Keywords:

Customer attitudes

End-use consumption

Energy efficiency

Load research

Residential Energy Usage

Comparison: Findings

Prepared by

Quantum Consulting Inc.

Berkeley, California

EPRI CU-7392

Project 2863-3

Final Report

August 1991

EE-RM-90-201 COMMENT OOC2

Keywords: 

Customer attitudes 

End-use consumption 

Energy efficiency 

Load research 

Residential Energy Usage 

Comparison: Findings 

Prepared by 

Quantum Consulting Inc. 

Berkeley, California 

EPRI CU-7392 

Project 2863-3 

Final Report 

August 1991 
REPORT s u M MAR Y

INTEREST CATEGORIES

Demand-side planning

Market assessment

Lighting and appliances

KEYWORDS

Customer attitudes

End-use consumption

Energy efficiency

Load research

EPRI CU-7392s

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 appliance 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 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

Electric Power Research Institute

EE-RM-90-201 COMMENT OOC2

INTEREST CATEGORIES 

Demand-side planning 

Market assessment 

Lighting and appliances 

KEYWORDS 

Customer attitudes 

End-use consumption 

Energy efficiency 

Load research 

EPRI CU-7392s 

RE PORT s u M MAR Y 

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 appliance 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 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 

Electric Power Research Institute 
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 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 EPRI'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-90-201 COMMENT OOC2

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 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 EPRI'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. 
DISCLAIMER OF WARRANTIES AND

LIMITATION OF LIABILITIES

THIS REPORT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN

ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER

RESEARCH INSTITUTE, INC. (EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY

COSPONSOR, THE ORGANIZATlON(S) NAMED BELOW, NOR ANY PERSON ACTING

ON BEHALF OF ANY OF THEM:

(A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR

IMPLIED. (I) WITH RESPECT TO THE USE OF ANY INFORMATION. APPARATUS,

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

MERCHANTABILITY AND FITNESS FOR APARTICULAR PURPOSE, OR (II) THAI' 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

(B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY

WHATSOEVER (INCLUDING ANY CONSEQUENTIAL DAMAGES. EVEN IF EPRI OR ANY

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

RESULTING FROM YOUR SELECTION OR USE OF THIS REPORT OR ANY INFORMATION,

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

ORGANIZATlON(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

B. A. Smith

R. 1. Uhlaner

1. 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-90-201 COMMENT OOC2

DISCLAIMER OF WARRANTIES AND 

LIMITATION OF LIABILITIES 

THIS REPORT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN 

ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER 

RESEARCH INSTITUTE, INC. (EPRI). NEITHER EPRI , ANY MEMBER OF EPRI, ANY 

COSPONSOR, THE ORGANIZATlON(S) NAMED BELOW, NOR ANY PERSON ACTING 

ON BEHALF OF ANY OF THEM: 

(A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR 

IMPLIED, (I) WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, 

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

MERCHANTABILITY AND FITNESS FOR A PARTICULAR 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 

(B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY 

WHATSOEVER (INCLUDING ANY CONSEQUENTIAL DAMAGES, EVEN IF EPRI OR ANY 

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

RESULTING FROM 'rUUR SELECTION OR USE OF THIS REPORT OR ANY INFORMATION, 

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

ORGANIZATION(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 

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 
Electric Power Research Institute and EPRI are registered service marks of Electric Power Research Institute. Inc.

Copyright © 1991 Electric Power Research Institute. Inc. All rights reserved.

ORDERING INFORMATION

Requests for copies of this report should be directed to Research Reports Center

(RRC), Box 50490, Palo Alto, CA 94303, (415) 965-4081. There is no charge for reports

requested by EPRI member utilities and affiliates, U.S. utility associations, U.S. government

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

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

EE-RM-90-201 COMMENT OOC2

Electric Power Research Institute and EPRI are registered service marks of Electric Power Research Institute. Inc. 

Copyright © 1991 Electric Power Research Institute. Inc. All rights reserved. 

ORDERING INFORMATION 

Requests for copies of this report should be directed to Research Reports Center 

(RRC), Box 50490, Palo Alto, CA 94303, (415) 965-4081 . There is no charge for reports 

requested by EPRI member utilities and affiliates, U.S. utility associations, U.S. government 

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

an information exchange agreement. On request, RRC will send a catalog of EPRI reports. 
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 profi les 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 REUC project data were also used to develop segmentation approaches that

portrayed load shapes for different customer groups. Several segmentation

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

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

shapes based only on customer attribute information.

iii

EE-RM-90-201 COMMENT OOC2

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 profi les 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 REUC project data were also used to develop segmentation approaches that 

portrayed load shapes for different customer groups. Several segmentation 

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

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

shapes based only on customer attribute information. 

iii 
EE-RM-90-201 COMMENT OOC2
EPRI FOREWORD

'f Ii ; re idential energy usage comparison (REUC) project has been a joint effort by EPRI and

, uth-rn California Edison Co. to measure and compare typical energy comsumption patterns

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

pr j t has produced valuable information about how actual households use electricity and

g for pace 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

v

EE-RM-90-201 COMMENT OOC2

EPRI FOREWORD 

1 Ii , r ' sidential energy usage comparison (REUC) project has been a joint effort by EPRI and 

llthern Ca lifornia Edison Co. to measure and compare typical energy comsumption patterns 

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

pr jc t has produced valuable information about how actual households use electricity and 

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

the: des ign, analysis methods, and results from the REUC project. 

Steven D. 8raithwait; Peter Cleary; Paul C. Meagher, Project Managers 

Customer Systems Division 

v 
EE-RM-90-201 COMMENT OOC2
ACKNOWLEDGEMENTS

The Quantum Consulting project staff wishes to acknowledge the assistance and

contributions of several organizations and persons in conducting this research. In

particular, we want to acknowledge the substantive contributions of Dr. Steven

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

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

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

valuable ingredient in our research.

VII

EE-RM-90-201 COMMENT OOC2

ACKNOWLEDGEMENTS 

The Quantum Consulting project staff wishes to acknowledge the assistance and 

contributions of several organizations and persons in conducting this research. In 

particular, we want to acknowledge the substantive contributions of Dr. Steven 

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

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

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

valuable ingredient in our research. 

VII 
EE-RM-90-201 COMMENT OOC2
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

ix

S-1

1-1

1-1

1-2

1-5

2-1

2-1

2-1

2-2

3-1

3-1

3-1

3-4

4-1

4-1

4-1

4-2

4-4

5-1

5-1

5-1

5-4

5-11

5-16

6-1

6-1

6-1

6-10

6-18

EE-RM-90-201 COMMENT OOC2

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 

I ntrod uction 

Load Profj les 

Using Household Characteristics to Explain Load Shapes 

Total Energy Regressions 

ix 

S-1 

1-1 

1-1 

1-2 

1-5 

2-1 

2-1 

2-1 

2-2 

3- 1 

3-1 

3-1 

3-4 

4-1 

4-1 

4-1 

4-2 

4-4 

5-1 

5-1 

5-1 

5-4 

5-11 

5-16 

6-1 

6-1 

6-1 

6-10 

6-18 
CONTENTS, cont.

Section

7

8

CUSTOMER PREFERENCES AND APPLIANCE ACCEPTANCE

Introduction

~uel Type Image

Space Heating

Water Heating

Cooking

Appliance Discount Factors

INTEGRATION OF LOAD AND SURVEY ANALYSES

Introduction

Load Shapes by REUC Customer Segment

Determination of 1I0ptimai ll Stratifying Variables

Appliance Life Cycle Costs

APPENDIX A

Supplementary Energy Usage Information and Load Profiles

APPENDIX B

7-1

7-1

7-2

7-6

7-14

7-18

7-21

8-1

8-1

8-2

8-5

8-7

A-l

Methodology to Assess the Generalizabilityof the REUC Customer

Sample B-1

APPENDIX C

Load Data Editing and Validation

GLOSSARY OF TERMS

x

C-l

G-l

EE-RM-90-201 COMMENT OOC2

CONTENTS, cont. 

Section 

7 

8 

CUSTOMER PREFERENCES AN D APPLIANCE ACCEPTANCE 

Introduction 

r::uel Type Image 

Space Heating 

Water Heati ng 

Cooking 

Appliance Discount Factors 

INTEGRATION OF LOAD AND SURVEY ANALYSES 

Introduction 

Load Shapes by REUC Customer Segment 

Determination of 1I0ptimai li Stratifying Variables 

Appliance Life Cycle Costs 

APPENDIX A 

Supplementary Energy Usage Information and Load Profiles 

APPENDIX 8 

Methodology to Assess the Generalizabilityof the REUC Customer 

7 -1 

7 -1 

7-2 

7-6 

7-14 

7-18 

7 -21 

8-1 

8-1 

8-2 

8-5 

8-7 

A-l 

Sample 8-1 

APPENDIX C 

Load Data Editing and Validation C-1 

GLOSSARY OF TERMS G-l 

x 
'-, I

'-, )

S -·1

s- ')

S-h

S-7

5-8

1-1

3-1

5- 1

5-2

5-3

5-4

5-5

5-6

5-7

FIGURES

Annual Average Energy Usage - Space Conditioning and

Water Heating

SCE REUC: Average Load Profiles - Winter Weekday

Space Conditioning

Customers' Required Return on a Small Riskless Investment

Customers l Required Return on an Investment in Appliance

Efficiency

REUC Participant Images of Electric vs. Gas

SCE REUC: Quality Factor Segments - Winter Weekday

Heat Pump

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

Heat Pump

REUC: Heat Pump Water Heater - Demographic Scenario

REUC Project Milestones

REUC Survey and Load Data Collection History

Annual Average Energy Usage Space Conditioning and Water

Heating

Annual Average Energy Usage - Clothes Drying and Cooking

Difference in Annual Energy Consumption between Gas

and Electric Appliances

Seasonal Average Weekday Electricity Usage Electric Sample

End-Uses

Whole House: Average Daily Electricity Usage

SCE REUC Weekday Average UEC Distribution - Winter

Electric Heat Pump: Pooled Cities

SCE REUC Weekday Average UEC Distribution - Summer

Space Conditioning: Pooled Cities

XI

S-3

S-4

S-6

S-7

S-8

S-9

5-10

5-11

1-3

3-4

5-4

5-5

5-7

5-10

5-11

5-13

5-13

EE-RM-90-201 COMMENT OOC2

" I 

'> ) 

'> 1 . , 

s·s 

S- h 

S- 7 

5- 8 

1 -1 

3 - 1 

5-1 

5-2 

5-3 

5-4 

5-5 

5-6 

5-7 

FIGURES 

Annual Average Energy Usage - Space Conditioning and 

W ater Heating 

SCE REUC: Average Load Profiles - Winter Weekday 

Space Conditioning 

Customersl Required Return on a Small Riskless Investment 

Customersl Required Return on an Investment in Appliance 

Efficiency 

REUC Participant Images of Electric vs. Gas 

SCE REUC: Quality Factor Segments - Winter Weekday 

Heat Pump 

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

Heat Pump 

REUC:Heat Pump Water Heater - Demographic Scenario 

REUC Project Milestones 

REUC Survey and Load Data Collection History 

Annual Average Energy Usage Space Conditioning and Water 

Heating 

Annual Average Energy Usage - Clothes Drying and Cooking 

Difference in Annual Energy Consumption between Gas 

and Electric Appliances 

Seasonal Average Weekday Electricity Usage Electric Sample 

End-Uses 

Whole House: Average Daily Electricity Usage 

SCE REUC Weekday Average UEC Distribution - Winter 

Electric Heat Pump: Pooled Cities 

SCE REUC Weekday Average UEC Distribution - Summer 

Space Conditioning: Pooled Cities 

XI 

S-3 

S-4 

S-6 

S-7 

5-8 

5-9 

5-10 

5-11 

1-3 

3-4 

5-4 

5-5 

5-7 

5-10 

5-11 

5-13 

5-13 
FIGURES, cont.

Figure

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

Gas Furnace: Pooled Cities

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

Electric Water Heating: Pooled Cities

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

Gas 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

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

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

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

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

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

6-9 SCE REUC Summer Weekday Electricity: Electric Sample 6-8

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

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

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

Estimated Load vs. Average Load

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

Estimated Load vs. Average Load

6-14 REUC Heat Pump Water Heater - House 50 - Second Step 6-16

. Estimated Load vs. Average Load

6-15 REUC Heat Pump Water Heater - House 86 - Second Step 6-16

Estimated Load vs. Average Load

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

xii

EE-RM-90-201 COMMENT OOC2

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 

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

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

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

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

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

6-9 SCE REUC Summer Weekday Electricity: Electric Sample 6-8 

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

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

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

Estimated Load vs. Average Load 

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

Estimated Load vs. Average Load 

6-14 REUC Heat Pump Water Heater - House 50 - Second Step 6-16 

. Estimated Load vs. Average Load 

6-15 REUC Heat Pump Water Heater - House 86 - Second Step 6-16 

Estimated Load vs. Average Load 

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

xi i 
FIGURES, cont.

