GRI-93/0204

Topical Report

Metered Ranges, Northern Illinois Cooktops, and

Gas Residential

Ovens in the

Load Study Data Base

Prepared by:

Battelle

Gas Research Institute

Appliance Research Department

Julv 1993

YOGI4

EE-RM-94-230 COMMENT 61B

GRI-93!o204 

Topiical Report 

Metered Ranges, Cooktops, and Ovens in the 

Northern·lllinois. Gas Residential Load Study Data Base_ 

. Prepared by: 

Battelle 

Gas Research Institute 

Appliance Research Deparrinenl 

lLlI\' 1993 
mi inMETERED RANGES, COOKTOPS AND OVENS IN THE

NORTHERN ILLINOIS GAS

RESIDENTIAL LOAD STUDY DATA BASE

TOPICAL REPORT

(July 1993)

by

John Menkedick, Nancy Niemuth

Pam Hartford, and D. K. Landstrom

BATTELLE

505 King Avenue

Columbus, Ohio 43201-2693

for

GAS RESEARCH INSTITUTE

8600 West Bryn Mawr Avenue

Chicago, Illinois 60631

and

NAECA RANGE TECHNICAL ADVISORY GROUP

GRI Project Manager

Rodney C. Rinholm

a

F

GRI-93/0204

IIPSfiffLP

Esreaaaserlsaaw.

EE-RM-94-230 COMMENT 61B

w 

, i 

GRI-93/0204 . r.t. 5M "'W 4 # it 5 A 9## t 

METERED RANGES, COOKTOPS AND OVENS IN THE 

NORTHERN ILLINOIS GAS 

RESIDENTIAL LOAD STUDY DATA BASE 

TOPICAL REPORT 

(July 1993) 

by 

John Menkedick, Nancy Niemuth 

Pam Hartford, and D. K. Landstrom 

BATTELLE 

505 King A venue 

Columbus, Ohio 43201-2693 

for 

GAS RESEARCH INSTITUTE 

8600 West Bryn Mawr Avenue 

Chicago, Illinois 60631 

- and 

NAECA RANGE TECHNICAL ADVISORY GROUP 

GRI Project Manager 

Rodney C. Rinholm 

¥ h $ $ 'b§ • 

ff 1 ( .. iH ·f .,.j i ; #g .,.m ;; t# i f g.>. kg 'i; kif : ;,;. ·i
LEGAL NOTICE

This report was prepared by Battelle as an account of work

sponsored by the Gas Research Institute (GRI). Neither GRI,

members of GRI, Battelle, officers, trustees, or staff of Battelle, nor

any person acting on behalf of either:

a. Makes any warranty or representation, expressed

or implied, with respect to the accuracy,

completeness, or usefulness of the information

contained in this report, or that the use of any

information, apparatus, software, method, or

process disclosed in this report may not infringe

privately owned rights; or

b. Assumes any liability with respect to the use of, or

for damages resulting from the use of, any

information, apparatus, software, method, or

process disclosed in this report.

Reference to trade names or specific commercial products,

commodities, or services in this report does not represent nor

constitute an endorsement, recommendation, or favoring by GRI or

Battelle of the specific commercial product, commodity, or service.

ii

EE-RM-94-230 COMMENT 61B

LEGAL NOTICE 

. This report was prepared by Battelle as an account of work 

sponsored by the Gas Research Instiblte (GRI). Neither GRI, 

members of GRI~ Battelle, officers, trustees, or staff of Battelle, nor 

any person acting on behalf of either: 

a. Makes any warranty ·or representation, expressed 

or implied, with respect to the accuracy, 

. completeness, or usefulness of the information 

contained in this report, or that the use of any . 

information, apparatus, software, method, or 

process. disclosed in this report may not infringe 

privately owned rights; or 

b. Assumes any liability with respect to the use of, or 

for damages resulting from the use of,. any 

information, apparabls, software, method, or 

process disclosed in this report. 

Reference to trade names or specific co~erCial products, 

commodities, or services in this report does not represent nor 

constiblte an endorsement, recommendation, or favoring by GRI or 

Battelle of the specific commercial product, commodity, or service. 

ii 
PREFACE

Northern Illinois Gas Company has offered access to its

Residential Load Study Data Base to the Gas Research Institute and

Battelle Memorial Institute for use in GRI's NAECA Projects. As

specified in the written agreement between Northern Illinois Gas,

Battelle, and GRI, the Data Base remains the property of Northern

Illinois Gas.

iii .U.l

EE-RM-94-230 COMMENT 61B

PREFACE 

Northern Illinois Gas Company has offered access to its 

Residential Load Study Data Base to the Gas Research Institute and 

Battelle Memorial Institute for use in GRI's NAECA Projects. As 

. specified in the written agreement between Northern Illinois Gas, 

Battelle, and GRI, the Data Base remains the property of Northern 

Illinois Gas. 

iii 
50272-101

REPORT DOCUMENTATION 1. REPORT NO. 2. 3. Recipient's Accession No.

PACE GRI-93/0204

4. Title and Subtitle 5. Report Date

July 1993

Metered Ranges, Cooktops and Ovens in the Northern Illinois Gas Residential Load 6. Study Data Base

7. Authors 8. Performing Organization

John Menkedick, Nancy Niemuth, Pam Hartford, and D. K. Landstrom Rept. No.

9. Performing Organization Name and Address 10. Project/Task/Work Unit N

N5875

Battelle

-- , „. ,~~~~~~~Battelle~~ 11. Contract (C) or Grant (G) 

505 King Avenue

Columbus, OH 43201-2693 (Cl 5086-241-1219 and

(c) 5092-281-2494

12. Sponsoring Organization Name and Address 13. Type of Report & Period

Covered

Gas Research Institute Topical

8600 West Bryn Mawr Avenue Jun 92 - Mar 93

Chicago, IL 60631 14.

15. Supplementary Notes

16. Abstract (Limit 200 Words)

Northern Illinois Gas Company conducted a two-year study of usage from 752 individually metered gas appliances i

354 single-family households in northern Illinois. The study included 103 ranges, drop-in surface units (cooktops), 

ovens metered in 101 households. Of these, 92 were stand-alone ranges, eight were cooktops metered separately, an

three were built-in ovens metered separately. Meter readings were collected daily over two years, from May 1990

through April 1992, and hourly during the months of July and January in that time frame. For each household,

demographic and appliance information was collected for over 150 variables.

This report was prepared for the GRI Technical Advisory Group for NAECA rulemaking on ranges with the approva

Northern Illinois Gas. This report describes the data base and the statistical analysis approaches, and presents estima

of annual gas usage that should prove helpful in the NAECA rulemaking process for gas ranges.

The range annual gas usage represents only 2 to 4 percent of the total gas usage (approximately 1400 therms) for a

typical household in the Northern Illinois Gas service area. The estimated average annual usage per range in the stu

was 22.4 therms for ranges with electric ignition and 56.1 therms for ranges with standing pilot.

17. Document Analysis a. Descriptors

Ranges, ignition, cooking

b. Identifiers/Open-Ended Terms

Residential gas usage, household gas usage, load studies, cooktops, ovens

c. COSATI Field/Group

18. Availability Statement 19. Security Class (This 21. No. of Pages

Report) 29

Unlimited . Unclassified

20. Security Class (This Page) 22. Price

Unclassified

(See ANSI-Z39.18) See Instructions on Reverse OPTIONAL FORM 272 (4-77)

(Formerly NTIS-35)

Department of Commerce

EE-RM-94-230 COMMENT 61B

50272·101 

r::R=EP=O=R=T=-=D::O::C=-U=M::ENT=:-:A::T::I=O::N:-rI1-=-.-:R::E::P::O::R=T=-N=o=-. ------.-2-. -----------r-3.-R-e-c-i-p-ie-n-t-'s-A-cc-e-s-s-io-n-N-o-

PACE GRI-93/0204 

4. Title and Subtitle 

Metered Ranges, Cooktops and Ovens in the Northern Illinois Gas Residential Load 

Study Data Base 

7. Authors 

John Menkedick, Nancy Niemuth, Pam Hartford, and D. K. Landstrom 

9. performing Organization Name and Address 

Battelle 

505 King A venue 

Columbus, OH 43201-2693 

12. sponsoring Organization Name and Address 

Gas Research Institute 

8600 West Bryn Mawr Avenue 

Chicago, IL 60631 

15. SUPPlementary Notes 

16. Abstract (Limit 200 Wordsl 

5. Report Date 

July 1993 

6. 

8. Performing Organization 

Rept. No. 

10. projectlTask/Work Unit N

N5875 

11. Contract (CI or Crant ICI 

(CI 5086-241-1219 and 

ICI 5092-281-2494 

13. Type of Report & period 

Covered 

Topical 

Jun 92 - Mar 93 

14. 

Northern Illinois Gas Company conducted a two-year study ·of usage from 752 individually metered gas appliances i

354 single-family households in northern Illinois. The study included 103 ranges, drop-in surface units (cooktops), 

ovens metered in 101 households. Of these, 92 were stand-alone ranges, eight were cooktops metered separately, an

three were built-in ovens metered separately. Meter readings were collected daily over two years, from May 1990 

through April 1992, and hourly during the months of July and January in that time frame. For each household, 

demographic and appliance information was collected for over 150 variables. 

This report was prepared for the GRI Technical Advisory Group for NAECA rulemaking on ranges with the approva

Northern Illinois Gas. This report describes the data base and the statistical analysis approaches, and presents estima

of annual gas usage that should prove helpful in the NAECA rulemaking process for gas ranges. 

The range annual gas usage represents only 2 to 4 percent of the total gas usage (approximately 1400 therms) for a 

typical household in the Northern Illinois Gas service area. The estimated average annual usage per range in the stu

was 22.4 therms for ranges with electric ignition and 56.1 therms for ranges with standing pilot. 

17. Document Analysis a. Descriptors 

Ranges, ignition, cooking 

b. Identiflers/Open-Ended Terms 

Residential gas usage, household gas usage, load studies, cooktops, ovens 

c. COSATI Field/Croup 

18. Availability Statement 

Unlimited 

(See ANSI-Z39.1S1 

19. Security Class (This 

Reportl 

Unclassified 

21. No. of pages 

29 

20. Security Class (This Pagel 22. Price 

Unclassified 

See Instructions on Reverse OPTIONAl. FORM 272 (4-771 

(Formerly NTIS-3S1 

Department of Commerce 
RESEARCH SUMMARY

Metered Ranges, Cooktops and Ovens in the Northern Illinois Gas Residential

Load Study Data Base

Contractor

Contract

Number

Principal

Investigators

Report

Period

Objective

Technical

Perspective

Results

Battelle

GRI 5086-241-1219

GRI 5092-281-2494

John Menkedick, Nancy Niemuth, Pam Hartford, and D. K. Landstrom

June 1992 - March 1993

Topical Report, July 1993

To study the Northern Illinois Gas residential load study data base and to evaluate

its effectiveness in predicting annual household gas usage.

The Department of Energy has proposed a rulemaking process to set minimum

standards for the efficiency of gas ranges under the National Appliance Energy

Conservation Act (NAECA). The Gas Research Institute is compiling information

on annual household range gas usage in order to prepare a response to the

proposed rulemaking. The data base from the Northern Illinois Gas residential

load study provides recent information (data taken from 1990 through 1992) over

a comprehensive study of household types and appliance and user characteristics

(150 variables noted for each household). The Gas Research Institute expects to

use the information in the data base to prepare a response that fairly represents the

interests of gas appliance manufacturers and users in the DOE rulemaking

process.

The range annual gas usage represents only 2 to 4 percent of the total gas usage

(approximately 1400 therms) for a typical household in the Northern Illinois Gas

service area. The estimated average annual usage per range in the study was

22.4 therms for ranges with electric ignition and 56.1 therms for ranges with

standing pilot. At present, the most significant factors in predicting annual range

gas usage were found to be the type of ignition system (electronic ignition or

standing pilot) and the age of adult occupants. The report cautions that increased

gas usage shown by older adults may be due more to a difference in gas usage

between generations than to the aging of the subjects. If this is so, range usage.

may not increase as the younger adults age.

v

Title

EE-RM-94-230 COMMENT 61B

Title 

Contractor 

Contract 

Number 

Principal 

Investigators 

Report 

Period 

Objective 

Technical 

Perspective 

Results 

RESEARCH SUMMARY 

Metered Ranges, Cooktops and Ovens in the Northern Illinois Gas Residential 

Load Study Data Base 

Battelle 

GRI 5086-241-1219 

GRI 5092-281-2494 

John Menkedick, Nancy Niemuth, Pam Hartford, and D. K. Landstrom 

June 1992 - March 1993 

Topical Report, July 1993 

To study the Northern Illinois Gas residential load study data base and to evaluate 

its effectiveness in predicting annual household gas usage. 

