GRI-93/0204



# Topical Report 

# Metered Ranges, Northern Illinois Cooktops, and Gas ResidentialOvens in the LoadStudy Data Base

Prepared by: Battelle



## Gas Research Institute ApplianceResearch Department Julv 1993 

# YOGI4 GRI-93!o204



# Topiical Report 

# Metered Ranges, Cooktops, and Ovens inthe Northern·lllinois.Gas Residential Load StudyData Base_

.Preparedby: Battelle



### Gas Research Institute ApplianceResearch Deparrinenl lLlI\' 1993

### mi in

## 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 Avenue Columbus, Ohio 43201-2693 for 

## GAS RESEARCH INSTITUTE 8600West Bryn Mawr Avenue Chicago, Illinois 60631 and 

## NAECA RANGE TECHNICAL ADVISORY GROUP
GRI Project Manager Rodney C. Rinholm 

## a

# F

GRI-93/0204

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# , i 

## GRI-93/0204

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## METERED RANGES, COOKTOPS AND OVENS INTHE NORTHERNILLINOIS GAS RESIDENTIALLOAD STUDY DATA BASE TOPICAL REPORT (July 1993) by


### JohnMenkedick, Nancy Niemuth PamHartford, and D. K. Landstrom 

## BATTELLE 505 King Avenue Columbus, Ohio 43201-2693
for 

## GAS RESEARCHINSTITUTE 8600 West Bryn MawrAvenue Chicago, Illinois 60631

-and 

## NAECA RANGETECHNICAL ADVISORY GROUP
GRI ProjectManager

Rodney C. Rinholm ¥h $ $'b§ •

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### LEGAL NOTICE This report wasprepared by Battelle as an account ofwork sponsored by the Gas Research Institute (GRI). Neither GRI, members ofGRI, Battelle, officers, trustees, or staffofBattelle, nor any person acting onbehalfofeither:
a. Makes any warranty or representation, expressed or implied, with respect tothe accuracy, completeness, orusefulness ofthe information contained in this report, orthat theuse ofany information, apparatus, software, method, or process disclosed inthis report may not infringe privately owned rights; or b. Assumes any liability with respect tothe useof, 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 notrepresent nor constitute an endorsement, recommendation, orfavoring by GRI or Battelle ofthe specific commercial product, commodity, or service.

ii LEGAL NOTICE

.This report was prepared byBattelle as an account ofwork sponsored by the Gas Research Instiblte (GRI). Neither GRI, members of**GRI~** Battelle, officers, trustees, orstaffofBattelle, nor any person acting on behalfofeither:
a. Makes any warranty·or representation, expressed or implied, with respecttothe accuracy,

.completeness, orusefulness ofthe information contained in this report, orthattheuse ofany . information, apparatus, software, method, or process.disclosed inthis report may not infringe privately owned rights; or b. Assumes any liability with respectto the use of, or for damages resulting from theuse of,.any information, apparabls, software, method, or process disclosed in this report. Reference to trade names orspecific**co~erCial** products, commodities, or services inthis report does notrepresentnor constiblte

## an endorsement, recommendation, orfavoring by GRI or Battelle ofthe specific commercial product, commodity, or service.



### ii

### PREFACE Northern Illinois GasCompany 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 theproperty ofNorthern Illinois Gas.

iii .U.l 

### PREFACE Northern IllinoisGas 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 ofNorthern Illinois Gas.

iii50272-101 REPORT DOCUMENTATION 1. REPORTNO. 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 No. N5875 Battelle -- , „. ,~~~~~~~Battelle~~ 11. Contract(C) or Grant(G) No. 505 King Avenue Columbus, OH 43201-2693 (Cl 5086-241-1219 and (c) 5092-281-2494 12. Sponsoring Organization Name andAddress 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 ofusage 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 ovens metered in 101 households. Of these, 92 were stand-alone ranges, eight were cooktops metered separately, and 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 ofJuly 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 approval ol Northern Illinois Gas. This report describes the data base and the statistical analysis approaches, and presents estimates ofannual 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 ofthe 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.
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. AvailabilityStatement 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 Instructionson Reverse OPTIONAL FORM 272 (4-77) (Formerly NTIS-35) Department of Commerce 50272·101 

### r::

R

=EP=O=R=T=-=D::O::C=-U=M::ENT=:-:A::T::I=O::N:-r I1-=-.-: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 andAddress Battelle 505 King Avenue Columbus, OH 43201-2693 12.sponsoring Organization NameandAddress 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 No. N5875 11. Contract (CI orCrant ICI No. (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·ofusage 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 ovens metered in 101 households. Ofthese, 92 were stand-alone ranges, eight were cooktops metered separately, and 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 ofJuly 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 approval oj Northern Illinois Gas. This report describes the data base and the statistical analysis approaches, and presents estimates ofannual 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 ofthe 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.
17. DocumentAnalysis 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 ofCommerce

### 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 arulemaking process to set minimum standards forthe 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 tothe proposed rulemaking. The data base fromthe Northern Illinois Gas residential load study provides recent information (data taken from 1990through 1992) over a comprehensive study ofhousehold types and appliance and user characteristics (150 variables noted for each household). The Gas Research Institute expects to usethe information in the data base toprepare a response that fairly represents the interests ofgas appliance manufacturers and users in the DOE rulemaking process. The range annual gas usage represents only 2 to4percent ofthe total gas usage (approximately 1400 therms) for atypical household in the Northern Illinois Gas service area. The estimated average annual usage per range in the study was 22.4 therms forranges with electric ignition and 56.1 therms for ranges with standingpilot. At present, the most significant factors inpredicting annual range gas usage were found to be the type ofignition system (electronic ignitionor standing pilot) and the age ofadult occupants. The report cautions that increased gas usage shown by older adults may be due more to adifference in gas usage between generations than to the aging ofthe subjects. Ifthis is so, range usage. may not increase as the younger adults age.

v 

### Title 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 DataBase 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 studythe Northern Illinois Gas residential load study data base and to evaluate its effectiveness in predicting annual household gas usage. The Department ofEnergy has proposed a rulemaking process to set minimum standards for the efficiency ofgas ranges under the National Appliance Energy ConservationAct (NAECA). The Gas Research Institute is compiling information on'annual household range gas usage in orderto prepare a response to the proposed rulemaking. The data base from the Northern Illinois Gas residential load studyprovides recent information (data taken from 1990through 1992) over a comprehensive study ofhousehold types and appliance and user characteristics (150 variables noted for each houseQold). The Gas Research Institute expects to use the information inthe data base to prepare a response that fairly represents the interests ofgas appliance manufacturers and users in the DOE rulemaking process. The range annual gas usage represents only 2 to 4 percent ofthe total gas usage (approximately 1400 therms)for a typical household in the NorthernIllinois 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 standingpilot. At present, the most significant factors in predicting annual range gas usage were found to be the type ofignition system (electronic ignition or standing pilot) and the age ofadult occupants. The report cautions that increased gas usage shownby older adults may be due more to a difference in gas usage between generations than to the aging ofthe subjects. Ifthis is so, range usage.

