OE/EIA-0431

jsidential Energy 

onsumption Survey:

legression 

Analysis of 

Energy 

Consumption 

by End Use

Energy Information Administration

Washington, D.C. ' '

October 1983

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Residential Energy 

Consumption Survey:

Regression 

Analysis of 

Energy 

Consumption 

by End Use

Prepared by: 

Robert B. Latta

This report was prepared by the 

Energy Information Administra 

tion, the independent statistical 

and analytical agency within the 

Department of Energy. The in 

formation contained herein 

should not be construed as 

advocating or necessarily reflect 

ing any policy position of the 

Department of Energy or any 

other organization.

Energy Information Administration

Office of Energy Markets and End Use

Energy End Use Division 

U.S. Department of Energy 

Washington, D.C.

October 1983

DOE/EIA-0431 

Dist. Category UC-98
Contact

This report was prepared by Robert B. Latta. Specific information 

regarding the preparation or contents of this report may be obtained 

from the author (202-252-1121).

ii
CONTENTS

Page

1. Introduction .................................................. 1

2. Description of the Residential Energy Consumption Surveys ..... 3

3. Previous Reports .............................................. 7

4. Availability of Data for Fitting Regression Equations ......... 9

5. Description of Regression Procedure ........................... 17

6. Results of Regression Analysis ................................ 29

7. Discussion of Regression Results .............................. 43

8. Disaggregation of Annual Utility Bills ........................ 47

9. Discussion of Disaggregation Results .......................... 75

10. Effect of Electricity Prices .................................. 79

11. Calendar Year Consumption Estimates ........................... 87

Appendixes

A. Definition of Variables Used in Regression Analysis ............ 91

B. Results of Regression Analysis ................................. 105

C. Adjustments to Space-Heating Component to Reflect Calendar

Year Consumption ............................................... 113

Figures

1. Electricity Consumption by Cooling Degree-Days for Households 

That Use Electricity for Central Air Conditioning But Not for 

Space Heating or Water Heating (Million Btu) ................... 19

2. Electricity Consumption by Heating Degree-Days for Households

Whose Main Space-Heating Fuel Is Electricity (Million Btu) ..... 20

3. Natural Gas Consumption by Heating Degree-Days for Households

Whose Main Space-Heating Fuel Is Natural Gas (Million Btu) ..... 21

4. Fuel Oil Consumption by Heating Degree-Days for Households

Whose Main Space-Heating Fuel Is Fuel Oil (Million Btu) ........ 22

5. LPG Consumption by Heating Degree-Days for Households Whose

Main Space-Heating Fuel Is LPG (Million Btu) ................... 23

6. Wood Consumption by Heating Degree-Days for Households Whose

Main Space-Heating Fuel Is Wood (Cords) ........................ 24

Tables

1. Total Households in the Sample That Use Electricity, Have Type 

I Electricity Data, Use Electricity for Main Heating Fuel, and 

Use Electricity for Main Heating Fuel and Have Type I 

Electricity Data (Classified by Housing Type) .................. 11

2. Total Households in the Sample That Use Natural Gas, Have 

Type I Natural Gas Data, Use Natural Gas for Main Heating 

Fuel, and Use Natural Gas for Main Heating Fuel and Have 

Type I Natural Gas Data (Classified by Housing Type) ........... 12

iii
Page

3. Total Households in the Sample That Use Fuel Oil, Have Type 

I Fuel Oil Data, Use Fuel Oil for Main Heating Fuel, and 

Use Fuel Oil for Main Heating Fuel and Have Type I Fuel Oil 

Data (Classified by Housing Type) .............................. 13

4. Total Households in the Sample That Use Kerosene, Have Type I 

Kerosene Data, Use Kerosene for Main Heating Fuel, and Use 

Kerosene for Main Heating Fuel and Have Type 1 Kerosene Data 

(Classified by Housing Type) ................................... 14

5. Total Households in the Sample That Use LPG, Have Type I

LPG Data, Use LPG for Main Heating Fuel, and Use LPG for Main

Heating Fuel and Have Type I LPG Data (Classified by Housing

Type) .......................................................... 15

6. Measures of Goodness of Fit of Regression Equations ............ 37

7. Standard Deviations of Error Terms ............................. 37

8. Measures of Fit for Secondary Regression Equations for

Non-Single-Family Detached Homes ............................... 42

9. Standard Deviations for Secondary Regression Equations for

Non-Single-Family Detached Homes ............................... 42

10. Average Household Electricity Consumption by End Use for

All Households ................................................. 54

11. Average Household Natural Gas Consumption by End Use for

Households Us ing Natural Gas ................................... 56

12. Average Household Fuel Oil and Kerosene Consumption by End

Use for Households Using Fuel Oil or Kerosene .................. 58

13. Average Household LPG Consumption by End Use for Households

Using LPG ...................................................... 60

14. Average Household Electricity Consumption for Space Heating

for Households Whose Main Space-Heating Fuel Is Electricity .... 62

15. Average Household Natural Gas Consumption for Space Heating

for Households Whose Main Space-Heating Fuel Is Natural Gas .... 64

16. Average Household Fuel Oil and Kerosene Consumption for

Space Heating for Households Whose Main Space-Heating Fuel

Is Fuel Oil or Kerosene ........................................ 66

17. Average Household LPG Consumption for Space Heating for

Households Whose Main Space-Heating Fuel Is LPG ................ 68

18. Average Household Electricity Consumption and Natural Gas 

Consumption for Water Heating for Households Whose Main 

Water-Heating Fuel Is Electricity or Natural Gas ............... 70

19. Average Household Fuel Oil and Kerosene Consumption and

LPG Consumption for Water Heating for Households Whose Main 

Water-Heating Fuel Is Fuel Oil, Kerosene, or LPG ............... 72

20. Households Using Electricity, by End Use ....................... 80

21. Average Household Electricity Consumption by End Use ........... 81

22. Average Household Electricity Consumption for Space Heating

for Households Whose Main Space-Heating Fuel Is Electricity .... 82

23. Estimates of Coefficients for Regression Using Marginal

Electricity Rates with All Components .......................... 84

24. Estimates of Coefficients for Regression Using Marginal

Electricity Rates Only When They Are Significant ............... 84

25. Residential Energy Consumption for Calendar Year 1980 by

Census Division and Fuel Type (Trillion Btu) ................... 88

iv
Page

26* Residential Energy Consumption for April 1980 Through March

1981 by Census Division and Fuel Type (Trillion Btu) ........... 88

Al. Variables Used in Regression Equations ......................... 93

Bl. Estimates of Coefficients for Regression Using Households 

Living in Single-Family Detached Homes Where Electricity 

Consumption Is the Dependent Variable .......................... 107

B2. Estimates of Coefficients for Regression Using Households 

Living in Single-Family Detached Homes Where Natural Gas 

Consumption Is the Dependent Variable .......................... 108

B3. Estimates of Coefficients for Regression Using Households 

Living in Single-Family Detached Homes Where Fuel Oil 

Consumption Is the Dependent Variable .......................... 109

B4. Estimates of Coefficients for Regression Using Households

Living in Single-Family Detached Homes Where LPG Consumption

Is the Dependent Variable ...................................... 109

B5. Estimates of Coefficients for Regression Using Households

Not Living in Single-Family Detached Homes Where Electricity 

Consumption Is the Dependent Variable .......................... 110

B6. Estimates of Coefficients for Regression Using Households Not 

Living in Single-Family Detached Homes Where Natural Gas 

Consumption Is the Dependent Variable .......................... 110

B7. Estimates of Coefficients for Regression Using Households 

Not Living in Single-Family Detached Homes Where Fuel Oil 

Consumption Is the Dependent Variable .......................... 110

B8. Estimates of Coefficients for Regression Using Households Not 

Living in Single-Family Detached Homes Where LPG Consumption 

Is the Dependent Variable ...................................... Ill

B9. Estimates of Coefficients for Regression Where Kerosene

Consumption Is the Dependent Variable Using All Households ..... Ill

1. INTRODUCTION

This report covers two main topics. The first is a description of a 

regression analysis of energy consumption data obtained from the Residential 

Energy Consumption Surveys (REGS). The sections that cover this topic 

are technical in nature and would appeal mainly to other researchers who 

plan to analyze the RECS data set. The second topic is a presentation of 

estimates of residential energy consumption by end use. The resulting 

estimates of consumption by end use will be of interest to a wide audience. 

This includes State and Federal agencies that are formulating programs 

for low-income energy assistance, utilities that need to forecast load 

demands, and other researchers who are studying the demand for energy 

in the residential sector. Readers who are mainly interested in the 

end-use estimates and not the methodology used to obtain the estimates 

can skip to Section 8.

The Residential Energy Consumption Surveys were designed by the Energy 

Information Administration (EIA) to provide information concerning 

energy consumption within the residential sector. Information concerning 

the housing unit is collected through personal interviews with adult 

residents of a representative national sample of households. Data con 

cerning actual energy consumption are obtained from fuel records maintained 

by the households' fuel suppliers.

This report is based on the data obtained from the 1980 RECS. The energy 

consumption data are collected for four major fuels (electricity, natural 

gas, liquid petroleum gas (LPG), and fuel oil). Kerosene consumption is 

included in the fuel oil consumption figures. For each household in 

RECS, the consumption data are condensed and adjusted so that the amount 

reported reflects the consumption for the period from April 1980 to 

March 1981.

This report presents a discussion of the variability of energy consumption 

within the home. The variability of energy consumption for transportation 

may be the subject of later reports. Regression equations are presented 

that express the predicted home energy consumption for an individual 

housing unit as a function of main heating fuel, water-heating fuel, 

weather, dwelling size, appliance stock, and other variables. Separate 

equations are presented for electricity, natural gas, liquid petroleum 

gas, and fuel oil. These equations are then used to estimate the amount 

of energy consumed by end use.

The design of RECS is briefly discussed in Section 2. A reference is given 

in Section 2 to reports containing more detailed discussion. The National 

Interim Energy Consumption Survey (NIECS) was a preliminary survey that 

preceded RECS. Two reports^ on the variability in energy consumption

^ Energy Information Administration, National Interim Energy Consumption 

Survey; Exploring the Variability in Energy Consumption, DOE/EIA-0272 

(Washington, D.C., July 1981) and Energy Information Administration, 

National Interim Energy Consumption Survey; Exploring the Variability 

in Energy Consumption A Supplement, DOE/EIA-0272/S (Washington, D.C., 

October 1981).

-1-
were produced by using the data in NIECS. These studies are discussed 

in Section 3 and referenced repeatedly throughout this report. Section 

4 discusses what subset of the households surveyed in REGS is used to 

develop the regression equations and why this subset was chosen. The 

model that was fit and the procedure used to fit the model are discussed 

in Section 5. The resulting regression equations are given in Section 6, 

along with a measure of their goodness of fit. Section 7 presents a 

discussion of the regression equations, in particular the limitations 

that one must realize before interpreting the results. The estimation 

of energy consumption by end use is discussed in Section 8. Tables of 

end-use energy consumption by Census division, income, dwelling size, and 

weather are also given in Section 8. Section 9 contains a discussion of 

the end-use estimates including the trends that appear in the results. 

The effects of electricity prices on electricity consumption are discussed 

in Section 10. Section 11 presents a procedure for adjusting the April 

1980 through March 1981 consumption amounts to reflect the consumption 

for the 1980 calendar year.

-2-
2. DESCRIPTION OF THE RESIDENTIAL ENERGY CONSUMPTION SURVEYS

Before REGS, two surveys were conducted by Response Analysis Corporation 

(RAC) for EIA. These were the National Interim Energy Consumption Survey 

(NIECS) and the Household Screener Survey (SCREENER). Several reports 1 

have been produced using the results of these surveys. Both surveys used 

the same sample design. It was not custom tailored to EIA's needs, but 

created previously as an all-purpose design. The design did not cover 

households in Alaska, in Hawaii, or on military bases. The experience 

gained in conducting these two surveys was used in designing REGS.

The sample design for REGS was specifically constructed to obtain data on 

residential energy consumption and related housing characteristics. It 

covers all households in the United States, including those living in 

Alaska and in Hawaii and those living on military bases. The households 

used in the survey were selected by using probability sampling. The 

probability of selection varied across the country. This variation was 

partly a result of oversampling in some areas to insure a minimum level 

of precision for energy consumption estimates in those areas.

The sample design included many stages. Initially, the country was divided 

into 1,782 primary sampling units (PSU's). The PSU's were grouped into 

131 strata with roughly the same population. Thirty-one strata contained 

only one PSU; the rest contained two or more PSU's. Within each stratum, 

one PSU was selected.

A number of intermediate probability sampling stages preceded the final 

selection of REGS households. These stages included the selection of minor 

civil divisions (MCD's) within each PSU. Within the MCD's, Census tracts 

or enumeration districts (ED's) were selected. A segment of 25 or more 

housing units was selected within a tract or ED. A cluster of 25 housing 

units was selected from the sample segments. The ultimate cluster to be 

contacted for interviews (averaging about four housing units) was 

systematically selected from the cluster. The number of ultimate clusters 

totaled 1,667.

From the 1,667 ultimate clusters, 7,338 units were selected to be in the 

sample. Of these units, 106 were found to be nonresidential units, 430 

were vacant residential units, and 168 were seasonal or vacation units. 

This left 6,634 eligible residential units. From these units, the occupants 

of 5,804 units were personally interviewed, either during the initial

*A list of these reports is given in Energy Information Administration, 

Residential Energy Consumption Survey: 1978-1980 Consumption and 

Expenditures, Part 2; Regional Data, DOE/EIA-0262/2 (Washington, D.C., 

May 1981).

-3-
attempt or during follow-up contacts. Finally, mail followup resulted 

in responses from 247 additional households. In the end, 6,051 households 

responded, 551 occupied units were classed as nonrespendents, and the 

occupants of 32 units moved between the initial contact and the end of 

possible followups.

Most of the questions asked for REGS were similar to those asked for 

NIECS. The SCREENER survey was much briefer and was employed primarily 

to obtain a panel of households to use in running a residential 

transportation survey. Two of the major changes between NIECS and REGS 

concern square footage and wood consumption.

In NIECS, the respondent estimated the square footage for the dwelling, 

which proved to be an unsatisfactory way of obtaining data. In REGS, 

the interviewer's measurements were used to obtain the square footage of 

the dwelling.

For NIECS and SCREENER, no attempt was made to obtain the amount of wood 

consumed as fuel; for REGS, the respondent was asked to estimate the 

amount of wood that the household used the previous year. In future 

REGS's, the wood consumption question may be changed to get a more 

reliable response.

For all three surveys, some form of heating degree-days (HDD) and cooling 

degree-days (CDD) was obtained for all households. Let T^ be the highest 

temperature in degrees Fahrenheit for a particular day. Let T£ be the 

lowest. Set T = (Tj + T£)/2. The heating degree-days for this particular 

day base 65 F equal the maximum of 0 and 65-T. The cooling degree-days 

equal the maximum of 0 and T-65. Degree-days for other bases are 

calculated in a similar manner. The annual degree-days (HDD and CDD) are 

obtained by summing the daily degree-days.

In NIECS and SCREENER, the only annual values for HDD and CDD that were 

calculated were those based at 65 F. For REGS, annual HDD and CDD values 

were calculated for bases of 50 , 55 , 60 , 65 , 70 , 75 , and 80 F. 

For NIECS, the values of HDD and CDD were long-term averages adjusted by 

Census division for April 1978 through March 1979 weather. For SCREENER 

and RECS, the values of HDD and CDD were calculated using the actual 

daily highs and lows experienced by weather stations in the same National 

Oceanic and Atmospheric Administration (NOAA) district.

For all surveys, the fuel consumption amounts were annualized to reflect 

the consumption from April of one year through March of the next year. 

In most cases, the annual amount was based on at least 330 days of actual 

fuel bills. In other cases, the annual amount was based on fewer days or 

imputed based on a preliminary regression estimate.

-4-
Missing values for variables such as household income, main heating 

fuel, and age of respondent were imputed in some cases, usually by a 

hot-deck procedure.

A previous report^ gives more details on the sample design, imputation, 

etc., for REGS.

^Energy Information Administration, Residential Energy Consumption Survey; 

Housing Characteristics, 1980, DOE/EIA-0314 (Washington, D.C., June 1982).

-5-

3. PREVIOUS REPORTS

The regression results presented in this report are similar to the 

results presented in two previous reports.* The two present an 

analysis of residential energy consumption data from NIECS covering the 

period from April 1978 through March 1979. Regression equations were 

developed that express the natural gas, electricity, fuel oil, and LPG 

consumption of individual households in terms of household, dwelling, 

and weather characteristics.

The theoretical model used for residential consumption of each fuel is

KM N

Fh = AO + I AkXhk + ( I BmY hm)HDDh + ( £ Cn Zhn)CDDh + Eh .

k=l m-1 n-1

In this equation, Ao , Ak, Bm , and Cn are parameters that are estimated by 

using a regression technique; HDD^ is heating degree-days; CDD^ is 

cooling degree-days; X^k , Y^mHDD^, and Z^CDD^ are the independent 

variables used in the regression; E^ is the error term; and h the index 

for the households in the survey. The dependent variable is F^, which 

is the energy consumption of household h.

Theoretically, all the space-heating and space-cooling characteristics are 

represented by Y^ and Z^n , respectively. In particular, if the fuel in 

question was not used for space heating, then all Y^'s equal zero; 

similarly, if the fuel in question was not used for air conditioning, 

then all Z^n 's equal zero. In the actual models that were fit, some 

of the terms involving space-heating characteristics were not used in 

conjunction with HDD^. For example, the fuel oil regression equation 

for households living in single-family detached dwellings contains an 

indicator variable for fuel oil heating that has a large coefficient.

The variance of E^, the error term, depends on the predicted fuel 

consumption. For example, the variance of electricity consumption 

across households living in small apartments that use electricity only 

for appliances is much smaller than the variance of electricity 

consumption across households living in large, all-electric, single-family 

detached homes.

The models were fit under the assumption that the variance of E^ was 

proportional to F^ - E^, which equals the predicted consumption for 

household h. Examination of residual plots reveals that this assumption 

is approximately valid.

For the NIECS reports,^ the regression equations were fit by using an 

iterative procedure. TLe regressions were initially fit by using a 

standard unweighted least squares procedure. The predicted consumption

*National Interim Energy Consumption Survey; Exploring the Variability 

in Energy Consumption (July 1981) and National Interim Energy Consumption 

Survey; Exploring the Variability in Energy Consumption A Supplement 

(October 1981).

-7-


based on the initial regression results was computed for each household. 

The regression was next fit by using a weighted regression where the 

weights were equal to the inverse of the predicted consumption. Next, 

outliers were deleted from the data set. The weighted regression and 

outlier deletion were alternatively performed until no more outliers 

were found or seven weighted regressions were run.

Only part of the data was used in the regression procedure. The subset 

used for the regression procedure varied from fuel to fuel. For all 

fuels, the households whose characteristics were obtained by mail 

questionnaire were not used because many of the responses for these 

households were imputed. In addition, for each fuel, only those households 

that used the fuel in question but did not use it for nonresidential 

purposes were used in the regression procedure. For electricity, only 

those households whose annual electricity consumption was based on at 

least 330 days of billing data were used. Similarly, for natural gas, 

only those households whose annual natural gas consumption was based on 

at least 330 days of billing data were used. For fuel oil, kerosene, 

and LPG, only those households where the annual consumption was not imputed 

by a regression estimate were used.

In only three instances were there enough cases to fit a model with many 

dependent variables. These were electricity consumption, natural gas 

consumption, and fuel oil consumption for households living in singlefamily detached homes. In the rest of the cases, only a few independent 

variables were used. The ones that were used were either the predicted 

consumption based on one of the three major cases or parts of the predicted 

consumption.

Tables 3 through 7 in the NIECS report supplement^ show the number of 

cases available for regression runs. Tables 2 through 5 in the NIECS 

report-^ and Table 9 in the NIECS report supplement^ give the final 

regression estimates.

^National Interim Energy Consumption Survey: Exploring the Variability 

in_Energy Consumption A Supplement (October 1981).

^National Interim Energy Consumption Survey: Exploring the Variability 

in Energy Consumption (July 1981).

-8-
4. AVAILABILITY OF DATA FOR FITTING REGRESSION EQUATIONS

The quality of the response obtained from households covered by the 1980 

RECS varies from household to household, and the quality of the energy 

consumption data varies depending on the fuel as well as on the household. 

For these reasons, not all households were used in fitting the regression 

equations and the set of households varies from fuel to fuel. A procedure 

similar to that in the NIECS analysis was employed to determine which 

households to use in the regression procedures.

The set of households was first reduced to the set with type I overall data. 

Type I data are defined as data that are acceptable for use in a regression 

procedure. Most of the data for households that were interviewed by 

mail were imputed. Hence, all households whose responses were obtained 

by mail questionnaire were dropped from the set of households with type I 

overall data. Any household where the main space-heating fuel, secondary 

space-heating fuel, or water-heating fuel was imputed was dropped from 

the set of households with type I overall data because space heating and 

water heating are two of the most important end uses. The fact that the 

listed fuel for one or both of these end uses was imputed makes the 

observation flawed for purposes of fitting regression equations. The 

remaining households were denoted as the set of households with type I 

overall data. This set consists of 5,769 households out of the 6,051 

households that responded in the 1980 RECS. It includes a large number 

of households with at least one variable that was imputed. A desire 

to preserve a large number of households in the set used to fit the 

regression equations prevented the deletion of all households with any 

imputed data. Even if these households were deleted, the resulting set 

would still contain errors due to coding, responses, and other reasons.

In fitting the regression equations for each fuel, the researchers started 

with the set of households with type I overall data and then deleted those 

households that did not use the fuel, those households with flawed con 

sumption data for that fuel, and those households with square footage 

data based on a regression estimate when the fuel in question was used 

as the main heating fuel. The last deletion was made because square 

footage will be used in conjunction with main heating fuel in fitting the 

regression equation. The resulting set was denoted as the set of households 

with type I consumption data for that fuel.

For fuel oil, kerosene, and LPG, the reported consumption was the amount 

purchased, not the amount actually consumed. Hence, some households 

have an annual consumption amount equal to zero, really zero purchases, 

yet are still classified as users of fuel oil, kerosene, or LPG. This 

convention differs from the NIECS analysis, in which households with zero 

purchases were classified as nonusers.

*In the NIECS report, data that were acceptable for regression analysis 

were denoted as good data.

-9-
Two criteria were used for electricity to determine if the consumption 

data were acceptable or flawed for regression purposes. If the electric 

ity bill had to be adjusted for nonresidential uses, the consumption 

data were deemed to be flawed. If the annual consumption amount was 

obtained by a regression estimate or by a ratio estimate where fewer 

than 330 days of actual data were available, then the consumption data 

were deemed to be flawed. The latter criterion can be determined by 

looking at the variable ORIGELQ on the 1980 REGS public-use tape.

If the natural gas bill had to be adjusted for nonresidential use, the 

natural gas consumption for that household was deemed to be flawed. If 

the annual consumption amount was obtained by a regression estimate or 

a ratio estimate where fewer than 330 days of actual data were available, 

then the consumption data were deemed to be flawed.

For LPG, the same two criteria that were used with natural gas were used 

to determine if the LPG consumption data were acceptable or flawed.

The fuel oil and kerosene consumption amounts were combined when the data 

were condensed into annual consumption figures, because some equipment 

can burn both fuel oil and kerosene. In most cases, only one of the two 

fuels was used. The fuel used can be determined by looking at the variables 

that describe fuel oil uses and those that describe kerosene uses. Those 

households that appear to use both fuel oil and kerosene were classified 

as using only fuel oil, but their consumption was classified as flawed 

data. The two criteria employed for the other fuels were also used to 

determine additional households with flawed consumption data for fuel 

oil consumption or kerosene consumption.

Tables 1 to 5 give the number of households in the data set that used 

each fuel, and the number for each fuel with both type I overall data and 

type I consumption data for that fuel. These two numbers are also 

given for the case where the only households that are considered are 

those whose main heating fuel is the fuel in question. These numbers 

are shown for each main housing type. The two types of single-family 

attached homes were merged into one set. The three types of dwelling in 

small apartment buildings were also merged into a single set.

-10-
Table 1. Total Households in the Sample That Use Electricity, Have Type I 

Electricity Data, Use Electricity for Main Heating Fuel, and 

Use Electricity for Main Heating Fuel and Have Type I Electricity 

Data (Classified by Housing Type)________________________

Housing 

Type

Mobile

SingleFamily 

Detached 

Unit ..........

SingleFamily 

Attached 

Unit ..........

Units in 

Building 

with Two to

Use 

Have Type I Electricity 

Use Electricity for Main 

Electricity Data Heating Fuel

348 227 80

4,068 3,177 584

215 151 39

709 402 107

Use 

Electricity 

for Main 

Heating Fuel 

and Have 

Type I 

Electricity 

Data

53

430

23

52

Units in 

Building 

with Five or 

More Units ., 708 277 256 98

-11-
Table 2. Total Households in the Sample That Use Natural Gas, Have Type I 

Natural Gas Data, Use Natural Gas for Main Heating Fuel, and 

Use Natural Gas for Main Heating Fuel and Have Type I Natural

_______Gas Data (Classified by Housing Type)_____________________

Housing 

Type

Use 

Natural 

Gas

Have

Type I 

Natural 

Data Gas

Use Natural 

Gas for Main 

Heating Fuel

Use Natural

Gas for Main

Heating Fuel 

and Have Type I 

Natural Gas Data

Mobile 

Home .. 116 64 111 62

SingleFamily 

Detached 

Unit ....

SingleFamily 

Attached 

Unit ....