The Department of Energy has proposed a rulemaking process to set minimum 

standards for the efficiency of gas ranges under the National Appliance Energy 

Conservation Act (NAECA). The Gas Research Institute is compiling information 

on 'annual household range gas usage in order to prepare a response to the 

proposed rulemaking. The data base from the Northern Illinois Gas residential 

load study provides recent information (data taken from 1990 through 1992) over 

a comprehensive study of household types and appliance and user characteristics 

(150 variables noted for each houseQold). The Gas Research Institute expects to 

use the information in the data base to prepare a response that fairly represents the 

interests of gas appliance manufacturers and users in the DOE rulemaking 

process. 

The range annual gas usage represents only 2 to 4 percent of the total gas usage 

(approximately 1400 therms) for a typical household in the Northern Illinois Gas 

service area. The estimated average annual usage per range in the study was 

22.4 therms for ranges with electric ignition and 56.1 therms for ranges with 

standing pilot. At present, the most significant factors in predicting annual range 

gas usage were found to be the type of ignition system (electronic ignition or 

standing pilot) and the age of adult occupants. The report cautions that increased 

gas usage shown by older adults may be due more to a difference in gas usage 

between generations than to the aging of the subjects. If this is so, range usage . 

. may not increase as the younger adults age. 

v 
Results

(Continued)

Based on three independent estimation methods, an estimate of the difference in

annual gas usage between an electronic ignition range and a standing pilot range is

32.7 therms when the approximately 1 therm of electric energy used by the

electronic ignition device is subtracted.

There were not enough data in the Northern Illinois Gas study to conclusively

confirm the DOE's assumption of a 50-50 split in energy use between the

separately metered cooktop and stand-alone oven.

Technical

Approach.

Northern Illinois Gas Company conducted a two-year study of usage from 752

individually metered gas appliances in 354 single-family households in northern

Illinois. The study included 103 ranges, drop-in surface units (cooktops), or

drop-in ovens metered in 101 households. Of these, 92 were stand-alone ranges,

8 were cooktops metered separately, and 3 were built-in ovens metered separately.

Meter readings were collected daily over two years, from May 1990 through

April 1992, and hourly during the months of July and January in that time frame.

Attached to the individual meter was a Metscan unit that dialed the meter readings

into a central computer.

For each household, information was collected for over 150 demographic and

appliance'variables which could not be controlled but were only recorded. The

data base was designed to include three sets of-information: actual hourly or daily

meter readings, appliance characteristics, and demographic information on the

household. The. data base was used to predict annual household gas usage, pilot

gas usage, cooktop and stand-alone oven gas usage, and range gas usage as a

percent of total household gas usage.

Potential problems addressed were: (1) unbalanced demographic data due to

unequal frequencies in the classes and subclasses of a variable and (2) the

confounding of the effect of secondary variables when assessing the effects of

primary variables on gas usage; The effects of multiple variables were considered

in a statistical model generated to estimate annual gas usage. Each variable's

correlation with gas usage was assessed through linear regression or an analysis of

variance, as well as through an overall analysis of covariance.

Project

Implications

This report was prepared for the GRI Technical Advisory Group for NAECA

rulemaking on ranges with the approval of Northern Illinois Gas. This report

describes the data base and the statistical analysis approaches, and presents

estimates of annual gas usage that should prove helpful in the NAECA rulemaking

process for gas ranges.

GRI Project Manager

Rodney Rinholm

Project Manager, Appliances

vi

EE-RM-94-230 COMMENT 61B

Results 

(Continued) 

Technical 

Approach· 

Project 

ImplicatiolllS 

Based on three independent estimation methods, an estimate of the difference in 

annual gas usage between an electronic ignition range and a standing pilot range is 

32.7 therms when the approximately 1 thenil of electric energy used by the 

electronic ignition device is subtracted. 

There were not enough data in the Northern Illinois Gas study to conclusively 

confinn the DOE's assumption of a 50-50 split in energy use between the 

separately metered cooktop and stand-alone oven. 

Northern Illinois Gas Coinpany conducted a two-year study of usage from 752 

individually metered gas appliances in 354 single-{amily households in nonhern 

Illinois. The study included 103 ranges, drop-in surface units (cooktops), or: 

drop-in ovens metered in 101 households .. Of these, 92 were stand-alone ranges, 

8 were cooktops metered separately, and 3 were built-in ovens metered separately. 

Meter readings were collected 'daily over two years, from May 1990 through 

April 1992, and hourly during the months of July aiId January in that time frame. 

Attached to the individual meter was a Metscan unit that dialed the meter readings 

into a central computer. 

For each household, information waS collected for over 150 demographic and 

appliance"variables which could not be .controlledbut were only recorded. The 

data base was designed to include three sets of ,information: actual hourly or daily 

meter readings, appliance characteristics, and demographic information on the 

household. The.data base was used to predict annual household gas usage, pilot 

gas usage, cooktop and stand-alone oven gas usage, and range gas usage as a 

percent of total household gas usage. . 

Potential problems addressed were: (1) unbalanced demographic data due to 

unequal frequencies in the classes and subcl~ses of a variable and (2) the· 

confounding of the effect of secondary variables when assessing the effects of 

primary variables on gas usage .. The effects of multiple variables were considered 

in a statistica1model generated to estirilate annual gas usage. ,Each variable's 

correlation with gas usage was assessed through linear regression or an analysis of 

variance, as well as through an overall analysis of covariance. . 

This report was prepared for the GRI Technical Advisory Group for NAECA 

rulemakirig on ranges with the approval of Northern Illinois Gas. This repon 

• describes the data base and the statistical analysis approaches, and presents 

estimates of annual gas usage that should prove helpful in the NAECA rulemaking 

process for gas ranges. 

GRI Project Manager 

Rodney Rinholm 

Project Manager, Appliances 

vi 
CONTENTS

EXECUTIVE SUMMARY .. ix

SECTION 1

INTRODUCTION .. .............. ....................

SECTION 2

DATA BASE DESCRIPTION ...............................

.SECTION 3

STATISTICAL ANALYSIS ISSUES AND APPROACH ..............

I

2

4

3.1 MISSING VALUES AND THE CREATION OF SUMMARY FILES

3.2 UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS ..

3.3 VARIABLE SELECTION IN THE MODEL BUILDING PROCESS . ..

SECTION 4

RESULTS .:.........................................

4.1 MODEL TO PREDICT ANNUAL GAS USAGE ..............

4.2 ESTIMATES OF PILOT GAS USAGE .....................

4.3 COMPARISON OF NIGAS ANNUAL USAGE ESTIMATES WITH

OTHER CURRENT ESTIMATES ........................

4.4 ESTIMATES OF THE COOKTOP/OVEN PERCENTAGE

SPLIT IN GAS USAGE ...............................

4.5 RANGE GAS USAGE AS A PERCENT

OF TOTAL HOUSEHOLD GAS USAGE ...................

APPENDIX A. 1

ANNUAL GAS USAGE SUMMARY STATISTICS ...........

APPENDIX A.2

SELECTED STATISTICAL ANALYSIS OUTPUT ............

APPENDIX B

UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS

LIST OF TABLES

. . .. . . . . . 4

. . . . . . . . . . . 5

. . . . . . . . . .

7

7

11

12

13

14

. . .. .. . .... ... .. .A -1

A-2

. . . . . . .. . . . . . . . . . B -1

Table ES-I.

Table 4-1.

Table 4-2.

Table 4-3.

Average Annual Gas Usage by Type of Ignition Per Ranges and Pilot

Significance of Including a Variable in Annual Gas Usage

Model with Type of Ignition System .....................

Parameter Estimates for the Annual Gas Usage Model ..........

Comparison of Least Squares Means with Simple Averages

of Annual Gas Usage in Therms For Each Type of Ignition System

. . . . . . . . .. x

. . . . . . . . . 7

. . . . . . . . . 8

. . . . . . . . . 10

vii

EE-RM-94-230 COMMENT 61B

• 

EXECUTIVE SUMMARY 

SECTION 1 

INTRODUCTION 

SECTION 2 

DATA BASE DESCRIPTION 

.SECTION 3 

CONTENTS 

ST A TIS TICAL ANALYSIS ISS UES AND APPROACH 

IX 

2 

4 

3.1 MISSING VALUES AND THE CREATION OF SUMMARY FILES ...... ~ .... - .. 4 

3.2 UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS. . . . . . . . . . . .. 5 

3.3 VARIABLE SELECTION IN THE MODEL BUILDING PROCESS .... '.' . . . . . . .. 5

SECTION 4 

RESULTS .;..................................................... 7 

4.1 MODEL TO PREDICT ANNUAL GAS USAGE ......................... , 7 

4.2 ESTIMATES OF PILOT GAS USAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 11 

4.3 COMPARISON OF NIGAS ANNUAL USAGE ESTIMATES WITH 

OTHER CURRENT ESTIMATES ................................... 12 

4.4 ESTIMATES OF THE COOKTOP/OVEN PERCENT AGE 

SPLIT IN GAS USAGE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 13 

4.5. RANGE GAS USAGE AS A PERCENT 

OF TOTAL HOUSEHOLD GAS USAGE .............................. 14 

APPENDIX A.l 

ANNUAL GAS USAGE SUMMARY STATISTICS A-I 

APPENDIX A.2 

SELECTED STATISTICAL ANALYSIS OUTPUT ............................. A-2 

APPENDIX B 

UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS ...... ' ........... B-1 

LIST OF TABLES 

Table ES-l. Average Annual Gas Usage by Type oflgnition Per Ranges and Pilot ........ " X 

Table 4-1. Significance of Including a Variable in Annual Gas Usage 

Model with Type of Ignition System . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7 

Table 4-2. Parameter Estimates for the Annual Gas Usage Model. . . . . . . . . . . . . . . . . .. 8 

Table 4-3. Comparison of Least Squares Means with Simple Averages 

of Annual Gas Usage in Thenns For Each Type of Ignition System .......... 10 

vii 
CONTENTS

Page

Table 4-4. Estimates of Annual Pilot Gas Usage For a Single Range; and a Single Pilot ... . 12

Table 4-5. The Department of Energy assumes a 50-50 split in energy use between the

cooktop and oven. The NIGAS study offers no evidence to change that estimate . 13

Table A-1. Average Annual Gas Usage by Type of Appliance .................... A-1

Table A-2. Average Annual Gas Usage by Type of Appliance

and Type of Ignition System ............................. .... A-1

Table A-3. Analysis of Covariance to Test for Equality of Slope in Annual Usage Model. ... A-2

Table A-4. Analysis of Covariance for Annual Usage Model ..................... A-3

Table B-1. Average Annual Gas Usage for Ranges by Type of Household ........... B-1

Table B-2. Average Annual Gas Usage for Ranges by Type of Ignition System and Type of

Household B-1

Table Table B-3. Average Average Annual Ga................. Gas Usage By Type of Household .................... ............... B-3 B-3

LIST OF FIGURES

Analysis of Covariance model fit. Lines with equal slopes but

different intercepts are fitted for each ignition type ....................

The least squares mean estimate for each type of ignition is the model

prediction when Age of Adult Occupants is at its mean value, 44.24 years......

Current annual usage estimates for gas ranges. ......................

Approximate annual gas usage for various metered household appliances

in comparison with total household gas usage ........................

viii

Figure 4-1.

Figure 4-2.

Figure 4-3.

Figure 4-4.

9

10

13

14

EE-RM-94-230 COMMENT 61B

Table 4-4. 

Table 4-5. 

Table A-I. 

Table A-2. 

Table A-3. 

Table A-4. 

Tabie B-1. 

Table B-2. 

Table B-3. 

CONTENTS 

Estimates of AIUlual Pilot Gas Usage For a Single Range; and a Single Pilot . . . . 

The Depanment of Energy assumes a 50-50 split in energy use between the 

cooktop and oven. The NIGAS study offers no evidence to change that estimate . 

Average AIUlualGas Usage by Type of Appliance ................... . 

Average Annual Gas Usage,by Type of Appliance 

and Type of Ignition System . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . 

Analysis of Covariance to Test for Equality of Slope in Annual Usage Model, . . . 