Lillure Page

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

REUC Heat Pump Water Heater (Model)

6-18 Effect of Quality Image of Electricity on Average Weekday Usage: 6-22

REUC Heat Pump Water Heater (Model)

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

REUC Heat Pump Water Heater (Model)

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

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

Heaters by Fuel Type

7-3a Importance of Heating System Attributes by Fuel Type Electric 7-11

Sample

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

Sample

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

Heaters by Fuel Type

7-5 Longitudinal Change in Electric Induction Range Subsample - 7-19

Participants' Fuel Type Preference for Cooking

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

Investment

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

in Appliance Efficiency

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

Conditioning Customer Preference & Behavior Stratification

8-2 REUC Summer Weekday Load Shape: Electric Space 8-4

Conditioning Electric Value/Gas Value Stratification

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

Electric Quality/Gas Value Stratification

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

Income by Square Feet Stratification

xiii

8-5

EE-RM-90-201 COMMENT OOC2

Lillure 

b -17 

6-18 

6-19 

7 -1 

7-2 

7-3a 

7-3b 

7-4 

7-5 

7-6 

7-7 

8-1 

8-2 

8-3 

8-4 

FIGURES, cont. 

Effect of Household Size on Average Weekday Usage: 

REUC Heat Pump Water Heater (Model) 

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 

Importance of Heating System Attributes by Fuel Type Electric 

Sample 

Importance of Heating System Attributes by Fuel Type Gas 

Sample 

REUC Participants' Comparisons between Old and New Water 

Heaters by Fuel Type 

Longitudinal Change in Electric Induction Range Subsample -

Participants' Fuel Type Preference for Cooking 

REUC Participants' Required Return on a Small Riskless 

Investment 

REUC Participants' Required Return on an Investment 

in Appliance Efficiency 

REUC Summer Weekday Load Shape: Electric Space 

Conditioning Customer Preference & Behavior Stratification 

REUC Summer Weekday Load Shape: Electric Space 

Conditioning Electric Value/Gas Value Stratification 

REUC Winter Weekday Load Shape: Heat Pump Water Heater 

Electric Quality/Gas Value Stratification 

REUC Winter Weekday Load Shape: Heat Pump Water Heater 

Income by Square Feet Stratification 

xiii 

Page 

6-21 

6-22 

6-23 

7-2 

7-10 

7 -11 

7-12 

7-14 

7-19 

7-23 

7-24 

8-3 

8-4 

8-4 

8-5 
FIGURES, cont.

Figure Page

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

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

A-1 Space Conditioning: 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 Conditioning: Summer Average Daily Usage A-4

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

A-7 SCE REUC Summer: Clothes Drying A-5

A-8 SCE REUC Winter: Cooking A-5

A-9 SCE REUC Summer: Cooking A-6

A-10 SCE REUCWinter: Whole House Electricity A-6

A-11 SCE REUC Summer: Whole House Electricity A-7

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

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

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

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

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

Conditioning

A-17 SCE REUC Weekday Average Load: Electric Sample Space A-l0

Conditioning

A-18 SCE REUC Weekday Average Load: Electric Sample Space A-l0

Conditioning

xiv

EE-RM-90-201 COMMENT OOC2

FIGURES, cont. 

Figure Page 

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

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

A-1 Space Conditioning: 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 Conditioning: Summer Average Daily Usage A-4 

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

A-7 SCE REUC Summer: Clothes Drying A-5 

A-8 SCE REUC Winter: Cooking A-5 

A-9 SCE REUC Summer: Cooking A-6 

A-l0 SCE REUCWinter: Whole House Electricity A-6 

A-ll SCE REUC Summer: Whole House Electricity A-7 

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

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

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

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

A-16 SCE REUC Weekday Average Load: 

Conditioning . 

Electric Sample Space A-9 

A-17 SCE REUC Weekday Average Load: Electric Sample Space A-l0 

Conditioning 

A-18 SCE REUC Weekday Average Load: Electric Sample Space A-l0 

Conditioning 

xiv 
FIGURES, cont.

'-igure

/\- 19 SCE REUC Weekday Average Load: Gas Sample Space A-11

Conditioning

A-20 SCE REUC Weekday Average Load: Gas Sample Space A-11

Conditioning

A-21 SCE REUC Weekday Average Load: Gas Sample Space A-12

Conditioning

A-22 SCE REUC Winter Weekday Electricity: Electric Sample A-12

A-23 SCE REUC Summer Weekday Electricity: Gas Sample A-13

A-24 SCE REUC Summer Weekday Electricity: Electric Sample A-13

A-25 SCE REUC Whole House Electricity: Electric Sample A-14

A-26 SCE REUC Whole House Electricity: Gas Sample A-14

A-27 SCE REUC Space Conditioning: Electric Sample A-15

A-28 SCE REUC Water Heating: Electric Sample A-15

A-29 SCE REUC Cooking: Electric Sample A-16

A-30 SCE REUC Clothes Drying: Electric Sample A-16

A-31 SCE REUC: Temperature A-17

B-1 REUC Sample Assessment Methodology B-2

xv

EE-RM-90-201 COMMENT OOC2

FIGURES, cont. 

/\ - 19 SCE REUC Weekday Average Load: Gas Sample Space A-11 

Conditioning 

A-20 SCE REUC Weekday Average Load: Gas Sample Space A-11 

Conditioning 

A-21 SCE REUC Weekday Average Load: Gas Sample Space A-12 

Conditioning 

A-2 2 SCE REUC Winter Weekday Electricity: Electric Sample A-12 

A-2 3 SCE REUC Summer Weekday Electricity: Gas Sample A-13 

A-24 SCE REUC Summer Weekday Electricity: Electric Sample A-13 

A-25 SCE REUC Whole House Electricity: Electric Sample A-14 

A-26 SCE REUC Whole House Electricity: Gas Sample A-14 

A-27 SCE REUC Space Conditioning: Electric Sample A-15 

A-28 SCE REUC Water Heating: Electric Sample A-15 

A-29 SCE REUC Cooking: Electric Sample A-16 

A-30 SCE REUC Clothes Drying: Electric Sample A-16 

A-31 SCE REUC: Temperature A-17 

8-1 REUC Sample Assessment Methodology 8-2 

xv 
TABLES

I.dd/' Page

·1 I REUC Participant Demographics - Sample Design Stratification 4-3

Variables

·1- :? REUC Participants Demographics - Highest Completed 4-4

Education Level Among Key Household Members

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

·1-4 REUC sample Representativeness by House Size Stratum 4-7

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

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

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

5-4 Estimated Linear Relationship between Average Morning Space 5-17

Heating and Morning Heating Degree-Days: All Households

6-1 Estimated and Simulated Harmonic Model Load Shape 6-12

Parameters

6-2 Differing Average Characteristics 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 7-7

Preferences for Space Heating Systems

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

Preferences for Space Heating Systems

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

Heating System to a Friend - By Fuel Type

7-4a Longitudinal Changes in Electric Sample Participants' Fuel Type 7-16

Preferences for Water Heaters

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

Preferences for Water Heaters

XVII

EE-RM-90-201 COMMENT OOC2

TABLES 

I. dd( ' Page 

-4 I REUC Participant Demographics - Sample Design Stratification 4-3 

Variables 

-1 ~ :! REUC Participants Demographics - Highest Completed 4-4 

Education Level Among Key Household Members 

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

-1-4 REUC sample Representativeness by House Size Stratum 4-7 

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

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

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

5-4 Estimated Linear Relationship between Average Morning Space 5-17 

Heating and Morning Heating Degree-Days: All Households 

6-1 Estimated and Simulated Harmonic Model Load Shape 6-12 

Parameters 

6-2 Differing Average Characteristics 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 7-7 

Preferences for Space Heating Systems 

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

Preferences for Space Heating Systems 

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

Heating System to a Friend - By Fuel Type 

7-4a Longitudinal Changes in Electric Sample Participants' Fuel Type 7 -16 

Preferences for Water Heaters 

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

Preferences for Water Heaters 

XVII 
TABLES, cont.

Table Page

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

Heater System to a Friend - By Fuel Type

7-6 Electric Induction Subsample ParticipantsI Willingness to 7-20

Recommend Their Range to a Friend

7-7 REUC Participants· Range/Oven Comparisons between Fuel 7-21

Types

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

8-1 REUC Stratification Scheme Variance Calculations 8-6

A-1 Average Annual and Seasonal Energy Usage - Mission Viejo A-18

Households

A-2 Average Annual and Seasonal Energy Usage - Irvine A-19

Households

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

A-4 Monthly Average Usage for Irvine Households A-22

B-1 Appliance Stock Inventory (AS!) Share by SCE Division B-4

B-2 Chi-Square Statistics for Customer Descriptors that Discriminate B-5

between Appliance Stock Inventories (AS!) and Pool Ownership

B-3 RSS-Based Stratification of Customer Descriptors B-6

B-4 Customer Descriptors that Meaningfully Discriminate Annual B-7

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

Division

C-1 IIReasonableli Connected Load Estimates C-4

C-2 IIReasonablell Ranges for Weekly Appliance Use C-4

C-3 ACUPA Error Flag Criteria C-5

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

xviii

EE-RM-90-201 COMMENT OOC2

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 7-20 

Recommend Their Range to a Friend 

7-7 REUC Participants' Range/Oven Comparisons between Fuel 7 -21 

Types 

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

8-1 REUC Stratification Scheme Variance Calculations 8-6 

A-1 Average Annual and Seasonal Energy Usage - Mission Viejo A-18 

Households 

A-2 Average Annual and Seasonal Energy Usage - Irvine A-19 

Households 

A-3 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 (AS!) and Pool Ownership 

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

8-4 Customer Descriptors that Meaningfully Discriminate Annual 8-7 

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

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

I his report presents the research methods and results from the Residential Energy

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

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

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

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

consumption patterns of energy efficient residential electric and gas appliances.

REUC PROJECT OVERVIEW

The REUC project has been designed to assess the energy performance and

customer acceptance of new electric and gas appliances. Traditionally, studies

Ih3t have examined the relative energy efficiencies of electric and gas end-use

technologies have relied on laboratory settings or computer simulation models.

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

relying on the energy demands and customer behavior of actual households

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

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

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

models. The project design is discussed more fully in Sections 1 through 3.

The results presented in this report pertain to a sample of 92 households in

Orange County, southeast of Los Angeles. Over the past decade, Orange County

has experienced significant residential and commercial growth. The REUC

participant sample was designed to represent an important segment of SCE's

residential customers, namely owner-occupied, new Single-family homes. More

details about the REUC sample are provided in Section 4.

The analysis results illustrate the value 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 mid:'1987. Both end-use data and survey research data collection concluded in

5-1

EE-RM-90-201 COMMENT OOC2

SUMMARY 

I h is report presents the research methods and results from the Residential Energy 

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

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

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

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

consumption patterns 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 

th at 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 

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

in mid:"1987. Both end-use data and survey research data collection concluded in 

S-1 
December 1989. The project's October 1990 interim report (CU-6952)

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

presents analysis results using all the available REUC data.

A summary 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 measure electric and gas

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

as point-of-us·e site energy; i.e., 1 kWh =3.412 kBTU. Southern California Edison

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

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

were selected because of the direct competition between electricity and natural

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

technologies.

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

REUC space conditioning appliances (electric heat pumps relative to gas

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

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

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

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

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

During the winter season, electric heat pumps consume less than 36 percent as

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

consume 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.

S-2

EE-RM-90-201 COMMENT OOC2

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 measure electric and gas 

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

as point-of-us·e site energy; i.e., 1 kWh = 3.412 kBTU. Southern California Edison 

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

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

were selected because of the direct competition between electricity and natural 

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

technologies. 

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

REUC space conditioning appliances (electric heat pumps relative to gas 

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

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

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

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

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

During the winter season, electric heat pumps consume less than 36 percent as 

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

consume 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. 

S-2 
Figure S-l

Annual Average Energy Usage

Space Conditioning and Water Heating (at point of use)

Space Conditioning

Water Heating

23.2

23.4

o 5 10 15

MBTU

20 25 30

[!a Gas Sample

• Electric Sample

I"igure 5-2 illustrates the average winter weekday load profiles for both gas and

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

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

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

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

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

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

I\dditional REUC appliance load profiles developed from end-use data are

presented in Section 6 and in Appendix A.

5-3

EE-RM-90-201 COMMENT OOC2

Figure S-1 

Annual Average Energy Usage 

Space Conditioning and Water Heating (at point of use) 

23.2 

Space Conditioning 

23.4 

Water Heating 

o 5 10 15 20 25 30 

MBTU 

~ Gas Sample 

III Electric Sample 

I" igure 5-2 illustrates the average winter weekday load profiles for both gas and 

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

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

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

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

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

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

Ad ditional 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: Average Load Profiles

Winter Weekday Space Conditioning (at point of use)

12

Electric Heat Pump

Gas Furnace

6

8

(\

, \

I \

,'

,'

, ' I ' I ,

, ,

, ' I ' I \

I ,

I \

"\ I \

I \ ./"-_...... I \ ;' \

/ \ I \

~~ \ I

2 ,~ \ 1

'- ",1

---

4

10

kBTU

per

Hour

3 6 9 12 15 18 21 24

O+-........,r-r-r--r-r-r-r-....-..--.r-r-r-or-,:--r.......-,......,........."""T""-r-..--,

o

Time of Day

CUSTOMER ACCEPTANCE OF APPLIANCES

Measuring customer acceptance of these energy efficient appliances was another

objective 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,

S-4

EE-RM-90-201 COMMENT OOC2

kBTU 

per 

Hour 

Figure S-2 

SCE REUC: Average Load Profiles 

Winter Weekday Space Conditioning (at point of use) 

12 

10 

8 

6 

4 

(\ 

, \ 

, \ 

, ' 

, ' 

, ' 

, ' , I 

, , 

, I 

I I 

I ~ , , 

, \ " \ 

Electric Heat Pump 

Gas Furnace 

, \ ,\ ../"'-_ ....... , \ ;' \ 

~/ \ " \ ",- , 2 ,,,,-/ \' 

'- ..... ' --- o 3 6 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, 

S-4 
gas heating systems are more likely to have proponents than heat pump systems.