.may not increase as the younger adults age.

v

### Results (Continued) Based on three independent estimation methods, an estimate ofthe difference in annual gas usage between an electronic ignition range and a standing pilot range is 32.7 therms when theapproximately 1therm ofelectric energy used by the electronic ignitiondevice is subtracted. There were not enough data in the Northern Illinois Gas study to conclusively confirm the DOE's assumption ofa50-50 split in energy use between the separately metered cooktop and stand-alone oven. Technical 

### Approach. Northern Illinois Gas Company conducted a two-year study ofusage 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. Ofthese, 92 were stand-alone ranges, 8were cooktops metered separately, and 3were built-in ovens metered separately. Meter readings were collected daily over two years, from May 1990through April 1992, and hourly during the months ofJuly 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 couldnot be controlled but were only recorded. The data base wasdesigned to include three sets of-information: actual hourly ordaily meter readings, appliance characteristics, and demographic information on the household. The.data base was used topredict annual household gas usage, pilot gas usage, cooktop and stand-alone oven gas usage, and range gas usage as a percent oftotal household gasusage. Potential problems addressed were: (1)unbalanced demographic data due to unequal frequencies in the classes andsubclasses ofavariable and (2) the confounding ofthe effect ofsecondary variables when assessing the effects of primary variables on gas usage; The effects ofmultiple variables were considered ina statisticalmodel 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 ofcovariance. Project Implications 

### This report was prepared for the GRI Technical Advisory Group for NAECA rulemaking onranges withthe approval ofNorthernIllinois Gas. This report describes the data base and the statistical analysis approaches, andpresents estimates ofannual gas usage that should prove helpful in the NAECA rulemaking process forgas ranges. GRI Project Manager Rodney Rinholm Project Manager, Appliances

vi Results (Continued)

Technical Approach·

Project ImplicatiolllS Based on three independent estimation methods, an estimate ofthe difference in annual gas usage between an electronic ignitionrange and a standingpilotrange is 32.7 therms when the approximately 1thenil ofelectric energy used by the electronic ignitiondevice is subtracted. There were not enough data in the Northern Illinois Gas study to conclusively confinn the DOE's assumption ofa 50-50split in energy use between the separately metered cooktop and stand-aloneoven. Northern Illinois Gas Coinpany conducted a two-year study ofusage from 752 individuallymetered gas appliances in 354 single-{amily households in nonhern Illinois. The study included 103 ranges, drop-in surface units (cooktops), or: drop-inovens metered in 101 households
..Ofthese, 92 were stand-alone ranges, 8were cooktops metered separately, and 3 were built-inovens metered separately. Meter readings were collected 'daily overtwo years, from May 1990through April 1992, and hourly during the months ofJuly aiId January in that time frame. Attached to the individual meter was a Metscan unit that dialed the meter readings into a central computer. Foreach household, information waS collected for over 150demographic and appliance"variables which could notbe.controlledbutwere only recorded. The data base was designedto includethree sets of,information: actual hourly ordaily meter readings, appliance characteristics, and demographic information onthe household. The.data base was used to predict annual household gas usage, pilot gas usage, cooktop and stand-aloneoven gas usage, and range gas usage as a percent oftotal household gas usage. . Potential problems addressed were: (1) unbalanced demographic data due to unequal frequencies inthe classes and**subcl~ses** ofa variable and (2) the· confounding ofthe effect ofsecondary variables when assessing the effects of primaryvariables on gas usage.. Theeffects ofmultiple 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 ofcovariance.
. This report was prepared for the GRI Technical Advisory Group for NAECA rulemakirig onranges with the approval ofNorthern Illinois Gas. This repon • describes the database and the statistical analysis approaches, and presents estimates ofannual gas usage that shouldprove 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 ofIgnition Per Ranges and Pilot Significance ofIncluding a Variable in Annual Gas Usage Model with Type ofIgnition System
..................... Parameter Estimates for the Annual Gas Usage Model
.......... Comparison ofLeast Squares Means with Simple Averages ofAnnual Gas Usage in Therms For Each Type ofIgnition System . . . . . . . . .. x

. . . . . . . . . 7 

### . . . . . . . . . 8

. . . . . . . . . 10 vii 

# •

EXECUTIVE SUMMARY SECTION 1 INTRODUCTION SECTION 2 DATA BASE DESCRIPTION .SECTION 3 

### CONTENTS

STA TISTICALANALYSIS ISSUES AND APPROACH IX

2
4 3.1 MISSING VALUES AND THECREATION OF SUMMARY FILES
......**~** .... - .. 4 3.2 UNBALANCED DATA ANDTHECONFOUNDING OF EFFECTS.
. . . . . . . . . . .. 5 3.3 VARIABLE SELECTION IN THE MODEL BUILDING PROCESS
.... '.'
. . . . . . .. 5. SECTION4 RESULTS .;..................................................... 7 4.1 MODEL TO PREDICT ANNUAL GAS USAGE
......................... , 7 4.2 ESTIMATES OFPILOT GAS USAGE
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 11 4.3 COMPARISON OF NIGAS ANNUALUSAGE ESTIMATES WITH OTHER CURRENT ESTIMATES
................................... 12 4.4 ESTIMATES OFTHE COOKTOP/OVEN PERCENTAGE SPLITIN GAS USAGE.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 13 4.5. RANGE GAS USAGE AS A PERCENT OF TOTALHOUSEHOLD GAS USAGE
.............................. 14 APPENDIX A.l ANNUAL GAS USAGE SUMMARY STATISTICS A-I APPENDIX A.2 SELECTED STATISTICALANALYSIS OUTPUT
............................. A-2 APPENDIX B UNBALANCED DATA AND THECONFOUNDING OFEFFECTS
...... '
........... B-1


### LIST OF TABLES Table ES-l. Average Annual Gas Usage by Type oflgnitionPer Ranges and Pilot
........ " X Table 4-1. Significance ofIncluding a Variable in Annual Gas Usage Model with Type ofIgnition System
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7 Table 4-2. Parameter Estimates for the Annual Gas Usage Model.
. . . . . . . . . . . . . . . . .. 8 Table 4-3. Comparison ofLeast Squares Means with Simple Averages ofAnnual Gas Usage in Thenns For Each Type ofIgnition System
.......... 10 vii

## CONTENTS Page
Table 4-4. Estimates ofAnnual Pilot Gas Usage For a Single Range; and a Single Pilot
... . 12 

### Table 4-5. The Department of Energy assumes a 50-50 split inenergy usebetween the cooktop and oven. The NIGAS studyoffers no evidence to change thatestimate
. 13 Table A-1. Average Annual Gas Usage by Type ofAppliance
.................... A-1 Table A-2. Average Annual Gas Usage by Type ofAppliance and Type ofIgnition System
............................. .... A-1 Table A-3. Analysis ofCovariance to Testfor Equality ofSlope in Annual Usage Model.
... A-2 Table A-4. Analysis ofCovariance for Annual Usage Model
..................... A-3 Table B-1. Average Annual Gas Usage for Ranges by Type ofHousehold
........... B-1 Table B-2. Average Annual Gas Usage for Ranges by Type ofIgnition System and Type of Household B-1 Table B-3. Average Annual Ga.................
............... B-3 Table Average Gas Usage By Type ofHousehold
.................... B-3



### LISTOF FIGURES Analysis ofCovariance model fit. Lines with equal slopes but different intercepts are fitted for each ignition type .................... 