Analysis of Covariance for Annual Usage Model . . . . . . . . . . . . . . . . . . . . . 

Average AIUlual Gas Usage for Ranges by Type of Household ........... . 

Average Annual Gas Usage for Ranges by Type of Ignition System and Type of 

Household . ., . . ......... ' ... .' . . . . . .. " . . . . . . . . . . . . . . . . . ... . 

Average Annual Gas Usage By Type of Household .................. . 

,LIST OF FIGURES 

Figure 4-1. Analysis of Covariance model fit. Lines with equal slopes but , 

12 

13 

A-I 

A-I 

. A-2 

A-3 

B-1 

B-1 

8-3 

different intercepts are fitted fot each ignition type. .................... 9 

Figure 4-2. The least squares mean estimate for each type of ignition is the, model 

prediction when Age of Adult Occupants is at its mean value, 44.24 years. . . . . .. 10 

Figure 4-3. Current annual usage estimates for gas ranges. ....................... 13 

Figure 4-4. ApproxiIrulte aIUlual gas usage for vadousmetered household appliances 

in comparison with total household gas usage. . .......... " . . . . . . . . . .. 14 

viii 
EXECUTIVE SUMMARY

Northern Illinois Gas Company conducted a two-year meter study of individual appliance gas

usage in 354 single-family households in northern Illinois. Gas meters were placed on 752 individual

appliances within the 354 homes. Meter readings were collected daily over two years (May, 1990

through April, 1992) and hourly during the months of July and January in that time frame. The

meter readings were collected through use of a Metscan unit attached to the individual meter which

dialed the meter readings into a central computer. For each household, demographic and appliance

information was collected for over 150 variables.

The study included 103 ranges, cooktops, or ovens metered in 101 households. Of these, 92

were stand-alone ranges, 8 were drop-in surface units (cooktops) metered separately, and 3 were

built-in ovens metered separately.

The NIGAS data base includes three primary sets of information-for the ranges. The

first set contains is the actual hourly or daily meter readings. The second set includes information on

equipment characteristics for the individual appliances metered. The third set contains demographic

information on the households in which the appliances reside. Initially, the data was checked for

incongruities, outliers and suspicious values through frequency tables, outlier tests and cross

validation. The effect of missing values on estimates was analyzed and strategies developed to

minimize bias introduced by missing data.

Information was collected in this meter study for over 150 demographic and equipment

variables which could not be controlled, but only recorded. This type of observational study

unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes and

subclasses of a variables) and to the confounding of the effect of secondary variables when attempting

to assess the effect of any given primary variable on gas usage. To address this problem, the effects

of multiple variables were considered in a statistical model generated to estimate the annual gas usage.

Variables included in the model were chosen by assessing each variable's correlation with gas usage

through linear regression or an analysis of variance, as well as through an overall analysis of

covariance. The practical reality of including any given variable was considered, as well as the effect

of missing data for each variable. Finally, an attempt was made to keep the model as simple as

possible without losing significant predictive power.

The model chosen to predict annual gas usage is an analysis of covariance model with Type of

Ignition System as a categorical variable with two levels (electric ignition and standing pilot) and Age

of Adult Occupants as the covariate (quantitative variable). Based on this model, our best estimates

of annual gas usage for ranges with each type of ignition system are given by the least squares means

for Type of Ignition System (the predicted values for annual gas usage when the value of Age of

Adult Occupants is taken as its mean value, 44.24 years). The least squares means estimate of annual

gas usage for electric ignition ranges is 22.4 therms, and for standing pilot ranges, 56.1 therms.

Note that the significance of the Age of Adult Occupants may be due to a difference in gas

usage between generations, rather than to any increase in gas usage as individuals get older. In this

case, range usage would not be expected to increase as the current younger adults age. At the present

ix

EE-RM-94-230 COMMENT 61B

EXECUTIVE SUMMARY 

Nonhem Illinois Gas Company conducted a two-year meter study of individual appliance gas 

usage in 354 single-family households in northern lllirlOis. Gas meters were placed on 752 individual 

appliances within the 354 homes. Meter readings were collected daily over two years (May, 1990 

through April, 1992) and hourly during the months of July and January in that time frame. The 

meter readings were collected through use of a Metscan unit· attached to the individual meter which 

dialed the meter readings into a central computer. For each household, demographic and appliance 

information was collected for over 150 variables. 

The study included 103 ranges, coolctops, or ovens metered in 101 households. Of these, 92 

, were stand-alone ranges, 8 were drop-in surface units (cooktops) metered separately, and 3 were 

built-in ovens metered separately. 

The NIGAS data base includes three primary sets of information.for the ranges. The 

first set contains is the actual hourly or daily meter readings. The second set includes infonnation on 

equipment characteristics for the individual appliances metered. The third set contains demographic 

information on the households in which the appliances reside. Initially, the data was checked for 

incongruities, outliers and suspicious values through frequency tables, outlier tests and cross 

validation. The effect of missing values on estimates was analyzed and strategies developed to 

minimize bias introduced by missing data. 

Information was collected in this meter study for over 150 demographiC and equipment 

variables which could not be controlled, but only recorded. This type of observational study 

unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes and 

. subclasses of a variables) and to the confounding of the effect of secondary variables when attempting 

to assess the effect of any given primary variable on gas usage. To address this problem, the effects 

of multiple variables were considered in a statistical model generated to estimate the annual gas usage. 

Variables included in the model were chosen by assessing e.ach variable'S correlation with gas usage 

through linear regression or an analysis of variance, as well as through an overall analysis of 

covariance. The practical reality of including any given variable was considered, as well as the effect 

of missing data for each variable. Finally, an attempt was made to keep the model as simple as 

possible without losing significant predictive power. 

The model chosen to predict annual gas usage is an analysis of covariance model with Type of 

Ignition System as a categorical variable with two levels (electric ignition and standing pilot) and Age 

of Adult Occupants as the covariate (quantitative variable). Based on this model, our best estimates 

of annual gas usage for ranges with each type of ignition system are given by the least squares means 

for Type of Ignition System (the predicted values for annual gas usage when the value of Age of 

Adult Occupants is taken as its mean value, 44.24 years). The least squares means estimate of annual 

gas usage for electric ignition ranges is 22.4 thenns, and for standing pilot ranges, 56.1 thenns. 

Note that the significance of the Age of Adult Occupants may be due to a difference in gas 

usage between generations, rather than to any increase in gas usage as individuals get older. In this 

case, range usage would not be expected to increase as the current younger adults age. At the present 

IX 
time, the model is a good predictor of annual gas usage for a range, but there is no guarantee it will

remain so in the future.

Several methods were used to estimate the average annual amount of gas used by a standing

pilot in a standing pilot range. One method calculated the difference between our best estimates of

the estimated annual gas usage by a standing pilot range and an electric ignition range. Two other

methods took different approaches to estimating the amount of gas usage by a standing pilot range

during the hours of midnight through 5:00 a.m. From the three estimates of 33.7, 33.5, and 34.9

therms, we have chosen 33.7 therms as a reasonable single estimate for the average annual gas usage

by a standing pilot in a standing pilot range in the NIGAS study.

There was no data in the NIGAS study to refute an assumption of a 50-50 split in energy use

between the cooktop and the oven.

A typical total annual gas usage for households with space heating in the NIGAS service area

is approximately 1400 therms. Moreover, the variance between annual usage of households in the

Residential Load Study and annual usage of all households in the NIGAS service area was very small.

The estimates of range annual gas usage as presented in Table ES-i below represent approximately 2-

4 percent of the total typical household gas usage in the NIGAS service area.

Table ES-1. Average Annual Gas Usage by Type of Ignition Per Ranges and Pilot

1 Therm = 100,000 BTU = 100 cubic feet (ccf)

x

Average-Annual. - Average Annual

Type of Usage Per .: Pilot Usage

Ignition Range (Therms) (Therms)

Electric 22.4 0

Standing Pilot 56.1 33.7

EE-RM-94-230 COMMENT 61B

time, the model is a good predictor of annual gas usage for a range, but there" is no guarantee it will 

remain so in the future. 

Se:veral methods were used to estimate the average annual amount of'gas used by a standing 

pilot in a standing pilot range. One method calculated the difference between our best estimates of 

the estimated annual gas usage by a standing pilot range and an electric ignition range. Two other 

methods took different approaches to estimating the amount of gas usage by a standing pilot range " 

during the hours of midnight through 5:00 a.m. From the three estimates of 33.7,33.5, and 34.9 

thenns, we have chosen 33.7 thennsas a reasonable single estimate for the average annual gas usage 

by a standing pilot in a standing pilot range in the NIGAS study. 

There was no data in the NIGAS study to refute an assumption of a 50':'50 split in energy use 

between the cooktop and the oven. 

A typical total annual gas usage for households with space heating in the NIGAS service area 

is approximately 1400 thenns. Moreover, the variance between annual usage of households in the " 

Residential Load Study and annual usage of all households in the" NIGAS service area was very small. 

The estimates of range "annual gas usage as presented in Table ES-l below represent approximately 2-

4 percent of the total typical household gas usage in the NIGAS service area. " 

Table ES-1. Average Annual Gas Usage by Type of Ignition Per Ranges and Pilot 

Average Annual":; "" Average Annual 

Type of Usage:Per" Pilot" Usage 

Ignition Range (Therms)"" • (Therms) 

Electric ·22.4 0 

Standing Pilot , 56.1 33.7 

1 Therm = 100,000 BTU = 100 cubic feet (ccf) 

x 
SECTION 1

INTRODUCTION

Northern Illinois Gas Company conducted a two-year meter study of individual appliance gas

usage in 354 single-family households in northern Illinois. Gas meters were placed on 752 individual

appliances within the 354 homes. Meter readings were collected daily over two years (May, 1990

through April, 1992) and hourly during the months of July and January in that time frame. The

meter readings were collected through use of a Metscan unit attached to the individual meter which

dialed the meter readings into a central computer. For each household, demographic and appliance

information was collected for over 150 variables.

The study included 103 ranges, cooktops, or ovens metered in 101 households. Of these, 92

were stand-alone ranges, 8 were drop-in surface units (cooktops) metered separately, and 3 were

drop-in ovens metered separately.

The Gas Research Institute (GRI) and Northern Illinois Gas Company (NIGAS) both

recognized the value of the data base in preparing a response to proposed rulemaking by the

Department of Energy on minimum efficiency standards for gas ranges under the National Appliance

Energy Conservation Act (NAECA).

This report was prepared for the GRI Technical Advisory Group for NAECA rulemaking on

ranges. with the approval of Northern Illinois Gas. This report describes the data base, and the

statistical analysis approaches, and presents estimates of annual gas usage that should prove helpful in

the NAECA rulemaking process for gas ranges.

1

EE-RM-94-230 COMMENT 61B

SECTION 1 

INTRODUCTION 

Northern Illinois Gas Company conducted a two-year meter study of individual appliance gas 

usage in 354 single-family households in northern Illinois. Gas meters were placed on 752 individual 

appliances within the 354 homes. Meter readings were collected daily over two years (May. 1990 

through April. 1992) and hourly during the months of July and January in that time frame. The 

meter readings were collected through use of a Metscan unit attached to the individual meter which 

dialed the meter readings into a central computer. For each household. demographic and appliance 

information was collected for over 150 variables. 

The study included 103 ranges, cooktops, or ovens metered in 101 households. Of these. 92 

were stand-alone ranges, 8 were drop-in surface units (cooktops) metered separately, and 3 were 

drop-in ovens metered separately. 

The Gas Research Institute (GRI) and Northern Illinois Gas Company (NIGAS) both 

recognized the value of the data base in preparing a response to proposed rulemaking by the 

Department of Energy on minimum efficiency standards for gas ranges under the National Appliance 

Energy Conservation Act (NAECA). 

This report was prepared for the GRI Technical Advisory Group for NAECA rulemaking on 

ranges. with the approval of Northern Illinois Gas. This report describes the data base, and the 

statistical analysis approaches, and presents estimates of annual gas usage that should prove helpful in 

the NAECA rulemaking process for gas ranges. 
SECTION 2

DATA BASE DESCRIPTION

There are three primary sets of information included in the NIGAS data base related to

ranges. The first is the actual hourly or daily meter readings. Second is information on equipment

characteristics for the individual appliances metered. And third is demographic information on the

households in which the appliances reside.

Meter Readings

Meter readings were originally collected as cubic feet. These were converted to therms using

a conversion factor furnished by Northern Illinois Gas. Each meter reading in therms has several

variables associated with it. These include:

METER ID

DATE

SEASON

WEEKDAY

APPL

This is a unique number assigned to a particular household. In

combination with the APPL field, it uniquely determines an individual

meter.