2,439

164

1,918 2,277 1,794

104 122 73

Units in 

Building 

with Two to 

Four Units . 538 215 447 160

Units in 

Building 

with Five or 

More Units ., 468 67 324 48

-12-
Table 3. Total Households in the Sample That Use Fuel Oil, Have Type I Fuel 

Oil Data, Use Fuel Oil for Main Heating Fuel, and Use Fuel Oil 

for Main Heating Fuel and Have Type I Fuel Oil Data (Classified

_______by Housing Type)______________________________________

Housing Use 

Type Fuel Oil

Mobile 

Home ........... 35

SingleFamily 

Detached 

Unit ........... 688

SingleFamily 

Attached 

Unit ........... 48

Units in 

Building 

with Two to 

Four Units ..... 144

Have Type I Use Fuel Oil 

Fuel Oil for Main HeatData ing Fuel

18 27

438 605

27 47

35 132

Use Fuel 

for Main 

ing Fuel 

Have Type 

Oil Data

13

385

26

34

Oil 

Heatand 

I Fuel

Units in 

Building 

with Five or 

More Units .. 144 121

-13-
Table 4. Total Households in the Sample That Use Kerosene, Have Type I 

Kerosene Data, Use Kerosene for Main Heating Fuel, and Use 

Kerosene for Main Heating Fuel and Have Type I Kerosene Data

_______(Classified by Housing Type)________________________

Housing 

Type

Use 

Kerosene

Have Type I 

Kerosene Data

Use Kerosene 

for Main Heat 

ing Fuel

Use Kerosene 

for Main Heating 

Fuel and Have 

Type I Kerosene 

Data

Mobile 

Home ..

SingleFamily 

Detached 

Unit ....

24 10 22 10

57 12 27 10

SingleFamily 

Attached 

Unit ....

Units in 

Building 

with Two to 

Four Units ..

Units in 

Building 

with Five or 

More Units ..

-14-
Table 5. Total Households in the Sample That Use LPG, Have Type I LPG 

Data, Use LPG for Main Heating Fuel, and Use LPG for Main 

Heating Fuel and Have Type 1 LPG Data (Classified by Housing

_______Type)___________________________________________

Housing 

Type

Have Type I 

Use LPG LPG Data

Use LPG 

for Main Heat 

ing Fuel

Use LPG for 

Main Heating 

Fuel and Have 

Type I LPG Data

Mobile 

Home ..

SingleFamily 

Detached 

Unit ....

SingleFamily 

Attached 

Unit ....

Units in 

Building 

with Two to 

Four Units .

142 76 49

396 253 177 109

22

Units in 

Building 

with Five or 

More Units .,

-15-
The following fact, similar to that noted about the NIECS data set, was 

noted concerning the 1980 REGS data set. All households with good data 

paid their own utility bills. As a result, the regression equations 

that were developed apply only to households that pay for their energy 

use directly. The energy consumption was imputed for households whose 

utility bills were part of the rent. Therefore, comparing the energy 

use of households that did pay their own bills with those that did not 

would be difficult using the NIECS or RECS data sets.

The following facts can be gleaned from Tables 1 to 5:

 Most households live in single-family detached units.

 Out of a total of 6,051 households that were interviewed, 

only 3 did not use electricity in the home.

 The dominant heating fuel is natural gas.

 The survey produces type I energy consumption data for most 

households living in single-family detached units but not 

for households living in multifamily units.

 The fuel oil, kerosene, and LPG consumption values are 

less likely to be acceptable for regression purposes 

than the electricity and natural gas consumption values.

 Most households that use natural gas use it as their 

main heating fuel. The same applies to fuel oil.

Separate regression equations were fit for electricity, natural gas, 

fuel oil, and LPG consumption for households living in single-family 

detached homes. The regression equations for mobile homes and multiunit 

buildings used parts of the regression equations for the single-family 

detached cases as indexes for energy consumption. This fact will be 

discussed in more detail in Section 6.

-16-
5. DESCRIPTION OF REGRESSION PROCEDURE

The theoretical model used for the RECS analysis is the same as the one 

used for the NIECS analysis. There are changes in the independent 

variables. The amount of heated floorspace is used in the RECS analysis. 

Reliable estimates of this quantity were not available for use in the 

NIECS data set. Other changes include additional information on appliances. 

The degree-days base used in RECS is different from that used in NIECS. 

Heating degree-days base 60 F and cooling degree-days base 75 F are used 

for the RECS analysis. The new bases result in a slightly higher value 

of R^ than the results for which degree-days base 65 F are used. For 

NIECS, both degree-days were based at 65 F.

An additional reason for using degree-days bases other than the more 

commonly used base of 65 F involves consumer behavior. The author 

felt that the average household did not turn on the main space-heating 

source when the average daily outdoor temperature was just below 65 F 

(for example, a high of 75 F and a low of 55 F) and turn on the air condi 

tioning when the average daily temperature was just above 65 F. There 

should be a range of temperature where the household used other means, 

such as opening or closing windows, to moderate indoor temperature. 

This suggests using a base for cooling degree-days that is higher than 

the base for heating degree-days.

Scatter Plots of Consumption Versus Degree-Days

Figures 1 through 6 show the rough relationship between degree-days and fuel 

consumption. The figures use only these households with type I overall data 

and that live in a single-family detached house with a heated square foot 

age of between 1,000 and 3,000 square feet. This set was further constricted 

for each plot according to which consumption trend was being investigated.

Figure 1 is a plot of electricity consumption versus cooling degree-days base 

75 F. Only those households with good electricity consumption data, whose 

main space-heating fuel, secondary heating fuel, and water-heating fuel 

were not electricity, and that had electric central air conditioning were 

used. A slight upward trend is visible in Figure 1, which suggests 

increased use of electricity among those houses with central air 

conditioning as the number of cooling degree-days increases. The small 

slope in the trend and the scatter about the trend indicate that many 

factors will have to be considered if the effect of cooling degree-days 

on annual electricity consumption for air conditioning is to be noticeable.

Figures 2 through 5 show the consumption of electricity, natural gas, fuel 

oil, and LPG, respectively, versus heating degree-days. For Figures 2 

through 5, only those households whose main heating fuel is the fuel in 

question and that had type I consumption data for that fuel were used. In 

each of Figures 2 through 5, there is an upward trend in consumption as 

heating degree-days increase. For all fuels the upward trend in consumption 

is partially hidden by the large scatter about the trend. This is especially 

true for fuel oil and electricity.

-17-
The large amount of scatter about the trends in Figures 2 through 5 

indicates that factors beside annual heating degree-days have a great 

influence on annual energy consumption for space heating* As a result 

the regression equations will contain many variables as well as degreedays in conjunction with indicator variables for the main space-heating 

fuels.

Figure 6 shows cords of wood burned versus heating degree-days for 

households where the main heating fuel is wood. The consumption amount 

for wood is an estimate provided by the household and is subject to many 

sources of errors.^ No analysis of wood data is presented in this 

report, but Figure 6 confirms the intuitive idea that consumption is 

higher in cooler climates.

Theoretical Model for RECS

Figures 1 through 5 suggest that the theoretical model used for the NIECS 

analysis' is still useful when analyzing the RECS data. In addition, 

the figures can be used to suggest many other theoretical models that 

take into account the upward trend in energy consumption for space heating 

as heating degree-days increase. For instance,

K M 

log(Fh ) - AO + I AkXhk + ( I BmYhm)HDDh

N 

+ < I CnZhn )CDDh + Eh (1)

or

K M

Fh - A0 + I A^ + ( I BmYhm)HDDh 

k=l m-1

N

+ < I CnZhn )CDDh + Eh . (2) 

n-1

The NIECS model was used because any nonlinear transformation of the 

consumption variable F^ will result in a nonadditive model when it is 

solved for F^. For instance, model 1 is a log-linear or multiplicative

*Residential Energy Consumption Survey; Housing Characteristics, 1980 

(June 1982).

^National Interim Energy Consumption Survey; Exploring the Variability 

in Energy Consumption (July 1981) and National Interim Energy Consumption 

Surveyt Exploring the Variability in Energy Consumption A Supplement (October 1981). ————————— ———— ————————————

-18-
FIGURE 1. ELECTRICITY CONSUMPTION BY COOLING DEGREE-DAYS FOR HOUSEHOLDS THAT USE ELECTRICITY FOR CENTRAL AIR CONDITIONING 

BUT NOT FOR SPACE HEATING OR WATER HEATING (MILLION BTU)

ELECTRICITY CONSUMPTION 

(MILLION BTU) 

I

160 +

vo 

I

120

100

80

60 +

20 +

C 

C 

I 

F 

E 

C 

G 

A

A 

B 

A 

D 

E 

E 

G 

G 

D 

G 

H 

B

A 

A 

A

A 

A

B

E 

B 

B 

A 

A

B 

A 

C

C 

B 

B 

B 

B 

B 

C 

B 

B

A 

C 

B 

A

C 

C 

C

B

A 

A 

C

E 

F 

E 

G 

E 

D 

B

A 

A 

A 

A

B 

B

A

C 

B

A 

A

A 

C 

A

A 

A 

C 

D

B 

C 

B 

B

A 

B

B 

D

E 

B 

B 

A 

B

200 400 600 800 1,000 1,200 1,400 1,600

COOLING DEGREE-DAYS (BASE 75 DEGREES FAHRENHEIT) 

NOTE: LETTERS OF THE ALPHABET REPRESENT THE NUMBER OF OBSERVATIONS: A = ONE OBSERVATION, B = TWO OBSERVATIONS, ETC.
FIGURE 2. ELECTRICITY CONSUMPTION BY HEATING DEGREE-DAYS FOR HOUSEHOLDS WHOSE MAIN SPACE-HEATING FUEL 

IS ELECTRICITY (MILLION BTU)

ELECTRICITY CONSUMPTION 

(MILLION BTU)

I 

160 +

100

NJ 

0

80

60

40

A 

A

• 

A

• 

B 

A

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•

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AB AA A A

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1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

HEATING DEGREE-DAYS (BASE 60 DEGREES FAHRENHEIT) 

NOTE' LETTERS OF THE ALPHABET REPRESENT THE NUMBER OF OBSERVATIONS: A = ONE OBSERVATION, B = TWO OBSERVATIONS, ETC.
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-21-
FIGURE <*. FUEL OIL CONSUMPTION BY HEATING DEGREE-DAYS FOR HOUSEHOLDS WHOSE MAIN SPACE-HEATING FUEL 

IS FUEL OIL (MILLION BTU)

FUEL OIL CONSUMPTION 

(MILLION BTU)

I 

400 +

350

300 +

250 A 

A

1

N)

M

1

200

150

100

50

0

A

A A A AA

A A A

A

> A A B

B A

AAA A

CA

AA C

A

^ A BA

A B BB

A AB

A A

A

K

A

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A ,

A

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A

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B B

A AA

BAAA

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B AA

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1,000 2,000 3,000 4,000 5.000 6,000 7,000 8,000 9,000

HEATING DEGREE-DAYS (BASE 60 DEGREES FAHRENHEIT) 

NOTE: LETTERS OF THE ALPHABET REPRESENT THE NUMBER OF OBSERVATIONS: A = ONE OBSERVATION, B = TWO OBSERVATIONS, ETC.
FIGURE 5. LPG CONSUMPTION BY HEATING DEGREE-DAYS FOR HOUSEHOLDS WHOSE MAIN SPACE-HEATING FUEL IS LPG (MILLION BTU)

I

S3

LPG CONSUMPTION 

(MILLION BTU) 

I <*oo +

350

300

250 * 

200

150

A A

00

50

0

A B

A

•

A

A A

A AA

A

A A A AC

1- A A

A A

A B A A

A A A

A

•

A B AA

A A C A A

A A A A A

AA A A

A A

AA A A A A

A AA A

A A

A

A

.______4.----------*----------4.-- —— —— - —— -4- —— —— -- —— --4- ———— -- —— ___4.______

1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

HEATING DEGREE-DAYS (BASE 60 DEGREES FAHRENHEIT) 

NOTE: LETTERS OF THE ALPHABET REPRESENT THE NUMBER OF OBSERVATIONS: A = ONE OBSERVATION, B = TWO OBSERVATIONS, ETC.
FIGURE 6. WOOD CONSUMPTION BY HEATING DEGREE-DAYS FOR HOUSEHOLDS WHOSE MAIN SPACE-HEATING FUEL IS WOOD (CORDS)

I

N3

WOOD CONSUMPTION 

(CORDS) 

I 40 +

35

30

25 +

20

15

0

5

0 J

A

A

A

A

B

A A

A

^ A B

———— + ———————— + — ———— —— + -_- 

0 1,000 2,000

A 

AAA

B

C A

ACIC

A

D CAE

A ABA

3

A

AD

AC

AB

B

C D

A CA _ + ———

,000

A

B 

A

B

A

BB

B A

CA

F

A

AA 

AA 

A A

A A 

A A

A AA

ABA

A ABA

AAB

B AC A

AABA --+----- 

4,000

A

B

A

A

AA 

A

B AA

A

A A

A

- — + -- —— - 

5,000

B 

A

A B

A

A

6

A 

C

B 

B 

A A

B

A A

A

A

_ + —— _. 

,000

B B 

A A 

A

C B A 

C

D D A

DAB

B B B

A

B A

A

——— — + —— _ ——— __ + _____ 

7,000 8,000

— _--+ ————— 

9,000

HEATING DEGREE-DAYS (BASE 60 DEGREES FAHRENHEIT) 

NOTE: LETTERS OF THE ALPHABET REPRESENT THE NUMBER OF OBSERVATIONS: A = ONE OBSERVATION, B = TWO OBSERVATIONS, ETC.
model. Hence, adding central air conditioning to a previously non-airconditioned home will multiply the previous electricity consumption 

estimate by a factor greater than 1, say 1.20. Hence, the increase in 

the estimated electricity consumption resulting from adding central air 

conditioning will vary. The increase will depend upon for what other end 

uses the household uses electricity. For instance, if the household 

uses electricity for space heating and water heating, the consumption 

estimate may be 60 million Btu per year. With the log-linear model, 

adding air conditioning may increase this by 12 million Btu. If the 

household does not use electricity for space heating or water heating, 

the consumption estimate may be 20 million Btu per year. Here the loglinear model will increase the consumption estimate by 4 million Btu 

when air conditioning is added. This difference in the increase for 

adding any end use is not a desirable trait.

On the other hand, the NIECS model requires the use of an error term, the 

variance of which is not a constant. Transforming F^ might result in a 

model in which the error term can reasonably be assumed to have a constant 

variance across all household and energy characteristics.

The problem with a nonconstant variance in the error term can sometimes 

be alleviated by normalizing the dependent variable. For residential 

energy consumption, possible normalizing procedures are dividing the 

energy consumption by the number of household members, dividing by the 

square footage of the dwelling or dividing by the degree-days. None of 

these normalizing procedures will alleviate the problem in this case. 

The reason they fail is that the data set contains households with various 

space-heating and water-heating fuels. If the regression procedure was 

preformed only over a set of households with a given space-heating and 

water-heating fuel, then a normalizing procedure might work.

It was decided that the nonadditivity of the transformed model was a more 

serious problem than the nonconstant variance of the error term in the 

NIECS model. With the NIECS model, the consumption can be expressed as

Fft = (Appliance Terms) + (Water-Heating Terms) + (SpaceHeating Terms) + (Air-Conditioning Terms).

This breakdown can be used as a basis for partitioning each household's 

energy consumption into the four major end uses. With a multiplicative 

model this partitioning would not be so easy. Section 8 presents a way 

to partition energy consumption using the NIECS (additive) model.

Regression Procedures with Nonconstant Variance of Error Term

There are various ways to adapt regression procedures to account for a 

nonconstant variance of the error term. These techniques include the 

use of weighted least squares regression and the use of nonlinear least 

squares regression.

-25-
Three factors were taken into account in choosing the regression procedure 

used:

1. How the procedure adapts to nonconstant variance 

of the error terms,

2. How the procedure adapts to outliers, and

3. How fast and convenient the procedure is.

To describe various regression techniques, the following terminology 

is needed: Set F^ » G^ 4- E^, where F^ is the consumption, G^ is the 

regression estimate, and E^ is the error term.

In preliminary runs, it was noticed that

Fa - F - r Eh Fh Gh

was more normally distributed than E^ and had approximately a constant 

variance as G^ varied from small to large values. This fact held for 

natural gas, fuel oil, and LPG consumption. For electricity, it appeared

Fb = v - r Eh Fh Gh

was even better. The model F^ - G^ + E^ could then be fit by using 

a nonlinear regression procedure that minimizes

For electricity, this could be minimized:

When this nonlinear procedure is used, the effect of outliers with very large 

consumption is reduced, but the effect of outliers with very small 

consumption is increased. The distribution of E^ tends to be skewed to 

the right. Hence, more outliers can be found on the high side. Therefore, 

the nonlinear procedure does reduce some of the problems of outliers. 

The use of higher order roots makes the effect of outliers on the low 

side more pronounced. One drawback of nonlinear regression is that 

the number of steps it takes to converge can be large and depends heavily 

on the starting estimates.

Another way of fitting the model F^ » G^ + E^ is to use an iterative 

weighted least squares procedure where the weights depend on 1/Gfo and/or 

on I/Eft. Weighting on 1/G^ that is obtained from a previous step is 

equivalent to assuming that the variance of E^ is proportional to G^. 

Weighting on 1/Eft gives least absolute value regression. It is also 

possible to weight on 1/Gjj x E^, which would be weighted least absolute 

value regression.

-26-
Least absolute value regression Is much less affected by outliers than 

least squares regression. Unfortunately, the procedure takes many steps 

to converge with the models the author tried to fit.

With either nonlinear regression or iterative weighted regression, one 

can systematically delete outliers and rerun the procedure. This would 

remove the effect of outliers on the procedure, but would take longer. 

The cut-off point of what is and is not an outlier would be subjective.

All these procedures were investigated. For the cases examined, all the 

resulting regression equations were very similar, probably because of 

the large number of observations.

For convenience, it was decided to use one of the simpler procedures, 

iterative weighting on G^ alone. The final procedure chosen involved 

four computations of regression estimates. The first step used unweighted 

least squares regression. The second step used weighted least squares 

regression where the weight equaled 1/G^, with G^ obtained by using 

the regression estimates from the first step. Steps 3 and 4 are weighted 

least squares regression where the weights equal l/G^. Here G^ was 

determined by the regression estimate obtained in the previous step.

It is not clear how long it would take for the estimates to converge, 

using the procedure chosen. The changes that take place in the 

estimates between two weighted regression runs are small compared with 

changes between the unweighted least squares estimates and the first 

weighted least squares estimates. For convenience, it was arbitrarily 

decided to use three weighted regression iterations, which gives a total 

of four steps. The entire procedure was performed by one program that 

makes use of the General Linear Model procedure in the Statistical 

Analysis System (SAS) programming package.

The procedure was used to fit the regression equations for four cases: 

the electricity consumption, the natural gas consumption, the fuel oil 

consumption, and the LPG consumption for households living in singlefamily detached homes.

The regression equations for the other housing types were obtained by 

constructing consumption indexes based on regression results for households 

living in single-family detached houses. This was done because there 

were fewer observations for these housing types and the researchers 

wanted to maintain comparability across housing types.

-27-

6. RESULTS OF REGRESSION ANALYSIS

The final regression equations for consumption of electricity, natural gas, 

fuel oil, and LPG in single-family detached homes are consolidated into 

end-use components. For electricity, there are four components; for natural 

gas, three components; for fuel oil, two components; and for LPG, three 

components. For each fuel, the sum of the components yields the estimated 

consumption according to the regression equations. The definitions of the 

terms used in the equations are contained in Appendix A. The standard 

errors and levels of significance of the estimated coefficients are contained 

in Appendix B.

In all the regression analyses, the dependent variable is the energy con 

sumption from April 1980 through March 1981 in thousand Btu (MBtu). The 

following list contains the conversion factors for this unit of measure 

used in the REGS data set.

1 MBtu = 1,000 Btu

= 0.2931 kilowatt-hours of electricity

= 0.9794 cubic feet of natural gas

= 0.007407 gallons of No. 1 fuel oil

= 0.007407 gallons of kerosene

= 0.007210 gallons of No. 2 fuel oil

= 0.04643 pounds of LPG

= 0.01095 gallons of LPG

= 0.3984 cubic feet of LPG.

Electricity

The total electricity regression estimate for households living in singlefamily detached homes is represented by ELCONSFD. The four components of 

ELCONSFD are represented by ELCONBS, ELCONWT, ELCONHT, and ELCONCL. The 

terms associated with appliances, lighting, and miscellaneous electricity 

consumption are consolidated into ELCONBS. Similarly, the water-heating, 

space-heating, and air-conditioning terms are consolidated into the 

components ELCONWT, ELCONHT, and ELCONCL, respectively. The discussion 

of the final set of independent variables used in the electricity regres 

sion analysis for households living in single-family detached homes will 

be divided into four parts. These parts correspond to the four components. 

A full description of the variables is given in Appendix A.

The intercept term was placed in the miscellaneous or appliance components. 

The electricity regression analysis was the only place that the intercept 

term was not constrained to be zero. For electricity the intercept term 

can be thought of as representing the baseline electricity consumption.

The appliance component also contains variables that describe the household 

members and the dwelling unit. These variables are the number of adults 

(NUMADULTS), the square footage of heated living space (HEATED), the 

percentage of storm windows (PCTSTORM), and a variable that scales the

-29-
dwelling according to the year it was built (BUILTYEAR). Higher values 

of BUILTYEAR correspond to newer homes. These variables can be thought 

of as life-style variables that affect the baseline electricity 

consumption.

The rest of the variables used in the electricity appliance component 

describe the stock of appliances or the amount of use they receive. The 

variables describing the stock of appliances are the number of electric 

refrigerators (NELFRIG), the number of frost-free or automatic defrost 

electric refrigerators (NFFELFRIG), the number of electric freezers 

(NELFREZ), the number of electric ovens or ranges (NELOVARG), the use of 

a wringer washing machine (HWRNGWSH), the use of an automatic washing 

machine (HAUTOWSH), the use of an electric clothes dryer (HELCLSDY), the 

number of black-and-white televisions (TVBLACK), the number of color 

televisions (TVCOLOR), the use of a central warm air furnace (CFAEQUIP), 

the presence of a swimming pool (HPOOL), and the presence of a heated 

swimming pool (HTPOOL). The variables CFAEQUIP and HTPOOL are not 

dependent upon which fuel is used to heat either the dwelling or the 

pool. They signify the equipment exists and not that electricity is the 

fuel that is used.

The only variable that reflects the amount of appliance usage is the 

interaction term HAUTOWSH x NHSLDMEM, where NHSLDMEM is the number of 

household members.

The independent variables used in the electric water-heating component 

are either the indicator variable for electric water heating (ELWTHT) 

or interactions of ELWTHT with other variables. These other variables 

are NHSLDMEM, an indicator variable for an electric dishwasher (HELDISHW), 

and the number of teenage household members (TEENS). The interaction of 

HELDISHW and ELWTHT will be discussed in Section 7.

The independent variables used in the electric space-heating component 

are all interaction terms. The only term involving the use of electricity 

for secondary space heating is the interaction term consisting of the 

product of the indicator variable for secondary electric space heating 

(ELSHEAT) and heating degree-days base 60 F (HDD). Note that HDD is in 

units of 100 degree-days. The terms for main electric space heating are 

all interaction terms that involve the indicator variable for main 

electric space heating (ELMHEAT) and HDD. For all except two of these 

interaction terms, the amount of heated square footage in the dwelling 

(HEATED) is also part of the term. Other variables used in these inter 

action terms are the percentage of doors and windows that are covered by 

storm doors and windows (PCTSTORM), number of bathrooms (NBATHRMS), and 

BUILTYEAR. In addition, the indicator variable for secondary wood space 

heating (SWOODHT) times the amount of wood burned (TRUNCORD) is used in 

one of the interaction terms. TRUNCORD is in units of one-tenth cord 

and has a maximum value of 60, which represents six cords.

-30-
Some of the terms in the space-heating component appear to be complex 

interaction terms that are hard to justify. The amount of electricity 

used for space heating will intuitively involve the heating degree-days 

and the size of the dwelling. The interaction terms that use additional 

variables represent adjustments to the effect of degree-days and dwelling 

size on energy consumption for space heating.

The cooling component is composed of six terms. All these terms involve 

one of two variables that describe the amount of living space that can be 

air conditioned. RACRMS is the number of rooms that can be air conditioned 

by electric window units, and CACRMS is the number of rooms that can be 

air conditioned by electric central air-conditioning systems. Five of the 

terms are interaction terms involving the cooling degree-days base 75 F 

(CDD). In addition, in two of the terms, a categorical variable describing 

the household income (INCOME79) is used.

Care must be taken in interpreting any of the terms in any of the 

components. This also applies to the other fuels. There is much 

colinearity in the data. Hence the variables in the terms may actually 

represent the effect of other factors. This is especially true for the 

space-heating component. This fact will be discussed more fully in 

Section 7.

The equations for the four components and the total estimate are as 

follows:

ELCONBS

ELCONWT =

2,614.8 

+ 1,088.6 

+ 399.60

- 19.280 

+ 1.0980 

+ 3,842.0 

+ 10,216 

+ 2,455.6 

+ 2,863.3 

+ 3,953.5 

+ 929.71

- 2,312.1

- 4,330.5 

+ 1,734.7 

+ 3,409.5 

+ 836.17 

+ 2,055.4 

+ 1,155.6

x NUMADULTS 

x BUILTYEAR 

x PCTSTORM 

x HEATED 

x HPOOL 

x HTPOOL 

x NELFRIG 

x NFFELFRIG 

x NELFREZ 

x NELOVARG 

x HWRNGWSH 

x HAUTOWSH 

x HAUTOWSH x 

x HELCLSDY 

x TVBLACK 

x TVCOLOR 

x CFAEQUIP.

NHSLDMEM

3,966.7 x ELWTHT 

+ 2,156.9 x ELWTHT x NHSLDMEM 

+ 3,337.8 x ELWTHT x TEENS 

+ 4,903.6 x ELWTHT x HELDISHW.

