FCSM 2023

L. Kaili Diamond and S. Grace Deng

October 25, 2023 | College Park, MD

Editing Tools for Reconciling Housing 

Characteristics and Energy Billing Data

The analysis and conclusions contained in this 

presentation are those of the authors and do not 

represent the official position of the U.S. Energy 

Information Administration or the U.S. Department of 

Energy.
Residential Energy Consumption Survey (RECS) overview

• RECS is a nationally-representative study of household energy use and 

energy-related characteristics

• First conducted in 1978

• The most recent iteration collected information on energy-related 

characteristics for the housing unit, usage patterns, and household 

demographics for the 2020 calendar year

• RECS consists of two parts

– The Household Survey (HS) is a sample survey that collects energy-related characteristics 

and household demographics for primary residences

– The Energy Supplier Survey (ESS) collects energy consumption and expenditure data from 

energy suppliers for the respondents to the HS. Fuels reported include electricity, natural 

gas, fuel oil, and propane

2 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
RECS editing overview, 2020

• 2020 RECS Data:

– Household Survey – 18,525 completed surveys between September 2020 and April 2021

– Energy Supplier Survey – 27,729 completed surveys between July 2021 and February 2022 

(16,894 electricity, 9,087 natural gas, 674 fuel oil, and 1,074 propane)

• Each survey was edited independently for internal data errors and 

inconsistencies. 

– Household Survey had 18,496 respondents after removing out of scope cases

– ESS had 16,706 electricity, 9,000 natural gas, 666 fuel oil, and 1,063 propane bills after 

individual editing

• Final data editing phase is Reconciliation of the two data sources 

– We compare the expected household energy consumption based on household survey 

responses to the actual energy consumption provided by suppliers

– Seemingly accurate housing characteristics can be incompatible with seemingly accurate 

supplier data. 

3 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Reconciliation editing overview, 2020

• 3 types of edits:

– ESS edits: Identify and remove invalid or incorrect ESS consumption data

– Batch edits: updates made programmatically to cases where the failure of an edit had known outcome

• Example: reported NG water heating but ESS NG consumption is too low to support water heating

– Household characteristic edits: Manually review and edit housing characteristics based on inconsistencies 

between bills and preliminary engineering models based on HS responses

• Example: HS respondent claimed one fuel as primary heating fuel but consumption for that fuel 

obtained during ESS does not indicate seasonality associated with space heating

• 39 total reconciliation edits including ESS, HS, and Batch edits

• Editing guidelines were created to reduce editing bias and enhance repeatability

4 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Reconciliation Editing Methodology

• Each edit was listed on a separate spreadsheet tab. Columns were created on each 

tab for all related variable changes

• Edits were performed in a hierarchal order. Editing programs were re-run periodically 

to reduce edit failure redundancy

• Analysts checked out cases and determined if they should be changed. Cases without 

a clearly defined resolution were discussed as a group

• A dashboard was created that showed all supplier bills for all fuels and the preliminary 

engineering end-use modeled estimates

• Supplier data were deleted in cases where it seemed inaccurate. For example, 

consumption appeared to be for a whole apartment building rather than the sampled 

unit

5 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Engineering end-use models (EUM) and dashboard 

overview

• Expected consumption was derived from engineering based end-use models using 

household survey responses

– End-use models rely on responses to the household survey and attached location and weather data

– End-use models predict consumption for many devices in the home 

• For example, we can estimate the electricity consumption for a home’s main 

refrigerator based on characteristics such as the size, age, and style (French door, top 

freezer, existence of in-door water or ice, etc.) of the refrigerator

• A dashboard was created to compare end-use modeled consumption to actual energy 

consumption provided by the energy suppliers

• Only obvious inconsistencies between the two data sources led to changes

6 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Reconciliation dashboard tool, consistent case

7 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

• Expected consumption 

(dashed line) closely matches 

reported energy consumption 

from suppliers (solid line)

• Expected seasonality from 

space heating shown in 

Natural Gas (NG)

• No changes to fuel or 

equipment for the case

Note: In this case, EL 

consumption data from the 

supplier was reported on a 

bimonthly basis.
Dashboard tool, potential natural gas vs propane 

reporting issue example

8 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

• Propane reported 

for main heating 

fuel

• Modeled use 

shows seemingly 

accurate EL data 

but shows 

unaccounted for 

NG use with 

seasonality 

consistent with 

NG space 

heating

• Change main 

heating fuel to 

NG

Missing
Dashboard tool, potential natural gas vs electric space 

heating example

9 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

• Reported main space heating 

fuel was natural gas with wood 

for back-up, but no natural gas 

use data was available

• ESS data shows seasonality 

consistent with electric space 

heating

• Main heating fuel changed to 

electricity
Reconciliation editing outcomes

• All reconciliation edits:

– 4631 (25%) cases failed at least one reconciliation edit

– 2917 (16%) cases required at least one data change

• HS reconciliation edits:

– 2958 (16%) cases failed at least one HS edit

– 1765 (10%) cases required at least one household characteristic change

• Changes to main heating fuel tended to be from electricity to natural gas

• 10% of single family and 6% of required at least one analyst edit to housing 

characteristics

10 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Results of reconciliation, data changes

• Most common variables changes:

 1,203 (7%) Main space heating fuel 

 931 (5%) Main water heating fuel

 453 (2%) Main space heating equipment

 230 (1%) Backup fuel for central heat pump

 186 (1%) Secondary space heating fuel

 181 (1%) Secondary space heating equipment

 Space heating equipment only changed when related fuel was changed and 

the reported equipment was not compatible with identified fuel

 For example, if main space heating equipment was reported as a built-in electric heater but 

reconciliation found that space heating must have been natural gas

11 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Impact of main space heating fuel reconciliation edits

12 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

Percent of homes using NG or EL for main space heating fuel, weighted estimates 

for homes that heat

Data source: U.S. Energy Information Administration, 2020 Residential Energy Consumption Survey

• Changes tended to be 

from electricity to natural 

gas

• Changing main space 

heating fuel required 

checking that the 

reported heating 

equipment is compatible 

with the new fuel

• 1,203 changes in fuel led 

to 453 equipment 

changes

48%

53%

42%

36%

0%

10%

20%

30%

40%

50%

Preliminary 2020 Final 2020

Natural Gas Electricity
Impact of main water heating fuel edits

13 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

Preliminary versus final distribution of main water heating fuel, weighted 

estimates for most changed fuel types types

• Most main water heating fuel edits 

were from electricity to natural gas

• No change in equipment was 

necessary for these edits

• Energy consumption for water 

heating has seasonality, though less 

than space heating

47% 47% 48% 46%

0%

10%

20%

30%

40%

50%

60%

Preliminary Final

Natural gas Electricity

Data source: U.S. Energy Information Administration, 2020 Residential Energy Consumption Survey
Percent of cases requiring a data change during 

reconciliation, by state

14 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

Note: Percents calculated based on reconciliation changes to main space heating fuel, main space heating equipment, 

secondary space heating fuel, secondary space heating equipment, or main water heating equipment
The Reconciliation process improves our estimates of 

household energy characteristics

15 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

• Impacts of weighted distributions to secondary space heating fuel and 

equipment were negligible at the national and regional levels

• Some data changes from reconciliation forced data inconsistencies with 

other variables that also needed to be updated

– Adding unreported residential on-site solar generation opened solar capacity and solar 

installation date. These variables were imputed using modal imputation

– Adding heat to a home that previously reported not using space heating required changes to 

HEATHOME, EQUIPM, and FUELHEAT

• Although changes had minimal impacts on distributions, these changes are 

necessary as EIA makes micro data files available to stakeholders 
16 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Impact of implementing a hierarchal editing system

• The 39 reconciliation edit specifications were run in 11 batches

– A single household characteristic reporting error could trigger multiple related edit failures

– Re-running edit criteria with previously updated housing characteristics should remove these 

cases from related edit failures

• Iterative and hierarchal editing lowered the number of cases requiring review

– Initial run of the reconciliation edits yielded 10,683 failures (Note: a single case can appear in 

more than one edit failure)

– Re-running reconciliation edit criteria in 11 batches lowered the failure count to 5,680, saving 

analysts from reviewing 5,003 failures, or 47%

17 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
Conclusions from the Reconciliation process

18 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023

• The use of the editing dashboard that combined modeled consumption estimates 

based on housing characteristics with reported consumption from suppliers greatly 

aided the reconciliation process 

• Applying updates and re-running the edit criteria between related edits helped reduce 

analyst burden and led to a timelier reconciliation process and data release

• 12% of paper respondents and 11% of web respondents required at least one change 

during reconciliation, showing minimal mode effect on editing

• Most of the changes to housing characteristics were to non-imputed values

• Making edits, even when the impact on overall estimates is minimal, limits the error 

associated with the entire vector of end-use level consumption estimates
Future research and possible changes to the reconciliation 

process

• Make consumption dashboard interactive to see how changes to household 

energy characteristics would impact the expected consumption in real-time

• Continue to evaluate editing thresholds and edit order for further reduction of 

analyst burden while maintaining data quality

• Build a more comprehensive editing process to include household survey 

edits, ESS data edits, and reconciliation edits

• Utilize outcomes of reconciliation editing to improve imputation methods

19 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023
For more information

U.S. Energy Information Administration home page | www.eia.gov

Residential Energy Consumption Survey (RECS) home page | www.eia.gov/recs

L. Kaili Diamond | Kaili.Diamond@eia.gov

S. Grace Deng | Shaofen.Deng@eia.gov

20 L. Kaili Diamond and S. Grace Deng, FCSM 2023

October 25, 2023