### The least squares mean estimate for each type ofignition is the model prediction when Age ofAdult Occupants is at its mean value, 44.24years...... Current annual usage estimates for gas ranges. ...................... Approximate annual gas usage for various metered household appliances in comparison withtotal household gas usage ........................

viii 

### Figure 4-1. Figure 4-2. Figure 4-3. Figure 4-4. 9 10 

### 13 14 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 ofAIUlual Pilot Gas Usage For a SingleRange; and a Single Pilot
. . . . The Depanment ofEnergy assumes a 50-50 split in energy use betweenthe cooktop and oven. The NIGAS study offers no evidence to change that estimate
. Average AIUlualGas Usage by Type ofAppliance
................... . Average Annual Gas Usage,by Type ofAppliance and Type ofIgnition System
. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . Analysis ofCovariance to Testfor Equality ofSlope in Annual Usage Model,
. . . Analysis ofCovariance for Annual Usage Model
. . . . . . . . . . . . . . . . . . . . . Average AIUlual Gas Usage for Ranges by Type ofHousehold
........... . Average Annual Gas Usage for Ranges by Type ofIgnition System and Type of Household
. ., . . ......... '
....'
. . . . . .. " . . . . . . . . . . . . . . . . .
... . Average Annual Gas Usage By Type ofHousehold
.................. .
,LIST OF FIGURES Figure4-1. Analysis ofCovariancemodel 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 foteach ignitiontype. .................... 9 Figure4-2. The least squares mean estimate for each type ofignitionis the,model predictionwhen Age ofAdult Occupants is at its mean value, 44.24 years. . . . . .. 10 Figure4-3. Current annual usage estimates for gas ranges. ....................... 13 Figure 4-4. ApproxiIrulte aIUlual gas usage for vadousmeteredhousehold appliances incomparison withtotal household gas usage. . .........." . . . . . . . . .
.. 14

viii

### EXECUTIVE SUMMARY
Northern Illinois Gas Company conducted atwo-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 ofJuly and January in that time frame. The meter readings were collected through use ofaMetscan 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. Ofthese, 92 were stand-alone ranges, 8were drop-in surface units (cooktops) metered separately, and 3were built-in ovens metered separately. The NIGAS data base includes three primary sets ofinformation-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 ofmissing 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 ofobservational study unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes and subclasses ofa variables) and to the confounding ofthe effect ofsecondary variables when attempting to assess the effect ofany given primary variable ongas usage. To address this problem, the effects ofmultiple 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 ofvariance, aswell as through an overall analysis of covariance. The practical reality ofincluding any given variable was considered, as well as the effect ofmissing data for each variable. Finally, an attempt was made tokeep the model as simple as possible without losing significant predictive power. The model chosen to predict annual gas usage isan analysis of covariance model with Type of Ignition System as a categorical variable with two levels (electric ignition and standingpilot) and Age ofAdult Occupants as the covariate (quantitative variable). Based on this model, our best estimates of annual gas usage for ranges with each type ofignition system are given by the least squares means for Type ofIgnition System (the predicted values for annual gas usage when the value ofAge of Adult Occupants istaken as its mean value, 44.24 years). The least squares means estimate ofannual gas usage for electric ignition ranges is 22.4 therms, and for standing pilot ranges, 56.1 therms. Note that the significance ofthe Age ofAdult Occupants may be due to a difference in gas usage between generations, rather than to any increase in gas usage as individuals get older. Inthis case, range usage would notbe expected to increase as the current younger adults age. Atthe present
ix 

### EXECUTIVE SUMMARY
Nonhem Illinois Gas Company conducted a two-year meter study ofindividual 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 ofJuly and January in that time frame. The meter readings were collected through use ofa 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. Ofthese, 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 ofinformation.for the ranges. The first set contains is the actual hourly or daily meter readings. The second set includes infonnationon 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, outliertests and cross validation. The effect ofmissing values on estimates was analyzed and strategies developed to minimize bias introduced by missing data. Information was collected in this meter study for over 150demographiC and equipment variables which could not be controlled, but only recorded. This type ofobservational study unavoidably leads to unbalanceddemographic data (unequal frequencies in the different classes and

.subclasses ofa variables) and to the confounding ofthe effect ofsecondary variables when attempting to assess the effect ofany givenprimary variable on gas usage. To address this problem, the effects ofmultiple 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 ofvariance, as well as through an overall analysis of covariance. The practical reality ofincluding any given variable was considered, as well as the effect ofmissing 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 annualgas usage is an analysis ofcovariance model with Type of Ignition System as a categorical variable with two levels (electric ignition and standingpilot) and Age ofAdult Occupants as the covariate (quantitative variable). Based on this model, our best estimates ofannual gas usage for ranges with each type ofignition system are givenby the least squares means for Type ofIgnition System (the predicted values for annual gas usage when the value ofAge of Adult Occupants is taken as its mean value, 44.24 years). The least squares means estimate ofannual gas usage for electric ignitionranges is 22.4 thenns, and for standing pilot ranges, 56.1 thenns. Note that the significance ofthe Age ofAdult 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 ofannual gas usage fora range, but there is no guarantee itwill remain so in the future. Several methods were used to estimate the average annual amount ofgasused 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 ofgas usage by a standing pilot range during the hours ofmidnight through 5:00 a.m. Fromthe three estimates of33.7, 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 anassumption of a 50-50 split in energy use between the cooktop and the oven. A typical totalannual gasusage for households with space heating in the NIGAS service area is approximately 1400 therms. Moreover, the variance between annual usage ofhouseholds in the Residential Load Study and annual usage ofallhouseholds in the NIGAS service area wasvery small. The estimates ofrange annual gas usage as presented inTable ES-i below represent approximately 2- 4 percent ofthe 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

1Therm = 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 time, the model is a good predictorofannual 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 ourbest estimates of the estimated annual gas usage by a standing pilot range and an electric ignitionrange. Two other methods took different approaches to estimating the amount ofgas usage by a standing pilot range " during the hours ofmidnightthrough 5:00 a.m. Fromthe three estimates of33.7,33.5, and 34.9 thenns, we have chosen33.7 thennsas a reasonable singleestimate 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 ofa 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 isapproximately 1400 thenns. Moreover, the variance between annual usage ofhouseholds in the " Residential Load Study and annual usage ofall households in the" NIGAS service area was very small. The estimates ofrange"annual gas usage as presented in Table ES-l below represent approximately 2- 4 percent ofthe total typical household gas usage inthe NIGAS service area. "
Table ES-1. Average Annual Gas Usage by Type of Ignition Per Ranges and Pilot AverageAnnual":; "" AverageAnnual 

### Type of Usage:Per" Pilot"Usage Ignition Range (Therms)"" • (Therms) Electric ·22.4 0 Standing Pilot , 56.1 33.7 1 Therm = 100,000BTU = 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 withinthe 354 homes. Meter readings were collected daily over two years (May, 1990 through April, 1992) and hourly during the months ofJuly and January in that time frame. The meter readings were collected through use ofa 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. Ofthese, 92 were stand-alone ranges, 8were drop-in surface units (cooktops) metered separately, and 3were drop-in ovens metered separately. The Gas Research Institute (GRI) and Northern IllinoisGas Company (NIGAS) both recognized the value ofthe data base in preparing aresponse to proposed rulemaking by the Department ofEnergy onminimum efficiency standards forgas ranges under the National Appliance Energy Conservation Act (NAECA). This report wasprepared for the GRI Technical Advisory Group for NAECA rulemaking on ranges.with the approval ofNorthern Illinois Gas. This report describes the data base, and the statistical analysis approaches, and presents estimates ofannual gas usage that should prove helpful in the NAECA rulemaking process forgas ranges.

1 

## SECTION 1 INTRODUCTION
NorthernIllinois Gas Company conducted a two-year meter study ofindividual 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 ofJuly and January in that time frame. The meter readings were collected through use ofa Metscan unit attached to the individual meter which dialed the meter readings into acentral 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. Ofthese. 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 ofthe data base in preparing a response to proposed rulemaking by the Department ofEnergy onminimum efficiency standards for gas ranges under the National Appliance Energy ConservationAct (NAECA). This report was prepared for the GRI Technical Advisory Group for NAECA rulemaking on ranges. with the approval ofNorthern Illinois Gas. This report describes the data base, and the statistical analysis approaches, and presents estimates ofannual gas usage that should prove helpful in the NAECA rulemaking process for gas ranges.