-31-
ELCONHT - 56.946 x ELSHEAT x HDD 

+ 990.07 x ELMHEAT x HDD

- 7.9569 x ELMHEAT x HDD x HDD 

+ 0.27790 x ELMHEAT x HDD x HEATED

- 0.0032083 x ELMHEAT x HDD x HEATED x SWOODHT x TRIJNCORD

- 0.037273 x ELMHEAT x HDD x HEATED x BUILTYEAR 

+ 0.075640 x ELMHEAT x HDD x HEATED x NBATHRMS

- 0.0014999 x ELMHEAT x HDD x HEATED x PCTSTOEM.

ELCONCL » 186.63 x RACRMS x CDD

+ 10.657 x RACRMS x CDD x INCOME79

+ 409.40 x CACRMS

+ 341.03 x CACRMS x CDD

+ 9.9592 x CACRMS x CDD x INCOME79

- 17.884 x CACRMS x CDD x CDD.

ELCONSFD = ELCONBS + ELCONWT + ELCONHT + ELCONCL.

Natural Gas

The total natural gas regression estimate for households living in singlefamily detached homes is represented by NGCONSFD. The three components of 

NGCONSFD are represented by NGCONBS, NGCONWT, and NGCONHT. The terms 

associated with appliances, outdoor gas lights, pool heating, and central 

gas air conditioning are consolidated into the component NGCONBS. 

Similarly, the water-heating and space-heating terms are consolidated 

into the components NGCONWT and NGCONHT. The discussion of the final set 

of independent variables used in the natural gas regression analysis for 

households living in single-family detached homes will be divided into 

three parts. These parts correspond to the three components. A full 

description of the variables is given in Appendix A.

No intercept term was used in the natural gas regression analysis. Many 

households use natural gas only for space heating and water heating. For 

these households, there is no baseline natural gas consumption that cannot 

be attributed to either space heating or water heating.

Four independent variables are used in the miscellaneous or appliance 

components. One of the variables is an index (NGAPLIDX) representing 

the natural gas consumption of appliances by the household. The relatively 

small number of households using some of the appliances and the noise in 

the consumption for space heating and water heating prevented accurate 

estimation of the individual coefficients for the natural gas appliance 

terms. Consequently, the appliance index was created. This index is 

based on a preliminary regression analysis of the electricity consumption. 

The coefficients in the index follow closely the coefficients of the 

corresponding terms in the ELCONBS component. The equation for NGAPLIDX 

is

NGAPLIDX = 2,400 x NNGFRIG

+ 2,900 x NFFNGFRIG

+ 930 x NNGOVARG

+ 3,400 x HNGCLSDY,

-32-
where NNGFRIG is the number of natural gas refrigerators, NFFNGFRIG is the 

number of frost-free or automatic defrost natural gas refrigerators, 

NNGOVARG is the number of natural gas ovens and ranges, and HNGCLSDY is 

an indicator variable for the use of a natural gas clothes dryer. A 

few households did report the presence of gas refrigerators, but all of 

these may actually be electric refrigerators. The households may have 

interpreted gas to mean Freon, which is used in refrigerators. The 

author assumed the households' reports were correct in this case.

The natural gas miscellaneous component also uses the variables HODGASLT, 

NGPOOLHT, and NGCACRMS x CDD. HODGASLT is an indicator variable for 

outdoor gas lights. NGPOOLHT is an indicator variable for natural gas 

pool heating. In the interaction term, NGCACRMS is the number of rooms 

that can be air conditioned with central natural gas systems.

The majority of the households claiming to have natural gas central air 

conditioning may actually have electric systems. This error may be caused 

by two things. Households may have confused Freon with the fuel running 

the compressor. In addition, households with central gas heating systems 

and electric air-conditioning systems may have thought they were both 

natural gas systems. The author assumed the households' reports on the 

air-conditioning fuels were correct.

The water-heating component uses two independent variables. These are 

NGWTHT, an indicator variable for natural gas water heating, and the 

interaction term NGWTHT x NHSLDMEM.

The space-heating component for natural gas is similar to that for 

electricity. It uses indicator variables for natural gas secondary 

(NGSHEAT) and main (NGMHEAT) space heating. All terms except one 

are interaction terms that use HDD. Many of the terms also involve 

HEATED. The following variables were also used in the interaction 

terms: BUILTYEAR, NBATHRMS, SWOODHT, TRUNCORD, PCTSTORM, CFAEQUIP, 

indicator variable for radiators (RADEQUIP), age of householder 

(HEADAGE), indicator variable for presence of small children (CHILDPRE), 

number of rooms (NROOMS), indicator variable for secondary heating fuel 

other than natural gas (SOTHHTNG), indicator variable for basement 

(BASESFU), unheated floorspace (UNHEATED), indicator variable for no 

upper insulation (NOUPPIN), and number of doors and windows (NDRSAWS).

The equations for the three natural gas components and the total estimate 

are as follows:

NGCONBS = 2.2728 x NGAPLIDX

+ 13,280 x HODGASLT

+ 47,255 x NGPOOLHT

+ 1,531.5 x NGCACRMS x CDD.

NGCONWT = 13,938 x NGWTHT

+ 5,670.3 x NGWTHT x NHSLDMEM.

-33-
NGCONHT = 775.54 x NGSHEAT x HDD 

+ 11,734 x NGMHEAT 

+ 1,154.8 x NGMHEAT x HDD 

+ 525.35 x NGMHEAT x HDD x RADEQUIP 

+ 215.23 x NGMHEAT x HDD x CFAEQUIP

- 0.045498 x NGMHEAT x HDD x HEATED x BUILTYEAR 

+ 0.0032722 x NGMHEAT x HDD x HEATED x HEADAGE 

+ 0.11042 x NGMHEAT x HDD x HEATED x CHILDPRE 

+ 0.036130 x NGMHEAT x HDD x HEATED x NROOMS 

+ 0.17794 x NGMHEAT x HDD x HEATED x NOUPPIN 

+ 0.088873 x NGMHEAT x HDD x HEATED x NBATHRMS

0.12413 x NGMHEAT x HDD x HEATED x SOTHHTNG 

14.600 x NGMHEAT x HDD x SWOODHT x TRUNCORD

0.16714 x NGMHEAT x HDD x HEATED x BASESFU 

+ 0.20638 x NGMHEAT x HDD x UNHEATED 

+ 12.216 x NGMHEAT x HDD x NDRSAWS

- 0.078175 x NGMHEAT x HDD x HDD x PCTSTORM.

NGCONSFD = NGCONBS + NGCONWT + NGCONHT.

Fuel Oil

The total fuel oil regression estimate for households living in singlefamily detached homes is represented by FOCONSFD. The two components of 

FOCONSFD are water heating and space heating, represented by FOCONWT and 

FOCONHT, respectively. One household in the sample used fuel oil 

for pool heating. As a result, that household was dropped from the set 

of households used in the regression. No household used fuel oil as the 

main cooking fuel. The use of fuel oil as a secondary cooking fuel was 

not collected. Hence the only components used in the fuel oil regression 

were space heating and water heating.

The independent variables used in the fuel oil regression analysis are 

similar to those used in the space-heating and water-heating components 

of the natural gas analysis. The main differences are that the fuel oil 

analysis uses fewer variables, HDD is dropped from some of the interaction 

terms in the space-heating component, and indicator variables for fuel oil 

usage are used instead of indicator variables for natural gas usage. In 

particular, the fuel oil analysis uses indicator variables for fuel oil 

water heating (FOWTHT), secondary space heating (FOSHEAT), and main space 

heating (FOMHEAT). In addition, an indicator variable for secondary heating 

fuel other than fuel oil (SOTHHTFO) is used.

-34-
The equations for the two fuel oil components and the total estimate are 

as follows:

FOCONWT - 30,999 x POWTHT.

FOCONHT = 22,180 x FOSHEAT

+ 42,938 x FOMHEAT

+ 20,531 x FOMHEAT x RADEQUIP

- 4,589.4 x FOMHEAT x BUILTYEAR

+ 0.59636 x FOMHEAT x HDD x HEATED

- 0.011390 x FOMHEAT x HDD x HEATED x SWOODHT x TRUNCORD

- 0.28183 x FOMHEAT x HDD x HEATED x BASESFU

+ 487.09 x FOMHEAT x HDD x NOUPPIN

- 0.14023 x FOMHEAT x HDD x HEATED x SOTHHTFO

+ 9.6239 x FOMHEAT x UNHEATED

+ 27.078 x FOMHEAT x HDD x NDRSAWS.

FOCONSFD = FOCONWT + FOCONHT.

Liquid Petroleum Gas

The total LPG regression estimate for households living in single-family 

detached homes is denoted by LPCONSFD. The three components of LPCONSFD 

are represented by LPCONBS, LPCONWT, and LPCONHT. The terms associated with 

LPG appliances and LPG central air conditioning are consolidated into 

the component LPCONBS. Similarly, the water-heating and space-heating 

terms are consolidated into the components LPCONWT and LPCONHT. The 

independent variables used in the LPG regression analysis are similar to 

those used in the natural gas analysis. The main differences are that 

the LPG analysis uses fewer variables, the air-conditioning term is 

included in the appliance index, HEATED is dropped from some of the 

interaction terms in the space-heating component, and variables describing 

LPG usage are used instead of variables describing natural gas usage. 

In particular, the LPG analysis uses indicator variables for LPG water 

heating (LPWTHT), secondary space heating (LPSHEAT), and main space 

heating (LPMHEAT).

The LPG appliance index is denoted by LPAPLIDX. The LPG index includes 

terms for central LPG air-conditioning systems. It was based on a pre 

liminary electricity regression analysis. The equation for LPAPLIDX is 

given by:

LPAPLIDX - 2,400 x NLPFRIG

+ 2,900 x NFFLPFRIG 

+ 930 x NLPOVARG 

+ 3,400 x HLPCLSDY 

+ 420 X LPCACRMS 

+ 340 x ODD x LPCACRMS 

+ 9.8 X CDD x LPCACRMS x INCOME79 

17 x CDD x CDD x LPCACRMS,

-35-
where NLPFRIG is the number of LPG refrigerators, NFFLPFRIG is the number of 

frost-free LPG refrigerators, NLPOVARG is the number of LPG ovens and ranges, 

HLPCLSDY is an indicator variable for using an LPG clothes dryer, and LPCACRMS 

is the number of rooms that can be air conditioned by an LPG central airconditioning system.

The few households that consumed LPG and reported that gas was the fuel 

used in a refrigerator or a central air-conditioning system may have reported 

the wrong fuel. It is possible that these households actually used 

electricity. The author assumed that the households accurately reported 

the fuels that were used in refrigerators and in air-conditioning units.

The equations for the three components and the total estimate are as 

f o Hows :

LPCONBS = 3.0014 x LPAPLIDX

LPCONWT = 6,916.6 x LPWTHT x NHSLDMEM

LPCONHT = 781.51 x LPSHEAT x HDD

+ 1,917.6 x LPMHEAT x HDD

+ 0.12887 x LPMHEAT x HDD x HEATED x NBATHRMS

- 741.27 x LPMHEAT x HDD x BASESFU.

LPCONSFD = LPCONBS + LPCONWT + LPCONHT.

Measures of Fit

Table 6 lists three measures of fit for the four regression equations. 

The first measure of fit is the usual measure of fit for unweighted 

least squares regression. The other two measures of fit take into account 

the nonconstant variance of the error term and the nonnormality of the 

error term.

The measures used are

R2 = 100 x t I (yh - Gh) 2 / J- (yh - y) 2 ] 

R2 = 100 x [ I (yh1/2 - Gh 1/2 ) 2/ j- (yh1/2 - Mx ) 2 ] 

R2 - 100 x [ j (y^/4 - G h1/4 ) 2 / £ (yh1/4 - M,,) 2 ], 

where

yh = observed consumption,

0^ = expected consumption calculated from the regression equation,

y - ( i yn >/H, 

Mi = ( y yh1/2 )/H,

and

( I yh1/4 )/H.

-36-
In the last three equations, H is the number of households used in the 

regression procedures.

The measures of fit were not obtained from the printout produced by the 

SAS general linear model procedure. Instead they were calculated from 

the actual consumption values and the predicted consumption values 

produced by the general linear model procedure.

Table 7 gives the standard deviation of the error term for the models, 

that is, the standard deviation of E. , E 3 , and E^ corrected for the 

number of parameters that were estimated. See Section 5 for a definition 

and discussion of E^ and E^. These estimates of the standard 

deviations are denoted by s, s a , and s^, respectively. The value of 

s is included for comparison only. It should not be used to calculate 

confidence intervals because the error term E^ does not have a constant 

variance and is skewed toward the positive direction. The second two 

terms can be used to compute confidence intervals around the estimate of 

the predicted consumption of a particular household with given 

characteristics. For natural gas, fuel oil, and LPG, use sa to calculate 

confidence intervals. For electricity, use s^ to calculate confidence 

intervals.

Table 6. Measures of Goodness of Fit of Regression Equations______

Fuel

Electricity

Natural Gas

Fuel Oil

LPG

R2

74.39

64.38

63.12

70.02

Ra2

76.06

67.85

65.35

75.43

R 2 Rb

75.93

69.39

55.06

70.92

Table 7. Standard Deviations of Error Terms

Fuel

Electricity

Natural Gas

Fuel Oil

LPG

12,151

36,037

37,281

26,449

28.60

51.22

64.32

52.36

1.053

1.453

2.854

2.183

-37-
Assume the regression estimate of the mean consumption over all households 

with a specified set of characteristics is correct. Let G be this 

regression estimate. Let F be the actual consumption for a particular 

household with this set of characteristics. Then s a or s^ can be used 

to compute a confidence interval for F around G.

For example, if the predicted electricity consumption is G = 83,000 MBtu, 

then G 1 / 2* = 17.0, and a 95-percent confidence interval for F 1 /^ is 

(14.9, 19.1). Hence, a 95-percent confidence interval for F is 

(49,000, 133,000). As another example, assume that the predicted natural 

gas consumption is 4,200 MBtu, then G^' 2 = 65, an(j a 95-percent confidence 

interval for F^-' 2 ± s (-35, 165) or (0, 165), because negative values of 

pi' 2 are not valid. Hence, a 95-percent confidence interval for F is 

(0, 27,000). Finally, assume that the predicted natural gas consumption 

is 100.000 MBtu, then G 1/ 2 = 316, and a 95-percent confidence interval 

for F^' 2 is (216, 416). Hence, in this case, a 95-percent confidence 

interval for F is (47,000, 173,000). Notice that the range of the con 

fidence interval is larger where G is large. Notice also that the con 

fidence intervals are very wide. This reflects the large amount of 

variance in energy consumption among households living in similar dwellings 

and having similar characteristics.

Regression Equations for Non-Single-Family Detached Households

It was decided to use the results of the regression analysis of 

electricity, natural gas, fuel oil, and LPG consumption for households 

living in single-family detached (SFD) units in analyzing the energy 

consumption for households living in other housing types. In addition, 

the results of the regression analysis of fuel oil consumption were used 

in analyzing the kerosene consumption for all housing types.

The equations for the components that were developed for households 

living in SFD homes were used as indexes for energy consumption for 

the other housing types. For example, the space-heating component equation 

developed for SFD units was used as an index for the potential use of 

energy for space heating for households living in other housing types.

An overall index that equals the sum of the component indexes was also 

used. The indexes were multiplied by indicator variables for housing 

types and used as independent variables in the regression analyses. The 

individual terms in the indexes were not used in the regression analyses 

of energy consumption for non-SFD households. The intercept terms in 

these analyses were set equal to zero.

The indexes were used to preserve similarity and comparability between the 

regression equations for the different housing types. For each housing 

type and each fuel, it would also be possible to use the exact same set 

of independent variables that was used for the SFD analysis for the 

corresponding fuel. Unfortunately, there are fewer observations avail 

able for use in a regression analysis for the other housing types. (See 

Tables 1 to 4.) Hence, the coefficients would have a larger standard deviation, 

and many of the coefficients would no longer be significantly different

-38-
from zero. Therefore, it would be deceptive to compare the resulting 

coefficients in the regression equations for the different housing types 

term by term, even if they correspond to the same fuel. Using the indexes 

decreases the number of independent variables and allows the coefficients 

of those that are used to be more easily interpreted.

For electricity and natural gas, some of the non-SFD housing types have 

more observations available for regression analysis than for the analysis 

of LPG consumption for households living in SFD units. A set of independent 

variables could have been established for use in these cases. It would be a 

smaller set than was used for the SFD analysis for the same fuel. In 

addition, some variables in the new set would not be in the set used 

for the SFD analysis. The desire to preserve comparability between 

regression equations for different housing types but with the same fuel 

dictated that the indexes be used.

The author did try using the natural gas space-heating component equation 

and the electricity water-heating component equation as indexes for 

space heating and water heating for all fuels in the SFD analysis. The 

natural gas space-heating component was chosen because more households 

used natural gas for space heating than any other fuel. As a result of 

the larger number of observations with natural gas space heating, the 

equation for the space-heating component with natural gas had more terms 

in it than the space-heating component for other fuels. With water 

heating, the use of a fuel for both space heating and water heating 

tends to add noise when one attempts to fit a regression equation for 

the water-heating component. The electricity water-heating component 

was chosen because more households used electricity for water heating 

but not for space heating than any other fuel.

When one uses the indexes based on the natural gas space-heating component 

and the electricity water-heating component, the resulting fits to the 

data are not so good as with the set of independent variables that were 

finally used. This may indicate that the dynamics of space heating and 

water heating differ among the fuels. There is not enough information 

available to rerolve these differences.

The regression analysis for energy consumption in non-SFD units was done 

separately for each fuel. The analysis for the different housing types 

was combined for each fuel. Only those households with type I overall 

data, with type I consumption data for the fuel in question, and not living 

in single-family detached homes were used in the regression procedure.

For each fuel, one of the indexes for the components was dropped; 

otherwise, the data matrix would be singular. The remaining component 

indexes and the overall index were multiplied by indicator variables for 

the different housing types to form interaction terms. These terms were 

used as the independent variables in the regression analysis. In all 

cases the intercept term was set equal to zero.

The regression analysis used the same iterative procedure that was used 

in the SFD analysis. This involves an initial unweighted least squares 

followed by three weighted least squares fits.

-39-
The interaction terms for indexes other than the overall index were 

dropped if they were not significant at the 0.01 level. If one of the 

overall index interaction terms was not significant, then one of the 

component index interaction terms was dropped.

For the electricity analysis, the water-heating index, the space-heating 

index, the cooling index, and the overall index were used. The appliance 

index was dropped to avoid singularity problems. Hence the appliance 

component is always contained in the overall index. The coefficient for 

the overall index gives the estimated proportion of energy used in non-SFD 

households relative to the regression estimate based upon SFD households. 

The coefficients for the individual indexes estimate the difference between 

the proportions for the individual indexes and that for the overall index. 

Four indicator variables for housing types were used. The indicator 

variable for mobile homes is MOBILEHM; for single-family attached units, 

SFATTACH; for units in small apartment buildings, SMLAPTBD; and for 

units in large apartment buildings, LRGAPTBD. Initially, the regression 

procedure used 16 independent variables, the interactions between the 

four indexes, and the four indicator variables. After some terms were 

removed because of nonsignificances, there were only six terms remaining.

The resulting equation is

ELCON = 1.1491 x ELCONSFD x MOBILEHM 

+ 0.83612 x ELCONSFD x SFATTACH 

+ 0.84548 x ELCONSFD x SMLAPTBD 

+ 0.82659 x ELCONSFD x LRGAPTBD

- 0.55746 x ELCONWT x MOBILEHM

- 0.39426 x ELCONHT x LRGAPTBD.

For the natural gas analysis, the appliance index, water-heating index, 

and the overall index were used. The space-heating index was dropped 

to avoid singularity problems. The space-heating index was included in 

the overall index and in most cases represents the largest proportion 

of it. The same four indicator variables for housing types were used as 

were used in the electricity analysis. Initially, the regression procedure 

used 12 independent variables, the interactions between the three indexes, 

and the four indicator variables. Initially, all the interaction terms 

involving NGCONBS were positive, but only one was significant. The lack 

of significance for some housing types may have been due to the small sample 

sizes for these housing types. The author decided to consolidate all 

these terms by using NGCONBS alone as an independent variable. The 

resulting equation involving NGCONBS will be discussed further in Section 7.

The resulting equation is

NGCON = 0.87748 x NGCONSFD x MOBILEHM 

+ 0.82201 x NGCONSFD x SFATTACH 

+ 0.87284 x NGCONSFD x SMLAPTBD 

+ 0.61270 x NGCONSFD x LRGAPTBD 

+ 1.20153 x NGCONBS.

-40-
For the fuel oil analysis, only two indexes were used, the overall index 

and the water-heating index. The number of housing types was reduced. 

The categories for units in small and large apartment buildings were 

combined. The indicator variable for units in large or small apartment 

buildings is APTBLDG. For all housing types, the water-heating index 

was removed because it was not significant. In addition, for all housing 

types, the coefficient for the overall index was not significantly 

different from 1.0 at the 0.05 level.

The final equation is

FOCON - 1.12327 x FOCONSFD x MOBILEHM 

+ 0.91895 x FOCONSFD x SFATTACH 

+ 0.97387 x FOCONSFD x APTBLDG.

For the LPG analysis, three indexes were initially used, the overall index, 

the water-heating index, and the appliance index. The number of housing 

types was reduced. The categories for single-family attached units, units 

in small apartment buildings, and units in large apartment buildings were 

combined into a category for units in multiunit buildings. The indicator 

variable for units in multiunit buildings is MULTIUNIT . This leaves only 

two housing types other than SFD. For these two housing types, the waterheating index and the appliance index were removed from the final 

equation. In addition, the overall index for units in multiunit buildings 

was not significantly different from one.

The final equation is

LPCON = 0.72418 x LPCONSFD x MOBILEHM

+ 1.22596 x LPCONSFD x MULTIUNIT.

For kerosene, the same procedure was used except for one change. One 

household, which listed kerosene as its main heating fuel and LPG as its 

secondary heating fuel, purchased no kerosene during the interview year, 

but did purchase a large amount of LPG. The consumption of kerosene 

was set at zero since there were no purchases. Hence, this household was 

an extreme outlier in the set of 23 households with type I kerosene data. 

To moderate its effect on the kerosene regression procedure, the main 

space-heating fuel for this household was changed to LPG, and kerosene 

was demoted to the secondary heating fuel. This was done only for the 

kerosene regression procedure.

In the kerosene regression procedure, the fuel oil indexes were used. 

All the housing types were combined because of the small sample size. 

The only coefficient that was significantly different from zero was the 

one for the overall index. Here again, the coefficient was not signifi 

cantly different from 1.0 at the 0.05 level.

The final equation is

KRCON = 0.92448 x FOCONSFD.

Appendix B contains the standard deviation and levels of significance for 

all the coefficients in the regression equations.

-41-
Measures of Fit for Non-SFD Analysis

Table 8 gives the measures of fit for the non-single-family detached 

regressions, and Table 9 gives the standard deviation of the error 

terms. Individual measures of fit and standard deviations are not 

given for each housing type. They are given for the different fuels. 

For kerosene, the measures of fit and standard deviations also apply to 

households living in single-family detached homes. The standard 

deviations listed in Table 9 can be used in the same way as those for 

households living in the single-family detached homes. Use sa with 

consumption statistics other than electricity. Use s^, with electricity 

consumption statistics.

Table 8. Measures of Fit for Secondary Regression Equations for NonSingle-Family Detached Homes

Fuel

Electricity

Natural Gas

Fuel Oil

LPG

Kerosene

R2

74.73

62.73

45.90

67.32

10.91

4

74.50

69.64

51.03

75.15

26.53

R2Rb

72.94

70.01

50.18

75.85

29.82

Table 9. Standard Deviations for Secondary Regression Equations for 

_______Non-Single-Family Detached Homes____________________

Fuel s So SH

Electricity

Natural Gas

Fuel Oil

LPG

Kerosene

9,207

34,504

45,122

24,809

31,592

27.95

56.92

70.20

50.20

72.43

1.188

1.903

2.380

1.843

3.326

-42-
7. DISCUSSION OF REGRESSION RESULTS

The independent variables used in the final regressions for the SFD analysis 

were chosen on the basis of intuition, experience gained by the NIECS 

analysis, and trial and error. Many terms and interactions were tried. Only 

those significant at the 0.01 level after the final weighted least 

squares regression were left in the equation. Clearly, there was not enough 

time to investigate all possible terms. This is particularly true of the 

interaction terms involved in the space-heating component. Hence, some 

terms that would have been significant were not tried and, as a result, 

were left out.

Many of the potential interaction terms were highly colinear. For instance, 

one could use degree-days squared instead of degree-days in the term. If two 

highly colinear terms were such that neither was significant when the other 

one was included, then the one that was less significant was dropped.

Because of the nature of this regression analysis, one cannot use the 

coefficients of an individual term to accurately assess the effect of an 

individual variable. There is much colinearity in the response variables 

and interaction terms. Some of the terms that are significant are in 

some ways surrogates for other variables such as income, fuel cost, and 

life-style. For example, in the electricity regression equation for house 

holds living in SFD homes, the coefficient for the indicator variable for 

a wringer washing machine is negative. This negative coefficient may 

represent the life-style of households that have wringer washing machines, 

and it does not signify that the machines are energy-saving devices.

The size of the coefficient in the electricity and natural gas regression 

equations related to swimming pools may partially reflect the life-style 

of households that have swimming pools.

The individual terms that make up the heating components in the regression 

equations are not comparable across fuels. Some terms were used for one 

fuel and not for another. The terms that were chosen were selected on the 

basis of which were the most significant. Changing the terms by adding 

or deleting a variable from the interaction may still result in significant 

coefficients. Using the new terms may result in fits that are almost as 

good as the fit with the final set of terms. Not all possible interactions 

were tried. Hence, some changes may result in improved fits.