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

participants were the perceived quality problems with the units installed in 1986

and 1987.

The survey responses disclosed that participants found their heat pump water

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

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

were well accepted; 80 percent would recommend their induction stoves to a

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

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

Additional results on appliance acceptance are provided in Section 7.

INTEGRATED MARKET RESEARCH

The collection of both end-use load data and participant survey data is yet

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

creative synthesis of load research information with market research/survey information to provide insight about customers l energy usage behavior patterns.

S-5

EE-RM-90-201 COMMENT OOC2

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

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

participants were the perceived quality problems with the units installed in 1986 

and 1987. 

The survey responses disclosed that participants found their heat pump water 

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

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

were well accepted; 80 percent would recommend their induction stoves to a 

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

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

Additional results on appliance acceptance are provided in Section 7. 

INTEGRATED MARKET RESEARCH 

The collection of both end-use load data and participant survey data is yet 

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

creative synthesis of load research information with market research/survey information to provide insight about customers l energy usage behavior patterns. 

5-5 
Figure S-3

Customers' Required Return on a Small Riskless Investment

DID

of 50 -r---------------------.,

Participants

40

30

20

10

o

6%-8% 9%-10%

Required Return

Income: >$80,000

1m Income: $50,001-$80,000

Income: $30,000-$50,000

11 %-13%

The REUC project surveys included questions designed to estimate participants'

appliance investment payback requirements based on their required rates of

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

appliance investment, the shorter the required payback period for such

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

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

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

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

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

expensive appliances must demonstrate large efficiencies to compete with

cheaper alternatives.

5-6

EE-RM-90-201 COMMENT OOC2

Figure 5-3 

Customers' Required Return on a Small Riskless Investment 

0/0 

of 50 -r---------------------, 

Participants 

40 

30 

20 

10 

° 6%-8% 

-

9%-10% 

Required Return 

~ Income: >$80,000 

1m Income: $50,001-$80,000 

m Income: $30,000·$50,000 

11 %-13% 

The REUC project surveys included questions designed to estimate participants' 

appliance investment payback requirements based on their required rates of 

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

appliance investment, the shorter the required payback period for such 

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

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

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

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

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

expensive appliances must demonstrate large efficiencies to compete with 

cheaper alternatives. 

5-6 
Figure 5-4

Customers· Required Return on an Investment in Appliance Efficiency

%

of 40..,..-----------------------,

Participants

30

20

10

o

<15% 15%-59%

Required Return

E21 Income: >$80,000

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

Income: $30,000-$50,000

5-7

>59%

EE-RM-90-201 COMMENT OOC2

Figure 5-4 

Customers' Required Return on an Investment in Appliance Efficiency 

% 

of 40,-----------------------------------------~ 

Participants 

30 

20 

10 

<15% 15%-59% 

Required Return 

12l Income: >$80,000 

f3 Income: $50,001-$80,000 

~ Income: $30,000.$50,000 

5-7 

>59% 
Figure 5-5

REUC Participant Images of Electric vs. Gas

Dirty

Dangerous

Hard to Hookup

Inconvenient

Poor Service

from Utility

Unreliable

Sluggish Heat

High Cost

Appliances

Expensive

High Monthly

Utility Bills

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

I---------tk-::~l:j;,..:;. ----f

\\.

Electricity Gas

Image Image

Clean

Safe

Easy to Hookup

)..

....

Convenient -

""'" :§

Good Service C'

from Utility

Reliable

Fast Heat

Low Cost

Appliances l.I.l

::J

""'" Inexpensive

Low Monthly

Utility Bills

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

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

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

customer segments. Factor analysis 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).

5-8

EE-RM-90-201 COMMENT OOC2

Dirty 

Dangerous 

Hard to Hookup 

Inconvenient 

Poor Service 

from Utility 

Unreliable 

Sluggish Heat 

High Cost 

Appliances 

Expensive 

High Monthly 

Utility Bills 

Figure 5-5 

REUC Participant Images of Electric vs. Gas 

Clean 

Safe 

Easy to Hookup 

).. 

.... 

-

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

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

Convenient ""'" :§ 

Good Service C' 

from Utility 

Reliable 

Fast Heat 

Low Cost 

Appliances 1.1.1 

::J 

""'" 

Inexpensive ~ 

Low Monthly 

Utility Bills 

Electricity Gas 

Image Image 

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

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

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

customer segments. Factor analysis 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). 

5-8 
Figure S-6

SCE REUC: Quality Factor Segments

Winter Weekday Heat Pump

La Electric/Hi Gas Quality

Hi Electric/La Gas Quality

12 15 18 21 24

,

\

\

\

\

\

\

\ ,

--

6 9

I

I

I

I

,

I

I

.J

Load 1.4

In

kW

1.2

1.0

0.8

0.6

0.4

0.2

0.0

0 3

Time of Day (in Hours PST)

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

EE-RM-90-201 COMMENT OOC2

Load 1.4 

In 

kW 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 

Figure S-6 

SCE REUC: Quality Factor Segments 

Winter Weekday Heat Pump 

\ 

\ 

\ 

3 6 9 12 

La Electric/Hi Gas Qual ity 

Hi Electric/La Gas Quality 

, 

--

15 18 21 24 

Time of Day (in Hours PST) 

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 Segments

Summer Weekday Heat Pump

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

inkW

0.8

0.6

0.4

0.2

Appear. Cons.

Res. Consrv.

Value Seekers

....."'~ .. ...".......,

.,,,. .. .•. .. :' :; /"'.,rI ..........

3 6 9 12 15 18 21 24

0.0 +-....,..--r-.....-""'T""""""'T-~-r---r-.--..,.--....-r---.---.,----,---l

o

Time of Day (in Hours psn

The EPRI Customer Preference and Behavior (CP&B) CLASSIFYTM survey was

administered to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure 5-7 presents average summer weekday

load profiles for heat pumps segmented by Appearance Conscious, Resource

Conserver and Value Seeker needs-based segments.

5-10

EE-RM-90-201 COMMENT OOC2

Figure S-7 

SCE REUC: CP&B Needs-Based Segments 

Summer Weekday Heat Pump 

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

inkW 

0.8 

0.6 

0.4 

0.2 

Appear. Cons. 

Res. Consrv. 

Value Seekers 

.~....,. ......................... ~ .. ~ .•. ~ •• ~ .. :' ...... :; ............. ................ /"'<J' " 

.. ..,.".. ..... . ,~,~ 

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

O.O+-~~--~-r~--~~~--~~~--~~--"'-~~ 

o 3 6 9 12 15 18 21 24 

Time of Day (in Hours psn 

The EPRI Customer Preference and Behavior (CP&B) CLASSIFYTM survey was 

administered to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure 5-7 presents average summer weekday 

load profiles for heat pumps segmented by Appearance Conscious, Resource 

Conserver and Value Seeker needs-based segments. 

5-10 
Figure S-8

REUC: Heat Pump Water Heater

Demographic Scenario

Older Households

Younger Households

;\

I' \

I \

I \

I \

I \

\

\

\

\

,

, /' ....

, I ,

, I \

, / \

\ I \

\ I \

\ ;' \ \.,;.. \

'--; \

........ \ -; ....._-

0.4

0.6

0.2

Load 0.8 ~-------------------------,

in kw

3 6 9 12 15 18 21 24

0.0 +----,r---r----,,----,.---.----,--.---r-...----r--r----r-~_,--r-_;

o

Time of Day (in Hours PST)

The household characteristic data from REUC participants were also used in the

project's load shape modeling research. End-use load shapes were modeled

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

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

the harmonic model parameters. Figure 5-8 illustrates an application of this

approach by comparing derived heat pump water heater load shapes for two

hypothetical demographic market segments produced with the load shape

modeling methodology. This figure shows how the load shapes may differ

between younger and older households.

Additional integrated market research results of the REUC project are presented

in Section 8.

5-11

EE-RM-90-201 COMMENT OOC2

Figure S-8 

REUC: Heat Pump Water Heater 

Demographic Scenario 

Load 0.8 ~-------------------------., 

in kw 

0.6 

0.4 

0.2 ....... -; ..... _-

o 3 6 

;'\ 

I' \ 

I \ 

I \ 

I \ 

I \ 

9 

\ 

\ 

\ 

\ 

, 

12 

Older Households 

Younger Households 

, i" ...... 

" I \ 

" I \ , / \ 

\ I \ 

\ I \ 

\ ;,' \ 

\., ;,f' 

....... -;' 

15 18 21 

Time of Day (in Hours PST) 

24 

The household characteristic data from REUC participants were also used in the 

project's load shape modeling research. End-use load shapes were modeled 

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

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

th e harmonic model parameters. Figure 5-8 illustrates an application of this 

approach by comparing derived heat pump water heater load shapes for two 

hypothetical demographic market segments produced with the load shape 

modeling methodology. This figure shows how the load shapes may differ 

between younger and older households. 

Additional integrated market research results of the REUC project are presented 

in Section 8. 

S-11 
Section 1

INTRODUCTION

This report presents the research activities and findings from the Residential

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

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

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

compare typical energy consumption patterns of energy efficient residential

electric and gas appliances.

OVERVIEW OF THE REUC PROJECT

The REUC project has been designed to provide information necessary for assessing the energy performance, the relative effectiveness and the customer acceptance of new electric and gas appliances. Southern California Edison has recognized that evaluating key residential end-uses such as space conditioning, water

heating, cooking and clothes drying can provide much needed insight into the

energy efficiencies 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, real istic assessments of actual customers l

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

IJboratory simulation study.

1-1

EE-RM-90-201 COMMENT OOC2

Section 1 

INTRODUCTION 

This report presents the research activities and findings from the Residential 

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

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

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

compare typical energy consumption patterns of energy efficient residential 

electric and gas appliances. 

OVERVIEW OF THE REUC PROJECT 

The REUC project has been designed to provide information necessary for assessing the energy performance, the relative effectiveness and the customer acceptance of new electric and gas appliances. Southern California Edison has recognized that evaluating key residential end-uses such as space conditioning, water 

heating, cooking and clothes drying can provide much needed insight into the 

energy efficiencies 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 IIfjeld" experiment are reflected in the value of more comprehensive, real istic assessments of actual customers' usage and response to new electrotechnologies. These advantages offset the reduction in IIcontrolll afforded by a 

laboratory simulation study. 

1-1 
Project Objectives

The REUC project was designed, in part, to meet several of SCEl 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

• general ize results to other residential customers.

This report will illustrate how these objectives have been met.

Project Milestones

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

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

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

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

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

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

REUC project milestones are illustrated in Figure 1-1.

EPRI INVOLVEMENT IN THE REUC PROJECT

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

• project design and planning;

• project analysis; and

• project management.

1-2

EE-RM-90-201 COMMENT OOC2

Project Objectives 

The REUC project was designed, in part, to meet several of SCEls needs for 

assessing new electrotechnologies available to residential customers. These 

needs have been satisfied by meeting several objectives: 

• measure energy usage of electric and gas residential appliances; 

• compare usage patterns 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 

REUC project milestones are illustrated in Figure 1-1. 

EPRI INVOLVEMENT IN THE REUC PROJECT 

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

• project design and planning; 

• project analysis; and 

• project management. 

1-2 
1985 - Idea Suggested by CPUC and SCE

Early 1986 - Discussions with EPRI

Summer 1986 - Customer Sample Design;

REUC-RSS Survey; Installations Begun

Winter 1986 - Mission Viejo Installations Complete

Fall 1987 - Project Analysis Plan; First REUC Survey

Winter 1987 - Irvine Installations Complete

Spring 1988 - Second REUC Survey

Summer 1989 - Third REUC Survey;

Winter 1989 - Final REUC Survey; EqUipment Removal

--'

I

W

Figure 1-1

REUC PROJECT MILESTONES

1985 1986 1987 1988 1989 1990

I I I I I I

4t lI~. ••• •..

4t

11

•

•

..

•

..

<It

EE-RM-90-201 COMMENT OOC2

--' 

, 

w 

1985 

I 

Figure 1-1 

REUC PROJECT MILESTONES 

1986 1987 1988 1989 

I I I I 

4t 1985 - Idea Suggested by CPUC and SCE 

~ Early 1986 - Discussions with EPRI 

~ Summer 1986 - Customer Sample Design; 

1990 

I 

REUC-RSS Survey; Installations Begun 

• Winter 1986 - Mission Viejo Installations Complete 

• Fall 1987 - Project Analysis Plan; First REUC Survey 

• Winter 1987 - Irvine Installations Complete 

.. Spring 1988 - Second REUC Survey 

.. Summer 1989 - Third REUC Survey; 

<II Winter 1989 - Final REUC Survey; Equipment Removal 
Project Design

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

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

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

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

accurately represent important segments of SCEls residential customer population. In addition, the REUC experimental design needed to determine how

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

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

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

the project objectives.