The date the meter reading was collected.

A coded value designating the season of the year.

A coded value designating whether the day was a weekday or a

weekend.

A coded value designating whether the meter was a stand-alone

complete range, a drop-in surface unit only, or a drop-in oven only.

Demographic and Equipment Information

Information from the demographic and equipment variables listed below was used for the

analyses in this report. (Variable names listed below are descriptive. The actual name of the variables

in the data base are given in the data base documentation, as are complete details of the variable

coding schemes.)

Demographic Variable Name

TYPE OF HOUSEHOLD

LOCATION

HOMES SIZE

MEALS PER WEEK

Description

EXISTING CONSTRUCTION - Households which had received

NIGAS services for more than 1 year

NEW CONSTRUCTION - Households connected to NIGAS

services less than 1 year before the start of the study

URBAN, SUBURBAN, RURAL

HOME SIZE IN SQUARE FEET

ESTIMATE OF MEALS COOKED ON RANGE PER WEEK

2

EE-RM-94-230 COMMENT 61B

SECTION 2 

DATA BASE DESCRIPfION 

. Thc:re are three primary s~ts of infonnation included in the NIGAS data base related to 

ranges. The first is the actual hourly or daily meter readings. Second is information on equipment 

. characteristics for the individual appliances metered. And third is demographic infonnation on the 

households in which the appliances reside. 

Meter Readings 

Me:ter readings were originally collected as cubic feet. These were convened to thenns using 

a conversion factor furnished by Nonhem Illinois Gas. Each meter reading in thenns has several 

variables associated with it. These include: 

METER ID 

/ 

DATE 

SEASON 

WEEKDAY 

APPL 

This is a unique number assigned to a particular household. In 

combination with the APPL field, it uniquely determines an individual 

meter. 

The date the meter reading was collected. 

A coded value designating the seaSon of the year. 

A coded value designating whether the day was a weekday or a 

weekend. 

A coded value designating whether the meter was a stand-alone 

complete range, a drop-in surface unit only, or a drop-in oven only. 

Demographic and Equipment Information 

Infonnation from the demographic and equipment variables listed below was used for the 

analyses in this repon. (Variable names listed below are descriptive. The actual name of the variables 

in the data base are given in the data base documentation, as are complete details of the variable 

coding schemes.) 

Demographic Variable Name 

TYPE OF HOUSEHOLD 

LOCATION 

HOMES SIZE 

MEALS PER WEEK 

Description 

EXISTING CONSTRUCTION - Households which·had received 

NIGAS services for more than 1 year 

NEW CONSTRUCTION - Households connected to NIGAS 

services less than 1 year before the stan of the study 

URBAN,SUBURBAN,RURAL 

HOME SIZE IN SQUARE FEET 

ESTIMATE OF MEALS COOKED ON RANGE PERWEEK 

2 
START OF GRILL USE

END OF GRILL USE

FREQUENCY OF GRILL

TYPE OF GRILL FUEL

NUMBER OF ADULTS

NUMBER OF CHILDREN

NUMBER OF RESIDENTS

DO BOTH ADULTS WORK

HOURS AT HOME

AGE OF MALE

AGE OF FEMALE

INCOME

Equipment Variable Name

RANGE AGE

TYPE OF IGNITION

TYPE OF CLEANING

NUMBER OF PILOTS

MONTH WHEN GRILL USE BEGINS

MONTH WHEN GRILL USE STOPS

ESTIMATED GRILL USE PER MONTH

GAS, CHARCOAL, LIQUID PROPANE, ELECTRIC

NUMBER OF ADULTS IN HOUSEHOLD

NUMBER OF CHILDREN IN HOUSEHOLD

NUMBER OF ADULTS + CHILDREN IN HOUSEHOLD

"NO" IMPLIES AT LEAST ONE ADULT STAYS HOME

ESTIMATED ADULT HOURS AT HOME EACH DAY

AGE OF MALE ADULT

AGE OF FEMALE ADULT

INCOME OF FAMILY

Description

AGE OF RANGE

ELECTRIC, STANDING PILOT

SELF CLEANING, CONTINUOUS, NONE

NUMBER OF PILOTS FOR STANDING PILOT RANGES

3

EE-RM-94-230 COMMENT 61B

G. 

START OF GRILL USE 

END OF GRILL USE 

FREQUENCY OF GRILL 

TYPE OF GRILL FUEL 

NUMBER OF ADULTS 

NUMBER OF CHILDREN 

NUMBER OF RESIDENTS 

DO BOTH AD UL TS WORK 

HOURS AT HOME 

AGE OF MALE 

AGE OF FEMALE 

INCOME 

Equipment Variable Name 

RANGE AGE 

TYPE OF IGNITION 

TYPE OF CLEANING 

NUMBER OF PILOTS 

MONTH WHEN GRILL USE BEGINS 

MONTH WHEN GRILL USE STOPS 

ESTIMATED GRILL USE PER MONTH 

GAS, CHARCOAL, LIQUID PROPANE, ELECTRIC 

NUMBER OF ADULTS IN HOUSEHOLD 

NUMBER OF CHILDREN IN HOUSEHOLD 

NUMBER OF ADULTS + CHILDREN IN HOUSEHOLD 

"NO" IMPLIES AT LEAST ONE ADULT STAYS HOME 

ESTIMATED ADULT HOURS AT HOME EACH DAY 

AGE OF MALE ADULT 

AGE OF FEMALE ADULT 

INCOME OF FAMILY 

Description 

AGE OF RANGE 

ELECTRIC, STANDING PILOT 

SELF CLEANING, CONTINUOUS, NONE 

NUMBER OF PILOTS FOR STANDING PILOT RANGES 

3 
SECTION 3

STATISTICAL ANALYSIS ISSUES AND APPROACH

This section discusses our approach to the statistical analysis including strategies for handling

missing values and outliers, adjustments for unbalanced data, and steps for selecting the variables to

be'included in the different statistical models.

3.1 MISSING VALUES AND THE CREATION OF SUMMARY FILES

For all ranges initially metered in the study, less than 4% of meter readings were missing

over the two-year study period. This was true of both the daily and the hourly meter readings. If

two meters that stopped producing readings early in the study are not included in the calculations,

then missing values represent less than 2.5% of the meter readings.

In general, our approach to missing values was to use all the data whenever possible, while

being careful to insure that missing values did not lead to inaccurate summary information, or bias

estimates such as yearly averages by weighting one season or type of day more heavily than another.

In particular, for the daily meter readings we adopted the following strategy:

* For those months where at least seven days.have a missing value we did not calculate

a monthly total. Subsequently, in summary files we created for the daily data, an

annual average for a given meter was not calculated for any year with a missing

month to alleviate seasonal bias, i.e., when calculating the average annual gas usage

statistic all the data in any year in which at least one month has missing data was

deleted. For those months where no more than 6 days have missing values, we

replaced the missing values with a daily average. The average was calculated as:

Total Monthly Therms / # Days not missing.

For the hourly meter readings we adopted the following strategy:

* Those meters having over one week (168 readings) of hourly readings missing in a

given month were removed from that month's hourly meter reading file and moved to

an outlier file. For those meters with less than one week (168 readings) of hourly

readings missing within a given month, an average meter reading computed from the

remaining data in the month for each hour and type of day (weekday/weekend)

replaced the missing values in the hourly data file. The average was calculated as:

(Total therms for a given hour and type of day in the specified month)/ (# of nonmissing meter readings in that hour and type of day for the specified month).

Two meters (on stand-alone ranges) that were lost early in the study and had over 75 %

missing values were removed from both the daily and the hourly meter reading files and not used in

any analysis. Therefore, all analyses in this report involve a maximum of 90 metered stand-alone

ranges, 8 metered cooktops, and 3 metered ovens. In any particular summary or analysis only a

subset of these meters may have been included due to missing values in the demographic or

4

EE-RM-94-230 COMMENT 61B

SECTION 3 

STATISTICAL ANALYSIS ISSUES AND APPROACH 

This section discusses our approach to the statistical ailalysis including strategies for handling 

missing values and outliers, adjustments for unbalanced data, and steps for selecting the variables to 

be· included in the different statistical models. 

·3.1 MISSING VALUES AND THE CREATION OF SUMMARY FILES 

For all ranges initially metered in the study, less than 4 % of meter readings were missing 

over the two-year study period. This was true of both the daily and the hourly meter readings .. If 

two meters that stopped producing readings·early in the study are not included in the calculations, 

then missing .values represent less than 2.5% of the meter readings. 

In general, our approach to missing values was to use all the data whenever possible, while 

being careful to insure that missing values did not lead to inaccurate summary infonnation, or bias 

estimates. such as yearly averages by weighting one season or type of day more heavily than another .. 

In particular, for the daily meter readings we adopted the following strategy: 

• For those months where at least seven days.have a missing value we did not calculate 

a monthly total. Subsequently, in sununary files we created for the daily data, an 

annual average for a given meter was not calculated for any year with a missing 

month to alleviate seasonal bias, i.e., when calculating the average annual gas usage 

statistic all the data. in anY year in which at least one month has missing data. was 

deleted. For those months where no more than 6 days have missing values, we 

replaced the missing values with a daily average. The average was calculated as: 

Total Monthly Thenns / # Days not missing. . 

For the hourly meter readings we adopted the following strategy: 

• .Those meters having over one week (168 readings) of hourly readings missing in a 

given month were removed from that month's hourly meter reading file and moved to 

an outlier file. For those meters with less than one week (168 readings) of hourly 

readings missing within a given month, an average meter reading computed from the 

remaining data in the month for each hour and type of day (weekday/weekend) 

replaced the missing values in the hourly data file. The average was calculated as: 

(Total thenns for a given hour and type of day in the specified month)/ (# of nonmissing meter readings in that hour and type of day for the specified month). 

Two meters (on stand-alone ranges) that were lost early in the study and had over 75 % 

miSSing values were removed from both the daily and the hourly meter reading files and not used in 

any analysis. Therefore, all analyses in this repon involve a maximum of 90 metered stand-alone 

ranges, 8 metered cooktops, arid 3 metered ovens. In any particular summary or analysis only a 

subset of. these meters may have been included due to missing values in the demographic or 

4 
equipment variables. Further details concerning the handling of missing values are given in the data

base documentation.

3.2 UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS

Information was collected in this meter study for over 150 demographic and equipment

variables which could not be controlled, but only recorded. This type of observational study

unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes and

subclasses of a variables) and to the confounding of the effect of secondary variables when attempting

to assess the effect of any given primary variable on gas usage. Further details on the problem of

unbalanced data and the confounding of effects is given in Appendix B.

The following steps were taken to address the problem of the confounding of effects:

1. Different statistical models were fit to the data by least squares linear regression,

analysis of variance, and analysis of covariance. An assessment of the fit of these

models provided an indication of which demographic and equipment variables were

most highly correlated with gas usage and how the estimate of gas usage was

improved as different variables were added or subtracted from the model.

2. Estimates of annual, monthly or daily usage were generated from statistical models

that included the effects of multiple variables.

3. "Least Squares Means" were given as estimates of gas usage for the levels of key

variables such as Type of Ignition System. The least squares means (or population

marginal means) adjust the estimate for the effect of other confounded variables in the

model.

3.3 VARIABLE SELECTION IN THE MODEL BUILDING PROCESS

The decision of which variables to include in the models to predict annual, monthly and daily

gas usage for ranges included the following steps:

1. A subset of the demographic and equipment variables was selected as the set of

possible variables to be used in the model. This subset included:

TYPE OF HOUSEHOLD

LOCATION

HOMES SIZE IN SQUARE FEET

MEALS PER WEEK COOKED ON RANGE

FREQUENCY OF GRILL USE (GRILL USE PER MONTH X

MONTHS USED)

NUMBER OF OCCUPANTS (ADULTS + CHILDREN)

DO BOTH ADULTS WORK

HOURS AT HOME

5

EE-RM-94-230 COMMENT 61B

equipment variables. Further details concerning the handling of missing values are given in the data 

base documentation. 

3.2 UNBALANCED DATA AND THKCONFOUNDING OF EFFECTS 

Information was collected in this meter study for over 150 demographic and equipment 

variables which could not be controlled, but only recorded. This type of observational study 

unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes aQd 

subclasses of a variables) and to the confounding of the effect of secondary variables when attempting 

to assess the effect of any given primary variable on gas usage. Further details on the problem of 

unbalanced data and the confounding of effects is given in Appendix B. 

