U.S. Energy Information Administration Independent Statistics & An

Highlights from the 2015 RECS:Energy consumption, expenditures, and end-use moWebinarJuly 31, 2018 at 2 p.m. EDTByChip Berry, Survey Manager Greg Lawson, End-Use Modeling SpecialistMaggie Woodward, Industry Economist
Agenda

• Introduction

– Accessing RECS data on the website

• Consumption and expenditures highlights from the 2015 RECS• Overview of end-use modeling for the 2015 RECS• A look to the future• Q&A

2015 Residential Energy Consumption Survey July 31, 2018 
2015 RECS data products

• Updated! Housing characteristics data tables• Updated! Square footage data• Updated! Microdata file• Updated! Methodology documents• New! Consumption and expenditures data

2015 Residential Energy Consumption Survey July 31, 2018 
The RECS website

www.eia.gov/consumption/residential/

2015 Residential Energy Consumption Survey July 31, 2018 
Consumption and expenditures

highlights from the 2015 RECS

2015 Residential Energy Consumption Survey July 31, 2018 
Step 1: Household Survey

• EIA’s only household program• Produces national estimates an

subpopulation variations 

• Describes the characteristics th

household energy demand

– Structural (housing type, age)– Systems (heating, cooling)– Devices (appliances, electronics)– Behavior (usage frequency, tempe– People

2015 Residential Energy Consumption Survey July 31, 2018 
Step 2: Monthly billing data are collected from energy 

2015 Residential Energy Consumption Survey July 31, 2018 

Billing Date kWh Cost

1/7/2015 813 $194.442/5/2015 627 $133.113/9/2015 615 $122.904/7/2015 758 $143.895/7/2015 689 $149.446/8/2015 703 $148.037/8/2015 965 $228.998/6/2015 1302 $335.739/4/2015 1467 $386.86

10/6/2015 1584 $387.1811/5/2015 1191 $300.2112/8/2015 963 $223.40

Total 11,677 kWh $2,754

Monthly billing data from electric utility 

Annualized total
Step 3: Annualized totals are disaggregated into energy 

estimates

2015 Residential Energy Consumption Survey July 31, 2018 

11,677 kWh
2015 Residential Energy Consumption Survey 

July 31, 2018

Nation’s 118 million households consumed 77 million Btu oin 2015Source: EIA, 2015 Residential Energy Consumption Survey

0

20406080

100120140

1993 1997 2001 2005 2009 

number of primary occupied housing units million

average 

million Btu 
2015 Residential Energy Consumption Survey 

July 31, 2018

Average site consumption was highest in Northeast and MidMidwest costs were lower than national average

Source: EIA, 2015 Residential Energy Consumption Survey

0

20406080

100

Total U.S. Northeast Midwest South Wes

Average household site consumption and cost, by region, 2015million Btu 

$

$

$

$

$
0

20406080

100

Before

1950

1950

to 1959

1960

to 1969

1970

to 1979

1980

to 1989

1990

to 1999

t

0

20406080

100

Before

1950

1950

to 1959

1960

to 1969

1970

to 1979

1980

to 1989

1990

to 1999

t

Average household site consumption by year built, 2015

million Btu per household sq

2015 Residential Energy Consumption Survey July 31, 2018

A home built since 2000 consumed the same amount of enebuilt in the 1960s

Source: EIA, 2015 Residential Energy Consumption Survey

$

1,874

year home built

2,346
49%

69%

35%

30%

47%

47%

26%

61%

46%

44%

2

0% 20% 40% 60% 80% 

West

South

Midwest

Northeast

Total U.S.

Household site energy consumption by Census region, 2015percentage of total site consumption by fuel

2015 Residential Energy Consumption Survey July 31, 2018

Electricity was the most consumed energy source across all households; in coldest areas natural gas was most-consumedSource: EIA, 2015 Residential Energy Consumption Survey

electricity natural gas fu

p
Share of home energy use, 2015

2015 Residential Energy Consumption Survey July 31, 2018

Heating, cooling, and ventilation accounted for about half of home energy use, but the share varies by climate regionSource: EIA, 2015 Residential Energy Consumption Survey

m

0% 20% 40% 60% 80% 

Total U.S.