The REUC experimental design activities took advantage of previous EPRIsponsored research results, including the design of the Athens Automation and

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

techniques in EPRI EA-3286.

Project Analysis

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

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

developed "integrated market research" techniques and "behavioral engineering

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

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

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

Data Analysis Workstation Project (RP2342).

Project Management

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

design and 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

EE-RM-90-201 COMMENT OOC2

Project Design 

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

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

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

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

accurately represent important segments of SCEls residential customer population. In addition, the REUC experimental design needed to determine how 

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

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

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

the project objectives. 

The REUC experimental design activities took advantage of previous EPRIsponsored research results, including the design of the Athens Automation and 

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

techniques in EPRI EA-3286. 

Proiect Analysis 

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

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

developed lIintegrated market researchll techniques and IIbehavioral engineering 

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

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

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

Data Analysis Workstation Project (RP2342). 

Project Management 

EPRI has assisted SCE staff by offering primary management 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 
EPORT ORGANIZATION

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

Design Objectives, describes the design of the REUC project. Section 3, REUC

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

Section 4, REUC Customer Sample, provides a summary of the REUC sample

design and a demographic description of the REUC sample. Section 5, REUC

load Data Analysis: Gas and Electric Appliance Energy Usage, presents results

from the analysis of appliance energy data. Section 6, REUC Appliance Load

Profiles, provides appliance load profiles for the REUC electric and gas appliances.

Section 7, Customer Preferences and Appliance Acceptance, discusses REUC

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

Integration of Load and Survey Analyses, presents results of integrated market

research analyses, such as participants' fuel images and load profiles by customer

segment. Appendix A provides supplementary energy usage information load

profiles. Appendix B contains a description of the methodology for generalizing

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

and validation methods.

1-5

EE-RM-90-201 COMMENT OOC2

H EPORT ORGANIZATION 

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

Design Objectives, describes the design of the REUC project. Section 3, REUC 

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

Section 4, REUC Customer Sample, provides a summary of the REUC sample 

design and a demographic description of the REUC sample. Section 5, REUC 

l oad Data Analysis: Gas and Electric Appliance Energy Usage, presents results 

f rom the analysis of appliance energy data. Section 6, REUC Appliance Load 

I>rofiles, provides appliance load profiles for the REUC electric and gas appliances. 

Section 7, Customer Preferences and Appliance Acceptance, discusses REUC 

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

Integration of Load and Survey Analyses, presents results of integrated market 

research analyses, such as participants l fuel images and load profiles by customer 

segment. Appendix A provides supplementary energy usage information load 

profiles. Appendix B contains a description of the methodology for generalizing 

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

and validation methods. 

1-5 
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 project1s research results?"

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

the analysis findings.

REUC DESIGN

The REUC project's experimental design developed a consolidated customer

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

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

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

with the available project resources. 1

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

sample nor to represent SCEls entire residential customer class of over 3.5 million

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

sample represents one specific and important segment of residential customers,

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

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

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

Competitive Assessment Experiment: The Residential Energy Usage Comparison

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

1298-1305.

2-1

EE-RM-90-201 COMMENT OOC2

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 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 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 

1 J.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

2-2

EE-RM-90-201 COMMENT OOC2

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 

2-2 
most natural set of descriptors is customer responses to the demographic/characteristic variables in utility saturation surveys. Furthermore, the kinds of data

collected and techniques employed on the REUC project can allow for the

successful application of these results to residential customers served by other

uti Iities.

The REUC project administered four different survey instruments, as described in

Section 3. The first survey was based on SCE's 1985 Residential Saturation Survey

(RSS). With this and the additional survey information, the REUC results were

assessed for their applicability to other SCE residential customers. This assessment

is described in Section 4.

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

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

attitudes and acceptance of these energy-efficient end-use technologies. This

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

penetration of utility demand-side programs that emphasize customers' efficient

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

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

likelihood of program participation.

Finally, the REUC project included load shape modeling analyses that used both

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

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

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

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

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

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

utilities may adapt the REUC load shapes to their service territories.

2-3

EE-RM-90-201 COMMENT OOC2

most natural set of descriptors is customer responses to the demographic!characteristic variables in utility saturation surveys. Furthermore, the kinds of data 

collected and techniques employed on the REUC project can allow for the 

successful application of these results to residential customers served by other 

uti I ities. 

The REUC project administered four different survey instruments, as described in 

Section 3. The first survey was based on SCE's 1985 Residential Saturation Survey 

(RSS)' With this and the additional survey information, the REUC results were 

assessed for their applicability to other SCE residential customers. This assessment 

is described in Section 4. 

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

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

attitudes and acceptance of these energy-efficient end-use technologies. This 

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

penetration of utility demand-side programs that emphasize customers' efficient 

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

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

likelihood of program participation. 

Finally, the REUC project included load shape modeling analyses that used both 

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

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

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

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

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

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

utilities may adapt the REUC load shapes to their service territories. 

2-3 
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 "equ ipment test sample"; and 92 homes located in Irvine and

Mission Viejo, the "REUC sample."

This report focuses only on data from the REUCs 92-household sample. Irvine and

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

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

transition from a lightly populated agriculturally based economy to a metropolitan

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

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

other parts 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

EE-RM-90-201 COMMENT OOC2

INTRODUCTION 

Section 3 

REUC PROJECT SCOPE AND 

DATA COLLECTION HISTORY 

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 Irvine and 

M ission Viejo, the "REUC sample." 

This report focuses only on data from the REUCs 92-household sample. Irvine and 

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

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

transition from a lightly populated agriculturally based economy to a metropolitan 

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

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

other parts 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 cond itioning 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 of approximately 6 kW, or 20.5 kBTU/hour. The heat pump systems have a resistance

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

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

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

3-2

EE-RM-90-201 COMMENT OOC2

strategy reduced variation induced by building envelope characteristics and 

weather. 

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

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

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

heating systems that have output capacities 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 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 

3-2 
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 Ilcontrol groupll of

comparably equipped gas customers.

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

energy usage comparisons; load profile analysis; and the integration 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 segmentations of the sample derived from the analysis of the market

research data. These energy usage comparisons account for seasonal and weather

effects.

Load Profiles

The REUC load profiles can aid utilities in assessing the effectiveness of encouraging customers' adoption of energy-efficient appliances. The estimated REUC load

profiles also have been combined with the project's market research/attitudinal

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

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

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

are rarely collected.

Market Research Analysis

The REUC market research was undertaken to better understand customer attitudes and behavior toward the electric and gas appliances. Analysis of the market

research data has concentrated on three basic issues. First, these data were used

to determine which aspects or features of electricity and gas affect customers'

images of those energy sources. Are these features limited to cost, or do they

include other qualities associated with the two fuels? Second, analysis 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

EE-RM-90-201 COMMENT OOC2

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 IIcontrol groupll of 

comparably equipped gas customers. 

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

energy usage comparisons; load profile analysis; and the integration 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 segmentations of the sample derived from the analysis of the market 

research data. These energy usage comparisons account for seasonal and weather 

effects. 

Load Profiles 

The REUC load profiles can aid utilities in assessing the effectiveness of encouraging customers' adoption of energy-efficient appliances. The estimated REUC load 

profiles also have been combined with the project's market research/attitudinal 

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

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

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

are rarely collected. 

Market Research Analysis 

The REUC market research was undertaken to better understand customer attitudes and behavior toward the electric and gas appliances. Analysis of the market 

research data has concentrated on three basic issues. First, these data were used 

to determine which aspects or features of electricity and gas affect customers' 

images of those energy sources. Are these features limited to cost, or do they 

include other qualities associated with the two fuels? Second, analysis 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 and 50 homes in Irvine.

Figure 3-1 summarizes the survey and load data collection activities from 5eptember 1986 through December 1989 of the REUC project.

Figure 3-1

REUC Survey and

load Data Collection History

9/86 6/87 3/88 6/89 12/89

Mission I I

Viejo 50 51 52 53 54

Irvine I I

50 51 52 53 54

I-I

50

51-54

=Load data collection

= REUC-R55 survey

= REUC Market Research survey

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

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

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

on the 5CE 1985 R55 survey but augmented by several attitudinal and demographic questions. The first REUC market research survey, administered in June

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

attributes of fuel types and end-uses as well as the customer1s propensity to lIinvestll

3-4

EE-RM-90-201 COMMENT OOC2

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 and 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 Surve~ and 

load Data Collection Histor~ 

9/86 

SO 

SO 

= Load data collection 

= REUC-RSS survey 

6/87 

I 

51 

51 

= REUC Market Research survey 

3/88 

52 

I 

52 

6/89 12/89 

I 

53 54 

53 54 

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

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

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

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

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

attributes of fuel types and end-uses as well as the customer1s propensity to lIinvestll 

3-4 
in energy-efficient appliances. The second REUC survey instrument included

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

(ustomer acceptance of the new appliances, and the EPRI CLASSIFYTM questionnaire. In order to perform longitudinal analysis of the participants' acceptance of

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

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

;md contained portions of SCE's residential attitude survey instrument. In this way,

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

provides a 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 and

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

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

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

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

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

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

compared.

load Data Analysis Workstation

All 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

EE-RM-90-201 COMMENT OOC2

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

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

( ustomer acceptance of the new appliances, and the EPRI CLASSIFYTM questionIla ire. In order to perform longitudinal analysis of the participants' acceptance of 

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

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

;md contained portions of SCE's residential attitude survey instrument. In this way, 

!he 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, 

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

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

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

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

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

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

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

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. 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 IIbuilding blocksll to

accomplish complex analysis tasks. These programs include routines to:

• design load research and demand-side program customer

samples;

• read load data in standard central station tape formats;

• 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 liThe Load Data Analysis Workstation User's

Guide,1I Version O.4RG, May 1990, EPRI RP2342-B.

3-6

EE-RM-90-201 COMMENT OOC2

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 IIbuilding blocksll to 

accomplish complex analysis tasks. These programs include routines to: 

• design load research and demand-side program customer 

samples; 

• read load data in standard central station tape formats; 

• 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 

1 For more information, see liThe Load Data Analysis Workstation User's 

Guide," Version OARG, May 1990, EPRI RP2342-B. 

3-6 
• perform other important program evaluation functions.

The idea of conducting all the analysis tasks for a large-scale end-use load monitoring program such as the REUC on a microcomputer would have been out of

the question until recently. Fortunately, continuing advances in microcomputer

technology have put sufficient computing power and disk storage into desktop

machines to make the Workstation a viable, cost-effective alternative to mainframe computers. In fact, under most circumstances, analysts can expect quicker

turnaround on the Workstation even on very large jobs than they would get from

a mainframe. By combining the ease of use of existing microcomputers with the

newly available computer power, the Workstation puts the control of program

data analysis directly in the hands of program analysis staff, not remote data

processing facilities.

3-7

EE-RM-90-201 COMMENT OOC2

• perform other important program evaluation functions. 

The idea of conducting all the analysis tasks for a large-scale end-use load monitoring program such as the REUC on a microcomputer would have been out of 

the question until recently. Fortunately, continuing advances in microcomputer 

technology have put sufficient computing power and disk storage into desktop 

machines to make the Workstation a viable, cost-effective alternative to mainframe computers. In fact, under most circumstances, analysts can expect quicker 

turnaround on the Workstation even on very large jobs than they would get from 

a mainframe. By combining the ease of use of existing microcomputers with the 

newly available computer power, the Workstation puts the control of program 

data analysis directly in the hands of program analysis staff, not remote data 

processing facilities. 

3-7 
Section 4

THE REUC CUSTOMER SAMPLE

INTRODUCTION

This section describes the REUC project customer sample. The sample design

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

an analysis of the generalizability of the REUC sample to other customers and

other regions of SCE's service territory.

REUC SAMPLE DESIGN

In order to identify a potential sample of residential customers, SCE administered

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

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

survey responses, a subset of potential participants was visited by SCE project staff

to gather additional information on specific appliances in the household,

household characteristics, and occupancy patterns.

As described elsewhere,' extensive pre..;experimental research was undertaken to

identify a sample of customers that could provide the most pertinent and valuable

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

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

the optimal allocation of the available 92 sets of sub-metering equipment and

appliances so that the difference in energy usage between electric and gas

appliances could be accurately measured. This allocation was achieved by

identifying observable household characteristics that best predict overall

household electric load shape and then stratifying the sample according to those

characteristics. Required sample sizes were calculated using estimated variances

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

Energy Usage Comparison Project," J.T. Powers, M.S. Shirilau, R.T. Uhlaner and

B.A. Smith, IEEE Transactions on Power Systems (August 1988), Vol. 3, No.3,

pp. 1298-1305.

4-1

EE-RM-90-201 COMMENT OOC2

Section 4 

THE REUC CUSTOMER SAMPLE 

INTRODUCTION 

This section describes the REUC project customer sample. The sample design 

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

an analysis of the generalizability of the REUC sample to other customers and 

other regions of SCE's service territory. 