The following steps were taken to address the problem of the confounding of effects: 

1. Different statistical models were fit to the data by least squares linear regression, 

analysis of variance, and analysis of covariance. An assessment of the fit of these 

models provided an indication of which demographic and equipment variables were 

most highly correlated with gas usage and how the estimate of gas usage was 

improved as different variables were added or subtracted from the model. 

2. Estimates of annual, monthly or daily usage were generated from statistical models 

that included the effects of multiple variables. 

3. "Least Squares Means" were given as estimates of gas usage for the levels of key 

variables such as Type of Ignition System. The least squares means (or population 

marginal means) adjust the estimate for the effect of other confounded variables in the 

model. 

3.3 VARIABLE SELECTION IN THE MODEL BUILDING PROCESS 

The decision of which variables to include in the models to predict annual, monthly and daily 

gas usage for ranges included the following steps: 

1. A subset of the demographic and equipment variables was selected as the set of 

possible variables to be used in the model. This subset included: 

TYPE OF HOUSEHOLD 

LOCATION 

HOMES SIZE IN SQUARE FEET 

MEALS PER WEEK COOKED ON RANGE 

FREQUENCY OF GRILL USE (GRILL USE PER MONTH X 

MONTHS USED) 

NUMBER OF OCCUPANTS (ADULTS + CHILDREN) 

DO BOTH ADULTS WORK 

HOURS AT HOME 

5 
AGE OF ADULT RESIDENTS (MALE AGE + FEMALE AGE)/2

INCOME

AGE OF RANGE

TYPE OF IGNITION

TYPE OF CLEANING

2. An assessment of each variable's correlation with gas usage was made through linear

regression or an analysis of variance, and through an overall analysis of covariance

using all variables in the model.

3. The practical reality of including any given variable was considered. For instance,

the number of meals cooked per week was not considered a reasonable variable to

include in a model to be used for predicting future gas usage, as this variable

presupposes a survey for collecting information not normally available.

4. The effect of missing data for each variable was taken into consideration.

5. An attempt was made to keep the model as simple as possible without losing

significant predictive power.

Based on the results of the above steps, a model was chosen to predict annual range gas

usage for each type of ignition. Other models are also reasonable and a final decision of which model

to use depends on the requirements of the user.

It must also be noted that the demographic characteristics of households in the study,

including the fact that all households were single family households in the Illinois area, must be taken

into account before using the statistical models or estimates from this data to draw inferences about

other populations.

6

EE-RM-94-230 COMMENT 61B

AGE OF ADULT RESIDENTS (MALE AGE + FEMALE AGE)/2 

INCOME 

AGE OF RANGE 

TYPE OF IGNITION 

TYPE OF CLEANING 

2. An assessment of each variable's correlation with gas usage was made through linear 

regression or an analysis of variance, and through an overall 'analysis of covariance 

. using all variables in the model. 

3. The practical reality of including any given variable was considered. For instance. 

the number of meals cooked per week was not considered a reasonable variable to 

include in a model to be used for predicting future gas usage, as this variable . 

presupposes 'a survey for collecting infonnation not nonnally available. 

4. The effect of missing data f~r. each variable was taken into consideration. 

5. An attempt was made to keep the Diodel as simple as possible without losing 

significant predictive power. 

Bas(:d on the results of the above steps, a model was chosen to predict annual range gas 

usage for each type of ignition. Other models are also reasonable and a final decision of which model 

to use depends on the requirements of the user. , 

It must also be noted that the demographic characteristics of households, in the study. 

including the fact that all households were single family households in the Illinois area, must be taken 

, into account before using the statistical models or estimates from this data to draw inferences about 

other populations. . 

6 
SECTION 4

RESULTS

4.1 MODEL TO PREDICT ANNUAL GAS USAGE

The steps outlined in Section 3.3 above were followed in assessing a model for predicting

annual gas usage. The inclusion of Type of Ignition System alone in the model resulted in an R2

value of .63. The R2 value can be described approximately as the percentage of the variance in gas

usage that can be attributed to the effect of the predictor variable(s). Given that Type of Ignition

System explained a large percentage of the variance in gas usage, all models considered included this

variable. Table 4-1 contains the results of the analysis when a single additional variable was added to

the Annual Gas Usage model that included Type of Ignition System. The p-value is an indicator of

how significant the variable is to the model. P-values less than 0.05 are generally considered

statistically significant.

Table 4-1. Significance of Including a Variable in Annual Gas

Usage Model with Type of Ignition System.

Only Age of Adult Occupants, Income, Meals Cooked Per Week, Do Both Adults Work,

Hours at Home, and Type of Ignition System were found to have any statistical significance when

included in a model. (Contrary to expectations, Type of Household-new construction versus existing

7

Variable Description P-Value

Do Both Adults Work 0.0081

Age of Adult residents: 0.0115

(Male Age + Female Age)/2

Hours at Home 0.0249

Meals Cooked per Week 0.0305

Income 0.0402

Age of Range 0.1121

Frequency of Grill Use 0.1289

Number of Occupants 0.1545

Home Size in Square Feet 0.2489

Location 0.2764

Type of Cleaning 0.2949

Type of Household 0.9282

EE-RM-94-230 COMMENT 61B

SECTION 4 

RESULTS 

4.1 MODEL TO PREDICT ANNUAL GAS USAGE 

The steps outlined in Section 3.3 above were followed in assessing a model for predicting 

annual gas usage. The inclusion of Type of Ignition System alone in the model resulted in an R2 

value of .63. The R2 value can be described approximately as the percentage of the variance in gas 

usage that can be attributed to the effect of the predictor variable(s). Given that Type of Ignition 

System explained a large percentage of the variance in gas usage, all models considered included this 

variable. Table 4-1 contains the results of the analysis when a single additional variable was added to 

the Annual Gas Usage model that included Type of Ignition System. The p-value is an indicator of 

how significant the variable is to the model. P-values less than 0.05 are generally considered 

statistically significant. . 

Table 4-1. Significance of Including a Variable in Annual Gas 

Usage Model with Type of Ignition System. 

Variable Description P-Value 

Do Both Adults Work 0.0081 

Age of Adult residents: 0.0115 

(Male Age + Female Age )/2 

Hours at Home 0.0249 

Meals Cooked per Week 0.0305 

Income 0.0402 

Age of Range 0.1121 

Frequency of Grill Use 0.1289 

Number of Occupants 0.1545 

Home Size in Square Feet 0.2489 

Location 0.2764 

Type of Cleaning 0.2949 

Type of Household 0.9282 

Only Age of Adult Occupants, Income, Meals Cooked Per Week, Do Both Adults Work, 

Hours at Home, and Type of Ignition System were found to have any statistical significance when 

included in a model. (Contrary to expectations, Type of Household-new construction versus existing 

7 
household--had no statistically significant effect on gas usage. Appendix B includes a detailed

discussion of the effect of this variable. Age of the range was also insignificant.)

Although statistically significant, Meals Cooked Per Week, Hours at Home, and Do Both

Adults Work were considered unlikely candidates for a model since they presupposed a survey to

collect information not normally or readily available. In addition, discrepancies were found in these

variables. For instance several households reported both adults working, yet estimated the average

hours at home for an adult to be 24. Therefore, these variables were not considered for inclusion in

the model.

The inclusion of either Age of Adult Occupants or Income raised the R2 value to .66.

However, income information was not available (refused) for 23 of the 90 households with standalone ranges. Therefore, inclusion of income in the model would reduce the number of metered

ranges upon which the model was based from 90 to 67. Therefore, the model for predicting annual

usage was based on Type of Ignition System and Age of Adult Occupants.

The model chosen is an analysis of covariance model with Type of Ignition System as a

categorical variable with two levels (electric ignition and standing pilot) and Age of Adult Occupants

as the covariate (quantitative variable). The form of the model is:

Annual Usage (Therms) = Bo + ai + B * (Age of Adult Occupants)

were

B0 = an overall intercept effect

a, = the effect of the type of ignition

B = the regression coefficient for the effect of Age of Adult Occupants

The parameter estimates are listed in Table 4-2.

Table 4-2. Parameter Estimates for the Annual Gas Usage Model

Although we would not expect the relationship of Age of Adult Occupants to gas usage to be

affected by the type of ignition on a range, a statistical test of the equality of slopes for Age of Adult

Occupants within each category of Type of Ignition System was performed. This revealed no

statistical evidence for different slopes within each category, as indicated by the PILOT*AGE

8

' Parameter -. Estimate

Bgo 42.12

Electric Ignition -33.61

Standing Pilot 0.000

6B 0.315

EE-RM-94-230 COMMENT 61B

household--had no statistically significant effect on gas usage. Appendix B includes a detailed 

discussion of the effect of this variable. Ag~ of the range was also insignificant.) 

: Although statistically significant, Meals Cooked Per Week, Hours at Home, and Do Both 

Adults Work were considered unlikely candidates for a model since they presupposed a survey to 

collect 'information not normally or readiJyavailable. In addition, discrepancies were found in these 

variables. For instance several households reponed both adults working, yet estimated the average 

hours at home for an adulno be 24. Therefore, these variables were not considered for inclusion in 

the model. 

The inclusion of either Age of Adult Occupants or Income raised the R2 value to .66. 

However, income information was not available (refused) for 23 of the 90 households with standalone ranges. Therefore, inclusion of income in the model would reduce the·number of metered 

ranges upon which the model was based from 90 to 67 .. Therefore, the model for predicting .annual 

usage was based on Type of ignition System and A,ge of Adult Occupants. 

The model chosen is an analysis of covariance model with Type of Ignition System as a . 

categorical variable with two levels (electric ignition and standing pilot) and Age of Adult Occupants 

as the covariate ,(quantitative variable). The form of.the model is: 

Annual Usage (Therms) = 60 + Qj + 6 * '(Age of.Adult Occupants) 

were 

60 = an overall intercept effect, 

Qj = the effect of the type of ignition 

6 = the regression coefficient for the effect of Age of Adult Occupants 

The parameter estimates are listed in Table 4-2. 

Table 4-2. Parameter Estimates for the Annual Gas Usage Model 

. Parameter EStimate 

60 42.12 

Electric Ignition -33.61 

Q. I 

Standing Pilot 0.000 

6 0.315 

Although we would not expect the relationship.of Age of Adult Occupants to gas usage to be 

affected by the type of ig~tion Oil a range, a statistical test of the equality of slopes for Age of Adult 

Occupants within each category of Type of Ignition System was performed. This revealed no . 

statistical evidence for different slopes within each category, as indicated by the PILOT * AGE 

8 
interaction term in the analysis of covariance table presented in Table A-3 in Appendix A. The

statistical analysis results from the final fitting of the model are shown in Table A-4 in Appendix A.

These result in a preliminary model for annual gas usage for each type of ignition as follows:

Electric Ignition

Annual Usage (Therms) = 8.507 + .315 * (Age of Resident)

Standing Pilot

Annual Usage (Therms) = 42.12 + .315 * (Age of Resident)

A plot of the data with the fitted regression lines is shown below:

Annual Range Gas Usage as a Function of Age of Resident

p

P

P

E E E

E

F

P

P

P

Pp

F

P ------------

-- E--

P

p E

E E

p E E

e

E

p

E

P

E

E E

25 30 35 40 45 50 55 60 65 70

Age of Resident

Type of Pilot Electric Ign. - - - Standing Pilot

E E E Elec. Ign. Data P P P St Pilot Data

Figure 4-1. Analysis of Covariance model fit. Lines with equal slopes but different

intercepts are fitted for each ignition type.

9

85

80

75

70

( 65 E

G 60'

t 55

a 50' C0

o 45'

_ 40'

C 35'

< 30'

25'

20-

15'

10'

5S

P

E

. . . . . . . ~~ . . . . . . l . . -- .

E

EE-RM-94-230 COMMENT 61B

interaction tenn in the analysis of covariance table presented in Table A-3 in Appendix A. The 

statistical analysis results from the final fitting of the model are shown in Table A-4 in Appendix A. 

These result in a preliminary model for annual gas usage for each type of ignition as follows: 

Electric Ignition 

Amiual Usage (Therms) = 8.507 + .315 * (Age of Resident) 

Standing Pilot 

Annual Usage (Therms) = 42.12 + .315 * (Age of Resident) 

A plot of the data with the fitted regression lines is shown below: 

85 

80 

75 

70 

Iii' 

E 65 

... 60 a> 

.r: 

t::. 55 

a> 50 - CI 

III 

III 45 

::> p 

iii 40 

:l 

c: 35 

c: 

ct: 30 

25 

20 

15 -t 

10 

5 

25 

Figure 4-1. 