To force comparability, the heating part of the natural gas equation was 

tried as a heating Index for the other fuels. The resulting fits were 

not so good as the fits in which the data were allowed to influence the 

choice of the interaction heating terms. In future analysis, it may be 

better to agree on a common index to use from year to year. This index 

may vary across fuels.

-43-
The coefficient corresponding to the independent variable that is the 

indicator variable for fuel oil main space heating is large. The one for 

electricity is not significantly different from zero; hence, the corres 

ponding independent variable was not used. The coefficient for the 

indicator variable for natural gas main space heating was significant 

but much smaller than that for fuel oil. The difference in these co 

efficients is one reason the heating components are not comparable term 

by term.

The reason that the coefficient for the indicator variable for fuel oil 

main space heating is large is not clear. One reason may be that few 

households in the warmer regions heat with fuel oil. Hence, it is harder 

to get a reasonable fit for warmer regions.

Similar to the space-heating terms, the water-heating terms are also 

inconsistent. With electricity and natural gas, there is an increase 

as the number of household members increases. In addition, with 

electricity, it was found that the number of teenagers in the household 

indicates a larger increase in the water-heating term than the number 

for any other age group. This increase does not necessarily mean that 

teenagers use more hot water than other household members. It only 

suggests that households with teenagers use more hot water than households 

with an equal number of household members where none of them are teenagers. 

Clearly one possible reason for the difference is that teenagers do use 

more hot water, but it is not the only possible reason.

With fuel oil, the indicator variable for fuel oil water heat is by itself 

highly significant. The interaction term'with the number of household 

members is not significant if the indicator variable is also used as an 

independent variable. These results are similar to those found in the 

N1ECS analysis. The difference between the water-heating term for fuel 

oil and that for electricity or natural gas may be related to the different 

types of water-heating equipment.

With electricity, the interaction term between electric water heating and 

the indicated variable for an electric dishwasher was significant. It 

is not clear what the positive coefficient for this term indicates. It 

could be a life-style response or an indication that households with 

electric dishwashers do use more energy for water heating. The interaction 

terms between electric dishwasher and natural gas water heating, fuel 

oil water heating, or LPG water heating were not significant. The NIECS 

analysis yielded similar results for these interaction terms. The lack 

of significance for all but the electricity case may be due to noise in 

the natural gas consumption about the space-heating term and the small 

number of observations in the other cases. It may also indicate that 

the interaction term for the electric water-heating case is really a 

life-style variable.

An index based on the electric water-heating component was tried with the 

other fuels. The fit was not so good as the fit when actual variables 

chosen were used.

-44-
The effect of secondary heating is accounted for in two ways. If the main 

heating fuel is the fuel in question, then the existence of a secondary 

heating fuel lowers the heating component of the main heating fuel. If 

the main heating fuel is not the fuel in question and the secondary 

heating fuel is, then the heating component is composed of the secondary 

contribution.

Information was not collected on the amount of secondary heat provided, 

only on the existence of secondary heating fuels and equipment. There is 

some information contained in the total consumption amounts given for the 

secondary heating fuel when the fuel in question is the main heating 

fuel. The only case in which this information was used involved the use 

of wood as a secondary space-heating fuel. Hence, the positive secondary 

heating term consists only of an indicator variable or an indicator 

variable interacted with heating degree-days. The negative term consists 

only of one term, except in the case of wood secondary heating, for 

which the secondary heating term is an interaction term that includes an 

indicator variable for wood secondary heating and the amount of wood 

burned. If the amount of wood burned exceeds six cords, the actual 

amount was not used. Instead, the amount was set at six cords.

Many variables that considerably affect energy consumption were not 

used. These variables either were not available, were only partly available, 

or were available in unreliable forms. These include marginal prices for 

fuels, thermostat settings, measures of air-conditioning use, measures 

of appliance use, measures of insulation, and measures of air infiltration. 

Marginal prices for electricity are available, but only for some house 

holds. The effect of marginal electricity will be discussed at the end 

of Section 10. Thermostat settings and some measures of air-conditioning 

use may be obtained in subsequent surveys.

The income variable is a categorical variable that is treated in the 

analysis as a numerical variable. (See Appendix A for definition of the 

categories.) For incomes between $3,000 and $15,000, the variable is 

approximately equivalent to rounding the income off to the nearest thousand 

dollars.

The income variable was tried in many places, in particular in the 

space-heating components. When the other terms were added, the only 

place it remained significant was in the air-conditioning terms. This 

does not mean that income does not affect the other components, but 

instead it indicates that income is taken into account by the other 

terms used in the equation, in particular, by the size of the house in 

the space-heating component and the number of appliances in the 

miscellaneous component.

The relatively large value for NGCONBS obtained in the non-SFD natural gas 

regression analysis is troubling. On the surface it appears to indicate 

that non-SFD households use natural gas appliances more intensely than 

households living in SFD houses. What may actually be happening may be 

related to the problem of people claiming to have gas refrigerators and 

central air-conditioning units when these appliances really use 

electricity.

-45-

8. DISAGGREGATION OF ANNUAL UTILITY BILLS

End-Use Estimates

One aspect of residential energy consumption that is not listed directly 

on the REGS data tape is energy consumption by end use. The end-use 

categories discussed here are space heating, water heating, air 

conditioning, and miscellaneous. In this section, two basic methods of 

obtaining estimates of end-use consumption are presented. There are 

two variations of the second method. The different methods of estimating 

end-use consumption are illustrated by describing how to estimate the 

amount of electricity used for air conditioning. The advantages and 

disadvantages of each method are also discussed.

Method 1: Estimation Using Differences in Strata Means

An obvious method of estimating the amount of electricity consumed for 

residential air conditioning would be to stratify the households in the 

country and estimate the amount used in each stratum. The households in 

each stratum should be similar in all major characteristics that affect 

electricity consumption, except that some households use electricity for 

air conditioning and some do not. Within each stratum, calculate the 

difference between the mean electricity consumption for users of electric 

air conditioning and the mean electricity consumption for nonusers of 

electric air conditioning. The difference is an estimate of the mean 

amount of electricity used for air conditioning among households 

represented by the stratum. Weighted sums of the differences can be used 

to estimate regional and national totals.

Two advantages of this method are that it is simple to estimate the enduse components once the strata are selected and the idea behind the 

method is easy to understand.

The main disadvantage of this procedure is that many factors influence 

electricity consumption. This makes selection of the strata difficult. 

In estimating air-conditioning use, it would be desirable to stratify 

according to use and nonuse of electricity for space heating and for 

water heating. In addition, it would be desirable to stratify according 

to weather zones and size of dwelling. If one does these two things, 

the number of observations in some strata becomes too small. The differences 

between users and nonusers of air conditioners within strata may still 

be substantial. For example, consider the stratum consisting of households 

living in the South that use electricity for water heating and space 

heating. Households in this stratum that have air conditioning tend to 

have higher incomes, larger homes, and more electrical appliances than 

those households that do not have air conditioning. This means that the 

estimate of the amount of electricity used for air conditioning in this 

stratum will be biased upward. For other strata it may be biased downward. 

The total national estimate of electricity used for air conditioning may 

depend heavily on the stratification procedure used.

-47-
One possible stratification scheme that may result in a national estimate 

with a small bias would be to stratify according to ELCONBS + ELCONHT + 

ELCONWT. This scheme would result in strata that contain households from 

all parts of the country. On the other hand, the households in the strata 

would theoretically use similar amounts of electricity for noncooling 

purposes.

Even if a stratification scheme that yields a reliable national estimate 

exists, it may not be usable for obtaining regional estimates, estimates 

by income categories, and estimates for other categories. To obtain these 

estimates, one may need to seek different stratification schemes. In 

addition, separate stratification schemes may be needed for each end use.

Method 2t Estimation Using End-Use Consumption Amounts for Each Household

One procedure that could be used to yield national, regional, and special 

category estimates would be to estimate the energy consumed for the major 

end uses by fuel for each household in the REGS data set. Given these 

numbers, one would then use the household weights to estimate the amount of 

energy used by end use for any category defined by variables in the REGS 

data set.

Variation 1; Regression Estimates. One way of estimating the energy 

consumed for the major end uses would be to use the estimated components 

ELCONBS, ELCONCL, ELCONHT, ELCONWT, FOCONHT, FOCONWT, LPCONBS, LPCONHT, 

LPCONWT, NGCONBS, NGCONHT, and NGCONWT as end-use consumption estimates. 

This would apply for households living in single-family detached homes. 

The variables for fuels not used by a particular household will be set 

equal to zero. For households not living in single-family detached 

homes, the variables would be multiplied by the appropriate coefficients 

in the regression equations for ELCON, FOCON, LPCON, and NGCON. The 

only adjustment in the equations is to change the fuel oil components so 

that they reflect both fuel oil and kerosene consumption. This was done 

by setting the indicator function for water heating, main space heating, 

and secondary space heating equal to one if either fuel oil or kerosene 

was used for these purposes. This was done because the consumption 

amounts for fuel oil and kerosene were combined on the REGS data set.

Using that scheme, one can estimate the amount of electricity used for 

air conditioning for any category. Unfortunately, the estimates depend 

heavily on the results of the regression analysis. Also, many households 

consume much more or much less than the regression equation predicts. 

In addition, not all factors that possibly affect energy consumption 

were examined during the regression analysis. If one of these factors 

happens to be used in the definition of the category over which one desires 

an estimate of the amount of electricity used for air conditioning, then 

the resulting estimates may be seriously biased. This will happen when 

the regression equations consistently overestimate or consistently under 

estimate the amount of electricity used for air conditioning by households 

falling in the category.

-48-
Variation 2; Normalized Regression Estimates. The possibility of serious 

bias can be reduced by normalizing the end-use estimates for each household 

in the data set so that the sum of the end-use estimates for each fuel 

equals the actual (or imputed) yearly consumption. For example, set pi 

= ELCONBS/ELCON, p2 = ELCONWT/ELCON, p 3 = ELCONHT/ELCON, and p4 =• 

ELCONCL/ELCON. Then p^, p2 , P3, and P4 are estimates of the proportion 

of the yearly electricity consumption that is used for miscellaneous 

uses, water heating, space heating, and air conditioning, respectively. 

The actual (or imputed) yearly electricity consumption is denoted by 

BTUEL. Set BTUELBS = pi x BTUEL, BTUELWT = p2 x BTUEL, BTUELHT - p3 x 

BTUEL, and BTUELCL = P4 x BTUEL. Then BTUELBS, BTUELWT, BTUELHT, and 

BTUELCL are estimates of the end-use consumption normalized so that 

BTUELBS + BTUELWT + BTUELHT + BTUELCL = BTUEL.

The same procedure can be used to produce normalized end-use consumption 

estimates for all fuels and for all households. The end-use estimates 

for fuel oil and kerosene are combined. To obtain the estimated pro 

portions for fuel oil and kerosene, the fuel oil regression equation is 

used but the indicator functions reflect both fuel oil and kerosene use.

When working with households living in dwellings other than single-family 

detached homes, one needs to take into account the coefficients in the 

equations defining ELCON and NGCON. For example, when estimating end-use 

electricity consumption for households living in mobile homes, set p^ » 

1.1491 x ELCONBS/ELCON, p 2 = 0.5916 x ELCONWT/ELCON, p 3 - 1.1491 x ELCONHT/ 

ELCON, and P4 = 1.1491 x ELCONCL/ELCON. Note in the equation for p 2 the 

coefficient, 0.5916, comes from the sum of the coefficients for ELCONSFD 

and ELCONWT in the equation for ELCON. Using the values of pi, p2 , P3, 

and P4, proceed as before. With fuel oil and LPG, the proportions 

obtained from FOCON and LPCON will be the same as those obtained from 

FOCONSFD and LPCONSFD.

As mentioned previously, the coefficients for some of the terms in the 

regression equations may be changed dramatically by adding new independent 

variables or by removing other independent variables. This was one of the 

reasons the author was hesitant to interpret the values of the individual 

coefficients. On the other hand, any reasonable set of independent 

variables should produce normalized end-use consumption estimates similar 

to the ones produced by the set of independent variables that were used. 

The biggest changes would occur in components composed of only one or 

two independent variables or components affected by response errors. 

These included the water-heating component for fuel oil, the appliance 

component for natural gas, the water-heating component for LPG, and the 

appliance component for LPG.

Using the normalized regression estimates, the regression results are 

used only to estimate the proportion of the energy consumption that goes 

for each end use. In addition, the normalized end-use estimates are 

easier to interpret than the coefficients of the individual terms in the 

regression equations. They represent broad end-use consumption classes, 

there are far fewer terms to interpret, and the estimates have been 

normalized so that they sum to the actual consumption.

-49-
A possible source of colinearity that may adversely affect the normalized 

end-use estimates is the fact that households that use natural gas, fuel 

oil, or LPG for the main water-heating fuel tend to use the same fuel as 

the main space-heating fuel. Any miscellaneous use of natural gas 

tends also to imply natural gas space heating.

A greater problem involves one of the assumptions behind the normalization 

procedure. Here, one assumes that if a household conserves or overuses 

energy for one end use, it will do so on all end uses for that fuel by 

the same proportion. For instance, if BTUEL/ELCON = 1.34, then BTUELBS - 

1.34 x ELCONBS, BTUELWT = 1.34 x ELCONWT, BTUELHT = 1.34 x ELCONHT, and 

BTUELCL » 1.34 x ELCONCL. It is clear that some households may conserve 

electricity by setting the thermostat low in the winter, but they may 

also use more electricity than comparable households with comparable airconditioning equipment for cooling in the summer. Other households may 

do the opposite.

For individual households, the normalized end-use estimates may not 

accurately reflect the true breakdown of the energy consumption for the 

household. If one averages the normalized end-use estimates over 

categories with a "reasonable" number of sample households in the 

category, then the average estimates should approximate the actual 

averages for the sample households in the category. In addition, when one 

computes the average estimates, one uses these numbers to represent the 

energy consumption of all households in the country that fall in the 

category. Hence, the sampling errors and the nonsampling errors involved 

in producing the reported (or imputed) yearly energy consumption for the 

households also affect the use of the author's estimates.

The desire to keep the errors in the end-use averages caused by either the 

normalized estimation procedure, sampling error, or nonsampling error points 

to estimating averages only over categories with a "reasonable" number of 

sample households. It is not obvious how large a number needs to be before 

it is considered to be "reasonable." In addition, the idea of a "reasonable" 

number may vary. For instance, if one wants to estimate the amount of fuel 

oil used for space heating by households that used fuel oil only for 

space heating, then the normalized end-use estimate equals the actual 

(or imputed) consumption. On the other hand, the normalized estimate of 

the amount of natural gas used for non-water-heating and non-space-heating 

purposes may be subject to large errors caused by the noise in the spaceheating and water-heating components.

Recommended Disaggregation Technique

It was decided that variation 2 of method 2, the normalized end-use estimate, 

was the best of the three proposed techniques. It allows one to produce 

estimates for subpopulations. In addition, the resulting end-use estimates 

will sum to the overall consumption estimate.

-50-
An additional criticism of the normalized end-use estimates is that they 

do not use the additional information contained in the billing-period 

data. They use only the annual consumption data. For that matter, none 

of the proposed procedures uses this additional information. Major 

problems that are encountered in using the billing-period information 

are that it is available for only some households, the lengths of the 

billing periods vary considerably, and the quality of the billingperiod data varies considerably.

One would probably need to divide the sample into several subsets 

according to the amount of useful billing-period data. The disaggregation 

technique could then vary between subsets. In some subsets, one may be 

able to use the billing-period data, while in others, one may have only 

the annual consumption amounts to use.

It is not clear how much using the billing-period data would improve 

the disaggregation procedure. The extra work needed to use the data 

means that it may be some time before a procedure can be developed that 

produces end-use estimates for all households and uses the billing-period 

data, when available, to calculate these estimates.

Indexes for Residential Energy Consumption

The national or regional averages for the normalized end-use estimates 

can be used as indexes for residential energy consumption. In addition, 

one can compute the averages for other ways of categorizing households.

It might be interesting to sum the normalized space-heating consumption 

estimates across fuels. In this way, one could estimate the total or average 

amount of energy used for space heating. This average value could be used 

as an index for residential space-heating consumption. It remains to be 

determined if a straight sum or a weighted sum should be used. The 

weights could reflect the efficiency with which the different fuels can 

be used. In particular, a Btu of electricity can be used more efficiently 

than a Btu of other fuels. On the other hand, more than a Btu of oil or 

coal is consumed in a power plant to produce a Btu of electricity. 

Additional energy is lost when electricity is transmitted a long distance 

over power lines. A general rule of thumb is that it takes approximately 

3.0 Btu of fuel at the power plant to produce 1.0 Btu of electricity. The 

additional amount of energy lost in transmission depends on the distance 

and the climate.

For each fuel, one could correct the normalized space-heating consumption 

estimate for weather by dividing by the average number of heating degreedays. This would give one the heating component in thousands of Btu per 

degree-day. In addition, if one also divides by the average number of 

square feet heated per household, the heating component is given in Btu 

per degree-day per square foot. This final index will reflect not only 

the thermal integrity of the housing stock and the efficiency of the 

residential heating equipment but also the life-style of the household 

members. In particular, a trend toward lower thermostat settings should 

result in a lower value of the Btu per degree-day per square foot index.

-51-
Tables of Results

The average normalized end-use consumption estimates for electricity, 

natural gas, fuel oil, and LPG are summarized in Tables 10 to 19. These 

tables give the national averages, regional averages, and averages for 

various categories. The averages are weighted averages where the weights 

equal the number of households in the population that the sample household 

represents. In all tables, the values in parentheses are the estimated 

standard deviations of the corresponding statistic.

The estimates for the standard deviations were obtained by using a balanced 

half-sample replication technique.^ The listed standard deviations reflect 

only the sampling variation. They do not take into account errors made in 

disaggregating the annual energy consumption for individual households. 

Hence, the listed standard deviations are biased toward the low side. In 

the author's opinion, the sampling error should be greater than the 

disaggregation error in most cases. Hence, one expects that the bias 

should be small in most cases. The cases where the half-sample estimates 

may be substantially too small involve components that contain only one 

independent variable or components with independent variables subject to 

considerable response error. The components with only one independent 

variable are the fuel oil water-heating component, LPG appliance component, 

and the LPG water-heating component. The components subject to possible 

large amounts of response error are the natural gas appliance component 

and the LPG appliance component. The response errors involved are the 

possible incorrect fuels for refrigerators and central air-conditioning 

systems.

Table 10 lists the weighted averages for the electricity end-use components 

by Census division, by income group, by amount of heated floorspace, and 

by heating degree-days. The averages are over all households, including 

the relatively few households that do not use electricity.

Tables 11, 12, and 13 list the weighted averages for the natural gas, fuel 

oil, and LPG end-use components by Census division, by income group, by 

amount of heated floorspace, and by heating degree-days. The averages 

are over only those households that use the fuel in question.

The heating component averages listed in Tables 10 through 13 depend 

heavily on the percentage of the households that use each fuel for heating. 

To study trends in space-heating consumption, it is necessary to study the 

averages over only those households that heat with the fuel that is 

being studied. The same applies to the water-heating components.

Table 14 applies only to households where the main space-heating fuel is 

electricity. Table 14 lists the average degree-days, average heated 

square footage, and average amount of electricity consumed for space 

heating in Btu, Btu per degree-day, and Btu per degree-day per square 

foot. Tables 15, 16, and 17 are the corresponding tables for natural 

gas, fuel oil, and LPG.

^For a description of how the balanced half-sample replication technique 

is applied to REGS data, see Energy Information Administration, Residential 

Energy Consumption Survey; Housing Characteristics, 1980 (Washington, B.C., 

June 1982).

-52-
In Tables 14 through 17, the averages for Btu per degree-day do not equal 

the results given by dividing the average Btu consumed for space heating 

by the average amount of heating degree-days. Instead the tabled values 

are the average values of the indexes. The difference between the average 

values of the indexes and the indexes computed from the average values 

reflects the skewness in the distribution of the indexes among the sample 

households. The same applies to the averages for Btu per degree-day per 

square foot.

The water-heating component for each fuel is isolated and displayed in 

Tables 18 and 19. Table 18 lists the average values of the electricity 

water-heating component over only those households whose main waterheating fuel is electricity. Table 18 also lists the corresponding 

average values for natural gas. Table 19 lists the corresponding average 

values for fuel oil and LPG.

The heating degree-day base used in Tables 10 through 19 to classify 

households by weather is 65 F. In addition, the heating degree-day base 

used to calculate the indexes given in Tables 14 through 17 is also 65 F. 

The tables could have been constructed using any degree-day base. The 

base does not have to be the same as was used in the regression analysis. 

The author chose 65 F because it is the most commonly used base for 

calculating heating degree-days. Changing the base used in the computation 

of the space-heating indexes may alter some of the trends that appear when 

using a base of 65 F.

-53-
Table 10. Average Household Electricity Consumption by End Use for All Households

Average Household Electricity 

Consumption (Million Btu) for

Location and Income

Number of

Households

(Millions)

Space 

Heating

Water 

Heating

Air 

Conditioning

Mi sc el la neou s 

Uses

National

Census Divisions

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central

iln West South Central

"T Mountain 

Pacific

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

81.6 (0.0) 3.4 (0.3) 3.8 (0.2) 3.9 (0.1) 19.0 (0.2)

4.3

13.4

14.8

6.3

14.0

5.2

7.7

4.1

11.8

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

3)

3)

4)

4)

5)

3)

3)

1)

1)

1.9

2.6

3.2

2.1

3.7

9.1

2.5

2.1

3.8

(0.4)

(0.5)

(0.8)

(0.5)

(0.8)

(1.2)

(0.5)

(0.6)

(0.7)

2.9

2.4

3.2

2.9

7.0

7.5

2.3

2.4

3.1

(0.3)

(0.5)

(0.5)

(0.5)

(0.5)

(0.8)

(0.5)

(0.8)

(0.4)

0.4

1.0

1.4

5.3

6.4

8.2

12.6

3.0

0.8

(0.

(0.

(0.

(0.

(0.

(0.

(1.

(0.

(0.

1)

2)

4)

6)

7)

7)

2)

5)

2)

17.1

16.1

19.5

20.8

19.9

21.9

20.8

19.3

17.9

(0.6)

(0.5)

(0.5)

(0.5)

(0.6)

(0.8)

(1.0)

(0.5)

(0.5)

10.4

13.9

13.8

11.9

9.9

12.4

9.4

(0.

(0.

(0.

(0.

(0.

(0.

(0.

5)

6)

5)

4)

5)

5)

6)

3.1

2.7

3.1

3.2

3.8

3.8

4.4

(0.4)

(0.3)

(0.4)

(0.4)

(0.5)

(0.6)

(0.6)

2.4

3.1

3.7

4.1

4.3

4.5

4.8

(0.2)

(0.3)

(0.4)

(0.3)

(0.3)

(0.4)

(0.4)

1.6

2.2

3.1

3.9

4.7

5.2

7.7

(0.

(0.

(0.

(0.

(0.

(0.

(0.

2)

2)

2)

3)

4)

3)

6)

13.1

15.3

16.4

19.8

22.1

22.3

26.3

(0.4)

(0.3)

(0.5)

(0.4)

(0.5)

(0.5)

(0.6)
Table 10. Average Household Electricity Consumption by End Use for All Households 

(Continued)

i

Ul 

UiI

Average Household Electricity 

Consumption (Million Btu) for

Space Heated 

and Heating 

Degree-Days

Number of 

Households 

(Millions)

Space 

Heating

Water 

Heating

Air 

Conditioning

Miscellaneous 

Uses

Heated Square 

Footage

0

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April

0.5

17.0

11.1

9.3

7.7

12.5

12.2

11.4

1980

(0.1)

(0.7)

(0.5)

(0.4)

(0.3)

(0.4)

(0.5)

(0.5)

Through March 1981

(Base 65 F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

11.7

7.2

9.8

8.6

17.5

15.7

7.3

3.9

(1.9)

(1.6)

(1.4)

(1.4)

(2.1)

(1.6)

(0.8)

(1.3)

0.5

17.0

11.1

9.3

7.7

12.5

12.2

11.4

(0.1)

(0.7)

(0.5)

(0.4)

(0.3)

(0.4)

(0.5)

(0.5)

0.0

2.6

3.0

3.2

3.6

3.4

4.7

3.7

(0.0)

(0.3)

(0.3)

(0.4)

(0.5)

(0.5)

(0.6)

(0.5)

2.4

2.3

3.2

3.7

4.4

4.4

4.8

4.7

(0.7)

(0.2)

(0.2)

(0.3)

(0.3)

(0.4)

(0.4)

(0.4)

0.6

2.0

2.9

3.5

4.4

4.9

5.2

5.4

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

4)

1)

3)

2)

4)

5)

4)

3)

14.

11.

14.

17.

19.

21.

23.

26.

4

9

9

5

7

4

9

8

(1.0)

(0.3)

(0.4)

(0.5)

(0.4)

(0.4)

(0.4)

(0.6)

1.6 

2.1 

-3.4 

8.8 

3.3 

3.2 

1.8 

3.2

(0.3) 

(0.5) 

(0.6) 

(1.4) 

(0.7) 

(0.8) 

(0.5) 

(0.6)

3.7 

2.3 

4.8 

7.3 

3.1 

2.8 

3.1 

5.2

(0.6) 

(0.8) 

(0.6) 

(0.8) 

(0.6) 

(0.4) 

(0.6) 

(1.0)

7.9 

8.8 

5.7 

4.8 

2.7 

1.1 

0.2 

0.2

(1.0)

(1.1) 

(0.5) 

(0.7) 

(0.3) 

(0.2) 

(0.1) 

(0.1)

18.

19. 

19. 

22.

17.

18. 

18.