REUC SAMPLE DESIGN 

In order to identify a potential sample of residential customers, SCE administered 

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

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

survey responses, a subset of potential participants was visited by SCE project staff 

to gather additional information on specific appliances in the household, 

household characteristics, and occupancy patterns. 

As described elsewhere,' extensive pre..;experimental research was undertaken to 

identify a sample of customers that could provide the most pertinent and valuable 

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

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

the optimal allocation of the available 92 sets of sub-metering equipment and 

appliances so that the difference in energy usage between electric and gas 

appliances could be accurately measured. This allocation was achieved by 

identifying observable household characteristics that best predict overall 

household electric load shape and then stratifying the sample according to those 

characteristics. Required sample sizes were calculated using estimated variances 

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

Energy Usage Comparison Project," J.T. Powers, M.S. Shirilau, R.T. Uhlaner and 

B.A. Smith, IEEE Transactions on Power Systems (August 1988), Vol. 3, No.3, 

pp. 1298-1305. 

4-1 
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

4-2

EE-RM-90-201 COMMENT OOC2

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 

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

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

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

sample.

Table 4-1

REUC Participant Demographics

Sample Design Stratification Variables

Electric Sample

Number of Households

House Size

Medium House

1,250 - 1,749 sq ft

Large House

1,750 sq ft or more

Total

Mission

Income Irvine Viejo Total

< $50K 12 3 15

$50K or more 2 6 8

< $50K 2 4 6

$50K or more 9 8 11

46

Gas Sample

Number of Households

Mission

House Size Income Irvine Viejo Total

Medium House < $50K 2 3 5

1,250 -1,749 sq ft $50K or more 12 6 18

Large House < $50K 1 4 5

1,750 sq ft or more $50K or more 10 8 JJ2

Total 46

2lbid., p. 1302.

4-3

EE-RM-90-201 COMMENT OOC2

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

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

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

sample 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 

Table 4-1 

REUC Participant Demographics 

Sample Design Stratification Variables 

Electric Sample 

Number of Households 

Mission 

Income Irvine Viejo 

< $50K 12 3 

$50K or more 2 6 

< $50K 2 4 

$50K or more 9 8 

Gas Sample 

Total 

15 

8 

6 

11 

46 

Number 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 JJ2 

46 

2lbid., p. 1302. 

4-3 
Table 4-2

REUC Participants Demographics

Highest Completed Education Level Among Key Household Members

Electric Sample

Level Completed

High School

2-Year Associate

Bachelor

Masters

Doctorate

Level Completed

High School

2-Year Associate

Bachelor

Masters

Doctorate

Gas Sample

Number of Households

Irvine Mission Viejo

2 3

3 4

14 3

5 6

1 2

Number of Households

Irvine Mission Viejo

1 2

5 3

8 8

10 2

2

REPRESENTATIVENESS OF THE REUC SAMPLE

To perform the assessment of the representativeness of the REUC customer

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

the relationships between energy usage and household characteristics. Customer

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

descriptors and summaries of household appliance holdings. Then, IMR

techniques were used to identify those customer descriptors that segment

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

methodology developed to asses the REUC sample's representativeness.

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

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

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

4-4

EE-RM-90-201 COMMENT OOC2

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 

1 2 

5 3 

8 8 

10 2 

2 

To perform the assessment of the representativeness of the REUC customer 

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

the relationships between energy usage and household characteristics. Customer 

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

descriptors and summaries of household appliance holdings. Then, IMR 

techniques were used to identify those customer descriptors that segment 

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

methodology developed to asses the REUC sample's representativeness. 

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

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

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

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

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

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

'lpplicability 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

(AS!). This segmentation by ASI 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 ASI definitions:

(1) gas space heating and electric central air-cond itioni ng

(GSH/CAC);

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

(GSH!WWAC); and

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

See Appendix Bfor details of the ASI definitions.

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

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

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

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

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

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

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

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

3See Appendix B for a definition of each AS/.

4-5

EE-RM-90-201 COMMENT OOC2

va lidity of the original model-based sample design. The second objective is met 

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

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

;1pplicability 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 

(AS!). This segmentation by ASI 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 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)' 

See Appendix B for details of the ASI definitions. 

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

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

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

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

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

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

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

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

3See Appendix B for a definition of each AS!. 

4-5 
Single Familyll is taken from the IITotal SCEII column of Table B-1. Multiplying the

IIREUC Share of ASIII by the liASI Share of SCE Single Familyll produces the IIREUC

Share of SCE Single Family by ASI.II Finally, the IIREUC Total Share of SCE Single

Familyll 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

Size

RSS Income

Stratum

Appliance Stock Inventory (AS!)

GSH/CAC GSH!WWAC GSH/NOAC

REUC Total Share of SCE Single Family

REUC Share of SCE Single Family by ASI 15.4

REUC Share of ASI 67.0

ASI Share of SCE Single Family 23.0

NA

<$30,000

$30,000-$49,999

$50,000+

NA

0/0

21/10

25/36

<$25,000

$25,000-$34,999

$35,000-$49,999

$50,000+

19.5%

13.5

25.0

42.0

48.8% 31.3%

17.8 18.5

17.4 21.5

16.0 28.7

33.4 50.2

18.0 37.0

6.0 18.6

40.0%

4-6

EE-RM-90-201 COMMENT OOC2

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

"REUC Share of ASI" by the liAS I 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 ASI of the REUC Share of SCE Single Family. Thus, 

the REUC customers represent 40 percent of SCE1s single-family households, by 

income strata (15.4 + 6.0 + 18.6), representing over 550,000 households 

throughout SCE1s service territory. 

Table 4-3 

REUC Sample Representativeness by Income Stratum 

REUC sample RSS Single-Family Subsample 

REUC Income 

Stratum 

ELE/GAS 

Size 

RSS Income 

Stratum 

Appliance Stock Inventory (AS!) 

GSH/CAC GSH!WWAC GSH/NOAC 

NA 

<$30,000 

$30,000-$49,999 

$50,000+ 

NA 

0/0 

21/10 

25/36 

<$25,000 

$25,000-$34,999 

$35,000-$49,999 

$50,000+ 

19.5% 

13.5 

25.0 

42.0 

REUC Share 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

GSH/CAC ASI is very high, 86.4 percent. Following the calculations discussed

above for the income strata, the IIREUC Total Share of SCE Single Familyll for the

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

Table 4-4

REUC Sample Representativeness by House Size Stratum

REUC sample

REUC SqFt

Stratum

RSS Single-Family Subsample

ELE/GAS RSS SqFt Appliance Stock Inventory (ASI)

Size Stratum GSH/CAC GSH!WWAC GSH/NOAC

REUC Share of SCE Single Family by ASI 19.9

REUC Share of ASI 86.4

ASI Share of SCE Single Family 23.0

REUC Total Share of SCE Single Family

< 1,250 SqFt

1,250-1,749 SqFt

1,750+ SqFt

0/0

23/23

23/23

< 1,250 SqFt

1,250-1,749 SqFt

1,750+ SqFt

13.6%

38.2

48.2

45.2% 31.1%

37.1 34.1

17.7 34.8

54.8 68.9

18.0 37.0

9.9 25.5

55.3%

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

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

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

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

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

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

income and square feet strata, respectively.

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

conditioning along with deciding to install a heat pump.

4-7

EE-RM-90-201 COMMENT OOC2

Table 4-4 provides similar results of the REUC Sample1s 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 

RSS Single-Family Subsample 

ELE/GAS RSS SqFt Appliance Stock Inventory (AS!) 

REUC sample 

REUC SqFt 

Stratum Size Stratum GSH/CAC GSH!WWAC GSH/NOAC 

< 1,250 SqFt 

1,250-1,749 SqFt 

1,750+ SqFt 

0/0 

23/23 

23/23 

< 1,250 SqFt 

1,250-1,749 SqFt 

1,750+ SqFt 

13.6% 

38.2 

48.2 

REUC Share of ASI 86.4 

ASI Share of SCE Single Family 23.0 

REUC Share of SCE Single Family by ASI 19.9 

REUC Total Share of SCE Single Family 

45.2% 31.1% 

37.1 34.1 

17.7 34.8 

54.8 68.9 

18.0 37.0 

9.9 25.5 

55.3% 

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

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

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

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

of SCEls single-family homes by income strata and almost 20 percent of SCEls 

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

income and square feet strata, respectively. 

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

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 that the REUC

sample design, based on income and square feet, was a valid sampling framework.

Finally, the IMR techniques appl ied 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

EE-RM-90-201 COMMENT OOC2

Conclusions 

From the above analysis results, it can be seen that the 92 household REUC 

customer sample is fairly representative of a number of important segments of 

SCE's 3.5 million residential customers. This analysis also confirms that the REUC 

sample design, based on income and square feet, was 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 
Section 5

REUC LOAD DATA ANALYSIS:

ELECTRIC AND GAS APPLIANCE ENERGY USAGE

INTRODUCTION

This section presents the results from the analysis of nearly two years of load data

collected from the REUC sample. The project monitored both electric and gas

appliances in these households. As mentioned before, all energy information

presented here is given as pOint-of-use or site energy; i.e., 1 kWh =3.412 kBTU.

This section includes a description of how the end-uses were monitored, how the

data were collected, and the analysis methodology. For each end-use, monthly

and yearly average total usage tables and charts indicate the average appliance

energy usage. These results are followed by comparison of the distribution of

average daily UECs for space conditioning and water heating. Finally, results of a

weather model of space conditioning usage are presented.

LOAD DATA COLLECTION AND ANALYSIS

End-use load data were gathered from the 92 households participating in the

REUC project with load monitoring devices, each equipped with four load

measurement channels that record usage readings in five-minute intervals. Each

household provided data on from eight to twelve channels.

Total household electric usage was collected from each site, providing an

important cross-check for validating the end-use data. Each load monitoring

device downloaded its data via modem to the central load data computer

approximately once per week.

Simultaneously monitoring both electric and gas appliances from a single

household including heat pump technologies posed new data collection and

validation challenges. Moreover, this was the first SCE load monitoring project

conducted entirely with microcomputers rather than mainframe computers --

from initial data collection to final data analysis. This has allowed rapid

5-1

EE-RM-90-201 COMMENT OOC2

Section 5 

REUC LOAD DATA ANALYSIS: 

ELECTRIC AND GAS APPLIANCE ENERGY USAGE 

INTRODUCTION 

This section presents the results from the analysis of nearly two years of load data 

collected from the REUC sample. The project monitored both electric and gas 

appliances in these households. As mentioned before, all energy information 

presented here is given as pOint-of-use or site energy; i.e., 1 kWh = 3.412 kBTU. 

This section includes a description of how the end-uses were monitored, how the 

data were collected, and the analysis methodology. For each end-use, monthly 

and yearly average total usage tables and charts indicate the average appliance 

energy usage. These results are followed by comparison of the distribution of 

average daily UECs for space conditioning and water heating. Finally, results of a 

weather model of space conditioning usage are presented. 

LOAD DATA COLLECTION AND ANALYSIS 

End-use load data were gathered from the 92 households participating in the 

REUC project with load monitoring devices, each equipped with four load 

measurement channels that record usage readings in five-minute intervals. Each 

household provided data on from eight to twelve channels. 

Total household electric usage was collected from each site, providing an 

important cross-check for validating the end-use data. Each load monitoring 

device downloaded its data via modem to the central load data computer 

approximately once per week. 

Simultaneously monitoring both electric and gas appliances from a single 

household including heat pump technologies posed new data collection and 

validation challenges. Moreover, this was the first SCE load monitoring project 

conducted entirely with microcomputers rather than mainframe computers --

from initial data collection to final data analysis. This has allowed rapid 

5-1 
turnaround of initial data validation so that malfunctioning monitors and

appliances can be quickly identified.'

The nearly two years of usage data presented in this report consist of the period

February 5, 1988 through November 30, 1989. Therefore, the complete 1988

and 1989 summer seasons and nearly two complete winter seasons are analyzed.

For consistency, a "month" contains either a complete four- or five-week period

that closely, though not exactly, corresponds to the true calendar month. In this

way, the same proportion of week days and weekends enter into each month.

Table 5-1 describes exactly which days constitute each month's data.2

Before energy usage estimates were made, the data were extensively screened

for validity. Questionable data were removed from the analysis data set, and

usage amounts for each channel were calculated by summing up all valid usage

readings. Average usage figures were calculated by normalizing each channel by

the number of valid days of data, giving the usage of typical appliances for the

entire year. Details of all these validation, editing and analysis procedures can be

found in Appendix C.

1For 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 1988L

2Monthly usage estimates were computed by normalizing the daily usage

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

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

month, etc. .

5-2

EE-RM-90-201 COMMENT OOC2

turnaround of initial data validation so that malfunctioning monitors and 

appliances can be quickly identified.' 

The nearly two years of usage data presented in this report consist of the period 

February 5, 1988 through November 30, 1989. Therefore, the complete 1988 

and 1989 summer seasons and nearly two complete winter seasons are analyzed. 

For consistency, a "month" contains either a complete four- or five-week period 

that closely, though not exactly, corresponds to the true calendar month. In this 

way, the same proportion of week days and weekends enter into each month. 

