.7 607 1.65 52.6 2.1 65 

Water Heating 9.2 281 0.70 22.1 NA NA 

Clothes Dryer 3.7 115 0.13 3.8 0.3 11 

Range/Oven 1.4 47 0.10 3.3 NA NA 

A-23 
Appendix B 

METHODOLOGY TO ASSESS THE GENERALlZABlLlN 

OF THE REUC CUSTOMER SAMPLE 

This appendix describes the methodology for assessing the generalizability of the 

REUC findings to other residential customers discussed in Section 4. The 

characteristics of REUC participants were compared with SCE's 1987 RSS sample 

of single-family, detached homes. The samples were compared by identifying 

important customer descriptors and appropriate appliance stock inventories (ASI). 

Figure 6-1 illustrates the steps taken in performing the sample assessment. These 

steps were as follows: 

Step 1: Develop Representative AS1 Definitions; 

Step 2: Identify Important Customer Descriptors; 

Step 3: Determine Customer Descriptors that Predict 

Household's ASI; 

Step 4: Determine Customer Descriptors that Discriminate 

Energy Usage Profile; and 

Step 5: Assess REUC sample by Relevant Segmentation Criteria. 

This methodology involved first developing a set of representative ASls and, 

second, a set of customer descriptors. The descriptors identified to be most useful 

in this analysis are income, square feet (SqFt), house vintage (Vintage), number of 

residents (NumRes), and resident's ethnicity (Ethnic). Absent from this analysis is 

education level, noted earlier for being an important customer descriptor, since it 

was not included in the 1987 RSS Survey. 

A two-step analysis of customer energy usage, comprised of Steps 3 and 4, was 

undertaken. Step 3 determined those customer descriptors that were correlated 

with ASI; that is, customer descriptors related to appliance holdings. Step 4 

identified those descriptors that explain intensity of energy usage. Annual energy 

usage profiles based on monthly bills for the RSS customer sample were stratified 

 EE-RM-94-230 COMMENT 61F

Appendix B ' 

METHODOlOGY TO ASSESS THE GENERALIZABILITY 

OF THE REUC CUSTOMER SAMPLE 

This appendix describes the methodology for assessing the generalizability of the 

REUC findings to other residential customers discussed in Section 4. The' 

characteristics of REUC participants were compared with SCE's'1987 RSS sample 

of single-family, detached homes. The samples were compared by identifying 

important customer descriptors and appropriate appliance stock inventories (AS!). 

Figure B-1 illustrates the steps taken in performing the sample assessment. These 

steps were as follows: 

Step 1: 

Step 2: 

Step 3: 

Step 4: 

Step 5: 

Develop Representative ASI Definitions; 

Identify Important Customer Descriptors; 

Determine Customer Descriptors that Predict 

Household's ASI; 

Determine Customer Descriptors that Discriminate 

Energy Usage Profile; and 

Assess RE,UC sample by Relevant Segmentation Criteria. 

This methodology'involved first developing a set of representative ASls and, 

second, a set of customer descriptors. The descriptors identified to be most useful 

in this analysis are income, square feet (SqFt), house vintage (Vintage), number of 

residents (NumRes), and resident'S ethnicity (Ethnic). Absent from this ani3lysis is 

education level, noted earlier for being an important customer descriptor, since it 

was not included in the 1987 RSS Survey. 

A two-step analysis of customer energy usage, comprised of Steps 3 and 4, was 

undertaken. Step 3 determined those customer descriptors that were correlated 

with ASI; that is, customer descriptors related to appliance holdings. Step 4 

identified those descriptors that explain intensity of energy usage. Annual energy 

usage profiles based on monthly bills for the RSS customer sample were stratified 

B-1 
Figure B-1 

REUC Sample Assessment Methodoloey 

RSS Survey 

Segmentation 

Criteria, 

REUC 

Customers 

Representativeness 

Assessment 4 

* 

Representative 

Customer 

Descriptors 

4 

Program Planning 

System/Assessment 

Customer 

Descriptors 

Discriminate 

Usage 

Customer 

Descriptors 

Discriminate AS1 

 EE-RM-94-230 COMMENT 61F

Figure 8-1 

REUC Sample Assessl1'!ent Methodology 

, 

RSS Survey 

........ 

I ~ 

+ + 

Representative Representative 

Customer ASI 

Descriptors Defi nitions 

+ + 

Customer Customer 

Descriptors Descriptors 

Discriminate Discriminate ASI 

Usage 

• • 

Segmentation 

Criteria. 

REUC 

Customers 

f "- .,,-

Representativeness 

Assessment -

+ 

Program Planning 

System/Assessment 

8-2 
by the customer descriptors within each AS1 grouping. The analysis was 

petiormed at the SCE Division level, although after controlling for key customer 

descriptors and ASI, distinctions across SCE Divisions were mostly attributable to + 

weather related effects. 

Appliance Stock lnventorv Definitions 

A key concept underlying this analysis rests on segmenting RSS and REUC 

customers by their AS!. his segmentation allows the linkage of the energy usage 

characteristics for the two customer samples. The 1987 RSS provides appliance 

inventory data including the major appliances that were monitored and analyzed 

in the REUC project. Appliance penetrations were calculated for the singlefamily RSS subsample. Based on these penetrations, AS1 definitions were 

developed as combinations of gas and electric appliances that characterize a 

large majority of this subsample's appliance holdings. Appliances with the highest 

penetrations and influence on energy usage consisted mainly of gas space heaters, 

gas water heaters and electric space cooling. 