## SECTION 2 DATA BASE DESCRIPTION
There are three primary sets ofinformation 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 onthe 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 aunique 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 ofthe 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 forthe analyses in this report. (Variable names listedbelow are descriptive. The actual name ofthe variables inthe data base are given in the data base documentation, as are complete details ofthe 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 1year NEW CONSTRUCTION

- Households connected to NIGAS services less than 1 year before the start ofthe study URBAN, SUBURBAN, RURAL HOME SIZE IN SQUARE FEET ESTIMATE OF MEALS COOKED ON RANGE PER WEEK 2 

## SECTION 2 DATA BASE DESCRIPfION

. Thc:re are three primary**s~ts** ofinfonnation included in theNIGAS data base related to ranges. The first is the actual hourly or dailymeter readings. Second is information on equipment

.characteristics for the individual appliances metered. And third is demographic infonnationon 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 conversionfactor furnished by NonhemIllinois Gas. Each meter reading in thenns has several variables associated with it. Theseinclude: METER ID

/ DATE SEASON WEEKDAY
APPL This is a unique number assigned to a particularhousehold. In combinationwith the APPL field, it uniquely determines an individual meter. The date the meter reading was collected. A coded value designating the seaSon ofthe 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, ora drop-in oven only. Demographic andEquipment 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 ofthe variables inthe data base are given in the database documentation, as are complete details ofthe variable coding schemes.) Demographic VariableName TYPE OFHOUSEHOLD

LOCATION HOMES SIZE MEALS PERWEEK Description EXISTING CONSTRUCTION

- Households which·had received NIGAS services for more than 1year NEW CONSTRUCTION

- Households connected to NIGAS services less than 1year before the stanofthe study URBAN,SUBURBAN,RURAL HOME SIZEIN SQUARE FEET ESTIMATE OFMEALS COOKED ON RANGE PERWEEK 2START 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 SELFCLEANING, CONTINUOUS, NONE NUMBER OF PILOTS FOR STANDING PILOT RANGES

3 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 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 OFCHILDREN IN HOUSEHOLD NUMBEROF 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 PILOTRANGES

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% ofmeter readings were missing over the two-year studyperiod. This was true ofboth the daily and the hourly meter readings. If two meters that stopped producing readings early in the study are not included in the calculations, thenmissing values represent less than 2.5% ofthe meter readings. In general, our approach to missing values was to use all the data whenever possible, while being careful to insurethat missing valuesdid not lead to inaccurate summary information, orbias estimates such as yearly averages by weighting one season or type ofday more heavily than another. Inparticular, forthe daily meter readings we adopted the following strategy: * For those months where at least sevendays.have amissing value we did not calculate amonthly total. Subsequently, in summary files we created for the daily data, an annual average fora given meter was not calculated for any year with amissing month to alleviate seasonal bias, i.e., when calculating the average annual gas usage statistic all the data in any year in which atleast one month has missing data was deleted. Forthose months where nomore than 6 days have missing values, we replaced the missing values with adaily average. The average was calculated as: Total Monthly Therms / # Days not missing. For the hourlymeter readings we adopted thefollowing strategy: * Those meters having over one week (168 readings) ofhourly readings missing in a givenmonth were removed from thatmonth's hourly meter reading file and moved to an outlier file. Forthose meters withless than one week (168 readings) ofhourly readings missing within a given month, an average meter reading computed from the remaining data in the month for each hour and type ofday (weekday/weekend) replaced the missing values in thehourly data file. The average was calculated as: (Total therms fora givenhour and type ofday inthe specified month)/ (# ofnonmissing meter readings in that hour and type ofday 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 inthis report involve amaximum of90 metered stand-alone ranges, 8 metered cooktops, and 3metered ovens. In any particular summary or analysis only a subset ofthese meters may have been included due to missing values in the demographic or 4 

## SECTION 3 STATISTICALANALYSIS 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 THECREATION OFSUMMARY FILES For all ranges initially metered in the study, less than4 %ofmeter readings were missing over the two-year study period. This was true ofboth the daily and the hourly meter readings
..If two meters that stoppedproducing readings·early in the study are not included in the calculations, then missing.values represent less than 2.5% ofthe meter readings. In general, our approach to missing values was to use all the data whenever possible, while being careful to insurethat missing values did not lead to inaccurate summary infonnation, orbias estimates.such as yearly averages by weighting one seasonortype ofday more heavily than another
.. 

### Inparticular, for the daily meter readings we adopted the following strategy:
• Forthose 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 dailydata, an annual average for a givenmeter was not calculated for any year with a missing month to alleviate seasonal bias, i.e., whencalculatingthe average annual gas usage statistic all the data. in anY year in which at least one month has missing data.was deleted. Forthose months where no more than 6 days have missing values, we replaced the missing values with adaily average. The average was calculated as: Total Monthly Thenns / # Days not missing.
. Forthe hourly meter readings we adopted the following strategy:
• .Thosemeters having overone week (168 readings) ofhourly readings missing in

## a givenmonth were removed from thatmonth's hourly meter reading file and moved to an outlier file. Forthosemeters with less 

### than one week (168 readings) ofhourly readings missing within a givenmonth, an average meter reading computed from the remaining data inthe month for each hour and type ofday (weekday/weekend) replaced the missing values inthe hourly data file. The average was calculated as: (Total thenns for a givenhour and type ofday in the specified month)/ (# ofnonmissing meter readings in that hour and type ofday 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 boththe daily and the hourly meter reading files and not used in any analysis. Therefore, all analyses in thisreponinvolve amaximum of90 metered stand-alone ranges, 8 metered cooktops, arid 3metered ovens. In any particular summary oranalysis 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 ofmissing values are given in the data base documentation. 

### 3.2 UNBALANCED DATA AND THE CONFOUNDING OF EFFECTS Information was collected in this meter study forover 150 demographic and equipment variables which could not be controlled, but only recorded. This type ofobservational study unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes and subclasses ofa variables) and to the confounding ofthe effect ofsecondary variables when attempting to assess the effect ofany given primary variable on gas usage. Further details on theproblem of unbalanced data and the confounding ofeffects is given in Appendix B. The following steps were taken to address the problem ofthe confounding ofeffects: 1. Different statistical models were fitto the data by least squares linear regression, analysis ofvariance, and analysis of covariance. An assessment ofthe fit ofthese models provided an indication ofwhich demographic and equipment variables were most highly correlated with gas usage and how the estimate ofgas 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 ofmultiple variables. 3. "Least Squares Means" were given as estimates ofgas usage for the levels ofkey variables such as Type ofIgnition System. The least squares means (or population marginal means) adjust the estimate for the effect ofother confounded variables in the model. 

### 3.3 VARIABLE SELECTION IN THEMODEL BUILDING PROCESS The decision ofwhich variables to include in the models to predict annual, monthly and daily gas usage for ranges included the following steps: 1. A subset ofthe demographic and equipment variables was selected as the setof 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 equipment variables. Further details concerning the handling ofmissing 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 ofobservational study unavoidably leads to unbalanced demographic data (unequal frequencies in the different classes aQd subclasses ofa variables) and to the confounding ofthe effect ofsecondary variables when attempting to assess the effect ofany given primary variable on gas usage. Further details on the problem of unbalanced data and the confounding ofeffects is given in Appendix B. The following steps were taken to address the problem ofthe confounding ofeffects: 1. Different statistical models were fit to the data by least squares linear regression, analysis ofvariance, and analysis ofcovariance. An assessment ofthe fit ofthese models provided an indication ofwhich demographic and equipment variables were most highly correlated with gas usage and how the estimate ofgas usage was improved as different variables were added or subtracted from the model. 