Annual Range Gas Usage as a Function of Age of Resident 

F 

P 

P P 

P 

F' F 

P P ----- ----- p 

--------- ----- ----- 13 

P 

~ E P 

P ~ 

r E 

E P 

E E E 

e p 

~ E E E E g I E ~ p E E 

E. t - E 

30 35 40 45 50 55 80 85 

Age of Resident 

Type of Pilot 

E E E 

Electric 19n. 

Elec. Ign. Data ppp 

Standing Pilot 

St. Pilot Data 

Analysis of Covariance model fit. Unes with equal slopes but different 

intercepts are fined for each ignition type. 

9 

E 

~ 

E 

70 
Caution should be exercised utilizing this model to predict annual gas usage in the future. A

strong argument could be made that the' significance of the Age of Adult Occupants may be due to a

difference in gas usage between generations, rather than to any "aging" effect. In this case, range

usage would not be expected to increase as the current younger adults age. At the present time, the

model is a good predictor of annual gas usage for a range, but there is no guarantee it will remain so

in the future.

Based on this model, our best estimates of annual gas usage for ranges with each type of

ignition system are given by the least squares means for Type of Ignition System (the predicted values

for annual gas usage when the value of Age of Adult Occupants is taken as its mean value, 44.24

years). The least squaresmeans estimate of annual gas usage for electric ignition ranges is 22.4

therms, and for standing pilot ranges, 56.1 therms. Table 4-3 below presents the least squares means

in comparison to the simple averages of annual gas usage for each pilot type.

Table 4-3. Comparison of Least Squares- Means with Simple Averages

of Annual Gas Usage in Therms For Each Type of Ignition System

The model is illustrated in the graph below.

Annual Range Gas Usage as a Function of Age of Resident

7

12

.-

C*

cc

Cr

Cr

6-

4-

3-

2 -

r r , , , T | I ' r f 'F 

25 30 35 40

A

- - - . I i

45

kge of Resident

p r r- Tm -r-r r

so 55 60 5s

Figure 4-2. The least squares mean estimate for each type of ignition is the model prediction

when Age of Adult Occupants is at its mean value, 44.24 years.

10

Least Squares Simple Averages

Electric Pilot 22.4 21.8

Standing Pilot 56.1 1 57.1

Adjusted Mean _ Standing _- p__ 

EA eCtro' n' gn.

Adjusted Mean

1

EE-RM-94-230 COMMENT 61B

Caution should be exercised utilizing this model to predict annual gas usage in the future. A 

strong argument 'could be made that the' significance of the Age of Adult Occupants may be due to a 

difff:rence in gas usage between generations, rather than to any "aging" effect. In this case, range 

usage would not be expected to increase as the current younger adults age. At the present time, the 

model is a good predictor of annual gas usage for a range, but there is no guarantee it will remain so 

in th.e future. 

Based on this model, our best estimates of annual gas usage for ranges with each type of 

ignition system are given by the least' squares means for Type of Ignition System (the predicted values' 

for annual gas usage when the value of Age of Adult Occupants is taken as its r:nean value, 44.24 

years). The least squares-means estimate of annual gas usage for electric ignition ranges is 22.4 

therms, and for standing pilot ranges, 56.1' therms. Table 4-3 below presents the least" squares means 

in comparison to the simple averages of annual gas usage for each pilot type. 

Table 4-3. Comparison of Least Squares' Means with Simple Averages 

of Annual Gas Usage in Therms For Each Type of Ignition System 

Least Squares· Simple Averages 

Electric Pilot 22.4 21.8 

Standing Pilot 56.1 57.1 

The model is illustrated in the graph below. 

Annual'Range Gas Usage as a Function of Age of Resident 

7 ~----~----------------------------------------~--------~ 

25 30 35 45 

Age of Resident 

50 55 eo 

, 

85 

Figure 4-2. The least squares mean estimate for each type of ignition is the model prediction 

when Age of Adult Occupants is at its mean value, 44.24 years. 

10 
For many purposes, a model based only on type of ignition may be appropriate. In this

instance the model would simply consist of the least squares means presented in Table 4-3.

Note: As mentioned earlier, if the statistical significance of Age of Adult Occupants is

actually due to a difference in cooking habits between generations, range gas usage in the future ma

be expected to decrease as the number of older generation users decrease.

4.2 ESTIMATES OF PILOT GAS USAGE

We estimated the'average annual amount of gas used by standing pilots in a standing pilot'

range in several ways.

The first estimate was the difference between our best estimates of annual gas usage for a

standing pilot range and an electric ignition range. From Table 4-3 we can calculate the difference 

56.1 - 22.4 = 33.7 therms.

A second way of estimating the gas used by the pilots is to compare average hourly gas

usage from midnight until 5:00 a.m.. These averages are listed in Table A-30 in Appendix A. If th

electric ignition average hourly estimate is subtracted from the standing pilot average hourly estimat

for hours 1 through 5, the average difference is 0.00382 therms which calculates to 33.5 therms per

year, in agreement with our first estimate.

The third way of estimating the gas used by the pilots involved a closer assessment of the

usage pattern in the hourly data for hours 1 through 5, and also offered the opportunity to calculate

an estimate of annual gas usage per single pilot. There were 23 standing pilot ranges that also had

information on the number of standing pilots. Frequency counts for the hourly gas usage for each o

these ranges revealed a clear pattern of usage that can be assumed to be typical of pilot usage alone.

In very general terms, this pattern consisted of usage of 1 cubic foot of gas every 1 to 3 hours. For

the twenty-three meters, only 46 hours out of 14,260 (23 meters x 5 hours x 124 days) had gas usag

over 1 cubic foot. Thirty-four of these hourly readings over 1 cubic foot occurred for a single mete

This meter was therefore dropped from the analysis because of the strong evidence that the gas usag

was not due solely to pilot usage. The hourly readings over 1 cubic foot for the remaining 12 hours

were adjusted in two different ways: 1) by replacing the reading with 1 cubic foot usage; and 2) by

replacing the reading with 0 cubic foot usage.

After this adjustment, the average therms used in the five hour period for each range was

calculated, and from this an estimate of the average annual pilot usage. In addition, since the numb

of pilots per range was known for these ranges, an average annual use per single pilot was also

calculated. Of the 22 ranges used in this analysis, two had two pilots, 19 had three pilots, and one

had four pilots. The results of the analysis are given in the Table 4-4 below:

11

EE-RM-94-230 COMMENT 61B

For many purposes, a model based only on type of ignition may be appropriate. In this 

instance the model would simply consist of the least squares means presented in l:able 4-3. 

Note: As mentioned earlier, if the statistical significance of Age of Adult Occupants is 

actually due to a difference in cooking habits between generations, range gas usage in the futUre ma

be expected to decrease as the number of older generation users decrease. 

4.2 ESTIMATES OF PILOT GAS USAGE 

We estimated the' average annual amount of gas used by standing pilots in a standing pilot' 

range in several ways. 

The first estimate was the difference between our best estimates of annual gas usage for a 

standing pilot range and an electric ignition range. From Table 4-3 we can calculate the difference 

56.1 - 22.4 = 33.7 therms. 

A second way of estimating the gas used by the pilots is to compare average hourly gas 

usage" from midnight until 5:00 a.m.. These averages are listed in Table A-30 in Appendix A. If th

electric ignition average hourly estimate is subtracted from the standing pilot average hourly estimat

for hours 1 through 5, the average difference is 0.00382 therms which calculates to 33.5 therms per 

year, in agreement with our first estimate. 

The third way of estimating the gas used by the pilots involved a closer assessment of the 

" usage pattern in the hourly data for hours I through 5, and also offered the opportunity to calculate 

an estimate of annual gas usage per single pilot. There were 23 standing pilot ranges that also had 

infonnation on the nwnber of standing pilots. Frequency counts for the hourly gas usage for each o

these ranges revealed a clear pattern of usage that can be asswned to be typical of pilot usage alone. 

In very general terms, this pattern consisted of usage of I cubic foot of gas every I to 3 hours. For 

the twenty-three meters, only 46 hours out of 14,260 (23 meters x 5 hours x 124 days) had gas usag

over I cubic foot. Thirty-four of these hourly readings over I cubic foot occurred for a single mete

This meter was therefore dropped from the analysis because of the strong evidence that the gas usag

was not due solely to pilot usage. The hourly readings over 1 cubic foot for the remaining 12 hour

were adjusted in two different ways: 1) by replacing the reading with 1 cubic foot usage; and 2) by 

replacing the reading with 0 cubic foot usage. 

After this adjustment, the average therms used in the five hour period for each range was 

calculated, and from this an estimate of the average annual pilot usage. In addition, since the nwnb

of pilots per range was known for these ranges, an average annual use per single pilot was also 

calculated. Of the 22 ranges used in this analysis, two had two pilots, 19 had three pilots, and one 

had four pilots. The results of the analysis are given in the Table 4-4 below: 

II 
Table 4-4. Estimates of Annual Pilot Gas Usage For a Single Range; and a Single Pilot

From the three estimates of 33.7, 33.5, and 34.9 therms, we have chosen 33.7 therms as a

reasonable single estimate for the average annual gas usage by a standing pilot in a standing pilot

range in the NIGAS study.

It should be noted that while 33.7 therms may be taken as an estimate of the difference in

annual gas usage between an electric ignition range and a standing pilot range, the difference in

annual energy usage would be 33.7 therms minus the BTU equivalent of the electric energy used by

the electric ignition device. Although the electricity used by a cooktop spark ignitor may be

negligible, calculations based ol te_.0tlectric energy use of a hot surface ignitor in an oven results in

v af an estimate of approximately (the_ n of annual energy usage by a hot surface ignitor for an oven

ell< with usage at the levels seen in the NIGAS study. Therefore the difference in annual energy usage

between an electric ignition range and a standing pilot range may be estimated as 32.7 therms which

is the increase in gas usage minus the electric usage for an electric ignitor.

4.3 COMPARISON OF NIGAS ANNUAL USAGE ESTIMATES WITH

OTHER CURRENT ESTIMATES

Figure 4-3 below illustrates the relationship of the estimates of annual gas usage for each

type of range (electric ignition and standing pilot) from the NIGAS Residential'Load Study to

previous estimates from the Department of Energy, Association of Home Appliance Manufacturers,

and two EPRI studies conducted with Southern California Edison Company in Irvine and Mission

Viejo, California. The EPRI studies are documented in an August, 1991 final report to EPRI

"Residential Energy Usage Comparison: Findings", Report CU-7392. The American Home

Appliance Manufacturers estimate for electric ignition ranges was generated from data furnished by

the American Gas Association Laboratories at a Gas Research Institute Technical Advisory Committee

meeting. The AHAM estimate for a standing pilot range was calculated as the AHAM electric

ignition estimate + 33.7 therms (an estimate of annual pilot usage from the NIGAS data as discussed

above).

12

Annual Pilot Gas Usage (Therms) When Hourly Readings

Over 1 Cubic Foot Were Replaced By 0.

N MEAN |STD MIN-MAX

verage Annual Usage Per Range 22 34.8 9.2 18.3 - 54.5

verage Annual Usage Per Pilot 22 11.7 2.4 7.8 - 17.5

Annual Pilot Gas Usage (Therms) When Hourly Readings

Over 1 Cubic Foot Were Replaced By 1.

Average Annual Usage Per Range 22 34.9 9.2 18.3 - 54.5

Average Annual Usage Per Pilot 22 11.8 2.4 7.8 - 17.5

EE-RM-94-230 COMMENT 61B

Table 4-4. Estimate~ of Annual Pilot Gas Usage For a Single Range; and a Single Pilot 

Annual Pilot Gas Usage (Thenns) When Hourly Readings 

Over 1 Cubic· Foot Were Replaced By O • 

N MEAN .. STD MIN-MAX 

iA verage Annual Usage Per Raflge 22 34.8 9.2 18.3 - 54.5 

iA verage Annual Usage Per Pilot 22 11.7 2.4 7.8 - 17.5 

. Amiual Pilot Gas Usage (Therms) When Hourly Readings 

Over 1 Cubic Foot Were Replaced By 1. 

iA vetage Annual Usage Per Range 22 34.9 9.2 18.3 - 54.5 

iAverage Annual Usage Per Pilot 22 11.8 2.4 7.8 - 17.5 

From the three estimates of 33.7, 33.5, and 34.9 therms, we have chosen 33.7 thermsas a 

reasonable single estimate for the average annual gas usage by a standing pilot in a standing pilot 

range in the NIGAS study. 