. , 

Seventy-eight percent of the RSS single-family subsample fell into one of three 

AS1 definitions: 

gas space heating and electric central 

(GSHICAC); 

air-conditioning 

(2) gas space heating and electric wall/window air-conditioning 

(GSHIWWAC); and 

(3) gas space heating and no air-conditioning (GSHINOAC). 

In each AS1 definition, gas space heating includes both wall and floor furnaces. 

Walllwindow air-conditioning includes electric wall or window air-conditioner 

units and evaporative coolers. Evaporative coolers were included with WWAC 

because of the similarity of customers' usage profiles owning this appliance and 

the profiles of customers owning wall air-conditioners. The AS1 definitions do not 

include consideration of water heater ownership, since gas water heaters tend to 

be present in homes with gas space heating. Therefore, omitting water heating 

irom the ASI definitions affords simplicity without sacrificing analytic accuracy. 

 EE-RM-94-230 COMMENT 61F

by the customer descriptors within each ASI grouping. The analysis was 

performed at the SCE Division level, although after controlling for key customer 

descriptors and ASI, distinctions across SCE Divisions were mostly attributable to . 

weather related effects. 

Appliance Stock Inventory Definitions 

A key concept underlying this analysis rests on segmenting RSS and REUC 

customers by their ASI. This segmentation allows the linkage of the energy usage 

characteristics for the two customer samples. The 1987 RSS provides appliance 

inventory data including the major appliances that were monitored and analyzed 

in the REUC project. Appliance penetrations were calculated for the singlefamily RSS subsample. Based on these penetrations, ASI definitions were 

developed as combinations of gas and electric appliances that characterize a 

large majority of this subsample's appliance holdings. Appliances with the highest 

penetrations and influence on energy usage consisted mainly of gas space heaters, 

gas water heaters and electric space cooling. 

Seventy-eight percent of the RS$ single-family subsample fell into one of three 

ASI definitions; 

(1) gas space heating and electric central air-conditioning 

(GSH/CAC); 

(2) gas space heating and electric wall/window air-conditioning 

(GSH/WWAC); and 

(3) gas space heating and no air-conditioning (GSH/NOAC). 

In each ASI definition, gas space heating includes both wall and floor furnaces. 

Wall/window air-conditioning includes electric wall or window air-conditioner 

units and evaporative coolers. Evaporative coolers were included with WWAC 

because of the similarity of customers' usage profiles owning this appliance and 

the profiles of customers owning wall air-conditioners. The ASI definitions do not 

include consideration of water heater ownership, since gas water heaters tend to 

be present in homes with gas space heating. Therefore, omitting water heating. 

irom the ASI definitions affords simplicity without sacrificing analytic accuracy. 

B-3 
Table B-1 shows the shares of each of the three ASls by SCE Division. Customers 

with electric heating fell into the "Othe~" appliance stock category. Electric 

heating customers were not used in this analysis because of the small size of this 

group. In addition to the AS1 definitions above, in some customer segments it 

was necessary to consider the additional effects of pool ownership. 

Table 8-1 

Appliance Stock inventory (ASI) Share by SCE Division 

GSHINOAC 

Other 

Total 

North - East - West - South - SCE 

18% 40% 11% 26% 23% 

13 2 1 2 0 16 18 

3 9 12 51 40 37 

3 0 2 7 18 18 22 

100 100 100 100 100 

GSH - Gas Space Heating 

CAC.- Central Air Conditioner 

WWAC - Window and/or Wall Air Conditioner 

NOAC - No Air Conditioner 

Customer Descriptors 

Demographic variables can account for energy usage through customers' 

appliance choice and desired levels of energy consumption. A number of 

demographic variables were available from the RSS data, including other 

household characteristics, dwelling characteristics and implemented conservation 

measures. These customer data were used in the analysis of the RSS data. 

Two'approaches were used to identify a parsimonious set of customer descriptors. 

First, regressions explaining monthly electricity usage were used to screen the 

characteristic variables. Second, annual energy usage profiles were computed 

 EE-RM-94-230 COMMENT 61F

 

 

 B-4

Table 8-1 shows the shares of each of the three ASls by SCE Division. Customers 

with electric heating fell into the "Other" appliance stock category. Electric 

heating customers were not used in this analysis because of the small size of this , group. In addition to the ASI definitions above, in some customer segments it 

was necessary to consider the additional effects of pool ownership. 

Table B-1 

Appliance Stock Inventory (ASH Share by SCE Division 

ASI North East West South SCE 

GSH/CAC 18% 40% 11 % 26% 23% 

GSH/WWAC 13 21 20 16 18 

GSH/NOAC 39 12 51 40 37 

Other 30 27 18 18 22 

Total 100 100 100· 100 100 

Notes: 

GSH - Gas Space Heating 

CAC.- Central Air Conditioner 

WWAC - Window and/or Wall Air Conditioner 

. NOAC - No Air Conditioner 

Customer Descriptors 

Demographic variables can account for energy usage through customers' 

appliance choice and desired levels of energy consumption. A number of 

demographic variables were available from the RSS data, including other 

household characteristics, dwelling characteristics and implemented conservation 

measures. These customer data were used in the analysis of the RSS data. 

Two·approaches were used to identify a parsimonious set of customer descriptors. 