### 2. Estimates ofannual, monthly or daily usage were generated from statistical models that included the effects ofmultiple variables. 3. "Least SquaresMeans" were given as estimates ofgas usage for the levels ofkey variables such as Type ofIgnition System. The least squares means (orpopulation marginal means) adjust the estimate for the effectofother confounded variables in the model. 

### 3.3 VARIABLE SELECTION IN THE MODEL BUILDING PROCESS The decision ofwhich variables to include in the models to predict annual, monthly and daily gas usage for ranges included the following steps: 1. A subset ofthe 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 SIZEIN 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 OFIGNITION TYPE OF CLEANING 2. An assessment ofeach variable's correlation with gas usage was made through linear regression or an analysis ofvariance, and through an overall analysis ofcovariance usingall variables in the model. 3. The practical reality ofincluding any givenvariable was considered. For instance, the number ofmeals cooked per week was not considered areasonable 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 ofmissing data foreach variable was taken intoconsideration. 5. An attempt was made to keep the model as simple as possible without losing significant predictive power. Based on the results oftheabove steps, a model was chosen topredict annual range gas usage for each type ofignition. Other models are also reasonable and a final decision ofwhich model touse depends on the requirements oftheuser. It must alsobe noted thatthe demographic characteristics ofhouseholds in the study, including the fact that all households were single family households inthe Illinois area, must be taken into account before usingthe statistical models or estimates from thisdata to draw inferences about other populations.

6 AGE OF ADULT RESIDENTS (MALE AGE 

### + FEMALE AGE)/2 INCOME AGE OF RANGE TYPE OF IGNITION TYPE OF CLEANING 2. An assessment ofeach variable's correlationwith gas usage was made through linear regression or an analysis ofvariance, and through an overall'analysis ofcovariance

. using all variables in the model. 3. Thepractical reality ofincluding any given variable was considered. For instance. the number ofmeals 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 infonnationnot nonnally available. 4. The effect ofmissing data**f~r.** each variable was taken into consideration. 5. Anattempt was made to keep the Diodel as simple as possible without losing significantpredictivepower. Bas(:d onthe results ofthe above steps, a model was chosento predict annual range gas usage for each type ofignition. Other models are also reasonable and a final decisionofwhich model to use depends onthe requirements ofthe user., Itmust also be noted that the demographic characteristics ofhouseholds,in the study. includingthe fact that all households were single family households inthe Illinois area, must be taken

,into account before using the statistical models orestimates from this data to draw inferences about otherpopulations.
.



### 6

## SECTION 4 RESULTS

4.1 MODEL TO PREDICT ANNUAL GAS USAGE The steps outlined in Section 3.3 above were followed in assessing amodel for predicting annual gas usage. The inclusion ofType ofIgnition System alone in the model resulted in an R 2 

### value of .63. The R2 

### value canbe described approximately as the percentage ofthe variance in gas usage that can be attributed to the effect ofthe predictor variable(s). Given that Type ofIgnition System explained a large percentage ofthe variance in gas usage, all models considered included this variable. Table 4-1 contains the results ofthe analysis when a single additional variable was added to the Annual Gas Usage model that included Type ofIgnition System. The p-value isan indicator of how significant the variable isto the model. P-values lessthan 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 ofAdult Occupants, Income, Meals Cooked Per Week, Do Both Adults Work, Hours atHome, and Type ofIgnition System were found to have any statistical significance when included in amodel. (Contrary to expectations, Type ofHousehold-new construction versus existing 7 

### Variable Description P-Value Do Both Adults Work 0.0081 Age ofAdult residents: 0.0115 (Male Age + Female Age)/2 

### Hours at Home 0.0249 Meals Cooked per Week 0.0305 Income 0.0402 Age ofRange 0.1121 Frequency ofGrill Use 0.1289 Number ofOccupants 0.1545 Home Size in Square Feet 0.2489 Location 0.2764 Type ofCleaning 0.2949 Type ofHousehold 0.9282 

## SECTION4

RESULTS

4.1 MODELTO 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 ofType ofIgnition System alone in the model resulted in an R2 value of .63. The R2 value can be described approximately as the percentage ofthe variance in gas usage that can be attributed to the effect ofthe predictorvariable(s). Given that Type ofIgnition System explained a large percentage ofthe variance in gas usage, all models considered included this variable. Table 4-1 contains the results ofthe analysis when a single additional variable was added to the Annual Gas Usage model that included Type ofIgnition 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. 

### VariableDescription P-Value Do Both Adults Work 0.0081 Age ofAdult residents: 0.0115 (Male Age 

### + Female Age)/2 Hours at Home 0.0249 Meals Cooked perWeek 0.0305 Income 0.0402 Age ofRange 0.1121 Frequency ofGrill Use 0.1289 Number ofOccupants 0.1545 Home Size in Square Feet 0.2489 Location 0.2764 Type ofCleaning 0.2949 Type ofHousehold 0.9282 Only Age ofAdult Occupants, Income, Meals Cooked PerWeek, Do Both Adults Work, Hours at Home, and Type ofIgnition System were found to have any statistical significance when included in a model. (Contrary to expectations, Type ofHousehold-new construction versus existing 7

### household--had no statistically significant effect on gas usage. Appendix B includes a detailed discussion ofthe effect ofthis variable. Age ofthe range was also insignificant.) Although statistically significant, Meals Cooked Per Week, Hours at Home, and Do Both Adults Work were considered unlikely candidates for amodel sincethey 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 ofeither Age ofAdult Occupants orIncome raised the R 2 

### value to .66. However, income information was notavailable (refused) for 23 ofthe 90households with standalone ranges. Therefore, inclusion ofincome inthe model would reducethe number ofmetered ranges upon which the model was based from 90 to 67. Therefore, the model for predicting annual usage was based on Type ofIgnition System and Age ofAdultOccupants. The model chosen is an analysis ofcovariance model with Type ofIgnition System as a categorical variable with twolevels (electric ignitionand standingpilot) and Age ofAdult Occupants as the covariate (quantitative variable). The form ofthe model is: Annual Usage (Therms) = Bo + a i + B * (Age ofAdult Occupants) 

### were B 0 

### = an overall intercept effect a, = the effect ofthe type ofignition B= theregression coefficient for the effect ofAge ofAdult Occupants The parameter estimates are listed inTable 4-2. Table 4-2. Parameter Estimates forthe Annual Gas Usage Model



### Although we would not expect the relationship ofAge of Adult Occupants to gas usage tobe affected by the type ofignition on a range, a statistical test ofthe equality ofslopes for Age ofAdult Occupants within each category ofType ofIgnition 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 household--had no statistically significant effect on gas usage. Appendix B includes a detailed discussion ofthe effect ofthis variable.**Ag~** ofthe range was also insignificant.)
: Although statistically significant, Meals Cooked PerWeek, 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 orreadiJyavailable. In addition, discrepancies were found in these variables. For instance several households reponed bothadults 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 ofeither Age ofAdult Occupants or Income raised the 