Itshould be noted that while 33.7 therms may be taken as an estimate of the difference in 

annual gM usage between ail electric ignition range and a standing pilot range, the difference in 

annual energy usage would be 33.7 therms minus the BTU equivalent of the electric energy used by 

the electric ignition device. Although the electricity used by a cooktop spark ignitor may be 

negligible, calculations based~. lectric energy use of a hot surface ignitor in an oven results in 

\J.~ . an estimate of approximately 1 the of annual energy usage by a hot surface ignitor for an oven ").<\ ,.., with usage at the levels seen in the NIGAS study. Therefore· the difference in annual energy usage 

between an electric ignition range arid a standing pilot range may be estimated as 32.7 therms which' 

is the increase in gas usage minus the electric usage for an electric ignitor. 

4.3 COMPARISON OF NIGAS ANNUAL USAGE ESTIl\1ATES WITH 

OTHER CURRENT ESTIMATES 

Figure4-3 below illustrates the relationship of the estimates of annual gas usage for each 

type of range (electric ignition and standing pilot) from the NIGAS Residential'Load Study to 

previous estimates from the Depar:nnent of Energy, Association of Home Appliance ·Manufacturers, 

and two EPRI studies conducted with Southern California Edison Company in Irvine and Mission 

Vif:jo, California. The EPRI studies are documented in an August, 1991 final report to EPRI 

"Residential Energy Usage Comparison: Findings", Report CU-7392. The American Home 

Appliance Manufacturers estimate for electric ignition ranges was generated from data furnished by 

the American Gas Association Laboratories at a Gas Research Institute Technical Advisory Committee 

meeting. ·The AHAM estimate for a standing pilot range was calculated as the AHAM electric 

ignition estimate + 33.7 therms (an estimate of annual pilot usage from the NIGAS data as discussed 

above). 

12 . 
Average Annual Range Gas Usage (Therms)

90-

80so70 i r- 60

t~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ F'50

so

040 

';: 3030, c

20

10

0

DOE. (using Metered AHAM Response Annual Useful Appliance to DOE (Metered

Energy Factor) Study Appliance Pilot

Usage)

EPRI

Mission

Veto. CA

Ignition Type * Ranges with Pilot Rngeges wh Electric Ignition

EPRI

Invine. CA

* Mix

Figure 4-3. Current annual usage estimates for gas ranges.

4.4 ESTIMATES OF THE COOKTOP/OVEN PERCENTAGE

SPLIT IN GAS USAGE

There were only 8 cooktops and 3 ovens metered separately in the study. Only two

households had both the gas cooktop and oven metered in the same household. The table below

offers details on the percentage split between the cooktop and oven.

Table 4-5. The Department of Energy assumes a 50-50 split in energy use between the cooktop

and oven. The NIGAS study offers no data to change that estimate.

13

Percent of Gas Usage Between Oven and Cooktop

Oven Cooktop

N Average Usage % Usage N Average Usage % Usage

All Cooktops and Ovens 3 11.1 55% 8 8.9 45%

Household #1 1 13.2 62.5% 1 7.9 37.5%

Household #2 1 6.5 30% 1 15.4 70%

Avg. of Household #1 and #2 2 9.9 46% 2 11.7 54%

EE-RM-94-230 COMMENT 61B

'0 

E 

4D 

~ 

t:.. 

4D 

CI 

as 

... 

::;) 

." 

C!) '" 

"iii 

.~ 

<:: 

<:: 

01( 

80 

70 

60 

50 

40 

30 

20 

10 

0 

Average Annual Range Gas Usage (Therms) 

DOE. (using 

Annual Useful 

Energy Factor) 

Malered 

AppUance 

Study 

AHAM Response to DOE (Metered 

Appliance Pilot 

Usage) 

EPRI 

Mission 

VI8jo.CA 

EPRI 

Irvine. CA 

Ignition Type I. Ranges wI!tI Pilot 0 Ranges wI!tI EIecIric Ignition • Mix 

Figure 4-3. Current annual usage estimates for gas ranges. 

4.4 ESTIMATES OF THE COOKTOP/OVlEN PERCENTAGE 

SPLIT IN GAS USAGE 

There were only 8 cooktops and 3 ovens metered separately in the study. Only two 

households had both the gas cooktop and oven metered in the same household. The table below 

offers details on the percentage split between the cooktop and oven. 

Table 4-5. The Department of Energy assumes a 50-50 split in energy use between the cooktop 

and oven. The NIGAS study offers no data to change that estimate. 

Percent of Gas Usage Between Oven and Cooktop 

Oven Cooktop 

N Average Usage % Usage N Average Usage % Usage 

iAlI Cooktops and Ovens 3 11.1 55% 8 8.9 45% 

Household # 1 1 13.2 62.5% 1 7.9 37.5% 

Household #2 1 6.5 30% 1 15.4 70% 

Avg. of Household #1 and #2 2 9.9 46% 2 11.7 54% 

13 
4.5 RANGE GAS USAGE AS A PERCENT

OF TOTAL HOUSEHOLD GAS USAGE

A typical total annual gas usage for households with space heating in the NIGAS service area

is approximately 1400 therms. Moreover, the variance between annual usage of households in the

Residential Load Study and annual usage of all households in the NIGAS service area was very small.

The estimates of annual range gas usage as presented in Table 4-3 of this report represent

approximately 2-4 percent of typical household gas usage in the NIGAS service area. A comparison

of the annual gas usage by a range with the annual gas usage by other appliances in the NIGAS study

is illustrated in Figure 4-4 below.

1400

1200

a

0

0

C.S

1000

oo -

600--

400.

200

0-

-______________i__ [___________----

Range

I T i

Clomes Dyor ' WaterHeater ' Other Total Household

Usa e

Figure 4-4. Approximate annual gas usage for various metered household

appliances in comparison with total household gas usage.

14

EE-RM-94-230 COMMENT 61B

4.5 RANGE GAS USAGE AS A PERCENT 

OF TOTAL HOUSEHOLD GAS USAGE 

A tyPical total annual gas usage for households with space heating in the NIGAS service area 

is appro1timately 1400 thenns. Moreover, the variance between annual usage of households in the 

Residemial Load Study and annual usage of all households in the NIGAS service area was very small. 

The estimates of annual range gas usage as presented in Table 4-3 of this report represent 

approximately 2-4 percent of typical household gas usage in the NIGAS service area. A comparison 

of the annual gas usage by a range with the annual gas usage by other appliances in the NIGAS study 

is illustrated in Figure 4-4 below. . 

1400 

1200 

';;" 1000 

l 

II 

Q BIio :I 

:) 

::I 

" "iii 

:s 

c: 600 

.i 

II 

CI 

~ 

~ 400 

200 

0 

Figure 4-4. Approximate annual gas usage for various metered household 

appliances in comparison with total household gas usage. 

14 
A.1 ANNUAL USAGE SUMMARY STATISTICS

Note: Any reference to ranges refers to stand-alone ranges.

Table A-1. Average Annual Gas Usage by Type of Appliance

Type

Surface Unit Only -8 8.9 4.7

Oven Only 3 11.1 4.0

Range 90 34.8 21.5

Total 101 32.0 21.8

Table A-2. Average Annual Gas Usage by Type of Appliance

and Type of Ignition System

Avg. Annual Therms

N Average STD

Type Ignition Estimate of the

Annual Therm Usage

Surface Unit Electric When Adjusted for the

Only 8 8.9 4.7 Effect of Age of

Adult Occupants*

Oven Only Electric 3 11.1 4.0 (Least Squares Means)

Range. Electric 57 21.8 8.9 22.4

Standing Pilot 33 57.1 18.3 56.1

Total 101 32.0 21.8

A-1

Avg. Annual Therms

| Average STD

* Recommended Estimate for Annual Usage for Each Type of Ignition System.

----------- EE-RM-94-230 COMMENT 61B

A.1 ANNUAL USAGE SUMMARY ST ATISTles 

Note: Any reference to ranges refers to stand-alone ranges. 

Type 

Table A-1. Average Annual Gas Usage by Type of Appliance 

Avg. Annual Therms 

N Average STD 

Type 

Surface Unit Only 8 8.9 4.7 

Oven Only 3 11.1 4.0 

Range 90 34.8 21.5 

Total 101 32.0 21.8 

Table A-2. Average Annual Gas Usage by Type of Appliance 

and Type of Ignition· System 

Avg. Annual Therms 

N Average STD 

Ignition Estimate of the 

Annual Thenm Usage 

Surface Unit Electric When Adjusted for the 

Only 8 8.9 4.7 Effect of Age of 

Adult Occupants· 

OVen Only Electric 3 11.1 4.0 (Least Squares Means) 

Range, Electric 57 21.8 8.9 22.4 

Standing Pilot 33 57.1 18.3 56.1 

Total 101 32.0 21.8 

• RecOlllllended Estimate for Annual Usage for Each Type of Ignition System.' 

A-I 
A.2 SELECTED STATISTICAL ANALYSIS OUTPUT

Table A-3. Analysis of Covariance to Test for Equality of Slope in Annual Usage Model

General Linear Models Procedure

Dependent Variable: ANNTHERM

Sun of Mean

Source DF Squares Square F Value Pr > F

Model 3 27146.090 9048.697 55.17 0.0001

Error 86 14105.490 164.017

Corrected Total 89 41251.580

R-Square C.V. Root MSE ANNTHERM Mean

- 0.658062 36.83051 12.807 34.77259

Source DF Type I SS Mean Square F Value Pr > F

Pilot 1 26041.393 26041.393 158.77 0.0001

AGE 1 1081.795 1081.795 6.60 0.0119

AGE*Pilot 1 22.903 22.903 0.14 0.7096

Source DF Type 111 SS Mean Square F Value Pr > F

Pilot 1 1752.6261 1752.6261 10.69 0.0016

AGE 1 1027.9057 1027.9057 6.27 0.0142

AGE*Pilot 1 22.9028 22.9028 0.14 0.7096

A-2

EE-RM-94-230 COMMENT 61B

, 

, . , 

, 

Table A-3. 

A.2 SELECTED STATISTICAL ANALYSIS OUTPUT 

Analysis of Covariance to Test for Equality of Slope in Annual Usage Model 

General Linear Models Procedure 

Dependent Variable: ANNTHERM 

SI.n of Mean 

Source OF Squares Square F Value Pr > F 

Model 3 27146.090 9048.697 55.17 0.0001 

Error 86 14105.490 164.017 

Corrected Total 89 41251.580 

R-Square C.V. Root HSE ANNTHERH Mean 

- 0.658062 36.83051 12.807 34.77259 

Source OF Type I SS Mean Square F Value Pr > F 

Pilot 26041.393 26041.393 158.77 0.0001 

AGE 1081.795 1081.795 6.60 0.0119 

AGE*Pilot 22.903 22.903 0.14 0.7096 

Source OF Type J II SS Mean Square F Value Pr > F 

Pilot 1752.6261 1752.6261 10.69 0.0016 

AGE 1027.9057 1027.90S7 6.27 0.0142 

AGE*Pilot 22.9028 22.9028 0.14 0.7096 

A-2 
Table A-4. Analysis of Covariance for Annual Usage Model

General Linear Models Procedure

Dependent Variable: ANNTHERM

Source DF

Model 2

Error 87

Corrected Total 89

R-Square

0.657507

Source

Pilot

AGE

Source

Pilot

AGE

Parameter

INTERCEPT

Pilot

AGE

DF

1

DF

1

1

Estimate

42.11522950 E

E -33.60804912 B

P 0.00000000 E

0.31516299

Sun of

Squares

27123.188

14128.392

41251.580

C.V.

36.64795

Type I SS

26041.393

1081.795

Type III SS

22371.169

1081.795

T for HO:

Parameter:

1 6.i

1 -11.;

2.!

Mean

Square

13561.594

162.395

,Root MSE

12.743

Mean Square

26041.393

1081.795

Mean Square

22371.169

1081.795

F Value

83.51

Pr > F

0.0001

ANNTHERM Mean

34.77259

F Value

160.36

6.66

F Value

137.76

6.66

Pr > F

0.0001

0.0115

Pr > F

0.0001

0.0115

Pr > IT0 Std Error of

Estimate

0.0001 6.22549941

0.0001 2.86342435

0.0115 0.12210950

NOTE: The X'X matrix has been found to be singular and a generalized

inverse was used to solve the normal equations. Estimates

followed by the letter 'B' are biased, and are not unique

estimators of the parameters.