First, regressions explaining monthly electricity usage were used to screen the 

characteristic variables. Second, annual energy usage profiles were computed 
using stratifications based on the customer descriptors. This second approach 

allowed further screening of the explanatory power of the variables and provided 

independent validation of the regression results. Among the significant variables, 

affecting energy usage were Income, SqFt, Vintage and NumRes. Customers' 

reports of their implemented conservation actions was also useful for explaining 

usage in some cases. 

Customer characteristics can affect energy usage in two ways. First, customer 

characteristics may be related to the appliance holdings of a household. For 

example, if high income customers tend to own central air conditioners, the 

Income variable would be useful in predicting a customer's ASI. Second, given a 

household's appliance inventory, some customer characteristics may be related to 

the intensity that appliances are used. Again, income level may influence a 

customer's decision to cool to 68°F versus 70°F. 

Chi-square statistics were calculated to identify those customer descriptors that 

were correlated with ASI. Table B-2 reports the chi-square statistic for each 

variable with respect to the three-way AS1 categorization. In addition, chi-square 

statistics were computed to determine the customer descriptors that explained 

pool ownership. All the statistics are highly significant; Income, SqFt, and Vintage 

appear to demonstrate the strongest relationships. This result suggests that these 

customer descriptors could be used to predict a customer's ASI. 

Table B-2 

Chi-Square Statistics for Customer Descri~tors that Discriminate 

between Appliance Stock Inventories (ASI) and Pool Ownership 

Customer Descri~tor - AS1 . Pool owners hi^ 

Income 204* 240* 

Sq.Ft. 252* 245* 

Vintage 641 * 83* 

NumRes 30** 43' 

Ethnic 40* 28* 

*Significant at the 0.001 probability level. 

**Significant at the 0.005 probability level. 

 EE-RM-94-230 COMMENT 61F

 

 

 B-5

using stratifications based on the customer descriptors. This second approach 

allowed further screening of the explanatory power of the variables and provided 

independent validation of the regression results. Among the significant variables, 

affecting energy usage were Income, SqFt, Vintage and NumRes. Customers' 

reports of their implemented conservation actions was also useful for explaining 

usage in some cases. 

Customer characteristics can affect energy usage in two ways. First, customer 

characteristics may be related to the appliance holdings of a household. For 

example, if high income customers tend to own central air conditioners, the 

Income variable would be useful in predicting a customer's ASI. Second, given a 

household's appliance inventory, some customer characteristics may be related to 

the intensity that appliances are used. Again, income level may influence a 

customer's decision to cool to 68°F versus 70°F. 

Chi-square statistics were calculated to identify those customer descriptors that 

were correlated with AS!. Table B-2 reports the chi-square statistic. for each 

variable with respect to the three-way ASI categorization. In addition, chi-square 

statistics were computed to determine the customer descriptors that explained 

pool ownership. All the statistics are highly significant; Income, SqFt, and Vintage 

appear to demonstrate the strongest relationships. This result suggests that these 

customer descriptors could be usedto predict a customer's ASI. 

Table 8-2 

Chi-Square Statistics for Customer Descriptors that Discriminate 

between Appliance Stock Inventories (AS!) and Pool Ownership 

!.:ustQmer Descri~tor ASI 

Income 204* 

Sq:Ft. 252* 

Vintage 641' 

NumRes 30*' 

Ethnic 40* 

'Significant at the.O.OOl probability level. 

"Significant ilt the 0.005 probability level. 

Pool Qwnershi~ 

240* 

245* 

83' 

43* 

28* 
Determining the important customer descriptors that influence intensity of 

appliance usage initially involves~identifying strata for each of the characteristic 

variables. The purpose ofthis-step is to partially aggregate the categorical data 

collected for each RSS survey variable without losing information that may help 

explain energy usage. Table 8-3 lists for each variable, three ranges that were 

derived from analyzing energy usage profiles. For example, the lncome variable is 

split into three categories "Low lncome," "Medium lncome," and "High income," 

corresponding to "under $25,000," "$25,000 to $49,999," and "$50,000 and 

over." The results for the lncome and SqFt variables are consistent with the cell 

definitions applied in the design of the REUC sample. 

Table B-3 

RSS-Based Stratification of Customer Descri~tors 

Customer 

Descri~tor 

Income 

Vintage 

Stratum 

Low Medium High 

Income Income Income 

<$25,000 $25,000-49,999 $50,000-t 

Small House Medium House Large House 

<I ,250 1,250-1,749 1,750+ 

Very Old Old 

Pre-1960 196x9 75 

Recent 

1975+ 

NumRes Small Family Medium Familv Larve Familv 

1-2 3-4 5+ 

The strata listed for each characteristic variable in Table 8-3 are used to segment 

the RSS customers. Table 8-4 lists for each subsample of customers stratified by 

division and AS1 those variables that, when used to segment customers, resulted 

in significant differences in annual energy usage profiles. In almost every case, 

Income, NumRes, and SqFt continue to explain differences in energy usage. In 

the cases of the GSHNWAC and GSHINOAC ASIS, vintage of dwelling, when 

split before and after 1960, also helped meaningfully segment annual usage 

profiles. This result is interesting in that the expected effects of Title 24, 

California building efficiency standards implemented in 1978, are not significantly 

 EE-RM-94-230 COMMENT 61F

Determining the important customer descriptors that influence intensity of 

appliance usage initially involves· identifying strata for each of the characteristic 

variables. The purpose of--this,step is to partially aggregate the ca,tegorical data 

collected for each RSS survey variable without losing information that may help 

explain energy usage. Table B-3 lists for each variable, three ranges that were 

derived from analyzing energy usage profiles. For example, the Income variable is 

split into three categories "Low Income," "Medium Income," and "High Income," 

corresponding to "under $25,000," "$25.000 to $49,999," and "$50,000 and 

over." The results for the Income and SqFt variables are consistent with the cell 

definitions applied in the design of the REUC sample. 