### R2 value to .66. However, income information was not available (refused) for 23 ofthe 90households with standalone ranges. Therefore, inclusionofincome inthe model would reduce the·number ofmetered ranges upon which the model was based from 90to 67
.. Therefore, the model for predicting.annual usage was based on Type ofignitionSystem and A,ge ofAdult Occupants. The model chosen is an analysisofcovariance model with Type ofIgnition System as a . categorical variable with two levels (electric ignition and standingpilot) and Age ofAdult Occupants as the covariate,(quantitative variable). The form of.the model is: Annual Usage (Therms) 

## = 60 

### + Qj 

### + 6 

## * '(Age of.Adult Occupants) were 6 0 

## = anoverall intercept effect,
Qj 

### = the effect ofthe type ofignition 6 = the regressioncoefficient for the effect ofAge ofAdult Occupants The parameter estimates are listed inTable4-2. Table 4-2. Parameter Estimates for the Annual Gas Usage Model



### .Parameter EStimate 

### 6

0 42.12 Electric Ignition 

### -33.61 Q.

I StandingPilot 

### 0.000 6 

### 0.315 Although we would not expect the relationship.ofAge ofAdult Occupants to gas usage to be affected by the type of**ig~tion** Oila range, a statistical test ofthe equality ofslopes for Age ofAdult Occupants within each category ofType ofIgnition System was performed. This revealed no . statistical evidence for different slopes within each category, as indicated by the PILOT

### * AGE
8interaction term in the analysis ofcovariance table presented in Table A-3 in Appendix A. The statistical analysis results from the final fitting ofthe model are shown in Table A-4 in Appendix A. These result in apreliminary model for annual gas usage for each type of ignition as follows: Electric Ignition Annual Usage (Therms) = 8.507 + .315 * (Age ofResident) Standing Pilot Annual Usage (Therms) = 42.12 + .315 * (Age ofResident)
A plot ofthe data with the fitted regression lines is shown below:


### Annual Range Gas Usage as a Function ofAge of Resident

p

P

P E E E E F P P P P p 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 interaction tenn in the analysis ofcovariance table presented in Table A-3 in Appendix A. The statistical analysis results from the final fitting ofthe model are shown in Table A-4 in Appendix A. These result in apreliminary model for annual gas usage for each type ofignition as follows: Electric Ignition Amiual Usage (Therms) = 8.507 

### + .315 

### * (Age ofResident) Standing Pilot Annual Usage (Therms) = 42.12 

### + .315 

### * (Age ofResident)
A plot ofthe 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 asa Function ofAge 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 

## t E.


### - 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 thismodel topredict annual gas usage in the future. A strong argument could be made that the'significance ofthe Age ofAdult Occupants may be due to a difference in gas usage between generations, rather than to any "aging" effect. Inthis case, range usage would not be expected to increase as the current younger adults age. At the present time, the model isa goodpredictor ofannual gas usage for arange, but there is no guarantee itwill remain so in the future. Based on thismodel, ourbest estimates ofannual gas usage for ranges with each type of ignition system are given bythe least squares means for Type ofIgnition System (thepredicted values for annual gas usage when the value ofAge ofAdult Occupants istaken as its mean value, 44.24 years). The least squaresmeans estimate ofannual gas usage for electric ignitionranges 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 ofannual gas usage foreach pilottype. Table 4-3. Comparison of Least Squares- Means with Simple Averages of Annual Gas Usage inTherms For Each Type of Ignition System



### The model is illustrated in the graph below. Annual Range Gas Usage as a Function ofAge 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 isthe 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 Caution should be exercised utilizing this model to predict annual gas usage in the future. A strong argument'could be made that the'significance ofthe Age ofAdult 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 predictorofannual gas usage for a range, but there is no guarantee it 

### will remain so in th.e future. Based on this model, ourbest estimates ofannual gas usage for ranges with each type of ignition system are given by the least'squares means for Type ofIgnition System (the predicted values' for annual gas usage when the value ofAge ofAdult Occupants is taken as its r:nean value, 44.24 years). The least squares-means estimateofannual gas usage for electric ignition ranges is 22.4 therms, and for standing pilot ranges, 56.1'therms. Table4-3 below presents the least"squares means in comparison to the simple averages ofannual gas usage for each pilottype. 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· SimpleAverages Electric Pilot 22.4 21.8 Standing Pilot 56.1 57.1
The model is illustrated in the graph below. Annual'Range Gas Usageas a Function ofAge 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 ofignition may be appropriate. In this instance the model would simply consist ofthe least squares means presented in Table 4-3. Note: As mentioned earlier, ifthe statistical significance ofAge ofAdult Occupants is actually due to adifference in cooking habits between generations, range gas usage in the future may be expected to decrease as the number ofolder generation users decrease. 

### 4.2 ESTIMATES OF PILOT GAS USAGE We estimated the'average annual amount ofgas used by standing pilots in a standing pilot' range in several ways. The first estimate was the difference between our best estimates ofannual gas usage for a standing pilotrange and an electric ignition range. From Table 4-3 we can calculate the difference as 56.1

- 22.4 = 33.7 therms. 

### A second way ofestimating 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. Ifthe electric ignition average hourly estimate is subtracted from the standing pilot average hourly estimate for hours 1through 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 ofthe usage pattern in the hourly data for hours 1through 5, and also offered the opportunity to calculate an estimate ofannual gas usage per single pilot. There were 23 standing pilot ranges that also had information onthe number ofstanding pilots. Frequency counts for the hourly gas usage for each of. these ranges revealed a clear pattern ofusage that can be assumed tobe typical ofpilotusage alone. Invery general terms, this pattern consisted ofusage of 1cubic foot ofgas every 1to 3 hours. For the twenty-three meters, only 46 hours out of 14,260 (23 meters x 5hours x 124 days) had gas usage over 1cubic foot. Thirty-four ofthese hourly readings over 1cubic foot occurred for a single meter. This meter was therefore dropped from the analysis because ofthe strong evidence that the gas usage was not due solely to pilotusage. The hourly readings over 1cubic foot for the remaining 12 hours were adjusted in two different ways: 1)by replacing the reading with 1cubic foot usage; and 2) by replacing the reading with 0 cubic foot usage. After this adjustment, the average therms used in the five hourperiod for each range was calculated, and from this an estimate ofthe average annual pilotusage. In addition, since the number ofpilots perrange was known for these ranges, an average annual use per single pilot was also calculated. Ofthe 22 ranges used in this analysis, two had two pilots, 19 had three pilots, and one had four pilots. The results ofthe analysis are given in the Table 4-4 below:

11 For many purposes, a model based only on type ofignition may be appropriate. In this instance the model would simply consist ofthe least squares means presented in l:able 4-3. Note: As mentioned earlier, ifthe statistical significance ofAge ofAdult Occupants is actually due to a difference in cooking habits between generations, range gas usage in the futUre may be expected to decrease as the number ofoldergeneration users decrease. 

### 4.2 ESTIMATES OFPILOT GAS USAGE We estimated the'average annual amount ofgas used by standing pilots in a standing pilot' range in several ways. The first estimate was the difference between our best estimates ofannual gas usage for a standing pilot range and an electric ignition range. From Table 4-3 we can calculate the difference as 56.1

- 22.4 = 33.7 therms. A second way ofestimating 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. Ifthe electric ignition average hourly estimate is subtracted from the standing pilot average hourly estimate for hours 1through 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 ofestimating the gas used by the pilots involved a closer assessment ofthe "usage pattern in the hourly data for hours I through 5, and also offered the opportunityto calculate

an estimate ofannual gas usage per singlepilot. There were 23 standing pilot ranges that also had infonnationon the nwnber ofstandingpilots. Frequency counts for the hourly gas usage for each of these ranges revealed a clearpatternofusage that can be asswned to be typical ofpilot usage alone. 