Table 5-1 describes exactly which days constitute each month's data.2 

Before energy usage estimates were made, the data were extensively screened 

for validity. Questionable data were removed from the analysis data set, and 

usage amounts for each channel were calculated by summing up all valid usage 

readings. Average usage figures were calculated by normalizing each channel by 

the number of valid days of data, giving the usage of typical appliances for the 

entire year. Details of all these validation, editing and analysis procedures can be 

found in Appendix C. 

1 For more information, see John T. Powers and Bruce A. Smith, "Load 

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

System," IEEE 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

January

February

March

April

May

June

July

August

September

October

November

December

Actual Dates

Dec 30, 1988- Feb 2, 1989

Feb 5- Mar 3, 1988

Feb 3- Mar 2, 1989

Mar 4-Mar 31, 1988

Mar 3-Mar 30, 1989

Apr 1- Apr 28, 1988

Mar 31- Apr 27,1989

Apr 29- Jun 2, 1988

Apr 28- Jun 1, 1989

Jun 3- Jun 30, 1988

Jun 2- Jun 29, 1989

Jul 1- Jul 28, 1988

Jun 30- Aug 3, 1989

Jul 29- Aug 25, 1988

Aug 4- Aug 31, 1989

Aug 26- Sep 29, 1988

Sep 1 -Sep 28, 1989

Sep 30- Oct 27, 1988

Sep 29- Nov 2, 1989

Oct 28- Dec 1, 1989

Nov 3- Nov 30, 1989

Dec 2- Dec 29,1988

Number of Days

35

28

28

28

28a

28

28

28

35

28

28

28

35a

28

28

35

28

28

35

35

28

28

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

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

5-3

EE-RM-90-201 COMMENT OOC2

Table 5-1 

REUC: Table of Month Label to Date Correspondence 

Month Label Actual Dates Number of Da~s 

January Dec 30, 1988- Feb 2, 1989 35 

February Feb 5- Mar 3, 1988 28 

Feb 3- Mar 2, 1989 28 

March Mar 4-Mar 31, 1988 28 

Mar 3-Mar 30, 1989 28a 

April Apr 1- Apr 28, 1988 28 

Mar 31- Apr 27,1989 28 

May Apr 29- Jun 2, 1988 28 

Apr 28- Jun 1, 1989 35 

June Jun 3- Jun 30, 1988 28 

Jun 2- Jun 29, 1989 28 

July Jul 1- Jul 28, 1988 28 

Jun 30- Aug 3, 1989 35a 

August Jul 29- Aug 25, 1988 28 

Aug 4- Aug 31, 1989 28 

September Aug 26- Sep 29, 1988 35 

Sep 1 -Sep 28, 1989 28 

October Sep 30- Oct 27, 1988 28 

Sep 29- Nov 2, 1989 35 

November Oct 28- Dec 1, 1989 35 

Nov 3- Nov 30, 1989 28 

December 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 therms)

and are broken down by season in Table 5-2.4

Figure 5-1

Annual Average Energy Usage

Space Conditioning and Water Heatinga

Water Heating

23.4

o

a At Point of Use

5 10 15

MBTU

20 25 30

Ea Gas Sample

• Electric Sample

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

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

these numbers.

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

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

385 kWh vs. 224 kWh; Clothes Drying: 1070 kWh vs. 749 kWh). The usage

estimates are further broken down by city, month and day type (weekend vs.

weekday) in Appendix A.

5-4

EE-RM-90-201 COMMENT OOC2

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) These values 

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

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

and are broken down by season in Table 5-2.4 

Figure 5-1 

Annual Average Energy Usage 

Space Conditioning and Water Heatinga 

Space Conditioning 

Water Heating 

o 5 

a At Point of Use 

10 15 

MBTU 

~ Gas Sample 

• Electric Sample 

20 

23.4 

25 30 

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

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

these numbers. 

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

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

385 kWh vs. 224 kWh; Clothes Drying: 1070 kWh vs. 749 kWh). The usage 

estimates are further broken down by city, month and day type (weekend vs. 

weekday) in Appendix A. 

5-4 
figure 5-2

Annual Average Energy Usage

Clothes Drying and Cookinga

Clothes Drying

Cooking

4.7

o

a At Point of Use

2 3

MBTU

Ea Gas Sample

• Electric Sample

4 5 6

The REUC load data confirm that, with the exception of clothes drying, households with gas appliances consume considerably more energy at point of use than

households with electric appliances. As illustrated in Figure 5-1, the Gas Sample

consumes two to three times the total energy consumed by the Electric Sample

for space conditioning. Figure 5-1 also indicates that gas water heaters consume

two to three times the energy consumed by electric heat pump water heaters at

point of use. Gas ranges, as can be seen by Figure 5-2, consume more energy at

point of use than electric ranges by a similar factor. Clothes drying, in contrast,

does not appear to differ as much in energy usage across fuel types as the other

appliances. As shown in Figure 5-2, gas dryers use about 25 percent more energy

at point of use than electric dryers, which average 3.7 MBTU per year.

5-5

EE-RM-90-201 COMMENT OOC2

Clothes Drying 

Cooking 

o 

figure 5-2 

Annual Average Energy Usage 

Clothes Drying and Cookinga 

2 4 

a At Point of Use 

3 

MBTU 

Ea Gas Sample 

• 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 Sample 

consumes two to three times the total energy consumed by the Electric Sample 

for space conditioning. Figure 5-1 also indicates that gas water heaters consume 

two to three times the energy consumed by electric heat pump water heaters at 

point of use. Gas ranges, as can be seen by Figure 5-2, consume more energy at 

point of use than electric ranges by a similar factor. Clothes drying, in contrast, 

does not appear to differ as much in energy usage across fuel types as the other 

appliances. As shown in Figure 5-2, gas dryers use about 25 percent more energy 

at point of use than electric dryers, which average 3.7 MBTU per year. 

5-5 
Table 5-2

Average Annual and Seasonal Energy Usagea

Pooled Cities

Appliance

Electric Sample

kWh MBTUa

Gas Sample Gas Sample

(Gas Components) (Electric Components)

MBTU kWh MBTUa

Annual Average Household Usage (12 months)

Whole House 13000 44.40 NA 7540 25.70

Space Conditioning 2950 10.10 19.20 1170 3.99

Water Heating 2810 9.60 23.40 NA NA

Clothes Dryer 1070 3.66 4.32 110 0.38

Range/Oven 385 1.31 3.24 NA NA

Winterb Average Household Usage (8 months)

Whole House 9230 31.50 NA 4720 16.10

Space Conditioning 2190 7.50 19.00 437 1.49

Water Heating 2080 7.10 17.00 NA NA

Clothes Dryer 760 2.60 2.97 75 0.26

Range/Oven 274 0.94 2.24 NA NA

SummerC Average Household Usage (4 months)

Whole House 3770 12.90 NA 2820 9.60

Space Conditioning 760 2.60 0.20 733 2.50

Water Heating 730 2.50 6.40 NA NA

Clothes Dryer 310 1.06 1.35 35 0.12

Range/Oven 111 0.37 1.00 NA NA

aAt point of use.

bJanuary through May and October through December.

CJune through September.

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

illustrated in Figure 5-3. This figure indicates that an all-electric end-use home

can use nearly 30 MBTUs less per year relative to an all-gas end-use home, a

difference of almost 450 MBTUs over the assumed 15-year appliance lifetime.

5-6

EE-RM-90-201 COMMENT OOC2

Table 5-2 

Average Annual and Seasonal Energy Usagea 

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 NA 7540 25.70 

Space Conditioning 2950 10.10 19.20 1170 3.99 

Water Heating 2810 9.60 23.40 NA NA 

Clothes Dryer 1070 3.66 4.32 110 0.38 

Range/Oven 385 1.31 3.24 NA NA 

Winterb Average Household Usage (8 months) 

Whole House 9230 31.50 NA 4720 16.10 

Space Conditioning 2190 7.50 19.00 437 1.49 

Water Heating 2080 7.10 17.00 NA NA 

Clothes Dryer 760 2.60 2.97 75 0.26 

Range/Oven 274 0.94 2.24 NA NA 

SummerC Average Household Usage (4 months) 

Whole House 3770 12.90 NA 2820 9.60 

Space Conditioning 760 2.60 0.20 733 2.50 

Water Heating 730 2.50 6.40 NA NA 

Clothes Dryer 310 1.06 1.35 35 0.12 

Range/Oven 111 0.37 1.00 NA NA 

aAt point of use. 

bJanuary through May and October through December. 

CJune through September. 

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

illustrated in Figure 5-3. This figure indicates that an all-electric end-use home 

can use nearly 30 MBTUs less per year relative to an all-gas end-use home, a 

difference of almost 450 MBTUs over the assumed 15-year appliance lifetime. 

5-6 
Figure 5-3

Difference in Annual Energy Consumption

between Gas and Electric Appliancesa

Space Conditioning·

Water Heating

Clothes Drying

Cooking

o 5 10 15 20

a At Point of Use Annual Energy Difference (MBTU)

To show which time of year has the highest and lowest relative energy

consumption for the end-uses, Table 5-3 provides daily and monthly average

usage by month. This table is disaggregated by city in Appendix A so that the

reader can make inter-city comparisons.

5-7

EE-RM-90-201 COMMENT OOC2

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 At Point of Use AnnualE Difference (MBTU) 

20 

To show which time of year has the highest and lowest relative energy 

consumption for the end-uses, Table 5-3 provides daily and monthly average 

usage by month. This table is disaggregated by city in Appendix A so that the 

reader can make inter-city comparisons. 

5-7 
Table 5-3

Monthly Average Usage for Pooled Cities

Electric Sample Gas Sample Gas Sample

(Gas Components) (Electric Components)

kWh Therms kWh

Appliancea Weekday Monthly Weekday Monthly Weekday Monthly

. Average Average Average Average Average Average

January

Whole House 50.4 1625 NA NA 19.0 598

Space Condo 19.8 644 1.84 60.2 2.0 65

Water Heating 10.0 314 0.78 24.4 NA NA

Clothes Dryer 3.3 108 0.13 4.2 0.3 11 Range/Oven 1.2 38 0.09 2.8 NA NA

February

Whole House 41.1 1177 NA NA 18.2 517

Space Condo 12.3 344 1.22 34.2 1.6 45

Water Heating 9.2 261 0.77 21.9 NA NA

Clothes Dryer 2.9 92 0.12 3.5 0.3 9

Range/Oven 1.1 32 0.08 2.3 NA NA

March

Whole House 32.3 1048 NA NA 17.6 559

Space Condo 5.0 176 0.47 16.3 1.4 51

Water Heating 8.7 272 0.74 23.4 NA NA

Clothes Dryer 2.8 98 0.12 3.8 0.3 9

Range/Oven 1.0 33 0.08 2.5 NA NA

April

Whole House 31.2 955 NA NA 19.4 579

Space Condo 4.5 131 0.24 7.3 2.8 78

Water Heating 8.3 251 0.67 20.2 NA NA

Clothes Dryer 2.7 92 0.11 3.5 0.3 9

Range/Oven 1.0 32 0.09 2.8 NA NA

May

Whole House 28.0 888 NA NA 18.2 569

Space Condo 2.4 79 0.13 4.6 1.6 50

Water Heating 7.8 245 0.64 20.0 NA NA

Clothes Dryer 2.7 91 0.11 3.7 0.3 9

Range/Oven 1.0 30 0.08 2.6 NA NA

June

Whole House 27.3 825 NA NA 18.5 558

Space Condo 2.6 74 0.04 1.6 2.5 71

Water Heating 6.7 204 0.58 17.7 NA NA

Clothes Dryer 2.5 80 0.11 3.6 0.3 9

Range/Oven 0.9 29 0.08 2.5 NA NA

5-8

EE-RM-90-201 COMMENT OOC2

Table 5-3 

Monthly Average Usage for Pooled Cities 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Electric Components) 

kWh Therms kWh 

ARRliancea Weekday Monthly Weekday Monthly Weekday Monthly 

. Average Average Average Average Average Average 

January 

Whole House 50.4 1625 NA NA 19.0 598 

Space Condo 19.8 644 1.84 60.2 2.0 65 

Water Heating 10.0 314 0.78 24.4 NA NA 

Clothes Dryer 3.3 108 0.13 4.2 0.3 1 1 Range/Oven 1.2 38 0.09 2.8 NA NA 

February 

Whole House 41.1 1177 NA NA 18.2 517 

Space Condo 12.3 344 1.22 34.2 1.6 45 

Water Heating 9.2 261 0.77 21.9 NA NA 

Clothes Dryer 2.9 92 0.12 3.5 0.3 9 

Range/Oven 1.1 32 0.08 2.3 NA NA 

March 

Whole House 32.3 1048 NA NA 17.6 559 

Space Condo 5.0 176 0.47 16.3 1.4 51 

Water Heating 8.7 272 0.74 23.4 NA NA 

Clothes Dryer 2.8 98 0.12 3.8 0.3 9 

Range/Oven 1.0 33 0.08 2.5 NA NA 

April 

Whole House 31.2 955 NA NA 19.4 579 

Space Condo 4.5 131 0.24 7.3 2.8 78 

Water Heating 8.3 251 0.67 20.2 NA NA 

Clothes Dryer 2.7 92 0.11 3.5 0.3 9 

Range/Oven 1.0 32 0.09 2.8 NA NA 

May 

Whole House 28.0 888 NA NA 18.2 569 

Space Condo 2.4 79 0.13 4.6 1.6 50 

Water Heating 7.8 245 0.64 20.0 NA NA 

Clothes Dryer 2.7 91 0.11 3.7 0.3 9 

Range/Oven 1.0 30 0.08 2.6 NA NA 

June 

Whole House 27.3 825 NA NA 18.5 558 

Space Condo 2.6 74 0.04 1.6 2.5 71 

Water Heating 6.7 204 0.58 17.7 NA NA 

Clothes Dryer 2.5 80 0.11 3.6 0.3 9 

Range/Oven 0.9 29 0.08 2.5 NA NA 

5-8 
Table 5-3, Cont.