A-3

EE-RM-94-230 COMMENT 61B

" 

Table A-4. Analysis of Covariance for Annual Usage Model 

General Linear Models Procedure 

Dependent Variable: ANNTHERM 

SIJII of Mean 

Source DF Squares Square F Value Pr > F 

Model 2 27123.188 13561.594 83.51 0.0001 

Error 87 14128.392 162.395 

Corrected Total 89 41251.580 

R-Square C.V. ,Root MSE ANNTHERM Mean 

0.657507 36.64795 12.743 34.77259 

Source DF Type I 55 Mean Square 'F Value Pr > F 

Pilot 26041.393 26041.393 160.36 0.0001 

AGE 1081.795 1081.'795 6.66 0.0115 

Source DF Type III SS Mean Square F Value. Pr > F 

Pilot 22371.169 22371.169 137.76 0.0001 

AGE 1081.795 1081.795 6.66 0.0115 

T for HO: Pr > :T: Std Error of 

Parameter' Estimate Parameter=O Estimate 

INTERCEPT 42.11522950 B 6_76 0.0001 6.22549941 

Pi lot' E -33.60804912 B -11.74 0.0001 2.86342435 

P 0.00000000 B . . . AGE 0.31516299 2.58 0.0115 0.12210950 

NOTE: The X'X matrix has been found to be singular and a generalized 

inverse was used to solve the normal ,equations. Estimates 

followed by the letter 'B' are biased, and are not unique estimators 'of the parameters. 

A-3 
UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS

Information was collected in this meter study for over 150 demographic and equipment

variables which could not be controlled, but only recorded. This type of observational study

unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes and

subclasses of a variables) and to the confounding of the effect of secondary variables when attempting

to assess the effect of any given primary variable on gas usage.

As an example, consider the assessment of the effect of Type of Household on annual range

gas usage. Households with metered ranges were classified into two types: 1) new construction, and

2) existing households. Of the 90 stand-alone ranges, 59 were in existing households and 31 were in

new construction. As shown in Table B-l below, the average annual range gas usage in existing

households was 37.5 therms, while the average annual range gas usage in new construction was 29.6

therms.

Table B-1. Average Annual Gas Usage for Ranges by Type of Household

An initial interpretation might be that the new construction households use their range less on average

than the existing construction households. However, an examination of Table B-2 below reveals that

the apparent difference in annual gas usage is most likely a result of the confounding of the Type of

Ignition System variable with Type of Household.

Table B-2. Average Annual Gas Usage for Ranges by Type of Ignition

System and Type of Household

Avg. Annual Therms

N Average STD

Ignition Group

Electric Existing

Household 33 22.5 10.1

New Const. 24 20.9 7.0

Standing Existing

Pilot Household 26 56.5 19.6

New Const. 7 59.4 13.6

B-l

Avg. Annual Therms

N Average STD

Range Existing

Household 59 37.5 22.6

New Const. 31 29.6 18.5

EE-RM-94-230 COMMENT 61B

UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS 

Infonnation was collected in this meter srudy for over 150 demographic and equipment 

variables which could not be controlled, but only r"ecorded. This type of observational study 

unavoidably leads to unbalanced demographic data (unequal frequencies in the different Classes and 

subclasses of a variables) and to the confounding of the effect of secondary variables when attempting 

to assess the effect of any given primary variable on gas usage. 

" " 

As an example, consider the assessment of the effect of Type of Household on annual range 

gas usage. Households with metered ranges were classified into two types: 1) new construction, and 

2) existing households. Of the 90 stand-alone ranges, 59 were in existing households and 31 were in 

new construction. As shown in Table a-I below, the average annual range gas usage in existing 

households was 37.5 therms, while the average annual range gas usage in new construction was 29.6 

therms. 

Table B-1. Average Annual Gas Usage for Ranges by Type of Household 

Avg. AMual Therms 

N "Average STD 

Range Existing 

Household 59 37.5 22.6 

New Const. 31 29.6 18.5 

An initial interpretation might be that the new construction households use their range less on average 

than the existing construction households. However, an examination of Table B-2 below reveals that 

the apparent difference in annual gas usage is most likely a result of the confounding of the Type of 

" Ignition System variable with Type of Household. 

Table 8-2. Average Annual Gas Usage for Ranges by Type of Ignition 

System and Type of Household " 

Avg. Amual Therms 

N Average STD 

Ignition Group 

Electric Existing 

Household 33 22.5 10.1 

New Const. 24 20.9 7.0 

Standing Existing 

Pilot Household 26 56.5 19.6 

New Const. 7 59.4 13.6 

B-1 
When averages are calculated for each type of household for electric ranges and standing pilo

ranges separately, there appears to be no significant difference in gas usage between types of

household. The effect of Type of Ignition System has been confounded with Type of Household. I

the existing household classification, 56% of the ranges have electric ignition, whereas in the new

construction classification, 77% of the ranges have electric ignition. Of course, with so many

variables, there could be other confounding factors as well. The new construction households may,

on average, have much higher incomes, or use the grill much more frequently, or have fewer

residents. A strategy for addressing this confounding of factors is presented below. However, in a

study such as this where the frequencies in the levels of a variable can only be recorded, but not

controlled, caution should always be exercised in the interpretation of simple averages for any

variable.

ADJUSTMENTS FOR MISSING VALUES AND THE CONFOUNDING OF EFFECTS

The following steps were taken to address the problem of the confounding of variables:

1. Different statistical models were fit to the data by least squares linear regression,

analysis of variance, and analysis of covariance. An assessment of the fit of these

models provided an indication of which demographic and equipment variables were

most highly correlated with gas usage and how the estimate of gas usage was improve

as different variables were added or subtracted from the model.

2. As expected, Type of Ignition System was the variable most highly correlated with

range gas usage. Indeed, no other variable appeared nearly as significant in predicting

gas usage. Therefore, tables of average gas usage for any demographic or equipment

variable always include a presentation for each Type of Ignition System. If there

appears to be no confounding of Type of Ignition System with an individual variable,

i.e., the number of electric ignition and standing pilot ranges is approximately equal i

all categories of the variable, then combined averages across the types of pilot are als

presented.

3. Estimates of annual, monthly or daily usage were generated from a statistical model

that included multiple variables as determined by the statistical analysis described in

step 1 above.

4. "Least Squares Means" were given as estimates of gas usage for the levels of key

variables such as Type of Ignition System. The least squares means (or population

marginal means) adjust the estimate for the effect of other confounded variables in the

model. For example, Table B-3 below shows the least squares means (in the last

column) for the different levels of Type of Household, calculated from a model which

includes Type of Ignition System and the Age of Adult Occupants.

B-2

EE-RM-94-230 COMMENT 61B

When averages are calculated for each type of household for electric ranges and standing pilo

ranges separately, there appears to be no significant difference in gas usage between types of 

household. The effect of Type of Ignition System has been confounded with Type of Household. I

the existing household classification, 56% of the ranges have elecu:ic ignition, whereas in the new 

construction classification, 77% of the ranges have electric ignition. Of course, with so many 

variables, there could be other confounding factors as well. The new construction households may, 

on average, have much higher incomes, or use the grill much more frequently, or have fewer 

residents. A strategy for addressing this confounding of factors is presented below. However, in a 

study such as this where the frequencies in the levels of a variable can onJy be recorded, but not 

controlled, caution should always be exercised in the interpretation of simple averages for any 

variable. 

ADJUSTMENTS FOR MISSING V ALVES AND TIlE CONFOUNDING OF EFFECTS 

The following st.eps were taken to address the problem of the confounding of variables: 

1. Different statistical models were fit to the data by least squares linear regression, 

analysis of variance, and analysis of covariance. An assessment of the fit of these 

models provided an indication of which demographic and equipment variables were 

most highly correlated with gas usage and how the estimate of gas usage was imprOVe

as different variableS were added or subtracted from the model. 

2. As expected, Type of Ignition System was the variable most highly correlated with 

range gas usage. Indeed, no other variable appeared nearly as significant in predictin

gas usage. Therefore, tables of average gas usage for any demographic or equipment 

variable always include a presentation for each Type of Ignition System. If there 

appears to be no confounding of Type of Ignition System with an individual variable, 

i.e., the number of electric ignition and standing pilot ranges is approximately equal i

all categories of the variable, then combined averages across the types of pilot are als

presented. 

3. Estimates of annual, monthly or daily usage were generated from a statistical model 

that included multiple variables as determined by the statistical analysis described in 

step 1 above. 

4. "Least Squares Means" were given as estimates of gas usage for the levels of key 

variables such as Type of Ignition System. The least squares means (or popUlation 

marginal means) adjust the estimate for the effect of other confounded variables in the 

model. For example, Table B-3 below shows the least squares means (in the last 

column) for the different levels of Type of Household, calculated from a model which 

includes Type of Ignition System and the Age of Adult Occupants. 

B-2 
Table B-3. Average Annual Gas Usage By Type of Household

Estimates of the

Annual Therm Usage

When Adjusted for the

Avg. Annual Therms Effect of Pilot Type &

Age of Adult Occupants

N Average STD (Least Squares Means)

Range Existing

Household 59 37.5 22.6 38.60

New Const. 31 29.6 18.5 40.62

Total 90 32.0 21.8

In this instance, the least squares means or "adjusted" means are calculated from the least

squares linear model assuming equal numbers of electric ignition and standing pilot ranges and

assuming the mean value of 44.24 years for the covariate, Age of Adult Occupants. This results in

an estimate of annual gas usage of 38.6 therms in the Existing Household group and 40.62 therms in

the New Construction group. The estimates offer no evidence that the annual gas usage is affected by

type of construction. However, it is known that the new construction and older existing households

categories will not contain equal numbers of electric ignition and standing pilot ranges, i.e. that type

of pilot is expected to be confounded with type of construction. Therefore, these may not be the

appropriate estimates to use for predicting gas usage in each type of construction. The appropriate

estimate could be calculated from the model given in Section 4.1 after specifying the percentage of

electric ignitions and standing pilot ranges in each construction type category. Further illustration of

the use of least squares means is given in Section 4.1 in the discussion of the model to predict annual

usage.

B-3

EE-RM-94-230 COMMENT 61B

5 

Table B-3. Average Annual Gas Usage By Type of Household 

Estimates of the 

Annual Therm Usage 

When Adjusted for the 

Avg. Annual Thenms Effect of Pilot Type & 

Age of Adult ·Occupants 

N Average STD (Least Squares Means) : 

Range Existing , Household 59 37.5 22.6 38.60 

New Canst. 31 29.6 18.5 40.62 

Total 90 32.0 21.8 

][n this instance, the least squares means or "adjusted" means are calculated from the least 

squares linear model assuming equal numbers of electric ignition and standing pilot ranges and 

assuming the mean value of 44.24 years for the covariate, Age of Adult Occupants. This results in 

an estimate of annual gas usage of 38.6 therms in the Existing Household group and 40.62 therms in 

the New Construction group. The estimates offer no evidence that the annual gas usage is affected by 

type of construction. However, it is known that the new construction and older existing households 

categories will not contain equal numbers of electric igriition and standing pilot ranges, i.e. that type 

of pilot is expected to be confounded with type of construction. Therefore, these may not be the 

appropriate estimates to use for predicting gas usage in each type of construction. The appropriate 

estimate could be calculated from the model given in Section 4.1 after specifying the percentage of 

electric ignitions and standing pilot ranges in each construction type category. Further illustration of 

the use of least squares me~ is given in Section 4.1 in the discussion of the model to predict annual 

usage. 

B-3 
Headquarters

Gas Research Institute

8600 West Bryn Mawr Avenue

Chicago, Illinois 60631-3562

312/399-8100

Washington Operations

Gas Research Institute

1331 Pennsylvania Avenue, N. W

Suite 730 North

Washington. D.C. 20004-1703

202/662-x 8Q

EE-RM-94-230 COMMENT 61B

Headquaners 

Gas Research Institute 

8600 West Bryn Mawr Avenue 

Chicago, Illinois 60631-3562 

312/399-8/00 

Washingtoll Operatiolls 

Gas Research Institute 

/33/ Penn.n'/I'Gnia Avenue, N. W 

Suite 730 Nonh 

Washington. D.C. 20004-1703 

::n2lfifi2 -8Q8Q