21.8

(0.7) 

(0.8) 

(0.7) 

(0.6) 

(0.4) 

(0.5) 

(0.6) 

(0.9)
Table 11. Average Household Natural Gas Consumption by End Use 

_______for Households Using Natural Gas_______________________

Average Household Natural Gas

Location and 

Income

Number of House 

holds That Use 

Natural Gas 

(Millions)

Consumption (Million Btu) for

Space 

Heating

Water 

Heating

Miscellaneous

Uses

National

Census Divisions

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central 

West South Central 

Mountain 

Pacific

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

51.6 (1.4) 64.4 (1.3) 24.1 (0.4) 7.3 (0.3)

1.9

9.0

10.9

4.6

4.9

2.3

6.1

3.0

8.9

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

3)

9)

4)

5)

6)

3)

3)

3)

4)

52.

59.

99.

87.

57.

58.

44.

70.

34.

0

6

3

9

0

9

5

0

1

(7.0)

(5.4)

(2.8)

(4.1)

(5.2)

(4.5)

(3.4)

(1.8)

(1.3)

17.5

17.9

27.8

24.3

22.7

20.9

28.0

24.7

25.6

(0.

(1.

(0.

(1.

(2.

(2.

(1.

(1.

(1.

9)

2)

5)

2)

1)

5)

2)

2)

0)

6.

9.

7.

5.

5.

4.

9.

3,

7.

8

0

7

6

7

8

5

5

1

(0.5)

(0.5)

(0.6)

(0.5)

(0.5)

(0.9)

(1.0)

(0.6)

(1.0)

6.6

8.9

8.4

7.6

6.4

7.5

6.1

(0.

(0.

(0.

(0.

(0.

(0.

(0.

4)

5)

5)

4)

5)

5)

4)

55.5

56.6

56.6

61.8

74.1

70.7

81.1

(3.

(2.

(2.

(2.

(3.

(2.

(4.

4)

4)

6)

4)

3)

4)

9)

19.

20.

21.

24.

28.

27.

28.

9

8

2

7

6

0

3

(0.

(0.

(0.

(0.

(0.

(1.

(1.

7)

7)

8)

9)

9)

0)

0)

6.2

7.2

6.6

6.9

7.4

8.0

9.0

(0.3)

(0.7)

(0.5)

(0.4)

(0.5)

(0.7)

(1.3)

-56-
Table 11. Average Household Natural Gas Consumption by End Use 

for Households Using Natural Gas (Continued)

Space Heated 

and Heating 

Degree-Days

Heated Square

Footage

0

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April

Number of House 

holds That Use 

Natural Gas 

(Millions)

0.1

10.8

7.5

5.6

4.9

7.9

7.7

7.0

1980

(0.0)

(0.6)

(0.5)

(0.4)

(0.3)

(0.4)

(0.4)

(0.4)

0.

37.

49.

61.

59.

69.

81.

103.

Average Household Natural Gas 

Consumption (Million Btu) for

Space 

Heating

0

8

2

8

9

0

3

4

(0.

(1.

(2.

(3.

(3.

(2.

(3.

(4.

0)

8)

0)

2)

0)

1)

2)

1)

Water 

Heating

47.8

15.3

20.7

25.4

25.8

26.9

29.2

29.8

(10.

(0.

(0.

(0.

(1.

(0.

(1.

(1.

4)

7)

8)

7)

1)

9)

3)

0)

Miscellaneous 

Uses

4.4

6.2

6.5

6.6

9.0

7.3

7.4

8.9

(1.6)

(0.3)

(0.4)

(0.4)

(1.1)

(0.5)

(0.6)

(1.1)

Through March 1981

(Base 65 F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

7.4

5.7

5.5

3.5

11.7

11.4

4.8

1.6

(1.2)

(1.3)

(0.8)

(0.8)

(1.1)

(1.1)

(0.6)

(0.6)

24.

40.

54.

63.

61.

89.

98.

102.

8

7

6

9

8

4

9

2

(1.

(2.

(3.

(3.

(3.

(2.

(2.

(9.

2)

5)

5)

5)

3)

5)

6)

5)

26.8

25.3

25.0

23.2

20.1

25.6

24.7

22.4

(1.

(1.

(1.

(1.

(1.

(0.

(0.

(2.

2)

6)

4)

8)

2)

4)

8)

1)

9.1

6.4

6.2

5.2

8.4

7.5

5.7

5.5

(1.0)

(1.0)

(0.7)

(0.7)

(0.6)

(0.4)

(0.4)

(1.2)

-57-
Table 12. Average Household Fuel Oil and Kerosene Consumption by End Use 

________for Households Using Fuel Oil or Kerosene________________

Average Household Fuel Oil and Kerosene Consumption (Million Btu) for

Location and 

Income

Number of House 

holds That Use Fuel 

Oil or Kerosene 

(Millions)

Space 

Heating

Water 

Heating

National

Census Divisions

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central 

West South Central 

Mountain 

Pacific

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

15.4 (0.9)

2.7 

6.5 

1.5 

0.5 

3.5

(0.2) 

(0.5) 

(0.3) 

(0.1) 

(0.5)

0.5 (0.1)

2.2 (0.3) 

2.8 (0.2) 

(0.3) 

(0.3) 

(0.1) 

(0.2) 

(0.2)

2.9 

2.2 

1.5 

2.3 

1.5

85.5 (1.9)

98.0 (3.6) 

95.8 (2.8) 

73.9 (4.8) 

76.0 (9.5) 

69.2 (5.1)

61.7 (4.2)

15.3 (0.7)

20.2 (1.1)

23.1 (1.4)

3.2 (1.5)

1.0 (0.7)

7.1 (2.6)

1.5 (0.7)

86.0 (6.8) 

84.4 (4.1) 

77.7 (2.8) 

82.1 (4.5) 

91.1 (7.5) 

85.0 (3.2) 

101.6 (3.9)

16.2 (2.4) 

18.1 (1.9) 

13.8 (1.3) 

12.9 (1.3) 

12.0 (2.4) 

14.1 (1.9) 

19.9 (1.6)

-58-
Table 12. Average Household Fuel Oil and Kerosene Consumption by End Use 

________for Households Using Fuel Oil or Kerosene (Continued)________

Average Household Fuel Oil and Kerosene Consumption (Million Btu) for

Space Heated 

and Heating 

Degree-Days

Number of House 

holds That Use Fuel 

Oil or Kerosene 

(Millions)

Space 

Heating

Water 

Heating

Heated Square 

Footage

0

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65°F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

-

3.4 (0.4)

1.8 (0.2)

1.7 (0.2)

1.3 (0.2)

2.2 (0.2)

2.1 (0.2)

2.8 (0.3)

0.4 (0.2) -

1.1 (0.3)

1.3 (0.4)

6.2 (0.8)

3.2 (0.5)

1.7 (0.3)

1.4 (0.7)

-

73.1 (3.9)

73.4 (4.2)

78.1 (5.8)

79.9 (4.7)

87.2 (3.1)

92.1 (6.0)

109.9 (4.6)

45.8 (4.0) -

68.6 (6.3)

75.5 (9.2)

88.7 (3.8)

96.5 (4.0)

96.3 (6.4)

69.7 (7.3)

-

21.1 (2.1)

16.4 (1.3)

15.7 (2.7)

12.1 (1.6)

11.7 (1.3)

12.8 (1.6)

13.4 (1.7)

2.5 (1.3) -

1.6 (1.2)

10.5 (3.6)

21.4 (2.2)

16.1 (1.3)

13.7 (1.7)

7.3 (1.8)

Note: A dash "-" represents data withheld because there were not enough 

sample households in this category to produce accurate estimates.

-59-
Table 13. Average Household LPG Consumption by End Use for Households 

________Using LPG________________________________________

Average Household LPG Consumption 

(Million Btu) for

Location and

Income

Number of

Households

That Use LPG

(Millions)

Space

Heating

Water

Heating

Miscel 

laneous

Uses

National 

Census Division

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central 

West South Central 

Mountain 

Pacific

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

7.7 (0.7) 32.3 (2.8) 9.6 (0.8) 5.7 (0.4)

0.5

0.7

1.2

0.9

2.4

0.5

0.6

0.4

0.4

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

1)

3)

2)

2)

5)

1)

2)

1)

1)

8.0

12.8

56.4

54.8

23.2

40.3

31.1

51.9

8.8

(7.

(9.

(5.

(8.

(3.

(6.

(5.

(7.

(2.

1)

3)

4)

4)

8)

4)

1)

3)

7)

4.

4.

12.

11.

7.

6.

14.

12.

18.

8

6

7

9

3

2

7

0

4

(0.

(1.

(0.

(0.

(1.

(1.

(5.

(3.

(1.

8)

2)

8)

9)

8)

1)

4)

2)

8)

6.5

5.5

5.6

4.8

6.4

4.0

5.9

3.5

6.9

(0.7)

(0.7)

(0.5)

(0.6)

(0.9)

(1.1)

(0.5)

(0.5)

(2.3)

1.2

1.6

1.6

1.0

0.5

1.0

0.6

(0.

(0.

(0.

(0.

(0.

(0.

(0.

2)

2)

2)

2)

1)

1)

1)

24

30

34

27

46

34

39

.7

.9

.2

.6

.2

.2

.6

(3.

(3.

(3.

(6.

(8.

(5.

(7.

5)

2)

3)

0)

9)

5)

5)

7.4

5.8

9.7

9.4

18.5

11.2

13.6

(2.

(1.

(1.

(1.

(6.

(2.

(2.

0)

1)

2)

9)

2)

2)

3)

5.9

5.0

5.0

5.6

5.7

6.7

7.4

(0.8)

(0.6)

(0.7)

(0.7)

(0.9)

(1.1)

(1.4)

-60-
Table 13. Average Household LPG Consumption by End Use for Households 

________Using LPG (Continued)_________________________________

Average Household LPG Consumption 

(Million Btu) for

Space Heated 

and Heating 

Degree-Days

Heated Square

Footage

0

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65°F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

Number of 

Households 

That Use LPG 

(Millions)

0.2

2.3

0.9

0.9

0.6

0.9

0.9

0.9

1.0

0.5

1.7

0.9

1.2

0.9

0.8

0.7

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

0)

3)

1)

2)

1)

1)

2)

2)

3)

2)

5)

4)

3)

2)

3)

3)

Space 

Heating

0.

31.

25.

41.

28.

30.

32.

42.

12.

32.

33.

33.

45.

28.

44.

25.

0

1

4

9

7

7

0

8

3

6

1

6

3

4

3

8

(0.

(4.

(4.

(4.

(4.

(3.

(5.

(9.

(3.

(6.

(4.

(9.

(8.

(11.

(8.

(12.

0)

6)

4)

8)

1)

9)

8)

2)

9)

6)

0)

1)

1)

2)

6)

7)

Water 

Heating

22.5

7.7

7.7

8.2

6.1

7.1

10.8

19.0

13.3

17.2

7.8

7.3

9.8

8.0

10.0

7.3

(2.7)

(1.4)

(2.0)

(2.0)

(1.5)

(1.3)

(1.9)

(4.0)

(4.5)

(4.3)

(1.8)

(1.6)

(1.9)

(1.7)

(1.4)

(2.4)

Miscel 

laneous 

Uses

4.3

6.2

5.5

5.6

4.7

4.5

6.5

5.8

8.8

5.7

3.2

4.3

4.7

5.7

5.4

6.3

(1.1)

(0.7)

(0.9)

(0.6)

(0.6)

(1.1)

(1.3)

(0.8)

(0.8)

(1.7)

(1.0)

(1.0)

(0.6)

(0.5)

(0.8)

(1.1)

-61-
NS 

I

Table 14. Average Household Electricity Consumption for Space Heating for Households Whose Main SpaceHeating Fuel Is Electricity

Average

Location and

Income

National

Census Divisions

New England

Middle Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific

Number of 

Households

(Millions)

14.3 (1.0)

0.3 (0.1)

1.3 (0.4)

1.7 (0.4)

0.4 (0.1)

4.3 (0.8)

1.8 (0.3)

1.6 (0.3)

0.7 (0.2)

2.2 (0.3)

Heating 

DegreeDays (Base

65 F)

3,956

6,774

6,441

6,634

5,713

2,290

4,173

2,393

3,120

4,176

(278)

(275)

(393)

(249)

(332)

(663)

(184)

(193)

(467)

(209)

Average Electricity Consumption for 

Space Heating

Average 

Heated 

Square

Footage

1,366

1,350

1,552

1,415

1,889

1,387

1,497

1,289

1,216

1,075

(32)

(201)

(172)

(172)

(231)

(79)

(121)

(101)

(97)

(64)

Thousand Btu

Million Btu per Degree-Days

18.2

20.1

25.3

26.1

28.0

11.6

24.9

12.3

11.6

19.7

(1.5)

(3.4)

(3.4)

(3.3)

(2.2)

(3.9)

(1.3)

(0.9)

(2.3)

(1.8)

4.6

3.1

4.0

4.0

5.1

4.6

6.0

5.3

3.7

4.4

(0.2)

(0.6)

(0.6)

(0.5)

(0.4)

(0.6)

(0.2)

(0.6)

(0.2)

(0.4)

Btu 

per Degree-Day

per Square Foot

3.9

2.6

2.9

3.1

3.2

3.7

4.9

4.2

3.4

4.9

(0.2)

(0.3)

(0.4)

(0.2)

(0.4)

(0.4)

(0.4)

(0.3)

(0.3)

(0.2)

Income Group 

(Estimated 1979 

Income)

Less than $5,000

$5,000 to

$10

$15

$20

$25

$35

,000

,000

,000

,000

,000

to

to

to

to

or

$9,999

$14,999

$19,999

$24,999

$34,999

More

1.8

2.1

2.5

2.0

1.7

2.4

1.8

(0.

(0.

(0.

(0.

(0.

(0.

(0.

3)

3)

2)

3)

2)

3)

3)

4

3

3

4

3

3

3

,313

,842

,930

,085

,851

,907

,789

(201)

(434)

(282)

(456)

(336)

(305)

(483)

904

1,032

1,220

1,222

1,589

1,626

2,029

(64)

(46)

(41)

(69)

(78)

(71)

(78)

17.4 (1.4) 

16.4 (2.2) 

16.3 (1.6) 

17.4 (2.2) 

20.5 (2.0) 

18.8 (2.2) 

21.9 (3.1)

4.0 (0.2) 

4.3 (0.3) 

(0.3) 

(0.3) 

(0.3) 

4.8 (0.5) 

5.9 (0.4)

4.2

4.3 

5.2

5.2 (0.3) 

4.7 (0.3) 

3.6 (0.2) 

3.9 (0.2) 

3.7 (0.3) 

3.3 (0.3) 

3.2 (0.2)
Table 14. Average Household Electricity Consumption for Space Heating for Households Whose Main SpaceHeating Fuel Is Electricity (Continued)

OJ

Average

Space Heated Number of 

and Heating Households 

Degree-Days (Millions)

Heated Square

Footage

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65 F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

3.6 (0.4)

2.2 (0.2)

1.7 (0.2)

1.4 (0.2)

2.0 (0.2)

2.0 (0.3)

1.4 (0.2)

3.8 (1.1)

1.2 (0.3)

1.7 (0.4)

2.8 (0.5)

2.0 (0.5)

2.0 (0.5)

0.4 (0.1)

0.3 (0.1)

Heating 

DegreeDays (Base 

65 F)

4,063

3,839

3,777

3,535

3,543

4,394

4,446

1,094

2,447

3,565

4,658

5,414

6,556

7,475

8,925

(334)

(214)

(406)

(535)

(357)

(326)

(314)

(319)

(78)

(83)

(58)

(43)

(92)

(59)

(407)

Average 

Heated 

Square 

Footage

611

891

1,079

1,288

1,581

2,073

3,152

1,258

1,324

1,244

1,379

1,608

1,368

1,555

1,562

(10)

(5)

(6)

(5)

(9)

(18)

(86)

(72)

(88)

(93)

(99)

(98)

(140)

(322)

(285)

Average Electricity Consumption 

Space Heating

Thousand Btu 

Million Btu per Degree-Days

11.8

14.5

16.0

19.4

19.9

26.6

27.6

4.9

11.9

18.0

26.2

27.2

23.5

24.4

32.6

(1.0)

(1.1)

(2.0)

(3.1)

(2.4)

(2.1)

(2.5)

(1.8)

(1.1)

(1.6)

(0.7)

(1.5)

(4.1)

(3.9)

(7.2)

2.9

3.8

4.3

5.1

5.6

6.3

6.6

4.1

4.9

5.1

5.6

5.0

3.6

3.3

3.8

(0.2)

(0.2)

(0.4)

(0.4)

(0.3)

(0.3)

(0.4)

(0.5)

(0.6)

(0.5)

(0.2)

(0.3)

(0.6)

(0.5)

(0.9)

per 

per

5.1

4.2

4.0

3.9

3.5

3.0

2.1

3.6

3.9

4.8

4.9

3.8

2.8

3.0

2.6

for

Btu 

Degree-Day 

Square Foot

(0.3)

(0.2)

(0.3)

(0.3)

(0.2)

(0.1)

(0.1)

(0.5)

(0.4)

(0.2)

(0.3)

(0.3)

(0.3)

(0.6)

(0.4)
-o 

i

Table 15. Average Household Natural Gas Consumption for Space Heating for Households Whose Main SpaceHeating Fuel Is Natural Gas

Location and 

Income

Number of 

Households 

(Millions)

Average 

Heating DegreeDays (Base 

65°F)

Average 

Heated 

Square 

Footage

Average Natural Gas Consumption for 

Space Heating

Thousand Btu 

Million Btu per Degree-Days

Btu 

per Degree-Day 

per Square Foot

National

Census Divisions

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central 

West South Central 

Mountain 

Pacific

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

44.6 (1.5) 4,847 (66) 1,533 (22) 74.1 (1.2)

5.5 (0.3) 

7.3 (0.4) 

6.9 (0.5) 

6.6 (0.4) 

6.0 (0.4) 

6.7 (0.4) 

5.6 (0.4)

15.6 (0.2)

4,633 (169) 

4,826 (129) 

4,932 (162) 

4,822 (121) 

5,090 (133) 

4,993 (122) 

4,574 (182)

1,066 (39)

1,193 (32)

1,250 (29)

1,426 (36)

1,664 (50)

1,854 (48)

2,379 (74)

66.9 (3.1) 

68.7 (1.9) 

68.4 (2.6) 

70.1 (2.2) 

79.5 (3.4) 

79.1 (2.3) 

87.9 (5.0)

15.0 (0.5)

14

14

(0.4) 

(0.3) 

15.0 (0.3) 

15.9 (0.5) 

16.1 (0.3) 

19.6 (0.6)

12.4 (0.2)

1.1

5.5

10.4

4.5

4.0

2.2

5.6

2.9

8.3

(0.2)

(0.9)

(0.5)

(0.5)

(0.6)

(0.3)

(0.3)

(0.3)

(0.5)

6,823

6,239

6,736

6,236

4,117

3,804

2,601

5,031

2,602

(86)

(163)

(112)

(156)

(282)

(164)

(136)

(165)

(115)

1,388

1,663

1,587

1,735

1,459

1,418

1,387

1,459

1,477

(115)

(60)

(42)

(99)

(52)

(94)

(57)

(92)

(75)

87.6

96.4

103.6

88.9

68.4

62.4

47.8

72.2

36.3

(6.9)

(2.8)

(2.3)

(4.2)

(4.8)

(3.5)

(3.7)

(2.7)

(1.2)

12.8

15.6

15.4

14.7

17.2

16.7

18.6

14.3

14.2

(0.9)

(0.6)

(0.3)

(0.7)

(0.7)

(0.8)

(1.2)

(0.4)

(0.4)

10.8

11.9

11.4

10.6

14.0

14.2

15.8

11.8

11.6

(0.4)

(0.3)

(0.2)

(0.8)

(0.8)

(0.6)

(0.7)

(0.5)

(0.5)

16.4 (0.6) 

14.4 (0.5) 

(0.4) 

(0.3)

13

11

10.9 (0.4) 

9.9 (0.2) 

9.6 (0.4)
Table 15. Average Household Natural Gas Consumption for Space Heating for Households Whose Main SpaceHeating Fuel Is Natural Gas (Continued)

Ui 

I

Average

Space Heated Number of 

and Heating Households 

Degree-Days (Millions)

Heated Sqaure

Footage

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65 F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

8.3 (0.5)

6.3 (0.5)

5.1 (0.3)

4.4 (0.3)

7.2 (0.4)

7.0 (0.4)

6.3 (0.4)

6.5 (1.1)

5.5 (1.2)

5.0 (0.7)

3.3 (0.7)

8.1 (0.9)

10.1 (1.0)

4.6 (0.6)

1.5 (0.6)

Heating 

DegreeDays (Base 

65 F)

4,636

4,697

4,768

4,336

4,776

5,187

5,400

1,707

2,576

3,579

4,496

5,552

6,620

7,343

8,349

(139)

(157)

(152)

(153)

(106)

(116)

(114)

(30)

(56)

(92)

(75)

(43)

(32)

(25)

(110)

Average Natural Gas Consumption for 

Space Heating

Ave rage 

Heated 

Square Thousand Btu 

Footage Million Btu per Degree-Days

602

890

1,094

1,290

1,584

2,073

3,279

1,390

1,441

1,347

1,463

1,647

1,629

1,634

1,686

(9)

(3)

(3)

(4)

(7)

(10)

(68)

(85)

(86)

(48)

(76)

(59)

(48)

(68)

(201)

48.3

57.7

68.5

67.3

75.8

89.2

115.1

27.8

42.4

58.6

68.3

88.4

100.4

104.3

107.1

(1.5)

(2.3)

(2.9)

(2.9)

(2.5)

(3.3)

(4.8)

(0.9)

(2.5)

(3.7)

(2.9)

(2.2)

(2.0)

(3.5)

(8.7)

10.9

13.0

14.8

15.7

16.3

17.9

22.0

16.5

16.6

16.3

15.2

16.0

15.2

14.3

12.8

(0.3)

(0.4)

(0.3)

(0.5)

(0.3)

(0.5)

(0.8)

(0.5)

(1.0)

(0.8)

(0.5)

(0.5)

(0.3)

(0.5)

(1.2)

Btu 

per Degree-Day 

per Square Foot

19.1

14.6

13.5

12.2

10.3

8.6

6.9

14.2

13.8

13.9

12.7

12.3

11.2

10.1

8.7

(0.6)

(0.4)

(0.3)

(0.4)

(0.2)

(0.2)

(0.2)

(0.6)

(0.9)

(0.5)

(0.7)

(0.3)

(0.2)

(0.4)

(0.9)
i ON

Table 16. Average Household Fuel Oil and Kerosene Consumption for Space Heating for Households Whose Main 

Space-Heating Fuel Is Fuel Oil or Kerosene

Average

Location and

Income

National

Census Division

New England

Middle Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific

Number of 

Households

(Millions)

13.4

2.3

5.9

1.2

0.4

3.0

0.4

(0.7)

(0.2)

(0.4)

(0.2)

(0.1)

(0.4)

-

-

-

(0.1)

Average Fuel Oil and Kerosene Consumption 

for Space Heating

Heating Average 

Degree- Heated 

Days (Base Square

65 F) Footage

5,827

6,960

5,987

7,179

7,284

4,121

4,939

(143) 1,571

(90) 1,800

(232) 1,516

(209) 1,751

(216) 1,640

(225) 1,407

-

-

-

(343) 1,779

(48)

(137)

(70)

(145)

(177)

(96)

-

-

-

(134)

Thousand Btu

Million Btu per Degree-Days

96.0

112.5

104.2

88.8

88.8

76.7

67.1

(2.0)

(3.7)

(3.1)

(3.3)

(8.6)

(3.9)

-

-

-

(4.3)

17.6

16.2

17.7

12.6

12.5

21.6

14.5

(0.5)

(0.4)

(0.7)

(0.7)

(1.1)

(1.7)

-

-

-

(1.7)

Btu 

per Degree-Day

per Square Foot

15.6

13.5

16.5

8.8

8.9

20.0

9.3

(0.8)

(1.5)

(1.4)

(0.6)

(1.0)

(2.8)

-

-

-

(0.6)

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

2.0

2.5

2.4

1.8

1.4

2.0

1.4

(0.2)

(0.2)

(0.2)

(0.2)

(0.1)

(0.2)

(0.2)

5

5

5

5

5

5

5

,552

,953

,797

,912

,887

,874

,810

(154)

(183)

(208)

(241)

(207)

(169)

(192)

1,192

1,158

1,307

1,619

1,940

1,933

2,357

(94)

(64)

(91)

(108)

(148)

(97)

(91)

94.0

95.2

88.8

95.0

98.7

96.7

110.8

(6.

(4.

(3.

(3.

(7.

(3.

(3.

9)

5)

7)

9)

7)

2)

8)

17.3

17.6

17.0

16.6

17.1

18.2

19.8

(1.2)

(1.3)

(1.1)

(0.9)

(1.2)

(0.9)

(0.9)

20.2 (1.9)

21. 

17, 

13. 