Very cold/Cold

Mixed-dry/Hot-dry

Mixed-humid

Marine

Hot-humid

space heating + air handlers / 

boiler pumps + humidifiers = 44.4%

AC + air handlers + evaporative coolers 

+ ceiling fans + dehumidifiers = 10.0%

all oth

= 
Electric Space Heating increased, Other decreased relative 

RECS and prior rounds

2015 Residential Energy Consumption Survey July 31, 2018
2015 RECS reports 26 electricity end-use estimates, up from 

previous rounds

2015 Residential Energy Consumption Survey July 31, 2018 

Source: EIA, 2015 Residential Energy Consumption Survey
End-use modeling methodology

from the 2015 RECS

2015 Residential Energy Consumption Survey July 31, 2018 
What information is available for estimating nation

use consumption?• Sadly, no submetering data across a representative, national sa• Hence, end-use consumption must be Estimated from available 

– RECS Housing Characteristics Survey data– RECS Annualized Billing data– Administrative data– Wider Community Knowledge!

2015 Residential Energy Consumption Survey July 31, 2018 
The basics of end-use estimation

• Housing Characteristics data + Weather data give Expectation

consumption from individual end uses

– Expectations are quantified by use of Models

• Annualized Billing data are essentially Measurements of the to

consumption across all end uses, for a particular fuel

• Combine Expectations and Measurements through Calibration

Estimates = Calibration( Expectations, Measurements 

2015 Residential Energy Consumption Survey July 31, 2018 
End-use energy expectations set by modeling

• Example: the end-use model for coffee makers has no usage in

if COFFEE = 1

Coffee_Consumption = P_coffee

else

Coffee_Consumption = 0

end

• Prior to the 2015 RECS, modeling was Statistical: 

P_coffee looks like a UEC value, but it is merely part of a set ounknown parameters that are sensitive to the specifications of a

• The 2015 RECS used Engineering Models:

P_coffee was specified based on published values for the UEmakers (e.g., “wider expectations”) 

2015 Residential Energy Consumption Survey July 31, 2018 
End-use energy expectations can get complicated

• A model for space conditioning clearly depends on 

many inputs, like building size, materials, and location, as well as weather and occupant behavior

• Prior to the 2015 RECS, analysts supposed 

functional forms and inserted unknown parameters:

“Does space heating consumption depend on the square-root of H

• In the 2015 RECS, analysts followed engineering principles and 

calculate an underlying “Heating Load” or “Cooling Load,” and thconsidered the efficiency of the fuel and equipment used to mee

– U-A calculations for heat conduction– Normalized Leakage for air infiltration– e.g., a Natural Gas furnace that is 10 to 14 years old 

2015 Residential Energy Consumption Survey July 31, 2018 
Calibration is capable of using more information, i

provide it• Prior to the 2015 RECS, the Calibration method was Simple No

where all residuals from regression were prorated across all end 

– Treats all modeled end uses as equally certain/valid

• In the 2015 RECS, the Calibration method follows a Minimum V

Estimation approach

– Does not treat all modeled end uses as equally certain/valid– Requires specifying the uncertainties of and correlations between – Execution is a constrained minimization problem

2015 Residential Energy Consumption Survey July 31, 2018 
A Simple Example: the available information

2015 Residential Energy Consumption Survey July 31, 2018 

• Consider a hypothetical RECS Housing Unit

– Housing Characteristics Survey 

Only 

3 end uses for Electricity: 

• AC • Refrigerator • Coffee Maker

– Administrative Data

Weather data, etc. 

– Energy Supply Survey 

Annualized billing total of 2,000 kWh

Σ 
A Simple Example: possible disaggregations

2015 Residential Energy Consumption Survey July 31, 2018 

• What are the end-use estimates of consumption? 