Customer 

. Descriptor 

Income 

Sq Ft 

Vintage 

NumRes 

Table B-3 

RSS-Based Stratification of Customer Descriptors 

Stratum 

Low Medium High 

Income Income Income 

<$25,000 $25,000-49,999 $50,000+ 

Small House Medium House Large House 

<1,250 1,250-1,749 1,750+ 

VeOC Old Old Recent 

Pre-1960 1960-1975 1975+ 

Small Famil~ Medium Famil~ Large Famil~ 

1-2 3-4 5+ 

The strata listed for each characteristic variable in Table B-3 are used to segment 

the RSS customers. Table 8-4 lists for each subsample of customers stratified by 

division and ASI those variables that, when used to segment customers, resulted 

in significant differences in annual energy usage profiles. In almost every case, 

Income, NumRes, and SqFt continue to explain differences in energy usage. In 

the cases of the GSH/WWAC and GSH/NOAC ASls, vintage of dwelling, when 

split before and after 1960, also helped meaningfully segment annual usage 

profiles. This result is interesting in that the expected effects of Title 24, 

California building efficiency standards implemented in 1978, are not significantly 

8-6 
observed in the data with the exception of the GSHICAC AS1 in the SCE 

Northern Division. 

. - - - 

Table B-4 

Customer Descriptors that Meanindully Discriminate Annual Usage 

Profiles by Appliance Stock Inventory (ASI) and SCE Division 

GSHICAC SqFt SqFt 

NumRes NumRes 

Income income 

Pool Pool 

Vintage 

GSHIWWAC NumRes SqFt 

NumRes 

Income 

Pool 

GSHINOAC SqFt SqFt 

NumRes NumRes 

income Pool 

Pool Vintage 

- East 

SqFt 

NumRes 

Income 

Pool' 

SqFt 

NumRes 

Income 

Vintage 

SqFt 

NumRes 

income 

Vintage 

SqFtNumRes 

Income 

Vintage 

*Sample size too small for analysis. 

 EE-RM-94-230 COMMENT 61F

 

 

 B-7

observed in the data with the exception of the GSH/CAC ASI in the SCE 

Northern Division. 

Table 8-4 

Customer Descriptors that Meaningfully Discriminate Annual Usage 

Profiles by Appliance Stock Inventory (ASI) and SCE Division. 

ASI North 

GSH/CAC SqFt 

NumRes 

Income 

Pool 

Vintage 

GSH/WWAC NumRes 

GSH/NOAC SqFt 

NumRes 

Income 

Pool 

'Sample size too small for analysis. 

South 

SqFt 

NumRes 

Income 

Pool 

SqFt 

NumRes 

Income 

Pool 

SqFt 

NumRes 

Pool 

Vintage 

East 

Sq Ft 

NumRes 

Income 

poor 

SqFt 

NumRes 

Income 

Vintage 

• 

SqFt 

NumRes 

Income· 

Vintage 

SqFt 

NumRes 

Income 

Vintage 
Appendix C 

LOAD DATA EDITING AND VALIDATION 

REUC DATA VALIDATION METHODS 

Before REUC appliance load profiles and UEC estimates are calculated, it is 

necessary to extensively screen the entire data set. for validity. Invalid and 

misleading data can arise because of the following factors: 

Defective or Inaccurate Load Monitoring Devices. It is 

possible that an appliance load monitor, in constant use over 

an extended period of time, can fail or can provide 

inaccurate readings. 

Inappropriate Pulse Multipliers. The monitors record energy 

usage with pulses that are converted to the appropriate units 

(i.e., watt-hours or cubic feet)' with pulse multipliers. 

Extensive validation of these multipliers is required to ensure 

the validity of the load research results. 

System Failure. Misleading load readings can result from 

voltage surges and power spikes. In addition, a source of 

possible system failure is in the transmission and recording 

link between the load monitor device and the REUC 

computer system. 

Three different levels of damage can result from errors that are found ir; end-use 

load data. First, a 5-minute interval can be found to be invalid for a single 

monitoring channel. This can occur because of system failure. Second, a 5- 

minute interval can be found to be invalid for all channels in the entire data set. 

This again is usually attributed to system failure. Finally, all readings for a long 

period of time (a month or more) can be invalid for a particular channel. This 

most serious type of error can occur due to misidentification of channels or 

because of defective load monitoring devices. 

DATA VALIDATION AND EDITING PROCESS 

The data validation process takes place in three phases in order to screen out 

inaccurate readings that can result from. data collection problems described 

.above. 

 EE-RM-94-230 COMMENT 61F

 

 

 C-1

Appendix C 

LOAD DATA EDITING AND VALIDATION 

REUC DATA VALIDATION METHODS 

Before REUC appliance load profiles and UEC estimates are calculated, it is 

necessary to extensively screen the entire data set for validity. Invalid and 

misleading data can arise because of the following factors: 

• Defective or Inaccurate Load Monitoring Devices. It is 

possible that an appliance load monitor, in constant use over 

an extended period of time, can fail or can provide 

inaccurate readings. 

• Inappropriate Pulse Multipliers. The monitors record energy 

usage with pulses that are converted to the appropriate units 

(i.e., watt-hours or cubic feet)' with pulse multipliers. 