10,

1 (2.2) 

3 (2.5)

1 (0.9)

2 (0.7)

12.0 (0.9) 

10.3 (0.5)
Table 16. Average Household Fuel Oil and Kerosene Consumption for Space Heating for Households Whose Main 

Space-Heating Fuel Is Fuel Oil or Kerosene (Continued)

-vl

I

Space Heated 

and Heating 

Degree-Days

Heated Square

Footage

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65 F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

Number of 

Households 

(Millions)

3.1

1.6

1.4

1.2

1.9

1.7

2.5

0.4

-

1.0

1.2

5.6

2.9

1.4

0.9

(0.4)

(0.2)

(0.2)

(0.2)

(0.2)

(0.2)

(0.2)

(0.1)

(0.3)

(0.4)

(0.7)

(0.5)

(0.2)

(0.5)

Average

Heating 

DegreeDays (Base 

65 F)

5,662 (157)

5,555 (138)

5,876 (237)

5,450 (174)

5,873 (344)

6,286 (212)

6,012 (176)

1,396 (376)

-

3,571 (92)

4,764 (106)

5,534 (25)

6,477 (39)

7,484 (39)

8,709 (333)

Average Fuel Oil and Kerosene Consumption 

for Space Heating

Ave rage 

Heated Btu 

Square Thousand Btu per Degree-Day 

Footage Million Btu per Degree-Days per Square Foot

578

911

1,103

1,295

1,607

2,105

3,241

1,296

-

1,396

1,624

1,411

1,778

1,954

1,540

(10)

(13)

(9)

(6)

(9)

(16)

(83)

(183)

(217)

(90)

(71)

(121)

(148)

(189)

80.8

81.5

90.1

85.9

98.7

107.3

122.9

48.5

-

73.5

85.0

96.9

105.0

109.4

100.6

(3.9)

(4.5)

(4.9)

(4.4)

(3.6)

(4.3)

(3.9)

(3.7)

(4.4)

(5.0)

(4.2)

(3.9)

(3.8)

(9.5)

15.7

15.8

15.9

18.0

17.8

18.0

21.3

43.8

-

20.7

17.9

17.5

16.3

14.6

11.6

(1.1)

(1.6)

(0.9)

(1.5)

(1.1)

(1.0)

(0.8)

(17.4)

(1.5)

(1.3)

(0.7)

(0.6)

(0.5)

(1.1)

29.6

17.3

14.4

14.0

11.2

8.5

6.8

47.4

-

18.7

12.8

17.3

12.1

10.8

10.0

(2.3)

(1.7)

(0.8)

(1.2)

(0.7)

(0.5)

(0.2)

(29.0)

(3.2)

(1.3)

(1.7)

(1.2)

(1.6)

(1.0)

Note: A dash "-" represents data withheld because there were not enough sample households in this category 

to produce accurate estimates.
Table 17. Average Household LPG Consumption for Space Heating for Households Whose Main Space-Heating Fuel 

Is LPG

Location and 

Income

Number of 

Households 

(Millions)

Average 

Heating 

DegreeDays (Base 

65°F)

Average LPG Consumption for Space Heating

Average 

Heated 

Square 

Footage Million Btu

Thousand Btu 

per Degree-Days

Btu 

per Degree-Day 

per Square Foot

National

Census Division

3.7 (0.4) 4,386 (210) 1,234 (64) 60.7 (2.8) 14.9 (0.7) 16.7 (1.6)

oo 

I

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central 

West South Central 

Mountain 

Pacific

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

0.7

0.5

1.1

0.4

0.5

0.3

0.1

(0.

(0.

(0.

(0.

(0.

(0.

(0.

1)

1)

2)

1)

1)

1)

0)

6,788

5,575

2,708

3,622

2,532

5,846

3,751

(622)

(253)

(338)

(128)

(265)

(446)

(841)

1,571

1,788

1,051

1,177

967

893

1,487

(155)

(264)

(79)

(119)

(145)

(133)

(477)

92.3

85.4

41.6

53.5

37.5

61.7

38.1

(5.

(8.

(4.

(5.

(4.

(6.

(5.

6)

6)

2)

6)

9)

3)

7)

13.8

15.7

17.1

15.1

14.9

11.0

10.3

(1.0)

(1.7)

(2.3)

(1.5)

(0.6)

(1.2)

(0.7)

0.5

0.9

0.9

0.4

0.3

0.5

0.3

(0.1)

(0.1)

(0.1)

(0.1)

(0.1)

(0.1)

(0.1)

3,725

4,191

4,427

4,597

5,008

4,706

4,404

(355)

(319)

(354)

(522)

(390)

(411)

(567)

908

1,090

1,106

1,132

1,467

1,498

2,046

(89)

(104)

(74)

(163)

(190)

(174)

(388)

54.6

55.0

60.3

61.8

71.0

60.8

77.2

(4.

(4.

(4.

(8.

(12.

(8.

(10.

2)

6)

0)

4)

0)

6)

6)

15.

15.

14.

14.

13.

12.

19.

3

5

6

5

8

8

7

(1.5)

(2.3)

(0.7)

(1.0)

(1.9)

(1.2)

(3.5)

10.6 (0.7) 

11.9 (2.0) 

22.3 (4.4) 

17.6 (4.3) 

18.4 (2.2) 

16.8 (3.7) 

8.4 (2.8)

23.8 (5.3) 

18.2 (4.2) 

15.4 (1.3) 

19.4 (3.4) 

12.1 (2.3) 

11.2 (1.1) 

16.0 (4.3)
Table 17. Average Household LPG Consumption for Space Heating for Households Whose Main Space-Heating Fuel 

Is LPG (Continued)

I

c^

Space Heated 

and Heating 

Degree-Days

Heated Square

Footage

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65°F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

Number of 

Households 

(Millions)

1.4

0.5

0.6

0.3

0.4

0.3

0.3

0.5

0.4

0.9

0.4

0.6

0.3

0.4

(0.2)

(0.1)

(0.1)

(0.1)

(0.1)

(0.1)

(0.1)

(0.1)

(0.2)

(0.3)

(0.1)

(0.2)

(0.1)

(0.2)

Average 

Heating 

DegreeDays (Base 

65 °F)

3,954

4,497

4,444

4,531

4,688

4,170

5,736

1,171

2,488

3,504

4,698

5,418

6,610

7,529

(346)

(335)

(376)

(417)

(442)

(347)

(617)

(340)

(94)

(59)

(85)

(76)

(35)

(92)

Average LPG Consumption for Space Heating

Average 

Heated 

Square 

Footage Million

627 (18)

893 (11)

1,088 (6)

1,287 (14)

1,566 (26)

2,092 (37)

3,230 (279)

978 (117)

1,053 (125)

1,098 (110)

1,116 (38)

1,616 (323)

1,504 (254)

1,251 (237)

51.6

44.4

66.1

56.5

61.1

73.4

103.8

22.5

36.7

53.9

67.8

80.7

79.2

82.6

Thousand Btu 

Btu per Degree-Days

(3.6)

(3.7)

(7.9)

(5.9)

(5.3)

(10.5)

(13.2)

(5.1)

(5.4)

(4.4)

(9.7)

(7.3)

(12.2)

(9.5)

15.8

9.9

15.3

13.0

13.9

17.1

18.8

20.4

14.6

15.4

14.5

14.9

12.0

10.9

(1.7)

(1.1)

(1.4)

(1.1)

(1.2)

(2.4)

(1.5)

(5.6)

(1.6)

(1.1)

(2.3)

(1.3)

(1.8)

(1.1)

Btu 

per Degree-Day 

per Square Foot

27.7

11.2

14.1

10.1

9.0

8.4

6.1

27.0

19.3

19.4

15.1

12.2

9.7

11.2

(3.1)

(1.2)

(1.3)

(0.9)

(0.8)

(1.3)

(0.6)

(10.0)

(5.5)

(3.0)

(1.8)

(1.1)

(1.1)

(2.5)

Note: A dash "-" represents data withheld because there were not enough sample households in this category 

to produce accurate estimates.
Table 18. Average Household Electricity Consumption and Natural Gas Consumption for Water Heating for Households 

________Whose Main Water-Heating Fuel Is Electricity or Natural Gas_________________________________

Location and 

Income

Households Whose Main Water Heating 

_____Fuel Is Electricity________

Households Whose Main Water Heating 

Fuel Is Natural Gas

Average Household Electric- Average Household Natural 

Number of ity Consumption for Number of Gas Consumption for 

Households Water Heating Households Water Heating 

(Millions)_______(Million Btu)_________(Millions)_______(Million Btu)

National 26.1 (1.2) 11.9 (0.2) 44.1 (1.3) 28.2 (0.4)

o i

Census Division

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central 

West South Central 

Mountain 

Pacific

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

1.0 (0.1) 

2.9 (0.5) 

3.7 (0.4) 

1.5 (0.3) 

8.5 (0.7) 

3.1 (0.4) 

1.6 (0.4) 

0.9 (0.3) 

2.9 (0.4)

3.1 (0.3)

4.5 (0.4)

4.6 (0.3) 

3.8 (0.3) 

3.1 (0.2) 

4.1 (0.4) 

2.8 (0.3)

12.7

11.1

12.8

12.0

11.5

12.7

11.0

11.0

12.5

(0.5) 

(0.8) 

(0.8) 

(0.9) 

(0.5) 

(0.6) 

(0.9) 

(0.9) 

(0.6)

8.1 (0.4) 

9.7 (0.4) 

(0.6) 

(0.5) 

(0.4) 

(0.6)

11.0

12.6

13.5

13.5

1.4 (0.2)

5.8 (0.8)

10.2 (0.4) 

(0.4) 

3.8 (0.5) 

1.8 (0.2) 

5.7 (0.4) 

2.8 (0.3) 

8.4 (0.4)

4.2

16.0 (0.5)

5.4 (0.3) 

7.1 (0.4) 

7.0 (0.5) 

6.6 (0.3) 

5.9 (0.4) 

6.7 (0.4) 

5.3 (0.4)

22.8

27.9

29.8

26.9

29.1

27.1

30.1

25.9

27.2

(0.9) 

(1.1) 

(0.6) 

(1.0) 

(1.3) 

(2.0) 

(1.7) 

(0.8) 

(1.1)

24.3 (0.8) 

26.1 (0.7) 

25.3 (0.8) 

28.5 (0.8) 

31.0 (0.9) 

30.4 (0.9) 

32.3 (0.9)
Table 18. Average Household Electricity Consumption and Natural Gas Consumption for Water Heating for Households 

Whose Main Water-Heating Fuel Is Electricity or Natural Gas (Continued)

Households Whose Main Water Heating 

Fuel Is Electricity

Space Heated 

and Heating 

Degree-Days

Heated Square

Footage

0

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65 F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

Number of 

Households 

(Millions)

0.2

5.5

3.5

3.2

2.6

4.0

3.9

3.2

4.1

1.5

4.0

4.8

4.6

3.5

1.9

1.6

(0.1)

(0.5)

(0.3)

(0.3)

(0.2)

(0.3)

(0.3)

(0.3)

(1.2)

(0.6)

(0.8)

(0.9)

(1.3)

(0.7)

(0.5)

(0.7)

Households Whose Main Water Heating 

Fuel Is Natural Gas

Average Household Electric- Average Household Natural 

ity Consumption for Number of Gas Consumption for 

Water Heating Households Water Heating 

(Million Btu) (Millions) (Million Btu)

7.4

7.1

10.1

10.7

13.2

13.9

14.8

16.4

10.7

11.2

11.7

13.3

11.5

12.5

11.9

12.3

(1.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

(0.

3)

3)

4)

5)

5)

5)

5)

6)

4)

9)

5)

5)

5)

6)

6)

6)

0.

8.

6.

4.

4.

7.

7.

6.

7.

5.

4.

2.

8.

10.

4.

1.

1

1

2

9

4

2

0

3

1

3

7

9

2

2

3

5

(0.0)

(0.4)

(0.4)

(0.3)

(0.3)

(0.4)

(0.3)

(0.4)

(1.1)

(1.3)

(0.7)

(0.7)

(0.9)

(0.9)

(0.6)

(0.5)

47.8

20.4

25.1

29.2

29.2

29.6

32.2

33.3

27.8

27.5

29.2

28.4

28.7

28.4

27.8

24.7

(10.

(0.

(0.

(0.

(1.

(0.

(1.

(0.

(1

(1

(1

(1

(1

(0

(0

(1

4)

7)

8)

6)

2)

9)

1)

7)

.4)

.5)

.4)

.3)

.0)

.5)

.7)

.8)

Note: A dash "-" represents data withheld because there were not enough sample households in this category 

to produce accurate estimates*
Table 19. Average Household Fuel Oil and Kerosene Consumption and LPG Consumption for Water Heating for 

________Households Whose Main Water-Heating Fuel Is Fuel Oil, Kerosene, or LPG__________________

Households Whose Main Water Heating 

Fuel Is Fuel Oil or Kerosene

Households Whose Main Water Heating 

Fuel Is LPG

Location and 

Income

Average Household Fuel Oil

Number of and Kerosene Consumption Number of 

Households for Water Heating Households 

(Millions) (Million Btu)_________(Millions)

Average Household LPG

Consumption for

Water Heating

(Million Btu)

National 7.1 (0.5) 32.8 (0.8) 3.6 (0.3) 20.6 (1.1)

S3

Census Division

New England 

Middle Atlantic 

East North Central 

West North Central 

South Atlantic 

East South Central 

West South Central 

Mountain 

Pacific

1.6 (0.1) 

4.5 (0.3) 

0.2 (0.1)

0.8 (0.3)

33.0 (1.4) 

33.4 (1.1) 

28.2 (3.3)

31.0 (2.8)

0.1 (0.1) 

0.2 (0.1) 

0.7 (0.1) 

0.5 (0.1) 

0.7 (0.2) 

0.2 (0.0) 

0.4 (0.2) 

0.3 (0.1) 

0.4 (0.1)

19.8 

16.7 

20.7 

20.7 

23.5 

18.1 

21.8 

14.2 

22.7

(3.7) 

(1.6) 

(0.7) 

(1.4) 

(5.6) 

(2.0) 

(3.1) 

(3.6) 

(1.4)

Income Group 

(Estimated 1979 

Income)

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $19,999 

$20,000 to $24,999 

$25,000 to $34,999 

$35,000 or More

1.0 (0.2) 

1.4 (0.1) 

1.3 (0.1) 

0.9 (0.1) 

0.6 (0.1) 

1.0 (0.1) 

0.9 (0.1)

33.4 (2.3) 

35.7 (1.8)

30.8 (2.5)

30.9 (1.6) 

30.1 (2.9) 

33.3 (2.2) 

33.9 (1.9)

0.5 (0.1) 

0.7 (0.2) 

0.9 (0.2) 

0.5 (0.1) 

0.2 (0.1) 

0.5 (0.1) 

0.3 (0.1)

18.5

14.2

18, 

18,

(3.7) 

(1.6) 

(1.4) 

(2.4)

42.6 (14.5)

24.7

25.6

(2.2) 

(1.7)
Table 19. Average Household Fuel Oil and Kerosene Consumption and LPG Consumption for Water Heating for 

Households Whose Main Water-Heating Fuel Is Fuel Oil, Kerosene, or LPG (Continued)

Households Whose Main Water Heating

Space Heated

and Heating

Degree-Days

Heated Square

Footage

0

1 to 799

800 to 999

1,000 to 1,199

1,200 to 1,399

1,400 to 1,799

1,800 to 2,399

2,400 or More

Heating DegreeDays for April 1980

Through March 1981

(Base 65 F)

0 to 1,999

2,000 to 2,999

3,000 to 3,999

4,000 to 4,999

5,000 to 5,999

6,000 to 6,999

7,000 to 7,999

8,000 or More

Fuel

Number of

Households

(Millions)

_

2.0 (0.3)

0.9 (0.1)

0.7 (0.2)

0.5 (0.1)

0.8 (0.1)

0.8 (0.1)

1.2 (0.2)

-

-

0.5 (0.3)

3.9 (0.3)

1.6 (0.3)

0.7 (0.1)

0.4 (0.2)

Is Fuel Oil or Kerosene

Average Household Fuel Oil

and Kerosene Consumption

for Water Heating

(Million Btu)

_

35.1 (1.9)

32.3 (2.1)

34.4 (1.7)

31.0 (1.7)

31.0 (1.8)

34.0 (2.7)

30.0 (0.9)

-

-

30.9 (3.2)

33.9 (1.3)

32.7 (1.7)

33.7 (2.5)

25.9 (2.1)

Households

Number of

Households

(Millions)

0.2 (0.0)

1.1 (0.2)

0.4 (0.1)

0.3 (0.1)

0.2 (0.1)

0.3 (0.1)

0.5 (0.1)

0.5 (0.1)

0.4 (0.1)

0.4 (0.2)

0.7 (0.2)

0.4 (0.2)

0.6 (0.2)

0.4 (0.1)

0.4 (0.2)

0.3 (0.1)

Whose Main Water Heating

Fuel Is LPG

Average Household LPG

Consumption for

Water Heating

(Million Btu)

22.5 (2.7)

15.8 (1.3)

17.5 (2.6)

23.4 (4.1)

17.1 (2.8)

17.7 (2.1)

21.5 (2.4)

34.2 (7.4)

35.2 (8.3)

22.9 (3.6)

18.3 (1.2)

15.6 (1.9)

19.0 (1.9)

19.2 (2.8)

21.0 (3.4)

 »

Note: A dash "-" represents data withheld because there were not enough sample households in this category 

to produce accurate estimates.

31ON OF DISAGGREGATION RESULTS

drawn from the results listed in Tables 10 

r end use. The results for the additional 

are discussed, along with the uncorrected 

results.

Air Conditioning

icity for air conditioning increases dramatically 

warm regions, from low-income to high-income 

seholds living in small units to those living in 

•eases are more pronounced than the trend for 

suits for household income and dwelling unit size 

, as is illustrated by Tables 14 through 17, where 

come households tend to live in larger units.

Miscellaneous Energy Usage

Miscellaneous uses ^..^lude cooking, lighting, dishwashing, clothes drying, 

pool heating, and other uses. The consumption of electricity for miscel 

laneous uses increases as one goes from low-income to high-income house 

holds and from households living in small units to those living in large 

units. As with air-conditioning use, the results for household income 

and dwelling unit size are somewhat confounded. Also, the trends for 

miscellaneous use are not so pronounced as the trends for air condition 

ing. There is no clear trend for miscellaneous electricity consumption 

according to heating degree-days. The small differences that do occur 

may be caused by different percentages of households using electricity 

for cooking and clothes drying in different parts of the country.

The consumption of natural gas for miscellaneous uses exhibits the same 

trends as that for electricity. The remarks concerning electricity also 

apply to natural gas. The tables do not show a clear trend for 

miscellaneous uses of LPG.

Water Heating

The average fuel consumption for water heating depends heavily on the 

percentage of households that use the particular fuel for water heating. 

Tables 18 and 19 give the averages over only those households that use 

the particular fuels for water heating. The results listed in Table 18 

show that the average electricity consumption for water heating increases 

as one goes from low-income to high-income households and from households 

living in small dwelling units to those living in large units. The 

same relations hold for natural gas, but the increases are not as pronounced. 

There does not seem to be any clear trend by weather or Census division for 

either of these two fuels.

-75-
The resulting trends for electricity and natural gas reflect the form of 

the regression equation for the water-heating components. The number 

of household members plays an important role in these equations. This 

suggests that the use of hot water is related to the number of household 

members. Multimember households tend to have higher incomes and live 

in larger dwellings than single-member households.

The average fuel oil consumption and the average LPG consumption for water 

heating do not show any clear trends in Table 19. This may be a result of 

the fact that the water-heating component for these two fuels is not as 

accurately estimated as for electricity arid natural gas.

Space Heating

The averages in Tables 14 through 17 are averages over only those 

households whose main heating fuel is electricity, natural gas, fuel oil, 

and LPG, respectively. Using these tables, one notices that the same 

trends appear for all fuels.

In all tables, the uncorrected average energy consumption for space 

heating and the consumption in Btu per degree-day increases as one goes 

from low-income to high-income households. The increase in average 

consumption does not match the larger increase in the average amount of 

heated square footage. As a result, the average value for the Btu per 

degree-day per square foot index decreases as one goes to the higher 

income categories. Similar trends appear when one goes from households 

living in small units to households living in large units, except the 

increases or decreases are more pronounced. This implies that the trends 

by income categories may be a result of the tendency of households with 

higher incomes to live in larger units.

As one goes from households living in warmer climates to those living 

in cooler climates, the average consumption for space heating in Btu 

increases but the average consumption in Btu per degree-day and Btu 

per degree-day per square foot decreases. Part of the trend for Btu 

per degree-day per square foot may be related to the fact that the 

average unit size increases as one goes toward the cooler climates.

The overall trends for the index giving Btu per degree-day per square 

foot are troubling. It would be better if one had an index that was less 

affected by climate and unit size. Unfortunately, such an index may not 

have the intuitive appeal that Btu per degree-day per square foot does. 

A possible change in the index that may give better results would be to 

use a higher degree-day base for heating degree-day and divide by the 

square root of the heated floorspace instead of by the heated floorspace 

itself. The higher base for degree-days would result in a smaller percent 

age change in degree-days as one goes from warm to cool climates. The 

square root of the heated floorspace would be roughly proportional to 

the area of the exterior walls of the dwelling unit. This would increase 

at a lower rate than the heated floorspace.

Tables 14 through 17 show that households that use electricity as their 

main space-heating fuel tend to use dramatically less Btu for space 

heating than those households that use natural gas, fuel oil, kerosene,

-76-
or LPG as the main space-heating fuel. This does not mean that it is 

more efficient to use electricity to heat homes. The Btu equivalent of 

electricity is measured as the heat equivalent of the electricity that is 

delivered to the household, not the amount of energy required to generate 

and transmit the electricity to the household. In addition, households 

that use electricity as the main space-heating fuel tend to live in newer 

homes and warmer climates than those that heat with other fuels. As a 

result, this report does not provide an answer to the question as to which 

fuel is the most efficient for residential space-heating purposes.

Trends According to Income and Heating Degree-Days

The trends according to income shown in the tables reflect differences 

in a cross section of the population at a particular point in time. 

The effect of a general increase in the average household income may be 

different from the cross-sectional differences. In particular, unless 

the average dwelling size also increases, increases in the average real 

income may not result in an increase in the average amount of energy 

used for space heating.

Similarly, the trends according to heating degree-days shown in the tables 

reflect cross-sectional differences. Hence, one can use the results shown 

in the tables to predict the effect of changes in the weather on energy 

consumption only if one assumes that the longitudinal differences caused by 

weather are the same as the cross-sectional differences. It is intuitively 

obvious that weather does affect the amount of energy used for heating 

and cooling. But the type of equipment, amount of insulation, and 

percentage of storm windows vary by weather zone. Hence, the size of the 

differences shown in the tables also reflect these facts.

This report is based on data collected from households surveyed during the 

1980 REGS. Some of these households are also being surveyed during the 

1982 RECS. The resulting longitudinal data will be helpful in studying 

the longitudinal effect of changes in income, weather, and other variables.

-77-

10. EFFECT OF ELECTRICITY PRICES

The price of the various fuels was not used in the regression results 

presented in Section 6. For each household and each fuel the average 

price could be computed by dividing the expenditures by the consumption. 

But these average prices would be affected by the block rate structures. 

Hence the coefficient of any term involving the average price may reflect 

the change in average price between large and small consumers caused by 

the block rate structure as well as the response of consumers to price. 

It would be more desirable to use a set of marginal prices that are the 

same for all consumers that are under the same rate structure.

Fuel prices do affect the regression results. The choice of space-heating 

fuels, water-heating fuels, and appliance stock is affected by prices. The 

effect of electricity prices on these choices is discussed later in this 

section. The use of indicator variables that describe these choices in 

the regression procedure will partially account for fuel prices. If the 

fuel prices and these variables were simultaneously used in the regression 

procedures, then the coefficient for the price variables would probably 

underestimate the true effect of price on cross-sectional differences in 

energy consumption.

The 1980 REGS data set is a cross-sectional data set. Hence even if the 

price effect could be accurately estimated, it would be a cross-sectional 

effect. The cross-sectional effect of price obtained from 1980 data may 

be substantially different from the longitudinal effect of price. It is 

the longitudinal effect that should be used in estimating the effect of 

future fuel prices on energy consumption. The 1982 REGS will contain 

some households that were also used in the 1980 REGS. The resulting 

longitudinal data will be helpful in studying the longitudinal effect of 

changes in price.

For some of the households interviewed during the survey, the marginal 

prices for the utilities that serviced the households were available. 

The marginal price data for major investor-owned utilities were estimated 

from data listed in Typical Residential, Commercial and Industrial Bills 

Investor Owned Utilities, published by the Edison Electric Institute 

(EEI). A convenient source for marginal price data for rural electric 

cooperatives or municipal (public) electric companies was not available. 

Six marginal prices were listed when data were available. These were 

the marginal prices at low, medium, and high consumption levels, for 

both winter and summer months.

Table 20 lists the percentage of households that use electricity for major 

appliances and major end uses by the average of the six marginal prices. 

Table 21 lists the average estimated amount of electricity used for space 

heating, water heating, air conditioning, and miscellaneous end uses by 

the average marginal price. Table 21 also lists the average cooling 

degree-days base 65 F. Table 22 applies only to households where the main 

space-heating fuel is electricity. Table 22 lists the average degree-days, 

average heated square footage, and average amount of electricity consumed 

for space heating in Btu, Btu per degree-day, and Btu per degree-day per 

square foot. These averages are listed by the marginal prices.