2,000 kWh

1,900

+ 50

+ 50

= 2,000

667

+ 667

+ 666

= 2,000

1,300

+ 600

+ 100

= 2,000 

...
A Simple Example: end-use energy expectations

• Plausible, hypothetical model estimates for the end uses:

– AC = 1,000 kWh– Refrig = 500 kWh– Coffee = 60 kWh

• Sum of model estimates is 1,560 kWh

This is 440 kWh less than the annualized billing total of 2,00

• How to correct the model estimates so that they add to 2,000 kW

2015 Residential Energy Consumption Survey July 31, 2018 
A Simple Example: calibration prior to the 2015 R

2015 Residential Energy Consumption Survey July 31, 2018 

• Previous Calibration: Simple Normalization (Proration)

– AC_norm = 1,000 kWh · ( 2,000 / 1,560 ) = 1,282 kWh– Refrig_norm = 500 kWh · ( 2,000 / 1,560 ) = 641 kWh– Coffee_norm = 60 kWh · ( 2,000 / 1,560 ) = 77 kWh

• These add to 2,000 kWh, but does it make sense to treat all est

they are all equally valid? No!

– Refrigerators are relatively easy to model– AC is difficult to model– Coffee Makers cannot be modeled beyond presence in housing un

Σ 
A Simple Example: specify uncertainties and corre

• In the 2015 RECS: include uncertainties and correlations in the • Plausible, hypothetical estimates for the uncertainties and corre

– AC has 50% relative uncertainty :: 1,000 ± 500 kWh

– Refrig has 20% relative uncertainty :: 500 ± 100 kWh– Coffee has 100% relative uncertainty :: 60 ± 60 kWh 

– All 3 are uncorrelated :: 

Corr( AC, Refrig ) = Corr( AC, Coffee ) = Corr( Refrig, C

– Uncertainty Propagation :: Sum = AC + Refrig + Coffee = 1,5

2015 Residential Energy Consumption Survey July 31, 2018 
A Simple Example: improved calibration in the 20

• Uncertainties and correlations lead naturally to Minimum Varian

– Weight model estimates by inverse variance-covariance matrix– Optimization problem – Assume the billing total has no uncertainty– Apply constraints to ensure no negative consumption

• Final estimates in this hypothetical problem:

– AC_cal = 1,000 kWh + ( 250,000 / 263,600 ) · 440 kWh = 1,417 kWh– Refrig_cal = 500 kWh + ( 10,000 / 263,600 ) · 440 kWh = 517 kWh– Coffee_cal = 60 kWh + ( 3,600 / 263,600 ) · 440 kWh = 66 kWh 

2015 Residential Energy Consumption Survey July 31, 2018 
A Simple Example: two calibration solutions

2015 Residential Energy Consumption Survey July 31, 2018 

• The Minimum Variance Estimation and Simple Normalization so

2,000 kWh

1,417

+ 517

+ 66

= 2,000

1,282

+ 641

+ 77

= 2,000

Minimum Variance:

Simple Normalization:
A Simple Example: comparing results

• Bringing the estimates together

2015 Residential Energy Consumption Survey July 31, 2018 

Modeled Previous 

Calibration

Relative Uncertainty

Absolute Uncertainty

MVaEs

AC 1,000 1,282 ±50% ±500 Refrig 500 641 ±20% ±100 Coffee 60 77 ±100% ±60 Total 1,560 2,000 

Most of the +440 kWh correctbeen given to AC, the end usthe largest absolute uncertain
A look ahead...

2015 Residential Energy Consumption Survey July 31, 2018 
Innovations and improvements for 2015 RECS present 

opportunities for future studiesFlexibility to:• Draw larger samples to cover 50 states and lower RSEs for key 

subpopulations

• Conduct more frequent and timely data collections• Study special populations in more depth• Measure end-use consumption using load disaggregation devicPlanning is underway for the 2020 RECS

2015 Residential Energy Consumption Survey July 31, 2018 
Questions?

(Please submit them through the Chat box)

2015 Residential Energy Consumption Survey July 31, 2018 
Contact us!

• www.eia.gov/consumption/residential/• Chip Berry, Survey Manager

– James.Berry@eia.gov

• Greg Lawson, End-Use Modeling Specialist

– Greg.Lawson@eia.gov

• Maggie Woodward, Industry Economist

– Maggie.Woodward@eia.gov

2015 Residential Energy Consumption Survey July 31, 2018