Extensive validation of these multipliers is required to ensure 

the validity of the load research results. . 

• System Failure. Misleading load readings can result from 

voltage surges and power spikes. In addition, a source of 

possible system failure is in the transmission and recording 

link between the load monitor device and the REUC 

computer system. 

Three different levels of damage can result from errors that are found in end-use 

load data. First, a 5-minute interval can be found to be invalid for a single 

monitoring channel, This can occur because of system failure. Second, a 5-

minute interval can be found to be invalid for all channels in the entire data set. 

This again is usually attributed to system failure. Finally, all readings for a long 

period of time '(a month or more) can be invalid for a particular channel. This 

most serious type of error can occur due to misidentification of channels or 

because of defective load 'monitoring devices. 

DATA VALIDATION AND EDITING PROCESS 

The data validation process takes place in three phases in order to screen out 

inaccurate readings that can result from. data collection problems described 

·above. 

r.1 
. Phase 1 : Self-validation. Comparison of load readings 

against a credible estimate of connected load. 

. Phase 2: Cross-Validation. ' Comparison of load readings 

versus whole house readings. 

Phase 3: Raw Data Testing and Editing. Testing for monthly 

appliance range accuracy and appliance usage pattern 

detection. 

The Self-validation phase compares load readings against a credible estimate of 

connected load. The entire set is checked for unreasonable readings or spikes. 

Maximum "reasonable" connected loads for each appliance are shown in Table 

C-1. Data readings that exceed these values are marked as missing in the source 

data set. These limits are conservative, set high to ensure that this phase of 

validation only excludes values that are clearly invalid. 

Furthermore, as part of the Self-Validation phase, a minimum of one week of 

each channel in the REUC sample was graphically screened by a load research 

analyst using ~oad~iew?~ This Microsoft Windows-based load data analysis tool 

allows the researcher to determine if the raw load data reflect appropriate 

patterns of usage by graphing one day of data at a time. The researcher can also 

quickly scroll through successive days of data. LoadView was invaluable for verifying and reconciling the pulse multipliers described above. By viewing all major 

end-use channels and whole house load on one screen for many days of data, it 

was possible to refine estimates of the pulse multipliers by matching estimated 

usage patterns to known appropriate appliance usage patterns. 

During the Cross-Validation phase, the eniire data set is checked to make sure 

that no appliance load reading is greater than the whole house reading. Data 

that fail this test are marked as missing in the source data set. 

During the Raw Data Testing and Editing phase, the entire data set is subjected to 

additional rigorous tests for validity. The first set of tests compares appliance load 

data with reasonable range estimates of likely usage. In order to easily manage 

the first set of tests, the original data set consisting of 5-minute observations for 

each of the monitored appliances is reduced to a data set containing the weekly 

 EE-RM-94-230 COMMENT 61F

• Phase 1: Self-Validation. Comparison of load readings 

against a credible estimate of connected load. 

• Phase 2: Cross-Validation.' Comparison of load readings 

versus whole house readings. 

• Phase 3: Raw Data Testing and Editing. Testing for monthly 

appl iance range accuracy and appliance usage pattern 

detection. 

The Self-Validation phase compares load readings against a credible estimate of 

connected load. The entire set is checked for unreasonable readings or spikes. 

Maximum "reasonable" connected loads for each appliance are shown in Table 

C-1. Data readings that exceed these values are marked as missing,in the source 

data set. These limits are conservative, set high to ensure that this phase of 

validation only excludes values that are clearly invalid. 

Furthermore, as part of the Self-Validation phase, a minimum of one week of 

each channel in the REUC sample was graphically screened by a load research 

analyst using LoadView.TM This Microsoft Windows-based load data analysis tool 

allows the researcher to determine if the raw load data reflect appropriate 

patterns of usage by graphing one day of data at a time. The researcher can also 

quickly scroll through successive days of data. LoadView was invaluable for verifying and reconciling the pulse multipliers described above. By viewing all major 

end-use channels and whole house load on one screen for many days of data, it 

was possible to refine estimates of the pulse multipliers by matching estimated 

usage patterns to known appropriate appliance usage patterns . 

. During the Cross-Validation phase, the entire data set is checked to make sure 

that no appliance load reading is greater than the whole house reading. Data 

that fail this test are marked as missing in the source data set. 

During the Raw Data Testing and Editing phase, the entire data set is subjected to 

additional rigorous tests for validity. The first set of tests compares appliance load 

data with reasonable range estimates of likely usage. In order to easily rnanage 

the first set of tests, the original data set consisting of 5-minute observations for 

each of the monitored appliances is reduced to a data set containing the weekly 

C-2 
usage for each appliance. This provides a weekly total whole household use and a 

weekly total use for each appliance. 

During Phase 3, the first test compares weekly appliance total usage (UECSUM) 

with a "reasonable" range to determine whether it should be included in the 

REUC study. Valid usage readings for particular appliances should fall into the 

"reasonable" ranges as defined in Table C-2. Data that do not fall into these 

ranges are marked as ipvalid and removed from the data set. 