Electric Sample Gas Sample Gas Sample

(Gas Components) (Eledric Components)

kWh Therms kwh

Weekday Monthly Weekday Monthly Weekday Monthly

Appliance Average Average Average Average Average Average

July

Whole House 32.8 1022 NA NA 26.1 803

Space Condo 8.3 257 0.00 0.1 8.9 272

Water Heating 5.7 177 0.51 15.8 NA NA

Clothes Dryer 2.5 82 0.11 3.4 0.3 9

Range/Oven 0.9 27 0.08 2.5 NA NA

August

Whole House 29.6 934 NA NA 22.8 719

Space Condo 5.9 191 0.00 0.1 5.4 177

Water Heating 5.5 173 0.50 15.3 NA NA

Clothes Dryer 2.2 75 0.10 3.3 0.3 9

Range/Oven 0.9 27 0.08 2.5 NA NA

September

Whole House 32.3 999 NA NA 24.5 748

Space Condo 7.4 244 0.01 0.4 6.6 211

Water Heating 5.7 173 0.51 15.2 NA NA

Clothes Dryer 2.3 75 0.10 3.2 0.3 8

Range/Oven 0.9 28 0.08 ' 2.6 NA NA

October

Whole House 28.7 912 NA NA 19.3 607

Space Condo 2.1 74 0.05 1.7 1.5 53

Water Heating 6.6 204 0.56 17.2 NA NA

Clothes Dryer 2.5 86 0.10 3.4 0.3 9

Range/Oven 1.0 31 0.09 2.7 NA NA

November

Whole House 34.8 1059 NA NA 19.3 582

Space Condo 6.2 184 0.55 16.4 1.2 37

Water Heating 7.9 239 0.66 19.6 NA NA

Clothes Dryer 2.7 90 0.11 3.6 0.3 9

Range/Oven 1.2 35 0.10 2.9 NA NA

December

Whole House 49.9 1564 NA NA 22.2 704

Space Condo 18.2 556 1.59 49.3 1.9 58

Water Heating 9.4 293 0.74 23.2 NA NA

Clothes Dryer 3.2 104 0.13 4.1 0.3 11

Range/Oven 1.3 42 0.11 3.7 NA NA

5-9

EE-RM-90-201 COMMENT OOC2

Table 5-3, Cont. 

Electric Sample Gas Sample Gas Sample 

(Gas Components) (Eledric Components) 

kWh Therms kwh 

Weekday Monthly Weekday Monthly Weekday Monthly 

Appliance Average Average Average Average Average Average 

July 

Whole House 32.8 1022 NA NA 26.1 803 

Space Condo 8.3 257 0.00 0.1 8.9 272 

Water Heating 5.7 177 0.51 15.8 NA NA 

Clothes Dryer 2.5 82 0.11 3.4 0.3 9 

Range/Oven 0.9 27 0.08 2.5 NA NA 

August 

Whole House 29.6 934 NA NA 22.8 719 

Space Condo 5.9 191 0.00 0.1 5.4 177 

Water Heating 5.5 173 0.50 15.3 NA NA 

Clothes Dryer 2.2 75 0.10 3.3 0.3 9 

Range/Oven 0.9 27 0.08 2.5 NA NA 

September 

Whole House 32.3 999 NA NA 24.5 748 

Space Condo 7.4 244 0.01 0.4 6.6 211 

Water Heating 5.7 173 0.51 15.2 NA NA 

Clothes Dryer 2.3 75 0.10 3.2 0.3 8 

Range/Oven 0.9 28 0.08 . 2.6 NA NA 

October 

Whole House 28.7 912 NA NA 19.3 607 

Space Condo 2.1 74 0.05 1.7 1.5 53 

Water Heating 6.6 204 0.56 17.2 NA NA 

Clothes Dryer 2 .5 86 0.10 3.4 0.3 9 

Range/Oven 1.0 31 0.09 2.7 NA NA 

November 

Whole House 34.8 1059 NA NA 19.3 582 

Space Condo 6.2 184 0.55 16.4 1.2 37 

Water Heating 7.9 239 0.66 19.6 NA NA 

Clothes Dryer 2.7 90 0.11 3.6 0.3 9 

Range/Oven 1.2 35 0.10 2.9 NA NA 

December 

Whole House 49 .9 1564 NA NA 22.2 704 

Space Condo 18.2 556 1.59 49 .3 1.9 58 

Water Heating 9.4 293 0.74 23.2 NA NA 

Clothes Dryer 3.2 104 0.13 4.1 0.3 11 

Range/Oven 1.3 42 0.11 3.7 NA NA 

5-9 
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 Average Weekday Electricity Usage

Electric Sample End-Uses

Water Heating

Space Conditioning

Cooking

Clothes Drying

t:a Winter Weekday

II Summer Weekday

8.8

o 1 2 3 4 5 6 7 8 9 10

5-10

kWh

EE-RM-90-201 COMMENT OOC2

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 Average Weekday Electricity Usage 

Electric Sample End-Uses 

Water Heating 

Space Conditioning ~~~~~~~~ 

Cooking 

Clothes Drying 

t:a Winter Weekday 

II Summer Weekday 

8.8 

o 1 2 3 4 5 6 7 8 9 10 

kWh 

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 Electricity Usage

Winter Weekend

Winter Weekday

Summer Weekend

Summer Weekday

o 10 20

kWh

30 40 50

E3 Gas Sample

• 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

EE-RM-90-201 COMMENT OOC2

Append ix 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 Electricity Usage 

Winter Weekend 

Winter Weekday 

Summer Weekend 

Summer Weekday 

o 10 20 30 40 50 

kWh 

E:a Gas Sample 

• 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.

5-12

EE-RM-90-201 COMMENT OOC2

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 Weekday Average UEC Distribution

Winter Electric Heat Pump: Pooled Cities

Percent of Total 40

35

30

25

20

15

10

5

o

<2 4 8 12 16 20 24 28 32 36 40 44

Daily kWh Midpoints

Figure 5-7

SCE REUC Weekday Average UEC Distribution

Summer Space Conditioning: Pooled Cities

Percent of Total 40

35

30

25

20

15

10

5

o

<2 4 8 12 16 20 24 28 32 36 40

Daily kWh Midpoints

5-13

EE-RM-90-201 COMMENT OOC2

Figure 5-6 

SCE REUC Weekday Average UEC Distribution 

Winter Electric Heat Pump: Pooled Cities 

Percent of Total 40 

35 

30 

25 

20 

15 

10 

5 

o 

<2 4 8 12 16 20 24 28 32 36 40 44 

Daily kWh Midpoints 

Figure 5-7 

SCE REUC Weekday Average UEC Distribution 

Summer Space Conditioning: Pooled Cities 

Percent of Total 40 

35 

30 

25 

20 

15 

10 

5 

o 

<2 4 8 12 16 20 24 28 32 36 40 

Daily kWh Midpoints 

5-13 
figure 5-8

SCE REVC Weekday Average VEC Distribution

Winter Gas furnace: Pooled Cities

Percent of Total 50

45

40

35

30

25

20

15

10

5

0

<2 50 100 150 200 250 300 350 400 450

Daily kBTU Midpoints

figure 5-9

SCE REVC Weekday Average VEC Distribution

Winter Electric Water Heating: Pooled Cities

Percent of Total 20

18

16

14

12

10

8

6

4

2

0-----"-

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.511 11.5 12 12.5 13 13.5

Daily kWh Midpoints

5-14

EE-RM-90-201 COMMENT OOC2

figure 5-8 

SCE REUC Weekday Average UEC Distribution 

Winter Gas furnace: Pooled Cities 

Percent of Total 50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

<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 

Percent of Total 20 

18 

16 

14 

12 

10 

8 

6 

4 

2 0-----"-

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.511 11.5 12 12.5 13 13.5 

Daily kWh Midpoints 

5-14 
Figure 5-10

SCE REUC Weekday Average UEC Distribution

Winter Gas Water Heating: Pooled Cities

Percent of Tolal 20

18

16

14

12

10

8

6

4

2

0

30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110

Daily kBTU Midpoints

5-15

EE-RM-90-201 COMMENT OOC2

Percent of Total 

Figure 5-10 

SCE REUC Weekday Average UEC Distribution 

Winter Gas Water Heating: Pooled Cities 

20 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 

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 10:00 a.m. is the most important period

for space heating. A model is estimated below that uses the morning heating

degree days to explain the morning heating energy usage.

The model that is estimated has the following general form:

(5.1)

where UftiS the morning usage in kBTU between 6:00 and 10:00 a.m. for k = gas

or electric, j indexes cities, and t ranges over all days of the winter. Hjt is the

number of heating degree days base 68°F in city j on day 1. Heating degree days

are defined as follows:

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

m=73 (5.2)

where m=73 to 120 are the five-minute time periods between 6:00 and 10:00

a.m. Both usage and heating degree days are subtracted from their means for the

estimation so that no intercept is required in the model; tjt is a random

disturbance term. Thus, two i3r are estimated, one for each fuel type. Estimation

is carried out after first averaging across all of the households. The estimated

parameters for Equation 5.1 are given in Table 5-4 for each fuel type, electricity

and gas.

5-16

EE-RM-90-201 COMMENT OOC2

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 UftiS the morning usage in kBTU between 6:00 and 10:00 a.m. for k = gas 

or electric, j indexes cities, and t ranges over all days of the winter. Hjt is the 

number of heating degree days base 68°F in city j on day t. Heating degree days 

are defined as follows: 

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

m=73 (5.2) 

where m=73 to 120 are the five-minute time periods between 6:00 and 10:00 

a.m. Both usage and heating degree days are subtracted from their means for the 

estimation so that no intercept is required in the model; tjt is a random 

disturbance term. Thus, two i3r are estimated, one for each fuel type. Estimation 

is carried out after first averaging across all of the households. The estimated 

parameters for Equation 5.1 are given in Table 5-4 for each fuel type, electricity 

and gas. 

5-16 
Table 5-4

Estimated linear Relationship between Average Morning Space

Heating and Morning Heating Degree-Days: All Households

Fuel ak Std. Error N R2

Gas 26.5a 0.45 841 0.81

Electricity 7.5a 0.14 841 0.76

aOenotes significance at the one percent level.

For gas furnaces, the morning usage increases about 25 kBTU for every heating

degree day and, for electric heat pumps, the morning usage increases about 7

kBTU for every heating degree day. This relationship is linear; higher 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

EE-RM-90-201 COMMENT OOC2

Table 5-4 

Estimated Linear Relationship between Average Morning Space 

Heating and Morning Heating Degree-Days: All Households 

Fuel ak Std. Error N R2 

Gas 26.5a 0.45 841 0.81 

Electricity 7.5a 0.14 841 0.76 

aOenotes significance at the one percent level. 

For gas furnaces, the morning usage increases about 25 kBTU for every heating 

degree day and, for electric heat pumps, the morning usage increases about 7 

kBTU for every heating degree day. This relationship is linear; higher 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

INTRODUCTION

This section provides the appliance load profiles developed from nearly two years

of data collected from the REUC sample. The REUC appliance load data are

unique in that daily energy usage patterns of both electric and gas residential

appliances have been calculated and compared from field data. The data were

collected at five-minute intervals and subsequently aggregated over half-hour

periods for graphical presentation.

This section presents load profiles highlighting the key comparisons made for the

monitored appliances. Appendix A contains a comprehensive set of load profiles.

Finally, this section concludes with load shape modeling results.

LOAD PROFILES

Figures 6-1 through 6-4 contain average load profiles for space heating and water

heating, for the summer and winter seasons. These usage profiles indicate the

relative energy usage of the different fuel types, as well as the different usage

patterns of electric and gas appliances. Cooking and clothes drying usage profiles

are provided in Appendix A.

The load profile results mirror the 'seasonal and annual energy usage presented in

the previous section. The load shapes for electric and gas appliances 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

EE-RM-90-201 COMMENT OOC2

Section 6 

REUC APPLIANCE LOAD PROFILES 

INTRODUCTION 

This section provides the appliance load profiles developed from nearly two years 

of data collected from the REUC sample. The REUC appliance load data are 

unique in that daily energy usage patterns of both electric and gas residential 

appliances have been calculated and compared from field data. The data were 

collected at five-minute intervals and subsequently aggregated over half-hour 

periods for graphical presentation. 

This section presents load profiles highlighting the key comparisons made for the 

monitored appliances. Appendix A contains a comprehensive set of load profiles. 

Finally, this section concludes with load shape modeling results. 

LOAD PROFILES 

Figures 6-1 through 6-4 contain average load profiles for space heating and water 

heating, for the summer and winter seasons. These usage profiles indicate the 

relative energy usage of the different fuel types, as well as the different usage 

patterns of electric and gas appliances. Cooking and clothes drying usage profiles 

are provided in Appendix A. 