### In very general terms, this patternconsisted ofusage of I cubic foot ofgas every I to 3hours. For the twenty-three meters, only 46 hours out of 14,260 (23 meters x 5 hours x 124 days) had gas usage over I cubic foot. Thirty-fourofthese hourly readings over I cubic foot occurred for a single meter. This meter was therefore dropped from the analysis because ofthe strong evidence that the gas usage was not due solely to pilot usage. The hourly readings over 1cubic foot for the remaining 12 hours were adjusted in two different ways: 1) by replacing the reading with 1cubic 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 ofthe average annual pilot usage. In addition, since the nwnber ofpilotsper range was known for these ranges, an average annual use per singlepilot was also calculated. Ofthe 22 ranges used in this analysis, two had two pilots, 19 had threepilots, and one had four pilots. The results ofthe analysis are given in the Table 4-4 below:

IITable 4-4. Estimates of Annual Pilot Gas Usage For aSingle Range; and a Single Pilot



### From the three estimates of33.7, 33.5, and 34.9 therms, we have chosen 33.7 therms as a reasonable single estimate for the average annual gas usage bya standing pilotin a standing pilot range inthe NIGAS study. It should be noted that while 33.7 therms may be taken as an estimate ofthe 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 ofthe electric energy used by the electric ignitiondevice. Althoughthe electricity used by acooktop spark ignitor may be negligible, calculations based ol te_.0tlectric energy useofa hot surface ignitor in an oven results in af v anestimate ofapproximately(the_n ofannual energy usage bya hot surface ignitor for an oven ell< withusage at the levels seen in the NIGAS study. Therefore the difference in annual energy usage between an electric ignitionrange 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 ofthe estimates ofannual gas usage foreach type ofrange (electric ignitionand standing pilot) from the NIGAS Residential'Load Study to previous estimates from the Department ofEnergy, Association ofHome 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 ignitionranges was generated from data furnished by the American Gas Association Laboratories at a Gas Research InstituteTechnical Advisory Committee meeting. The AHAM estimate for a standing pilot range was calculated as the AHAM electric ignitionestimate + 33.7 therms (an estimate ofannual pilot usage from the NIGAS data as discussed above). 12 Annual Pilot Gas Usage (Therms) When Hourly Readings Over 1Cubic 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 1Cubic 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 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·FootWere Replaced By O • N MEAN
..STD MIN-MAX iAverage Annual Usage Per Raflge 22 34.8 9.2 18.3

- 54.5 iAverage Annual Usage Per Pilot 22 11.7 2.4 7.8

- 17.5

. Amiual Pilot Gas Usage (Therms) When Hourly Readings Over 1 Cubic FootWere Replaced By 1. iAvetage 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
Fromthe three estimates of33.7, 33.5, and 34.9therms, we have chosen 33.7 thermsas a reasonable single estimate for the average annual gas usage by a standing pilot in a standingpilot range inthe NIGAS study. Itshouldbe noted that while 33.7 therms may be taken as an estimate ofthe difference in annual gMusage between ail electric ignitionrange and a standingpilot range, the difference in annual energy usage would be 33.7 therms minus the BTU equivalent ofthe electric energy used by the electric ignitiondevice. Althoughthe electricityused by a cooktop spark ignitormay be negligible, calculations**based~.**lectric energy use ofa hot surface ignitor in an oven results in**\J.~** .anestimate ofapproximately 1the ofannual 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 energyusage between anelectric ignitionrange 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 OFNIGAS ANNUAL USAGE ESTIl\1ATES WITH 

### OTHERCURRENTESTIMATES Figure4-3 below illustratesthe relationship ofthe estimates ofannual gas usage for each type ofrange (electric ignitionand standing pilot) from the NIGAS Residential'Load Studyto previous estimates from the Depar:nnent ofEnergy, AssociationofHome Appliance·Manufacturers, and two EPRI studies conducted with SouthernCalifornia EdisonCompany 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 ignitionranges was generated from data furnished by the American Gas Association Laboratories at a Gas Research InstituteTechnical Advisory Committee meeting. ·The AHAM estimate for a standing pilot range was calculated as the 

### AHAM electric ignitionestimate 

### + 33.7therms (an estimate ofannual 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 40 0
30 ';: 30, c 20
10
0 DOE. (using Metered AHAM Response

Annual Useful Appliance toDOE(Metered Energy Factor) Study AppliancePilot Usage) EPRI Mission Veto. CA Ignition Type * Rangeswith Pilot Rngegeswh ElectricIgnition 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. ofHousehold #1 and #2 2 9.9 46% 2 11.7 54% '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 AppliancePilot Usage) EPRI Mission VI8jo.CA EPRI Irvine. CA IgnitionType 

## I. RangeswI!tI Pilot 0 Ranges wI!tI EIecIricIgnition • Mix Figure 4-3. Current annual usage estimates for gas ranges. 

### 4.4 ESTIMATES OFTHE 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 ofGas Usage Between Ovenand 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. ofHousehold #1 and #2 2 9.9 46% 2 11.7 54% 13

### 4.5 RANGE GAS USAGE AS A PERCENT OFTOTAL HOUSEHOLD GAS USAGE
A typical total annual gas usage for households with space heating in the NIGAS service area is approximately 1400therms. Moreover, the variance between annual usage ofhouseholds inthe Residential Load Study and annual usage ofall households in the NIGAS service area was very small. The estimates ofannual range gas usage as presented in Table 4-3 ofthis report represent approximately 2-4percent oftypical household gas usage in the NIGAS service area. A comparison oftheannual gas usage by arange 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

ClomesDyor ' WaterHeater ' Other Total Household Usa e Figure 4-4. Approximate annual gas usage forvarious metered household appliances in comparison with total household gas usage.



### 14 

### 4.5 RANGE GAS USAGE AS APERCENT OFTOTAL 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 ofhouseholds in the Residemial Load Study and annual usage ofall households in the NIGAS service area was very small. The estimates ofannual range gas usage as presented in Table4-3 ofthis report represent approximately 2-4 percent oftypical household gas usage in the NIGAS service area. Acomparison ofthe 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 600 c: .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: Anyreference to ranges refers tostand-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. 

# -----------

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 , ,
. ,

,

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 JII 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-2Table 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 FValue 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 "

Table A-4. Analysis of Covariance for Annual Usage Model General Linear Models Procedure Dependent Variable: ANNTHERM SIJII of Mean Source DF Squares Square FValue 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 FValue. 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 Pilot' 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 OFEFFECTS
Information was collected in this meter study for over 150 demographic and equipment variables which could not be controlled, but only recorded. Thistype ofobservational study unavoidably leads tounbalanced demographic data (unequal frequencies in the different classes and subclasses ofavariables) and to the confounding oftheeffect ofsecondary variables when attempting to assess the effect ofany given primary variable on gasusage. As an example, consider the assessment oftheeffect ofType ofHousehold on annual range gas usage. Households with metered ranges were classified intotwo types: 1)new construction, and 2) existing households. Ofthe 90 stand-alone ranges, 59 were inexisting 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 bethatthe new construction households usetheir range lesson average than theexisting construction households. However, an examination ofTable B-2 below reveals that the apparent difference in annual gas usage is most likely aresult oftheconfounding ofthe Type of Ignition System variable with Type ofHousehold.
Table B-2. Average Annual Gas Usage for Ranges byType 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 

### UNBALANCED DATAAND THE CONFOUNDING OF EFFECTS
Infonnation was collected in this meter srudy for over 150 demographic and equipment variables which could not be controlled, butonly r"ecorded. This typeofobservational study unavoidably leads to unbalanced demographic data (unequal frequencies in thedifferent Classes and subclasses ofa variables) and to the confounding ofthe effect ofsecondary variables when attempting to assess theeffect ofany given primary variable on gas usage.