Because of the possibility of misidentified channels within the data set, Phase 3 

also determines whether a given set of daily load observations for an appliance 

reflects behavior expected of that appliance. This test uses the Appliance 

Consecutive Use Period Analyzer (ACUPA), a software package that calculates 

the amount of time a given appliance is drawing current and the amount of time 

it is off. The ACUPA incorporates a complex system for filtering out small current 

draws from, for example, a micro'wave oven clock. By screening out these small 

current draws, ACUPA prevents inaccurateusage pattern profiles from entering 

the data set. ACUPA works by taking advantage of the fact that certain aspects of 

usage patterns for specific appliances are well known, and any deviation is 

unlikely. For example, refrigerators and freezers cannot have load readings of 

zero for 24 consecutive hours. It is also extremely unlikely that heating and 

cooling appliances will be on continuously for 24 consecutive hours. Table C-3 

lists the usage pattern criteria that ACUPA employs to flag invalid or misidentified 

appliance channels. ONTIME refers to the mean number o'f consecutive hours an 

appliance is on. If an appliance channel has an ONTIME greater'than is listed, it is 

flagged. OFFTIME similarly refers tothe meannumber of consecutive hours an 

appliance is off. If an appliance channel has an OFFTIME greater than orequal to 

that listed, it is also flagged. 

Data that fail tests during Phase 3 of the Data Validation Process are flagged 

according to the Problem Codes listed in Table C-4. 

The three-phase data "alidation system thoroughly tests the data set to remove 

inaccurate load readings. With the data set'screened in this manner, highly 

accurate load profiles for each appliance are then created. 

 EE-RM-94-230 COMMENT 61F

 

 

 C-3

usage for each appliance. This provides a weekly total whole household use and a 

weekly total use for each appliance. 

.' 

During Phase 3, the first test compares weekly appliance total usage (UECSUM) 

with a "reasonable" range to determine whether it should b~ included in the 

REUC study. Valid usage readings for particular appliances should fall into the 

"reasonable" ranges as defined in Table C-2. Data that do not f<:lll into these 

ranges are marked as invalid and removed from the data set. 

Because of the possibility of misidentified channels within the data set, Phase 3 

also determines whether a given set of daily load observations for an appliance 

reflects behavior expected of that appliance. This test uses the Appliance 

Consecutive Use Period Analyzer (ACUPA), a software package that calculates 

the amount of time a given appliance is drawing current and the amount of time 

it is off. The ACUPA iricorporates a complex system for filtering out small current 

draws from, for example, a microwave oven clock. By screening out these small 

current draws, ACUPA prevents inaccurate usage pattern prqfiles from entering 

the data set. ACUPA works by taking advantage of the fact that certain aspects of 

usage patterns for specific appliances are well known, and any deviation is 

unlikely. For example, refrigerators and freezers cannot have load readings of 

zero for 24 consecutive hours. It is also extremely unlikely that heating and 

cooling appliances will be on continuously for 24 consecutive hours. Table C-3 

lists the usage pattern criteria that ACUPA employs to flag invalid or misidentified 

appliance channels. ONTIME refers to the mean number of consecutive hours an 

appliance is on. If an appliance channel has an ONTIME greater than is listed, it is 

flagged. OFFTIME Similarly refers to the mean number of consecutive hours an 

appliance is off. If an appli'ance channel has an OFFTIME greater than or equal to 

that listed, it is also flagged. 

Data that fail tests during phase 3 of the Data Validation Process are flagged 

according to the Problem Codes listed in Table C-4. 

The three-phase data validation system thoroughly tests the data set to remove 

inaccurate load readings. With the data set'screened in this manner, highly 

accurate load profiles for each appliance are ,then created. 
Table C-1 

"Reasonable" Connected Load Estimates 

A~oliance 

Electric Clothes Dryer 

Electric Oven Range 

Gas Clothes Dryer 

Gas' Water Heater 

Gas Furnace 

Electric HVAC 

Electric Heat Pump AC 

Heat Pump Water Heater - 

Gas Range 

Maximum Acceptable 

Connected Load Estimate 

CONL = 7kWhIhour 

CONL = 1 OkWh/hour 

CONL = 40 c.f./hour 

CONL = 59 c.f./hour 

CONL = 150 c.f./hour 

CONL = 12kWli/hour 

CONL = 12kWhlhour 

CONL = 6kWh 

CONL = 50 c.f./hqur 

Table C-2 

"Reasonable" Ranges for Weeklv Aodiance Use 

Aooliance 

Electric Clothes Dryer 

Electric Range Oven 

Whole House 

Gas Clothes Dryer 

Gas Water Heater 

Heat Pump Water Heatel 

Gas Range 

Lower Bound 

N/A 

N/A 

UECSUM < 1 kwh 

N/ A 

N/ A 

N/A 

N /A 

Uoper Bound 

4 

UECSUM-> 2OOkWh 

UECSUM > 70 kwh 

UECSUM > 1000kWh 

UECSUM > 800cf 

UECSUM > 2000cf 

UECSUM > 200kWh 

UECSUM > 700cf 

 EE-RM-94-230 COMMENT 61F

Table C-l . 

"Reasonable" Connected load Estimates 

Appliance 

Electric Clothes Dryer 

Electric Oven Range 

Gas Clothes Dryer 

Gas· Water Heater 

Gas Furnace 

Electric HVAC 

Electric Heat Pump AC 

Heat Pump Water Heater _ 

Gas·Range 

Table C-2 

Maximum Acceptable 

Connected Load Estimate 

CO,,!L = 7kWh/hour 

CONL = lOkWh/hour. 