The load profile results mirror the 'seasonal and annual energy usage presented in 

the previous section. The load shapes for electric and gas appl iances 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 Conditioning (at point of use)

kBTU per Hour 12

Elec. Weekdays

Elec. Weekends

Gas Weekdays

Gas Weekends

4

i" ,

ii\ ! I ,

! I i'

! , i'

!, i'

!Ii'

!/ i \ I • , ! ;\

, . ,

,

r , . ,

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

h \ \ .'" '.\

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

2 ,~'- ','., \ ., ,. .... ,~ _--_... " ........_-----,-

6

8

10

63 9 12 15 18 21 24

O-+-...-..-......."""T"-r...-T"""1.......--r-r.....-...-,......,r"""'T""T"""'T""~T""'lr"""'T--,

o

Time of Day

Figure 6-2

SCE REUC Summer: Space Conditioning (at point of use)

k8TU per Hour 3.0

2.5

2.0

1.5

1.0

0.5

Elec. Weekdays

Elec. Weekends

Gas Weekdays

Gas Weekends

963 12 15 18 21 24

0.0 +-............,--r.....,.-'T'-............,"""T"""T""'T'-............,-,..""T"",.-........,.....,-,.......,.-...,

o

Time of Day

6-2

EE-RM-90-201 COMMENT OOC2

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 

Time of Day 

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 

Gas Weekdays 

Gas Weekends 

O.O;-~ro~-r,-~rolT-r,-~ro~-r,-~ro~-r~ 

o 3 6 9 12 15 18 21 24 

Time of Day 

6-2 
Figure 6-3

SCE REUC Winter: Water Heating (at point of use)

kBTU per Hour 8

"

. ' Bee. Weekdays I'

7 i i Bee. Weekends

i " Gas Weekdays , ' I

\1\ Gas Weekends

6 I " , \ ), I \

S , Ii \ ,--\ • ,, \ , , .""' \ I

4 I 't • , ;. \ /! ' I

, " \ II t., • , \, "--1/ ,\ 3 • , , \"-.

I , " .' \, I ...-... , ,1 , " , 2 " ....._-, ,

,,

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

963 12 lS 18 21 24

O+-'-'--T"'""--r-.....,,.....--r--"'"'T--T"'""--'

o

TIme of Day

Figure 6-4

SCE REUC Summer: Water Heating (at point of use)

Elec. Weekdays

Elec. Weekends

Gas Weekdays

Gas Weekends

i'.

.! '., J ,

" "'/\

~I \

t \

1\ \ " , \ ':A

I ", \ .r ~, , \ \ ~'~ , .. \ ,..., , " '-./i \ ... , \ ~, \ ......, '-, ,--' , .-, \,

,,

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

kUTU per Hour 6

S

4 I

I

I

3 I

I

• 2

63 9 12 lS 18 21 24

0+--...........-....--...........-....--.....--...--.....----. o

Time of Day

6-3

EE-RM-90-201 COMMENT OOC2

Figure 6-3 

SCE REUC Winter: Water Heating (at point of use) 

kBTU per Hour 8 ,1 

.. , , Bee. Weekdays 

7 i i Bee. Weekends 

i .~ Gas Weekdays 

\1\ Gas Weekends 

6 ., \ , ), \ 

S , " \ ,-\ • , , \ , , ''''' \ I 

4 , , ;. \ I 't , , "' \ /! t. 

, /' . I \ "-.-J. .' ''. 3 • , \ I \ ... , \. . ' ~ \ ... _, , ,1 .. , , 2 " --. .. _-, .. , .. ......... _-" 

Oi----,----T_---r--~~--~--~----T_--, 

o 3 6 9 12 lS 18 21 24 

TIme of Day 

Figure 6-4 

SCE REUC Summer: Water Heating (at point of use) 

o 3 6 9 12 lS 18 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 differ by fuel

type, but the load shape differs as well. Interesting comparisons can be made

across seasons. By comparing Figure 6-1 with Figure 6-2, the magnitude of heat

pumps' average peak demand is observed to be approximately equal in both

summer and winter. Only the timing of this peak differs between the two

seasons. However, Figure 6-5, which expresses demand in kW rather than kBTU

per hour, illustrates that in the months of extreme temperatures (July and

January), the peak of the average heating load profile of the heat pump is

significantly higher than the peak of the average cooling load profile. Space

conditioning average load profiles for the other ten months are included in

Appendix A.

Figure 6-5

SCE REUC: Space Conditioning Electric Sample

21 24

January Weekdays

January Weekends

July Weekdays

July Weekends

96 12 15 18

I" /.i

I"

----,-/'

kw load 2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2 .~~ ...

.... ~~.:.

0.0

0 3

Time of Day

6-4

EE-RM-90-201 COMMENT OOC2

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 differ by fuel 

type, but the load shape differs as well. Interesting comparisons can be made 

across seasons. By comparing Figure 6-1 with Figure 6-2, the magnitude of heat 

pumps' average peak demand is observed to be approximately equal in both 

summer and winter. Only the timing of this peak differs between the two 

seasons. However, Figure 6-5, which expresses demand in kW rather than kBTU 

per hour, illustrates that in the months of extreme temperatures (July and 

January), the peak of the average heating load profile of the heat pump is 

significantly higher than the peak of the average cooling load profile. Space 

conditioning average load profiles for the other ten months are included in 

Appendix A. 

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 

.~~ ... 

.... ~~.:. 

0 3 

, 

, 

, 

\ 

, 

, 

, 

\ 

\ 

\ 

, 

\ 

\ 

, 

\ 

, 

, 

, 

\ 

, 

, 

, 

\ 

/" /.i 

,., 

----,-// 

6 9 12 

Time of Day 

6-4 

15 18 

January Weekdays 

January Weekends 

July Weekdays 

July Weekends 

21 24 
Figures 6-6 and 6-7 present the average load profiles across all lwclw ,l)ol1lhc

, ill

the year for heat pump and gas space conditioning, respectively. Th(,,,l' II1IlT

dimensional figures trace the changing height of the average load profile peJk i J1

each month.

All four electric end-uses I load profiles are presented in one plot in Figures 6-8

and 6-9 in order to illustrate the different magnitudes of average appliance load.

Finally, Figures 6-10 and 6-11 present whole house load shapes for the Electric

and Gas Samples. Space conditioning and whole house load are overlaid in

Figure A-22 in Appendix A.

These load shapes and average load profiles are consistent with other residential

load metering studies. For example, load profiles produced from the SCE

Residential Appliance End-Use Survey (RAEUS) project are consistent with the

REUC load profiles where comparisons can be made. The RAEUS project, while

metering only electric appliances, has a larger number of end-use profiles.'

The profiles provided in Appendix A include: (1) load profiles for cooking and

clothes drying; (2) load profiles for the electric appliances in kW rather than

KBTU; (3) two-dimensional space conditioning average load profiles for each

month; (4) load profiles for weekdays expressed as a percentage of total daily

usage; (5) whole house, space conditioning and water heating load profiles on a

common graph; and (6) a three-dimensional daily temperature profile by month.

1For more information about the RAEUS load profiles, see Southern

California Edison, "Residential Appliance End-Use Survey: Sample I Final Report,"

February 1989.

6-5

EE-RM-90-201 COMMENT OOC2

Figures 6-6 and 6-7 present the average load profiles across all lvvclw 111 011 I h c

, i )1 

the year for heat pump and gas space condition ing, respecti vely. Thc n ,(, I hr ('(' 

dimensional figures trace the changing height of the average load profil e peJk i f1 

each month. 

All four electric end-uses I load profiles are presented in one plot in Figures 6-8 

and 6-9 in order to illustrate the different magnitudes of average appliance load. 

Finally, Figures 6-10 and 6-11 present whole house load shapes for the Electric 

and Gas Samples. Space conditioning and whole house load are overlaid in 

Figure A-22 in Appendix A. 

These load shapes and average load profiles are consistent with other residential 

load metering studies. For example, load profiles produced from the SCE 

Residential Appliance End-Use Survey (RAEUS) project are consistent with the 

REUC load profiles where comparisons can be made. The RAEUS project, while 

metering only electric appliances, has a larger number of end-use profiles.' 

The profiles provided in Appendix A include: (1) load profiles for cooking and 

clothes drying; (2) load profiles for the electric appliances in kW rather than 

KBTU; (3) two-dimensional space conditioning average load profiles for each 

month; (4) load profiles for weekdays expressed as a percentage of total daily 

usage; (5) whole house, space conditioning and water heating load profiles on a 

common graph; and (6) 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 Pump Space Conditioning

6-6

EE-RM-90-201 COMMENT OOC2

figure 6-6 

SCE REUC: Heat Pump Space Conditioning 

kW Load 

~O 

Ui 

LO 

OJ) 

'nme (PST) 

6-6 
kBTU per Hour

26

20

lli

10

6

Figure 6-7

SCE REUC: Gas/Electric Space Conditioning

I"''''''''-7'- May M:r

February

January

Deamber

::>7s..::;:<:;~(c'"~ september

August

July

June

0:00

6-7

EE-RM-90-201 COMMENT OOC2

kBTU per Hour 

20 

10 

6 

Figure 6-7 

SCE REUC: Gas/Electric Space Conditioning 

---'-""""."'- May M;r FI!bruary 

January 

Detember 

Nlm!mber 

October 

september 

August 

July 

June 

0:00 

6-7 
Figure 6-8

SCE REUC Winter Weekday Electricity: Electric Sample

Space Conditioning

Water Heating

Cooking

Clothes Drying

kW Load 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

0 3 6 9 12 1S 18 21 24

Time of Day

Figure 6-9

SCE REUC Summer Weekday Electricity: Electric Sample

3 6 9 12 15 18 21 24

Space Conditioning

Water Heating

Cooking

Clothes Drying

/\

, .. , \ I ,

I ,

I ..

I ' .. I \ I

I

I

I

, .. I , I -.._---,

;'

;,' , /

".. ,/I'

I .. \

.' ., .,....-.._----,--... _---,' ' .........- 0.0 -F.,..,.--l~---T""""~_r_-......,.--r_""'-"'T""-;.:,

o

kW Load 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Time of Day

6-8

EE-RM-90-201 COMMENT OOC2

Figure 6-8 

SCE REUC Winter Weekday Electricity: Electric Sample 

kW Load 1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

0 3 6 9 12 

Time of Day 

Figure 6-9 

, -.-,' 

15 

Space Conditioning 

Water Heating 

Cooking 

Clothes Drying 

! 

18 21 24 

SCE REUC Summer Weekday Electricity: Electric Sample 

kW Load 1.0 

0.9 

0.8 

0.7 

0.6 

o.s 

0.4 

0.3 

0.2 

0.1 

Space Conditioning 

Water Heating 

Cooking 

Clothes Drying 

/\ 

I , 

, \ , , 

: " , '. ;' 

;,' , / 

, \ , 

, 

, 

, 

, '..... /I' 

" - " ~ I , \ ... _---' .' ., ~ ... , ... -.... _--_., ....... _-_ ... " '-... --- 0.0 ..p.~..,.--.,::.;-""T"-.....,---r--'""'r--T'"""'~;.:, 

o 3 6 9 12 1S 18 21 24 

Time of Day 

6-8 
Figure 6-10

SCE REUC Winter: Whole House Electricity

Elec. Sample Weekdays

Elec. Sample Weekends

Gas Sample Weekdays

Gas Sample Weekends

24

'\.

1815 21

II

,

I

I

I

" ,1 .....-..

3 96 12

0.4

0.2

0.0 t.-~T"""""'~T"""""'"-..-,.~.........~......,.. ..............--"':::;:::::::::;---------'

o

kW load 3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

Time of Day

Figure 6-11

SCE REUC Summer: Whole House Electricity

18 21 24

Elec. Sample Weekdays

Elec. Sample Weekends

Gas Sample Weekdays

Gas Sample Weekends..

15

," ..'\

I ,..... .,., ,--' ,_....-,

3 6 9 12

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

Time of Day

6-9

EE-RM-90-201 COMMENT OOC2

kW load 

Figure 6-10 

SCE REUC Winter: Whole House Electricity 

3.0 

2.8 

2.6 

2.4 

2.2 

2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

, 

"'-... ~ 

0.8 ,. I'... .. ... ~~ ,/,/ -----,'-- 0.6 .,._~ 

0.4 

0.2 

, 

, 

, 

, 

, 

I 

I 

I -

'\ . 

Elec. Sample Weekdays 

Elec. Sample Weekends 

Gas Sample Weekdays 

Gas Sample Weekends 

0.0 i-.~T"""'"~r-r-..........,.~"'--"~,.....,.. .......... '"'""T""""":::;:::::::::::;---------' 

o 3 6 9 12 15 18 21 24 

Time of Day 

Figure 6-11 

SCE REUC Summer: Whole House Electricity 

kW Load 2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 3 6 9 

6-9 

, ,.'" , 

I , ......... , ,--' ,_ ..... -, 

12 15 

Time of Day 

Elec. Sample Weekdays 

Elec. Sample Weekends 

Gas Sample Weekdays 

Gas Sample Weekends .. ~ . 

18 21 24