" " As an example, considerthe assessment ofthe effect ofTypeofHousehold on annual range gas usage. Households with metered ranges were classified into two types: 1) new construction, and 2) existing households. Ofthe 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, whilethe average annual range gas usage in new constructionwas 29.6 therms.
Table B-1. Average Annual Gas Usage for Ranges byType 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 mightbethatthenew constructionhouseholds usetheirrange less on average thantheexisting constructionhouseholds. However, an examination ofTableB-2 below reveals that the apparent difference in annual gas usage is most likely a result oftheconfounding oftheType of "Ignition System variable with TypeofHousehold.
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 ofhousehold for electric ranges and standing pilot ranges separately, there appears to be no significant difference in gas usage between types of household. The effect ofType ofIgnition System has been confounded with Type ofHousehold. In the existing household classification, 56% ofthe ranges have electric ignition, whereas inthe new construction classification, 77% ofthe ranges have electric ignition. Ofcourse, with so many variables, there could be other confounding factors as well. The new construction households may, on average, have much higher incomes, oruse the grill much more frequently, or have fewer residents. A strategy for addressing this confounding offactors is presented below. However, in a study such asthis where the frequencies in the levels ofa variable can only be recorded, but not controlled, caution should always be exercised in the interpretation ofsimple averages for any variable. ADJUSTMENTS FOR MISSING VALUES AND THE CONFOUNDING OF EFFECTS Thefollowing steps were taken to address the problem ofthe confounding ofvariables: 1. Different statistical models were fit to the data by least squares linear regression, analysis ofvariance, and analysis of covariance. An assessment ofthe fit ofthese models provided an indication ofwhich demographic and equipment variables were most highly correlated with gas usage and how the estimate ofgas usage was improved as different variables were added or subtracted from the model. 2. As expected, Type ofIgnition 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 ofaverage gas usage for any demographic or equipment variable always include apresentation for each Type ofIgnition System. Ifthere appears tobe no confounding ofType ofIgnition System with an individual variable, i.e., the number ofelectric ignition and standing pilot ranges is approximately equal in all categories ofthe variable, then combined averages across the types ofpilot are also presented. 3. Estimates ofannual, monthly or daily usage were generated from a statistical model that included multiple variables as determined by the statistical analysis described in step 1above. 4. "Least Squares Means" were given as estimates ofgas usage for thelevels ofkey variables such asType ofIgnition System. The least squares means (orpopulation marginal means) adjust the estimate for the effect ofother confounded variables in the model. For example, Table B-3 below shows the least squares means (in the last column) for the different levels ofType ofHousehold, calculated from amodel which includes Type ofIgnition System and the AgeofAdult Occupants.

B-2 t When averages are calculated for each type ofhousehold for electric ranges and standing pilot ranges separately, there appears to be no significant difference in gas usage between types of household. The effect ofType ofIgnition System has been confounded with Type ofHousehold. In the existing household classification, 56% ofthe ranges have elecu:ic ignition, whereas in the new construction classification, 77% ofthe ranges have electric ignition. Ofcourse, with so many variables, there could be other confounding factors as well. The new construction households may, on average, have much higher incomes, oruse thegrill much more frequently, orhave fewer residents. A strategyfor addressing this confounding offactors is presented below. However, in a study such as this where the frequencies in the levels ofa variable can onJy be recorded, but not controlled, caution should always be exercised in the interpretation ofsimple averages for any variable. 

### ADJUSTMENTS FOR MISSING VALVES AND TIlE CONFOUNDING OFEFFECTS The following st.eps were taken to address the problem ofthe confounding ofvariables: 1. Different statistical models were fit to the data by least squares linear regression, analysis ofvariance, and analysis ofcovariance. An assessment ofthe fit ofthese models provided an indication ofwhich demographic and equipment variables were most highly correlated with gas usage and how the estimate ofgas usage was imprOVed as different variableS were added or subtracted from the model. 2. As expected, Type ofIgnition 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 ofaverage gas usage for any demographic or equipment variable always include a presentation for each Type ofIgnition System. Ifthere appears to be no confounding ofTypeofIgnition System with an individual variable, i.e., the number ofelectric ignition and standing pilotranges is approximately equal in all categories ofthe variable, then combined averages across the types ofpilot are also presented. 3. Estimates ofannual, monthly ordaily usage were generated from a statistical model that included multiple variables as determined by the statistical analysis described in step 1above. 4. "Least Squares Means" were given as estimates ofgas usage for the levels ofkey variables such as Type ofIgnition System. The least squares means (or popUlation marginal means) adjustthe estimate for the effect ofother confounded variables in the model. For example, Table B-3 below shows the least squares means (in the last column) for the different levels ofType ofHousehold, calculated from a model which includesType ofIgnition System and the Age ofAdult Occupants.

B-2Table 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


### Inthis instance, the least squares means or "adjusted" means arecalculated from theleast squares linear model assuming equal numbers ofelectric ignitionand standing pilotranges and assuming the mean value of44.24 years for the covariate, Age ofAdult Occupants. This results in an estimate ofannual gas usage of38.6 therms in the Existing Household group and 40.62 therms in theNew Construction group. The estimates offer no evidence that the annual gas usage is affected by type ofconstruction. However, itis known that the new construction and older existing households categories will not contain equal numbers ofelectric ignition and standingpilot ranges, i.e. that type ofpilot is expected tobeconfounded with type ofconstruction. Therefore, these may not bethe appropriate estimates to use forpredicting gas usage in eachtype ofconstruction. The appropriate estimate could be calculated from the model given in Section 4.1 after specifying thepercentage of electric ignitions and standing pilotranges in each construction typecategory. Further illustration of theuse ofleast squares means is given in Section 4.1 in the discussion ofthe model to predict annual usage.



### B-3 5 Table B-3. Average Annual Gas Usage ByType 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 ofelectric ignition and standing pilot ranges and assuming the mean value of44.24 years for the covariate, AgeofAdult Occupants. This results in an estimate ofannual gas usageof38.6therms in the Existing Household group and 40.62 therms in the New Construction group. The estimates offer no evidencethat the annual gas usage is affected by type ofconstruction. However, it is known that the new construction and olderexisting households categories 

### will not contain equal numbers ofelectric igriition and standing pilotranges, i.e. thattype ofpilot is expected tobeconfounded with type ofconstruction. Therefore, these may not bethe appropriate estimates to usefor predicting gas usage in each type ofconstruction. Theappropriate estimate could be calculated from the model given in Section 4.1 after specifying the percentageof electric ignitions and standingpilotranges in each constructiontype category. Further illustration of theuseofleast squares**me~** is given in Section4.1 in thediscussion ofthe model to predictannual usage.

B-3Headquarters Gas Research Institute 8600 West BrynMawrAvenue Chicago,Illinois60631-3562 312/399-8100 WashingtonOperations Gas Research Institute 1331 Pennsylvania Avenue,N. W Suite 730North Washington.D.C. 20004-1703 202/662-x8Q Headquaners Gas Research Institute 8600 West Bryn MawrAvenue Chicago, Illinois 60631-3562 312/399-8/00 Washingtoll Operatiolls Gas Research Institute /33/Penn.n'/I'GniaAvenue, N. W Suite 730Nonh Washington. D.C. 20004-1703 ::n2lfifi2-8Q8Q