CONL = 40 c.f./hour 

CONL = 59 c.f./hour 

CONL = 150 c.f./hour 

CONL = 12kWh/hour 

CONL = 12kWh/hour 

CONL '= 6kWh 

CONL = 50 c.f./hqur 

"Reasonable" Ranges for Weekly Appliance Use 

Appliance Lower Bound Upper Bound 

Electric Clothes Dryer N/A UECSUM> 200kWh 

Electric Range Oven N/A UECSUM > 70 kWh 

Whole House UECSUM < 1 kWh UECSUM > 1000kWh 

Gas Clothes Dryer N/A UECSUM > BOOd 

Gas Water Heater N/A UECSUM > 2000cf 

Heat Pump Water Heater N/A UECSUM > 200kWh 

Gas Range N/A UECSUM> lOOd 

C-4 

• 
Table C-3' 

ACUPA Error Flag Criteria 

ADoliance 

Electric Clothes Dryer 

Electric Oven Range 

Whole House 

Gas Clothes Dryer 

Gas Water Heater 

Heat Pump Water Heater 

Ga's Range 

ONTIME 

ONTIME > 15 hours 

ONTIME > 12 hours 

NIA 

ONTIME > 15 hours 

N/A 

NIA 

ONTIME > 12 hours 

OFFTIME 

N/A 

N/A 

OFFTIME = 0.5 hours 

N/A 

OFFTIME = 24 hours 

OFFTIME = 24 hours 

NIA 

 EE-RM-94-230 COMMENT 61F

Table C-3 

ACUPA Error Flag Criteria 

Appliance ONTIME OFFTIME 

Electric Clothes Dryer ONTIME.> 15 hours N/A 

Electric Oven Range ONTIME > 12 hours N/A 

Whole House N/A OFFTIME = 0.5 hours 

Gas Clothes Dryer ONTIME > 15 hours N/A 

Gas Water Heater N/A OFFTIME = 24 hours 

Heat Pump Water Heater N/A OFFTIME = 24 hours 

Gas Range ONTIME > 12 hours N/A 

.. 

C-5 
Table C-4 

Kev to Invalid Data Flagged During Data Validation Process 

Problem Code - Test 

PROBUEC=O The appliance had an acceptable weekly usage. 

(acceptable WEEKLY USAGE) 

PROBU EC=1 The appliance had an inordinately high weekly usage. 

(WEEKLY USAGE too high) 

PROBUEC=2 The appliance had an inordinately lo'w weekly usage. 

(WEEKLY USAGE too low) 

PROBCONL=O The appliance had an acceptable connected load estimate. (acceptable CONNECTED LOAD) 

PROBCONL=l The appliance had an inordinately high connected load 

estimate. (CONNECTED LOAD too high) 

PROBCONL=2 The appliance had an inordinately low connected load 

estimate. (CONNECTED LOAD too low) 

PROBON=O The appliance had an acceptable mean number of consecutive usage hours. (acceptable ON time) 

PROBON=l The appliance had an inordinately high mean number of 

consecutive usage hours. (ON too much) 

PROBON=2 The appliance had an inordinately low mean number of 

consecutive usage hours. (not ON enough) 

PROBOFF=O The appliance had an acceptable meannumber of consecutive non-usage hours. (acceptable OFF time) 

PROBOFF=l The appliance had an inordinately high mean number of 

consecutive non-usage hours. (OFF too much) 

PROBOFF=2 The appliance had an inordinately low mean number of 

consecutive non-usage hours. (not OFF enough) 

 EE-RM-94-230 COMMENT 61F

Table C-4 

Key to Invalid Data Flagged During Data Validation Pr()cess 

Problem Code 

PROBUEC=O 

PROBUEC=l 

PROBUEC=2 

PROBCONl=O 

PROBCONl=l 

. PROBCONL=2 

PROBON=O 

PROBON=l 

PROBON=2 

PROBOFF=O 

PROBOFF=l 

PROBOFF=2 

Test 

The appliance had an acceptable weekly usage. 

(acceptable WEEKLY USAGE) 

The appliance had an inordinately high weekly usage. 

(WEEKLY USAGE too high) 

The appliance had an inordinately low weekly usage. 

(WEEKLY USAGE too low) 

The appliance had an acceptable connected load estimate. (acceptable CONNECTED LOAD) 

The appliance had an inordinately high connected load 

estimate. (CONNECTED LOAD too high) 

The appliance had an inordinately low connected load 

estimate. (CONNECTED LOAD too low) 

The appliance had an acceptable mean number of c~nsecutive usage hours. (acceptable ON time) 

The appliance had an inordinately high mean number of 

consecutive usage hours. (ON too much) 

The appliance had an inordinately low mean number of 

consecutive usage hours. (not ON enough) 

The appliance had an acceptable mean. number of consecutive non-usage hours. (acceptable OFF time) 

The appliance had an inordinately high mean number of. 

consecutive non-usage hours. (OFF too much) 

The appliance had an inordinately low mean number of 

consecutive non-usage hours. (not OFF enough) 

C-6 
GLOSSARY OF TERMS 

AS1 Appliance Stock Inventory. 

BEST Behavioral Engineering Statistical Techniques. 

CAC Central Air ~ondhionin~. 

COP Coefficient of Performance. 

Cost Payback 

Approach REUC Project methodology for comparing the cost and . energy efficiency investment characteristics of gas and 

CP&B Customer Preference and Behavior (from CLASSIFYTM, EPRl 

EM-5908). 

Electric Induction 

Subsample Mission Viejo participants that received new electric 

induction ranges. 

Electric Sample REUC Participants that received new electric heat.pumps, 

heat pump water heaters, electric clothes dryers and (for the 

Mission Viejo Sample) electric induction ranges. 