-79-
Table 20. Households Using Electricity, by End Use

i

00o 

i

Price of 

Electricity

National

Marginal 

Price 

(Cents/kWh)

Not 

Available

0 to 2.99 

3 to 3.99 

4 to 4.99 

5 to 5.99 

6 to 6.99 

7 to 7.99 

8 or more

Number of 

Households 

(Millions)

81.6

30.4

1.3 

5.1 

17.4 

9.6 

7.1 

6.5 

4.3

(0)

(1.4)

(0.3) 

(1.2) 

(1.7) 

(1.6) 

(0.9) 

(0.8) 

(0.2)

Main SpaceHeating 

Fuel

17.5 (1.3)

21.4 (1.8)

32.3 (4.4) 

25.5 (4.2) 

13.1 (1.8) 

29.9 (7.2) 

8.4 (1.8) 

4.6 (1.1) 

0.4 (0.4)

Main WaterHeating 

Fuel

31.9

36.7

67.5 

57.1 

30.9 

41.7 

16.5 

8.3 

0.8

(1.4)

(2.4)

(6.9) 

(8.7) 

(4.4) 

(6.9) 

(3.3) 

(1.3) 

(0.5)

Percentage

Clothes 

Drying

46.9 (1.2)

44.3 (1.8)

74.6 (2.5) 

59.6 (3.8) 

55.1 (2.6) 

51.1 (3.2) 

47.5 (4.5) 

30.3 (3.0) 

23.9 (3.9)

of Households Using Electricity for

Main 

Cooking 

Fuel

54.4 (1.7)

55.6 (2.3)

90.2 (2.8) 

75.2 (5.2) 

55.0 (2.8) 

63.7 (3.6) 

56.6 (4.7) 

26.7 (3.1) 

25.4 (3.0)

Central 

Air Con 

ditioning

25.1 (1.0)

27.3 (1.8)

6.2 (2.0) 

28.4 (4.2) 

28.3 (2.4) 

31.7 (3.0) 

16.0 (3.0) 

20.2 (3.7) 

5.7 (1.8)

Room 

Air Con 

ditioning

30.4 (0.9)

28.2 (1.3)

13.0 (6.2) 

28.8 (2.9) 

29.1 (2.5) 

32.5 (3.7) 

30.1 (2.2) 

37.6 (2.9) 

43.3 (2.4)

Freezer

38.1 (1.0)

36.5 (1.3)

49.4 (4.9) 

47.7 (4.9) 

47.2 (2.2) 

40.3 (4.4) 

35.9 (3.3) 

24.9 (3.2) 

16.7 (2.0)

Dishwasher

37.2 (1.1)

3.3.1 (1.5)

54.5 (3.5) 

38.2 (2.5) 

36.1 (2.0) 

45.4 (2.6) 

46.1 (5.6) 

35.5 (4.1) 

34.7 (3.6)
Table 21. Average Household Electricity Consumption by End Use

Average Household Electricity 

Consumption (Million Btu) for

Price of 

Electricity

National

Marginal

Price

(Cents/kWh)

Not

Available

0 to 2.99

3 to 3.99

4 to 4.99

5 to 5.99

6 to 6.99

7 to 7.99

8 or More

Cooling 

Number of DegreeHouseholds Days (Base 

(Millions) 65 °F)

81.6

30.4

1.3

5.1

17.4

9.6

7.1

6.5

4.3

(0.0)

(1.4)

(0.3)

(1.2)

(1.7)

(1.6)

(0.9)

(0.8)

(0.2)

1,180

1,317

175

933

1,320

1,497

675

858

857

(35)

(47)

(94)

(108)

(90)

(285)

(65)

(18)

(9)

Space 

Heating

3.4

4.1

8.5

6.1

3.3

2.8

2.3

1.1

0.2

(0.3)

(0.4)

(1.1)

(1.0)

(0.5)

(0.6)

(0.5)

(0.3)

(0.0)

Water 

Heating

3.8 (0.2)

4.4 (0.3)

9.0 (1.0)

7.0 (1.2)

3.6 (0.5)

4.7 (0.7)

2.2 (0.5)

1.1 (0.2)

0.0 (0.0)

Air Condi 

tioning

3.9

4.3

0.2

3.4

5.5

5.5

1.2

1.7

0.8

(0.1)

(0.3)

(0.1)

(0.7)

(0.7)

(1.2)

(0.2)

(0.3)

(0.1)

Miscel 

laneous 

Uses

19.0

18.0

23.2

21.4

21.1

20.2

18.9

17.0

14.4

(0.2)

(0.3)

(1.3)

(0.7)

(0.4)

(0.6)

(0.6)

(0.9)

(1.1)

-81-
I 

oo

ISJ 

I

Table 22. Average Household Electricity Consumption for Space Heating for Households Whose Main SpaceHeating Fuel Is Electricity

Ave rage 

Heating

Price of

Electricity

National

Marginal

Price

(Cents/kWh)

Not

Available

0 to 2.99

3 to 3.99

4 to 4.99

5 to 5.99

6 to 6.99

7 to 7.99

8 or More

Number of 

Households

(Millions)

14.3 (1.0)

6.5 (0.7)

0.4 (0.1)

1.3 (0.4)

2.3 (0.4)

2.9 (1.0)

0.6 (0.2)

0.3 (0.1)

"

DegreeDays (Base

65 F)

3,956

3,862

5,208

5,218

4,645

2,372

5,659

5,490

(278)

(180)

(157)

(382)

(281)

(1074)

(298)

(317)

Average

Average

Heated 

Square

Footage

1,366

1,305

1,190

1,409

1,521

1,346

1,649

1,229

(32)

(61)

(138)

(86)

(94)

(57)

(218)

(220)

Million Btu

18.2

18.3

25.8

23.2

23.6

8.9

24.6

21.1

(1.5)

(1.3)

(1.2)

(1.0)

(1.6)

(4.0)

(2.1)

(3.9)

Electricity Consumption for 

Space Heating

Thousand Btu

per Degree-Days

4.6

4.7

4.9

4.8

5.5

3.7

4.5

3.8

(0.2)

(0.2)

(0.2)

(0.5)

(0.4)

(0.4)

(0.5)

(0.7)

Btu 

per Degree-Day

per Square Foot

3.9

4.2

4.7

4.0

4.1

3.2

3.1

3.4

(0.2)

(0.1)

(0.3)

(0.5)

(0.2)

(0.4)

(0.4)

(0.6)

Note: A dash "-" represents data withheld because there were not enough sample households in this 

category to produce accurate estimates.
The author did not use the marginal electricity prices in the Initial 

regression procedure because these marginal prices were not available 

for many of the households. The marginal prices were used in a 

supplemental regression procedure. The only households used in this 

regression procedure were those households for which all six marginal 

prices were available and for which Type I electricity consumption 

data were available. The electricity consumption was used as the dependent 

variable. Eight independent variables were used. Four of these were 

ESTELBS, ESTELWT, ESTELHT, and ESTELCL. Where

ESTELBS - ELCONBS (1 + 0.1491 x MOBILEHM

- 0.1639 x SFATTACH

- 0.1545 x SMLAPTBD

- 0.1734 x LRGAPTBD),

ESTELWT - ELCONWT (1 - 0.4084 x MOBILEHM

- 0.1639 x SFATTACH

- 0.1545 x SMLAPTBD

- 0.1734 x LRGAPTBD),

ESTELHT - ELCONHT (1 + 0.1491 x MOBILEHM

- 0.1639 x SFATTACH

- 0.1545 x SMLAPTBD

- 0.5677 x LRGAPTBD),

and ESTELCL - ELCONCL (1 + 0.1491 x MOBILEHM

- 0.1639 x SFATTACH

- 0.1545 x SMLAPTBD

- 0.1734 x LRGAPTBD).

Here ESTELBS, ESTELWT, ESTELHT, and ESTELCL are the non normalized regression 

estimates of the miscellaneous, water-heating, space-heating, and airconditioning components of electricity consumption. For households living 

in single-family detached dwellings, these estimates will equal ELCONBS, ELCONWT, 

ELCONHT, and ELCONCL, respectively. For households living in other housing 

types, the estimates are adjusted to reflect the non-SFD regression results.

The other four independent variables were the interactions of these four 

terms with one of the marginal prices. These interaction terms will 

represent the effect of electricity prices beyond the effect on the choice 

of fuels, equipment, and other variables used in the initial regression 

analysis. The marginal price depended on the consumption component. 

The same robust procedure was used as was used for the initial regressions.

Table 23 lists the resulting estimates, their standard deviations, and their 

level of significance. The estimates of the coefficients for 

ELCONHT x RATE3W and ELCONCL x RATE3S are not significantly different 

from zero. These two terms were then dropped. Table 24 lists the results 

when only six independent variables are used.

-83-
Table 23. Estimates of Coefficients for Regression Using Marginal 

Electricity Rates with All Components

Independent 

Variable

ESTELBS

E STELES x RATE2S

ESTELWT

ESTELWT x RATE2S

ESTELCL

ESTELCL x RATE3S

ESTELHT

ESTELHT x RATE3W

Estimate of 

Coefficient

1.12186

-0.00024

1.14175

-0.00050

1.32160

-0.00039

0.96489

-0.00005

Standard Error 

of Estimates

0.03020

0.00005

0.11163

0.00023

0.15097

0.00030

0.08784

0.00020

Two-Sided 

Level of 

Significance

Less than 0.0001

Less than 0.0001

Less than 0.0001

0.03

Less than 0.0001

0.19

Less than 0.0001

0.81

Table 24. Estimates of Coefficients for Regression Using Marginal 

Electricity Rates Only When They Are Significant

Independent 

Variable

ESTELBS

ESTELBS x RATE2S

ESTELWT

ESTELWT x RATE2S

ESTELCL

ESTELHT

Estimate of 

Coefficient

1.13528

-0.00027

1.15063

-0.00053

1.13000

0.94410

Standard Error 

of Estimates

0.02849

0.00005

0.10604

0.00022

0.03328

0.03198

Two-Sided 

Level of 

Significance

Less than 0.0001

Less than 0.0001

Less than 0.0001

0.015

Less than 0.0001

Less than 0.0001

-84-
Conclusions Concerning Effect of Electricity Prices

The results shown in Tables 20 through 24 reflect cross-sectional 

differences. They should not be used to estimate the effect of a sudden 

increase in electricity prices without careful consideration of the 

relationship between cross-sectional differences and longitudinal 

differences. The results in Table 20 show that in areas with low 

electricity prices the prevalence of electric appliances is greater than 

in areas with high electricity prices. This is especially true for 

electric main space heating and electric water heating. The results in 

Tables 23 and 24 indicate that electricity prices result in crosssectional differences in electricity consumption that cannot be fully 

explained by all the independent variables used in the original 

regression procedure.

The effect of marginal electricity prices on the air-conditioning component 

is masked by the changes in the cooling degree-days when one goes from 

one part of the country to another. Table 21 reveals that both the 

average electricity consumption for air conditioning and the average 

cooling degree-days go up and then down as one goes from low-price areas 

to high-price areas. The drop in consumption is faster than the drop in 

cooling degree-days. This can be partially explained by examining the 

ratio between the percentage of households with electric window units to 

the percentage of households with electric central systems. Table 20 

reveals that in high-price areas, room air-conditioning units are much 

more prevalent than central systems. In areas with low electricity 

prices but not extremely low values of cooling degree-days, the ratio is 

approximately one to one.

-85-

11. CALENDAR YEAR CONSUMPTION ESTIMATES

The energy consumption estimates that have been presented are for the 

period from April 1980 through March 1981. There is some interest in 

presenting energy consumption estimates covering the period from 

January 1980 through December 1980, that is, calendar year estimates. It 

is possible to produce calendar year estimates using the April through 

March consumption estimates and the regression results. The procedure 

used was to produce calendar year estimates for each household by fuel. 

National and regional calendar year estimates can be obtained by using 

the household calendar year consumption estimates and the household 

sampling weights.

In estimating the calendar year consumption, the author adjusted the 

components separately for each household and each fuel. The author 

assumed that there was no need to adjust the air-conditioning component 

for electricity. This decision was made because there was at most a 

small change in the cooling degree-days between the calendar year and 

the interview year. The water-heating and appliance components for all 

fuels were increased by 0.274 percent because 1980 was a leap year. The 

space-heating components were adjusted to account for changes in the 

heating degree-days and the main space-heating fuel. The adjusted components 

were then added together by fuel and by household to obtain calendar year 

consumption estimates by fuel and by household.

In adjusting the space-heating component, the author made the following 

assumptions:

Assumption 1: Any change in the main spaceheating fuel takes place on 

April 1, 1980.

Assumption 2: There is no change in the

secondary space-heating fuel.

Assumption 3: The changes in a household's 

energy consumption for space 

heating due to changes in 

heating degree-days are 

proportional to the changes 

in the heating degree-days 

base 60 F.

Assumption 1 is approximately equivalent to assuming that the change in 

the main space-heating fuel occurred during the summer months. This 

happens because the majority of the heating degree-days occur during the 

winter months. Assumption 2 is necessary because the author has 

data only on changes in the main space-heating fuel. One could replace 

assumption 3 with another assumption on how changes in the heating 

degree-days affect energy consumption. It is not clear if assumption 3 

is the best assumption to use.

-87-
The details of the space-heating adjustments are given in Appendix C. 

The resulting calendar year estimates for national and regional energy 

consumption are given in Table 25. Table 26 gives the corresponding 

estimates for the period covering April 1980 through March 1981.

Table 25. Residential Energy Consumption for Calendar Year 1980 by Census 

Division and Fuel Type (Trillion Btu)

Energy Consumption

Census Division

New England

Middle Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific

Total

Electricity

96 (7)

296 (14)

406 (29)

197 (12)

520 (25)

243 (22)

297 (27)

111 (7)

308 (14)

2,473 (48)

Natural

Gas

137 (23)

770 (122)

1,521 (84)

590 (65)

416 (72)

201 (30)

504 (31)

306 (30)

604 (42)

5,050 (192)

Fuel Oil/

Kerosene

330 (26)

804 (52)

139 (28)

49 (16)

264 (49)

7 (3)

2 (2)

7 (6)

33 (6)

1,636 (89)

LPG

11 (5)

17 (9)

93 (18)

69 (12)

85 (22)

28 (5)

32 (13)

26 (6)

15 (5)

377 (34)

Table 26. Residential Energy Consumption for April 1980 Through March 

1981 by Census Division and Fuel Type (Trillion Btu)

Energy Consumption

Census Division

New England

Middle Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific

Total

Electricity

95 (7)

296 (14)

404 (29)

195 (12)

519 (24)

242 (21)

296 (28)

110 (7)

303 (14)

2,460 (47)

Natural

Gas

141 (24)

782 (122)

1,474 (80)

543 (59)

417 (71)

199 (29)

499 (32)

291 (28)

598 (42)

4,943 (190)

Fuel Oil/

Kerosene

318 (26)

772 (50)

117 (23)

39 (13)

263 (48)

6 (3)

1 (1)

6 (6)

30 (6)

1,552 (85)

LPG

10 (5)

17 (9)

89 (17)

63 (12)

87 (22)

28 (5)

31 (13)

25 (5)

15 (5)

365 (33)

-88-
The differences between the calendar year estimates and the April 1980 

through March 1981 estimates are small. The biggest change occurs with 

fuel oil consumption. Most of the change with fuel oil is caused by 

households switching from fuel oil to some other source of main heating 

fuel.

The results shown in Table 25 differ slightly from the corresponding 

results given in previous reports.* The difference is caused by a 

change in assumption 3 and the adjustment for the leap year.

^-Energy Information Administration, Energy Price and Expenditure Data 

Report, 1970-1980 (State and U.S. Total), DOE/EIA-0376 (Washington, 

B.C., July 1983); Energy Information Administration, Residential Energy 

Consumption Survey; Consumption and Expenditures April 1980 Through 

March 1981, Part 1; National Data, DOE/EIA-0321/1 (Washington, D.C., 

September 1982); Energy Information Administration, Residential Energy 

Consumption Survey; Consumption and Expenditures April 1980 Through 

March 1981, Part 2; Regional Data, DOE/EIA-0321/2 (Washington, D.C., 

June 1983).

-89-

Appendix A

DEFINITION OF VARIABLES USED IN REGRESSION ANALYSIS

-91-

Table Al. Variables Used in Regression Equations

Variables

On PublicUse Tape

Page First 

Used Description

APTBLDG No 41

BASESFU No 33

BUILTYEAR No 30

CACRMS

CDD

No

No

31

31

CFAEQUIP No 30

Household lives in an apartment build 

ing (Yes = 1, No = 0). Obtainable 

from the variable TYPEHOME on the 

public-use tape.

Household lives in a single-family 

unit that has a basement (Yes =1, 

No = 0). Obtainable from the 

variable BASEMENT on the publicuse tape.

Year dwelling was built;

Before 1940 = 1;

1940-1949 = 2;

1950-1959 = 3;

1960-1964 = 4;

1965-1969 = 5;

1970-1974 = 6;

After 1974 = 7.

Obtainable from the variable BUILTYEAR

on the public-use tape.

Number of rooms that can be air 

conditioned with an electric central 

system. Equals the number of rooms 

that can be air conditioned if the 

dwelling unit has an electric central 

system and no room air-conditioning 

units. If the dwelling unit has 

both types of electric systems, then 

CACRMS equals the number of rooms 

that can be air conditioned minus 

one. Obtainable from the variables 

HCENTAC, HROOMAC, NROOMAC, NROOMS, 

and KFLCNAC on the public-use tape.

Cooling degree-days base 75 F. 

Equals CDD75 on public-use tape. 

Units = 100 degree-days.

Main space-heating equipment is a 

central forced-air unit (Yes » 1, 

No = 0). Obtainable from the 

variable EQUIPM on the public-use 

tape.

-93-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

CHILDPRE

ELCON

No

No

33

40

ELCONBS No 29

ELCONCL No 29

ELCONHT

ELCONSFD

No

No

29

29

ELCONWT No 29

ELMHEAT No 30

ELSHEAT No 30

ELWTHT No 30

One of the household members is less 

than 3 years old (Yes = 1, No = 0). 

Obtainable from the variables NAGE01 - 

NAGE12 on the public-use tape.

Regression estimate of total electric 

ity consumption for households not 

living in single-family detached units.

Regression estimate of electricity con 

sumption for lighting, appliances, and 

miscellaneous use based on households 

living in single-family detached homes.

Regression estimate of electricity con 

sumption for air conditioning based 

on households living in single-family 

detached homes.

Regression estimate of electricity con 

sumption for space heating based on 

households living in single-family 

detached homes.

Regression estimate of total electricity 

consumption based on households living 

in single-family detached homes.

Regression estimate of electricity con 

sumption for water heating based on 

households living in single-family 

detached homes.

Use electricity as the main heating 

fuel (Yes =1, No - 0). Obtainable 

from the variable KFLMHEAT on the 

public-use tape.

Use electricity as a secondary heating 

fuel but not as the main heating fuel 

(Yes =1, No = 0). Obtainable from 

variables KFLMHEAT and KFLSHEAT on 

the public-use tape.

Use electricity as the main fuel for 

water heating (Yes = 1, No =0). 

Obtainable from the variable KWHEATFL 

on the public-use tape.

-94-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

ESTELBS No 83

ESTELCL No 83

ESTELHT No 83

ESTELWT No 83

Nonnormalized regression estimate of 

the miscellaneous component of electric 

ity consumption. For households living 

in single-family detached homes this 

equals ELCONBS. For households living 

in other housing types the estimate is 

adjusted to reflect the non-SFD regres 

sion results.

Nonnormalized regression estimate of 

the air-conditioning component of 

electricity consumption. For house 

holds living in single-family detached 

homes this equals ELCONCL. For house 

holds living in other housing types 

the estimate is adjusted to reflect 

the non-SFD regression results.

Nonnormalized regression estimate of 

the space-heating component of electric 

ity consumption. For households living 

in single-family detached homes this 

equals ELCONHT. For households living 

in other housing types the estimate is 

adjusted to reflect the non-SFD regres 

sion results.

Nonnormalized regression estimate of 

the water-heating component of electric 

ity consumption. For households living 

in single-family detached homes this 

equals ELCONWT. For households living 

in other housing types the estimate is 

adjusted to reflect the non-SFD regres 

sion results.

FOCON No 41

FOCONHT No

Regression estimate of total fuel oil 

consumption for households not living 

in single-family detached units.

Regression estimate of fuel oil con 

sumption for space heating based on 

households living in single-family 

detached homes.

FOCONSFD No 34

Regression estimate of total fuel oil 

consumption based on households 

living in single-family detached 

homes.

-95-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On Public- Page First 

Use Tape____Used Description

FOCONWT

FOMHEAT

FOSHEAT

FOWTHT 

HAUTOWSH

HDD

HEADAGE

HEATED

HELCLSDY 

HELDISHW

No

No

No

No 

Yes

No 

Yes 

Yes

Yes 

Yes

34

34

34

34

30

30

33

29

30

30

Regression estimate of fuel oil con 

sumption for water heating based 

on households living in singlefamily detached homes.

Use fuel oil as the main heating 

fuel (Yes =1, No = 0). Obtainable 

from the variable KFLMHEAT on the 

public-use tape.

Use fuel oil as a secondary heating 

fuel but not as main heating fuel. 

(Yes = 1, No = 0). Mathematical 

definition:

FOSHEAT = 1 if KFLMHEAT t 3

and (KFLSHEAT = 3 

or FOHEAT = 1).

The variables KFLMHEAT, KFLSHEAT, and 

FOHEAT are on the public-use tape.

Use fuel oil as a water-heating fuel 

(Used as main water-heating fuel = 1, 

Used as secondary water-heating 

fuel = 0.5, Not used as a waterheating fuel = 0). Mathematical 

definition:

FOWTHT =1 if KWHEATFL = 3

= 0.5 if KWHEATFL t 3,

and FOH20 = 1 

= 0 otherwise.

Use automatic clothes washer (Yes = 1, 

No = 0).

Heating degree-days base 60 F. Equals 

HDD60 on public-use tape. Units = 

100 degree-days.

Age of head of household.

Total square footage of heated area.

Use electric clothes dryer (Yes = 1, 

No = 0).

Use electric dishwasher (Yes =1, 

No = 0).

-96-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

HLPCLSDY No 35

HNGCLSDY 

HODGASLT

HPOOL

HTPOOL

HWRNGWSH 

INCOME?9

No 

Yes

No

No

Yes 

Yes

32

33

30

30

30

31

Use LPG clothes dryer (Yes = 1, 

No = 0). Obtainable from variables 

HGSCLSDY and BTULP on the publicuse tape.

Use natural gas clothes dryer (Yes = 1, 

No = 0). Obtainable from variables 

HGSCLSDY and BTUNG on the publicuse tape.

Use outdoor gas lights (Yes = 1, 

No = 0).

Have swimming pool (Yes = 1,

No = 0). Obtainable from variable

POOL in public-use tape.

Use a pool heater (fuel unspecified) 

(Yes = 1, No = 0). Obtainable from 

variable POOLHEAT on public-use tape.

Use electric wringer washing machine 

(Yes =1, No = 0).

Self-reported family income for 1979

Loss

$0 - $2

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

$9,000

$10,000

$11,000

$12,000

$13,000

$14,000

$15,000

$17,000

$20,000

$25,000

$30,000

$35,000

$40,000

$50,000

$75,000

i;

,999 = 2;

- $3,999 =

- $4,999 -

- $5,999 =

- $6,999 =

- $7,999 =

- $8,999 =

- $9,999 =

- $10,999

- $11,999

- $12,999

- $13,999

- $14,999

- $16,999

- $19,999

- $24,999

- $29,999

- $34,999

- $39,999

- $49,999

- $74,999 

or More =

3;

5; 

6; 7; 

8; 

9; = 10;

= 11;

• 12; 

= 13; 

= 14;

= 15; 

= 16; 

= 17; 

= 18;

- 19; 

= 20; 

= 21; 

- 22; 

23;

-97-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse TapePage First 

Used Description

KRCON 

LPAPLIDX

No 

No

LPCACRMS

LPCON

No

No

LPCONBS

LPCONHT

LPCONSFD

LPCONWT

No

No

No

No

LPMHEAT No

LPSHEAT No

Regression estimate of total kerosene 

41 consumption.

35 Appliance index for LPG appliances.

Number of rooms that can be air 

conditioned with an LPG central 

system. Equals the number of rooms 

that can be air conditioned if the 

dwelling unit has an LPG central system 

and no room air-conditioning units. 

If the dwelling unit has both room units 

and an LPG central system, then LPCACRMS 

equals the number of rooms that can be 

air conditioned minus one. Obtainable 

from the variables HCENTAC, HROOMAC, 

NROOMAC, NROOMS, KFLCNAC, and BTULP on 

35 the public-use tape.

Regression estimate of total LPG 

consumption for households not living 

41 in single-family detached units.

Regression estimate of LPG consumption 

for appliances, air conditioning, and 

miscellaneous uses based on households 

35 living in single-family detached homes.

Regression estimate of LPG consumption 

for space heating based on households 

35 living in single-family detached homes.

Regression estimate of total LPG con 

sumption based on households living 

35 in single-family detached units.

Regression estimate of LPG consumption 

for water heating based on households 

35 living in single-family detached homes.

Use LPG as the main heating fuel 

(Yes = 1, No = 0). Obtainable from 

the variable KFLMHEAT on the public35 use tape.

Use LPG as a secondary heating fuel 

but not as the main heating fuel 

(Yes = 1, No = 0). Obtainable from 

variables KFLMHEAT and KFLSHEAT on 

35 the public-use tape.

-98-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

LPWTHT No

LRGAPTBD No

MOBILEHM No

MULTIUNIT No

NBATHRMS No

NDRSAWS No

NELFRIG No

NELFREZ No

Use LPG as the main fuel for water 

heating (Yes = 1, No = 0). Obtain 

able from the variable KWHEATFL on 

35 the public-use tape.

Dwelling unit is in a large apart 

ment building (Yes = 1, No = 0). 

Obtainable from the variable 

40 TYPEHOME on the public-use tape.

Dwelling unit is a mobile home 

(Yes = 1, No « 0). Obtainable from 

the variable TYPEHOME on the public40 use tape.

Dwelling unit is either a singlefamily attached unit or a unit 

in an apartment building (Yes =1, 

No = 0). Obtainable from the 

variable TYPEHOME on the public-use

41 tape.

Number of bathrooms equals the number 

of full bathrooms plus half the number 

of half-bathrooms. Obtainable from 

the variables NCOMBATH and NHAFBATH 

30 on the public-use tape.

Number of windows and doors. Obtain 

able from the variables DOOR1ALL, 

DOOR2ALL, DOOR3ALL, WINDOWS, WIND1ALL 

WIND2ALL, and WIND3ALL on the public33 use tape.

Number of electric refrigerators. 

Obtainable from the variables NREFRIG, 

KREFREL1, and KREFRFL2 on the publicuse tape. Any refrigerator for which 

fuel is not listed is assumed to be an 

30 electric refrigerator.