Energy At 

Point of Use 1 kwh = 3.412 kBTU (i.e. not including generation and 

transmission losses). 

Ethnic Variable representing resident's ethnicity. 

Gas Sample REUC Participants that received new gas furnace/electric air 

conditioners, gas water heaters, gas clothes dryers and.gas 

ranges. 

GSH Gas Space Heating. 

HSPF Heating Season Performance Factor. 

I FS Image Factor Score. 

IMR Integrated Market Research. 

lrvine Sample The second 50 REUC participant households, located in 

Iwine, California (in Orange County). 

 EE-RM-94-230 COMMENT 61F

ASI 

BEST 

CAC 

COP 

Cost Payback 

Approach 

CP&B 

Electric Induction 

Subsample 

Electric Sample 

Energy At 

Point of Use 

Ethnic 

Gas Sample 

GSH 

HSPF 

IFS 

IMR 

Irvine Sample 

GLOSSARY OF TERMS 

Appliance Stock Inventory. 

Behavioral Engineering Statistical Techniques. 

Central Air Conditioning. 

Coefficient of Performance. 

REUC Project methodology for comparing the cost and 

energy efficiency investment characteristics of gas and 

electric appliances. 

Customer Preference and Behavior (from CLASSIFYTM, EPRI 

EM-5908). 

Mission Viejo participants that received new electric 

induction ranges. 

REUC Participants that received new electric heat. pumps, 

heat pump water heaters, electric clothes dryers and (for the 

Mission Viejo Sample) electric induction ranges. 

1 kWh = 3.412 kBTU (i.e. not including generation and 

transmission losses). 

Variable representing resident's ethnicity. 

REUC Participants that received new gas furnace/electric air 

conditioners, gas water heaters, gas clothes dryers and' gas 

ranges. 

Gas Space Heating. 

Heating Season Performance Factor. 

Image Factor Score. 

Integrated Market Research. 

The second 50 REUC participant households, located in 

Irvine, California (in Orange County). 

G-l 
LCC 

LDAW 

Load Data 

Load Profile 

Load Shape 

Load Shape 

Modeling 

MBTU 

Mission Viejo 

Sample 

NOACNumRes 

Quality Image 

RAEUS 

RSS 

SEER 

SqFt 

Therm 

U EC 

Value Image 

Thousand ~ritish thermal units; kBTU=lOO*Therms; 

kBTU=3.4127*kWh. 

Life Cycle Cost. 

The Load Data Analysis Workstation (EPRI RP2342-8). 

Five-minute readings of energy usage. 

Hourly (or half-hourly) average load over the 24-hour period 

from midnight to midnight; same as load shape. 

Hourly (or half-hourly) average load over the 24-h~ur'~eriod 

from midnight to midnight; same as load profile. 

The process of using customer characteristics to explain 

customer energy usage. 

Software tool for visually screening raw load data. 

Million British thermal units; MBTU=lOOO*kBTU. 

The first 42 REUC participant households, located in Mission 

Viejo, California (in Orange County). 

No Air Conditioning. 

Number of Household Residents. 

A composite of the survey respondent's Safety, Convenience, 

Service and Reliability responses. 

Residential Appliance End-Use Survey. 

Residential Saturation Survey. 

Seasonal Energy Efficiency Ratio. 

Household Square .Feet. 

A quantity of energy equal to 100,000 British thermal units, 

typical billing unit for natural gas. 

Unit Energy Consumption. 

A composite of the survey respondent's Appliance Cost, 

General Expense and Monthly Utility Bill responses. 

 EE-RM-94-230 COMMENT 61F

kBTU 

LCC 

LDAW 

Load Data 

Load Profile 

Load Shape 

Load Shape 

Modeling 

LoadViewTM 

MBTU 

Mission Viejo 

Sample 

NOAC 

, NumRes 

Quality Image 

RAEUS 

RSS 

SEER 

SqFt 

Therm 

UEC 

Value Image 

Thousand British ther,mal units; kBTU=lOO*Therms; 

kBTU=3.4127*kWh. 

Life Cycle Cost. 

The Load Data Analysis Workstation (EPRI RP2342-8l. 

Five-minute readings of energy usage. 

Hourly (or half-hourly) average load over the 24-hour period 

from midnight to midnight; same as load shape. 

Hourly (or half-hourly) average load over the 24-hour' period 

from midnight to midnight; same as load profi Ie. 

The process of using customer characteristics to explain 

customer energy usage. 

Software tool for visually screening raw load data. 

Million British thermal units; MBTU=1000*kBTU. 

The first 42 REUC participant households, located in Mission 

Viejo, California (in Orange County). 

No Air Conditioning. 

Number of Household Residents. 

A composite of the survey respondent's Safety, Convenience, 

Service and Reliability responses. 

Residential Appliance End-Use Survey. 

Residential Saturation Survey. 

Seasonal Energy Efficiency Ratio. 

Household Square ,Feet. 

A quantity of energy equal to 100,000 British thermal units, 

typical billing unit for natural gas. 

Unit Energy Consumption. 

A composite of the survey respondent's Appliance Cost, 

General Expense and Monthly Utility Bill responses. 

G-2 
Vintage 

WWAC 

House Vintage. 

WalliWindow Air Conditioning. 

 EE-RM-94-230 COMMENT 61F

Vintage 

WWAC 

House Vintage. 

WallIW.indow Air Conditioning. 

G-3