Number of electric freezers that are 

separate from refrigerators. Obtainable 

from the variables FREEZEN, FREEZE1F, 

and FREEZE2F on the public-use tape. 

Any freezer for which fuel is not listed 

30 is assumed to be an electric freezer.

-99-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

NELOVARG No

NFFELFRIG No

NFFLPFRIG No

NFFNGFRIG

NGAPLIDX

No

No

NGCACRMS No

Number of conventional electric ovens 

plus one if the household has an 

electric range. Obtainable from the 

variables OVENNUM, OVEN1F, OVEN2F, 

30 OVEN1M, OVEN2M, and HELRANGE.

Number of frost-free or automatic 

defrost electric refrigerators. Any 

refrigerator whose type is not specified 

is assumed to be a manual defrost 

refrigerator. Obtainable from the 

variables KREFRFL1, KREFRFL2, REFRIGT1, 

30 and REFRIGT2 on the public-use tape.

Number of frost-free or automatic defrost 

LPG refrigerators. Any refrigerator 

whose type is not specified is assumed 

to be a manual defrost refrigerator. 

Obtainable from the variables KREFRFL1, 

KREFRFL2, REFRIGT1, REFRIGT2, and 

35 BTULP on the public-use tape.

Number of frost-free or automatic 

defrost natural gas refrigerators. Any 

refrigerator whose type is not specified 

is assumed to be a manual defrost 

refrigerator. Obtainable from the 

variables KREFRFL1, KREFRFL2, REFR1GT1, 

REFRIGT2, and BTUNG on the public-use 

32 tape.

Appliance index for natural gas

32 appliances.

Number of rooms that can be air condi 

tioned with a natural gas central 

system. Equals the number of rooms 

that can be air conditioned if the 

dwelling unit has a natural gas 

central system and no room airconditioning units. If the dwelling unit 

has both room units and a natural gas 

central system, then NGCACRMS equals 

the number of rooms that can be air 

conditioned minus one. Obtainable 

from the variables NCENTAC, HROOMAC, 

NROOMAC, NROOMS, KFLCNAC, and BTUNG on

33 the public-use tape.

-100-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

NGCON No

NGCONBS No

NGCONHT

NGCONSFD

No

No

NGCONWT No

NGMHEAT No

NGPOOLHT No

NGSHEAT No

NGWTHT 

NHSLDMEM

NLPFRIG

No 

Yes

No

Regression estimate of total natural 

gas consumption for households not 

40 living in single-family detached units.

Regression estimate of natural gas 

consumption for appliances and miscel 

laneous uses based on households 

32 living in single-family detached homes.

Regression estimate of natural gas con 

sumption for space heating based on 

households living in single-family 

32 detached homes.

Regression estimate of total natural 

gas consumption based on households 

32 living in single-family detached units.

Regression estimate of natural gas con 

sumption for water heating based on 

households living in single-family

32 detached homes.

Use natural gas as the main heating 

fuel (Yes = 1, No = 0). Obtainable 

from the variable KFLMHEAT on the

33 public-use tape.

Use natural gas to heat a swimming pool 

(Yes = 1, No = 0). Obtainable from the 

variable POOLFUEL on the public-use 

33 tape.

Use natural gas as a secondary spaceheating fuel but not as the main 

space-heating fuel (Yes = 1, No = 0). 

Obtainable from variables KFLMHEAT 

33 and KFLSHEAT on the public-use tape.

Use natural gas as the main waterheating fuel (Yes = 1, No = 0). 

Obtainable from the variable KWHEATFL 

33 on the public-use tape.

30 Number of household members.

Number of LPG refrigerators. Obtain 

able from variables KREFRFL1, KREFRFL2, 

35 and BTULP on the public-use tape.

-101-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

NLPOVARG No

NNGFRIG No

NNGOVARL No

Number of LPG ovens and ranges. 

Obtainable from variables OVEN1F, 

OVEN2F, HGASRANG, and BTULP on the 

35 public-use tape.

Number of natural gas refrigerators. 

Obtainable from variables KREFRFL1, 

KREFRFL2, and BTUNG on the public32 use tape.

Number of natural gas ovens and 

ranges. Obtainable from variables 

OVEN1F, OVEN2F, HGASRANG, and 

32 BTUNG.

Presence of insulation in roof for 

single-family units:

NOUPPIN 

NROOMS

No 

Yes

NUMADULTS No

PCTSTORM

No insulation = 1;

Some insulation or unknown 0.

No

Obtainable from variable HINATTIC 

33 on the public-use tape.

33 Number of rooms.

Number of household members who 

are between 13 and 64 years of 

age. Obtainable from the variables 

NAGE01-NAGE12 on the public-use 

29 tape.

Percentage of windows and doors that 

are covered by storm windows or 

storm doors. Obtainable from the 

variables DOOR1ALL, DOOR1INS, 

DOOR2ALL, DOOR2INS, DOOR3ALL, 

DOOR3INS, WINDOWS, WIND1ALL, 

WIND1INDS, WIND2ALL, WIND2INS, 

29 WIND3ALL, and WIND3INS.

-102-
Table Al. Variables Used in Regression Equations (Continued)

Variables

On PublicUse Tape

Page First 

Used Description

RACRMS No

RADEQUIP

SFATTACH

No

No

SMLAPTBD No

SOTHHTFO No

SOTHHTNG No

SWOODHT No

TEENS No

Number of rooms that can be air 

conditioned with electric room units. 

Equals the number of rooms that can 

be air conditioned if the dwelling unit 

does not have a central air-conditioning 

system. Equals one if the dwelling unit 

has both room units and a central system. 

Obtainable from the variables HCENTAC, 

HROOMAC, NROOMAC, and NROOMS on the 

31 public-use tape.

Main space-heating equipment uses 

radiators or hot-water pipes (Yes = 1, 

No =0). Obtainable from the 

33 variable EQUIPM on the public-use tape.

Dwelling unit is a single-family 

attached unit (Yes = 1, No = 0). 

Obtainable from the variable TYPEHOME 

40 on the public-use tape.

Dwelling unit is in a small apart 

ment building (Yes = 1, No = 0). 

Obtainable from the variable TYPEHOME 

40 on the public-use tape.

Fuel oil is the main space-heating 

fuel, and the secondary space-heating 

fuel is not fuel oil or wood 

(Yes =1, No = 0). Obtainable from 

the variables KFLMHEAT and KFLSHEAT 

34 on the public-use tape.

Natural gas is the main space-heating 

fuel, and the secondary space-heating 

fuel is not natural gas or wood 

(Yes = 1, No = 0). Obtainable from 

the variables KFLMHEAT and KFLSHEAT 

33 on the public-use tape.

Secondary space-heating fuel is wood 

(Yes = 1, No = 0). Obtainable from 

the variables KFLSHEAT on the public30 use tape.

Number of household members between 

the ages of 13 and 19. Obtainable 

from the variable NAGE01-NAGE12 on 

30 the public-use tape.

-103-
Table Al. Variables Used in Regression Equations (Continued)____________

On Public- Page First 

Variables____Use Tape______Used___Description_____________________

Number of cords of wood burned 

truncated at six cords. Units = onetenth cord. Obtainable from the 

TRUNCORD No 30 variable CORD on the public-use tape.

Number of black-and-white television 

TVB1ACK Yes 30 sets.

TVCOLOR Yes 30 Number of color television sets.

Amount of unheated enclosed floorspace 

UNHEATED Yes 33 in the dwelling unit.

-104-
Appendix B

RESULTS OF REGRESSION ANALYSIS

The standard errors and levels of significance listed in this appendix 

were obtained from the fourth step in the regression procedure. They 

are based on the assumption that the variance of the error term is 

proportional to G, the estimated consumption. For selected coefficients 

the standard errors were also calculated using the half-sample technique. 

In this technique the entire four-step robust regression estimation 

procedure was run for each of 32 half-samples. The variances were 

computed using the differences between the full-sample estimates and the 

half-sample estimates. In most cases the results were very close to the 

values listed in this appendix.

-105-

Table Bl. Estimates of Coefficients for Regression Using Households 

Living in Single-Family Detached Homes Where Electricity 

Consumption Is the Dependent Variable

Independent 

Variable

INTERCEPT

NUMADULTS

BUILTYR

PCT STORM

HEATED

HPOOL

HTPOOL

NELFRIG

NFFELFRIG

NELFREZ

NELOVARG

HWRNGWSH

HAUTOWSH

HAUTOWSH x NHSLDMEM

HELCLSDY

TVBLACK

TVCOLOR

CFAEQUIP

ELWTHT

ELWTHT x NHSLDMEM

ELWTHT x TEENS

ELWTHT x HELD IS HW

ELSHEAT x HDD

ELMHEAT x HDD

ELMHEAT x HDD x HDD

ELMHEAT x HDD x HEATED

ELMHEAT x HDD x HEATED x

SWOODHT x TRUNCORD

ELMHEAT x HDD x HEATED x

BUILTYEAR

ELMHEAT x HDD x HEATED x

NBATHRMS

ELMHEAT x HDD x HEATED x

PCTSTROM

RACRMS x CDD

RACH4S x CDD x INCOME 7 9

CACRMS

CACEMS x CDD

CACRMS x CDD x INCOME? 9

CACRMS x CDD x CDD

Estimate of 

Coefficient

2,614.7530

1,088.5794

339.6044

-19.2800

1.0980

3,842.0099

10,215.6688

2,455.6169

2,863.3164

3,953.5273

929.7142

-2,312.1431

-4,330.5340

1,734.7495

3,409.5230

836.1728

2,055.3690

1,155.5738

3,966.6710

2,156.9070

3,337.8452

4,903.5595

56.9456

990.0705

-7.9569

0.2779

-0.0032

-0.0373

0.0756

-0.0015

186.6288

10.6565

409.4043

341.0278

9.9593

-17.8836

Standard Error 

of Estimate

766.6629

189.8177

103.9762

4.3472

0.2104

1,085.3462

2,886.4758

451.7848

362.8396

318.0743

181.1381

744.0982

728.4072

191.0041

411.2129

271.8875

316.2878

374.6923

954.5912

342.4235

715.5884

827.4341

13.0142

94.3625

1.9617

0.0735

0.0006

0.0063

0.0215

0.0004

38.8665

2.8786

134.2959

75.5023

2.7631

4.6018

Two-Sided 

Level of Signif 

icance

0.0007

Less than 0.0001

0.0011

Less than 0.0001

Less than 0.0001

0.0004

0.0004

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

0.0019

Less than 0.0001

Less than 0.0001

Less than 0.0001

0.0021

Less than 0.0001

0.0021

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

0.0002

Less than 0.0001

Less than 0.0001

0.0004

0.0007

Less than 0.0001

0.0002

0.0023

Less than 0.0001

0.0003

Less than 0.0001

-107-
Table B2. Estimates of Coefficients for Regression Using Households 

Living in Single-Family Detached Homes Where Natural Gas 

Consumption Is the Dependent Variable

Independent Estimate of 

Variable Coefficient

NGAPLIDX

HODGASLT 13

NGPOOLHT 47

NGCACRMS x CDD 1

NGWTHT 13

NGWTHT x NHSLDMEM 5

NGSHEAT x HDD

NGMHEAT 11

NGMHEAT x HDD 1

NGMHEAT x HDD x

RADEQUIP

NGMHEAT x HDD x

CFAEQUIP

NGMHEAT x HDD x

HEATED x BUILTYR

NGMHEAT x HDD x

HEATED x HEADAGE

NGMHEAT x HDD x

HEATED x CHILDPRE

NGMHEAT x HDD x

HEATED x NROOMS

NGMHEAT x HDD X

HEATED x NOUPPIN

NGMHEAT x HDD x

HEATED x NBATHRMS

NGMHEAT x HDD x

HEATED x SOTHHT

NGMHEAT x HDD X

HEATED x BASESFU

NGMHEAT x HDD x

SWOODHT x TRUNCORD

NGMHEAT x HDD x

UNHEATED

NGMHEAT x HDD x NDRSAWS

NGMHEAT x HDD x HDD x

PC T STORM

2.2728

,279.5561

,254.5513

,531.4794

,938.2185

,670.3061

775.5358

,734.1752

,154.7755

525.3524

215.2257

-0.0455

0.0033

0.1104

0.0361

0.1779

0.0889

-0.1241

-0.1671

-14.5995

0.2064

12.2160

-0.0782

Two-Sided 

Level of 

Standard Error Signifof Estimate icance

0.3388

4,197.5617

10,946.3122

190.6077

2,417.9503

607.8977

122.1136

2,592.0192

114.6721

103.4086

66.0483

0.0055

0.0007

0.0397

0.0069

0.0449

0.0211

0.0313

0.0284

2.8712

0.0476

4.0052

0.0127

Less than 0.0001

0.0016

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

0.0011

Less than 0.0001

Less than 0.0001

0.0054

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

0.0023

Less than 0.0001

-108-
Table B3. Estimates of Coefficients for Regression Using Households 

Living in Single-Family Detached Homes Where Fuel Oil 

Consumption Is the Dependent Variable

Independent 

Variable

FOWTHT

FOSHEAT

FOMHEAT

FOMHEAT x RADEQUIP

FOMHEAT x BUILTYR

FOMHEAT x HDD x

HEATED

FOMHEAT x HDD x

HEATED x SWOODHT x

TRUNCORD

FOMHEAT x HDD x

HEATED x BASESFU

FOMHEAT x HDD x

NOUPPIN

FOMHEAT x HDD x

HEATED x SOTHHTFO

FOMHEAT x UNHEATED

FOMHEAT x HDD x

NDRSAWS

Estimate of 

Coefficient

30,999.0166

22,180.0847

42,938.0614

20,530.6213

-4,589.3523

0.5964

-0.0114

-0.2818

487.0920

-0.1402

9.6239

27.0782

Two-Si ded 

Level of 

Standard Error Slgnifof Estimate icance

5,236.7468

2,705.4357

6,104.5076

5,757.5719

1,209.3584

0.0969

0.0012

0.0739

152.7990

0.0480

3.3383

7.0031

Less than

Less than

Less than

0.0004

0.0002

Less than

Less than

0.0002

0.0015

0.0037

0.0041

Less than

0.0001

0.0001

0.0001

0.0001

0.0001

0.0001

Table B4. Estimates of Coefficients for Regression Using Households 

Living in Single-Family Detached Homes Where LPG 

Consumption Is the Dependent Variable

Independent

Variable

LPAPLIDX

LPWTHT x NHSLDMEM

LPSHEAT x HDD

LPMHEAT x HDD

LPMHEAT x HDD x

HEATED x NBATHRMS

LPMHEAT x HDD x

BASESFU

Estimate of

Coefficient

3.0014

6,916.5963

781.5050

1,917.5892

0.1289

-741.2733

Standard Error

of Estimate

0.4258

729.6707

114.0048

131.6575

0.0329

170.2643

Two-Si ded

Level of

Signif 

icance

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

Less than 0.0001

-109-
Table B5. Estimates of Coefficients for Regression Using Households

Not Living in Single-Family Detached Homes Where Electricity 

________Consumption Is the Dependent Variable_________________

Independent 

Variable

Estimate of 

Coefficient

Standard Error 

of Estimate

Two-Sided 

Level of Signif 

icance

ELCONSFD

ELCONSFD

ELCONSFD

ELCONSFD

ELCONWT

ELCONHT

x MOBILEHM 

x SFATTACH 

x SMLAPTBD 

x LRGAPTBD 

x MOBILEHM 

x LRGAPTBD

1.1491

0.8361

0.8455

0.8266

-0.5575

-0.3943

0.0354

0.0245

0.0173

0.0281

0.1340

0.0703

Less than 

Less than 

Less than 

Less than 

Less than 

Less than

0.0001

0.0001

0.0001

0.0001

0.0001

0.0001

Table B6. Estimates of Coefficients for Regression Using Households 

Not Living in Single-Family Detached Homes Where Natural 

Gas Consumption Is the Dependent Variable

Independent 

Variable

NGCONSFD x MOBILEHM 

NGCONSFD x SFATTACH 

NGCONSFD x SMLAPTBD 

NGCONSFD x LRGAPTBD 

NGCONSBS

Estimate of 

Coefficient

0.8775 

0.8220 

0.8728 

0.6127 

1.2015

Standard Error 

of Estimate

0.0522 

0.0426 

0.0300 

0.0522 

0.2697

Two-Si ded 

Level of Signif 

icance

Less than 0.0001 

Less than 0.0001 

Less than 0.0001 

Less than 0.0001 

Less than 0.0001

Table B7. Estimates of Coefficients for Regression Using Households 

Not Living in Single-Family Detached Homes Where Fuel Oil 

________Consumption Is the Dependent Variable_______________

Independent 

Variable

Estimate of 

Coefficient

Standard Error 

of Estimate

Two-Sided 

Level of Signif 

icance

FOCONSFD x MOBILEHM 1.1233

FOCONSFD x SFATTACH 0.9189

FOCONSFD x APTBLDG 0.9739

0.1557 Less than 0.0001

0.0773 Less than 0.0001

0.0654 Less than 0.0001

-110-
Table B8. Estimates of Coefficients for Regression Using Households 

Not Living in Single-Family Detached Homes Where LPG 

Consumption Is the Dependent Variable

Independent 

Variable

LPCONSFD x MOBILEHM 

LPCONSFD x MULTIUNIT

Estimate of 

Coefficient

0.7242 

1.2260

Standard Error 

of Estimate

0.0466 Less 

0.1572 Less

Two-Sided 

Level of 

Signif 

icance

than 0.0001 

than 0.0001

Table B9. Estimates of Coefficients for Regression Where Kerosene 

________Consumption Is the Dependent Variable Using All Households

Independent 

Variable

Estimate of 

Coefficient

Standard Error 

of Estimate

Two-Sided 

Level of 

Signif 

icance

FOCONSFD 0.9245 0.1227 Less than 0.0001

-111-

Appendix C 

ADJUSTMENTS TO SPACE-HEATING COMPONENT TO REFLECT CALENDAR YEAR CONSUMPTION

This appendix contains the details of the procedure used to adjust the 

space-heating component of energy consumption to reflect consumption 

from January 1980 through December 1980 (calendar year 1980). The 

procedure is described in general terms. It can be applied to any of the 

fuels. In producing the calendar year 1980 estimates, the fuel oil and 

kerosene consumption amounts are combined. This last convention was also 

used in producing the consumption estimates for the April 1980 through 

March 1981 period.

The adjustments that were used can be divided into seven cases. The factors 

determining the cases are the main space-heating fuel on November 1979, the 

main space-heating fuel on November 1980, and the secondary space-heating 

fuels on November 1980. By assumption 1, the main space-heating fuel on 

November 1980 was also the main space-heating fuel during the period 

from April 1980 through March 1981. The main space-heating fuel on 

November 1979 was also the main space-heating fuel during the period 

from January 1980 through March 1980. By assumption 2, any fuel listed 

as a secondary space-heating fuel on November 1980 was also a secondary 

space-heating fuel from January 1980 through March 1981. In the regres 

sion procedure, the convention used was that if a fuel was the main 

space-heating fuel then it was not counted as a secondary space-heating 

fuel, even if the respondent listed it as one. This convention will be 

used for estimating calendar year consumption. The seven cases follow:

Case 1: The fuel was not used as a secondary or main 

space-heating fuel in either November 1979 

or November 1980.

Case 2: The fuel was listed as the main space-heating 

fuel in November 1980. The respondent 

stated that there was no change in the 

main space-heating fuel between 

November 1979 and November 1980.

Case 3: The fuel was listed as a secondary 

space-heating fuel on November 1980. 

The fuel was not listed as the main 

space-heating fuel in either November

1979 or November 1980.

Case 4: The fuel was not listed as the secondary 

or main space-heating fuel on November

1980 but was listed as the main 

space-heating fuel in November 1979.

-113-
Case 5: The fuel was listed as a secondary 

space-heating fuel but not the main 

space-heating fuel in November 1980. 

The fuel was listed as the main spaceheating fuel in November 1979.

Case 6: The fuel was listed as the main spaceheating fuel in November 1980, but the 

respondent stated that a different fuel 

was used as the main space-heating fuel 

in November 1979. The fuel was not 

listed as a secondary space-heating fuel 

in November 1980.

Case 7: The fuel was listed as the main spaceheating fuel in November 1980, but the 

respondent stated that a different fuel 

was used as the main space-heating fuel 

in November 1979. The fuel was also 

listed as a secondary space-heating fuel 

in November 1980.

In the adjustment procedure, the following heating degree-day amounts 

are used: HDDJM80 is the heating degree-days base 60 F for the period 

from January 1, 1980, through March 31, 1980; HDDAD80 is the total from 

April 1, 1980, through December 31, 1980; and HDDJM81 is the total from 

January 1, 1981, through March 31, 1981. These amounts were obtained for 

each household interviewed in the survey. The sum of HDDAD80 and HDDJM81 

equals the heating degree-days used in the regression procedure.

The adjustment made for each case is listed:

Case 1: No adjustments are necessary. The heating 

component is zero for both the April 1980 

through March 1981 period and the 1980 

calendar year.

Case 2: The space-heating component needs to be adjusted

for changes in heating degree-days. By assumption

3, one needs only to multiply the space-heating component

by (HDDJM80 + HDDAD80)/(HDDAD80 + HDDJM81).

Case 3: By assumption 2, it was assumed that the fuel is a 

secondary heating fuel from January 1980 through 

March 1981. By assumption 3, one needs only to multiply 

the space-heating component by (HDDJM80 + HDDAD80)/ 

(HDDAD80 + HDDJM81).

Case 4: By assumption 1, one can assume that the fuel 

was the main space-heating fuel from January 

1980 through March 1980 and was not used 

as the main or secondary space-heating

-114-
fuel from April 1980 through March 1981. 

Hence the space-heating component for the 

interview year is zero. This needs to be 

adjusted to reflect calendar year consumption 

by adding an estimate for the consumption 

from January 1980 to March 1980. Estimate 

this by calculating the space-heating regression 

estimate, assuming the fuel is the main spaceheating fuel, and multiply by (HDDJM80)/ 

(HDDAD80 + HDDJM81).

Case 5: By assumption 1, one can assume the fuel was 

the main space-heating fuel from January 

1980 through March 1980 and was a secondary 

space-heating fuel from January 1980 

through March 1981. The space-heating 

component must be adjusted by subtracting 

the secondary space-heating contribution for 

January 1981 through March 1981 and adding the 

main space-heating contribution for January 1980 

through March 1980. Let BTUSPH be the 

space-heating component for the period from 

April 1980 through March 1981. Estimate the 

secondary space-heating contribution for 

January 1981 through March 1981 by SSHJM81 - 

(BTUSPH) x (HDDJM81)/ (HDDAD80 + HDDJM81). 

Then SSHAD80 = BTUSPH-SSHJM81 is the space-heating 

consumption for April 1980 through December 

1980. Let REGMSH be the regression estimate 

for the space-heating component for April 

1980 through March 1981, assuming the fuel is 

the main space-heating fuel. Estimate the 

main space-heating contribution for January 

1980 through March 1980 by MSHJM80 = (REGMSH) 

(HDDJM80)/(HDDAD80 + HDDJM81). Set the 

space-heating component equal to SSHAD80 plus 

the maximum of SSHJM80 and MSHJM80, where 

SSHJM80 = (BTUSPH) x (HDDJM80)/(HDDAD80 + HDDJM81). 

Note that SSMJM80 is an estimate of the spaceheating consumption for secondary space-heating 

based on the estimate for April 1980 through 

March 1981. The maximum was used because the 

author did not want the switch from secondary 

space heating to main space heating to result in a 

decrease in the space-heating component.

Case 6: By assumption 1, one can assume the fuel was the 

main space-heating fuel from April 1980 through 

March 1981 and was not the main space-heating 

fuel from January 1980 through March 1980. By 

assumption 2, one can assume the fuel is not a 

secondary space-heating fuel from January 1980

-115-
through March 1980. One needs to adjust the spaceheating component by subtracting the main spaceheating consumption for January 1981 through 

March 1981. By assumption 3, one can accomplish 

this by multiplying the space-heating component 

by (HDDAD80)/(HDDAD80 + HDDJM81).

Case 7: By assumption 1, one can assume the fuel 

was the main space-heating fuel from 

April 1980 through March 1981 and was 

not the main space-heating fuel from 

January 1980 through March 1980. By 

assumption 2, one can assume the fuel was 

a secondary space-heating fuel from 

January 1980 through March 1980. One 

needs to adjust the space-heating component 

by subtracting the main space-heating con 

sumption for January 1981 through March 1981 

and adding the secondary space-heating con 

sumption for January 1980 through March 1980. 

Let BTUSPH be the space-heating component for 

the period from April 1980 through March 1981. 

Estimate the main space-heating consumption 

for January 1, 1981, through March 31, 1981, 

by MSHJM81 = (BTUSPH)(HDDJM81)/(HDDAD80 + 

HDDJM81). Then MSHAD80 = BTUSPH - MSHJM81 

is the estimated space-heating consumption 

for April 1980 through December 1980. Let 

REGSSH be the regression estimate for the 

space-heating component for April 1980 through 

March 1981, assuming the fuel is the secondary 

space-heating fuel but not the main spaceheating fuel. Estimate the secondary spaceheating consumption for January 1980 through 

March 1980 by SSHJM80 = (REGSSH) x (HDDJM80)/ 

(HDDAD80 + HDDJM81). Set the space-heating 

component equal to MSHAD80 plus the minimum of 

MSHJM80 and SSHJM80 where MSHJM80 = (BTUSPH) x 

(HDDJM80)/(HDDAD80 + HDDJM81). Note that 

MSHJM80 is an estimate of the space-heating 

consumption for main space heating based on 

the estimate for April 1980 through March 

1981. The minimum was used because 

the author did not want the switch from 

main space heating to secondary space heating to 

result in an increase in the space-heating 

component.

-116-

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