Dataset and details can be found at the US Energy Information Admininstration (EIA)'s RBECs website

Context

EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Traditionally, specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and household demographics. For the 2015 survey cycle, EIA used Web and mail forms, in addition to in-person interviews, to collect detailed information on household energy characteristics. This information is combined with data from energy suppliers to these homes to estimate energy costs and usage for heating, cooling, appliances and other end uses — information critical to meeting future energy demand and improving efficiency and building design.

First conducted in 1978, the fourteenth RECS collected data from more than 5,600 households in housing units statistically selected to represent the 118.2 million housing units that are occupied as a primary residence. Data from the 2015 RECS are tabulated by geography and for particularly characteristics, such as housing unit type and income, that are of particular interest to energy analysis.

The results of each RECS include data tables, a microdata file, and a series of reports. Data tables are generally organized across two headings; "Household Characteristics" and "Consumption & Expenditures." See RECS data tables.

The RECS and many of the EIA supplier surveys are integral ingredients for some of EIA's more comprehensive data products and reports, such as the Annual Energy Outlook (AEO) and Monthly Energy Review (MER). These products allow for broader comparisons across sectors, as well as projections of future consumption trends.

Content

The Residential Energy Consumption Survey (RECS) is a periodic study conducted by the U.S. Energy Information Administration (EIA) that provides detailed information about energy usage in U.S. homes. RECS is a multi-year effort (Figure 1) consisting of a Household Survey phase, data collection from household energy suppliers, and end-use consumption and expenditures estimation.

The Household Survey collects data on energy-related characteristics and usage patterns of a national representative sample of housing units. The Energy Supplier Survey (ESS) collects data on how much electricity, natural gas, propane/LPG, fuel oil, and kerosene were consumed in the sampled housing units during the reference year. It also collects data on actual dollar amounts spent on these energy sources.

EIA uses models (energy engineering-based models in the 2015 survey and non-linear statistical models in past RECS) to produce consumption and expenditures estimates for heating, cooling, refrigeration, and other end uses in all housing units occupied as a primary residence in the United States. Originally conducted by trained interviewers with paper and pencil, the 2015 study used a combination of computer-assisted personal interview (CAPI), web, and mail modes to collect data for the Household and Energy Supplier Surveys.

Banner image credit: https://www.flickr.com/photos/caribb/1518299093

This 2009 version represents the 13th iteration of the RECS program. First conducted in 1978, the Residential Energy Consumption Survey is a national sample survey that collects energy-related data for housing units occupied as a primary residence and the households that live in them. Data were collected from 12,083 households selected at random using a complex multistage, area-probability sample design. The sample represents 113.6 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2009 derived from their American Community Survey (ACS).

EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Traditionally, specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and household demographics. For the 2020 survey cycle, EIA used Web and mail forms to collect detailed information on household energy characteristics. This information is combined with data from energy suppliers to these homes to estimate energy costs and usage for heating, cooling, appliances and other end uses — information critical to meeting future energy demand and improving efficiency and building design. Archived from https://www.eia.gov/consumption/residential/

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into "https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• PUDL Documentation
• Other Catalyst Cooperative data archives

Abstract

This is a state-level summary of the Residential Energy Consumption Survey (RECS) 2020, for 50 states and the District of Columbia.

The survey measures characteristics that contribute to energy consumption in U.S. households.

The summarized datasets cover the following topics:

• Space heating
• Air conditioning
• Water heating
• Household characteristics
• Energy assistance

Methodology

• **Target population: **all a) occupied housing units in b) the 50 states & DC that are used as c) primary residences.
• Sample: Homes that are occupied as a primary residence
• Excludes vacant homes, seasonal housing units, group quarters (e.g. dormitories, nursing homes, prisons, military barracks), and common areas in apartment buildings.

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• Includes housing units located on military installations.
• **Sampling method: **Unclustered sampling, Jackknife method
• **Mode of survey: **self-administered questionnarie via 1) the web or 2) mail/paper
• **Sample size: **18,496 households (72% via web questionnaire; 27.2% via paper questionnaire)
• Response rate: Unweighted, 38.6%; Weighted, 37.9%

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Usage

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• The summarized datasets are weighted estimates of the number of households in each state.

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• Users should exercise caution in using estimates based on samples, as sample sizes could be small and the standard errors could be large.
• RECS is best suited for comparison across different characteristics of homes within the residential sector.
• **RECS is **%3Cu%3E%3Cstrong%3Enot%3C/strong%3E%3C/u%3E

** appropriate for comparing EIA's other residential energy data** as the scope of RECS is limited to homes occupied as a primary residence. As a result, RECS estimates are not comparable with sector-level totals defined in other EIA datasets

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The Building Energy Data Book (2011) is a compendium of data from a variety of data sets and includes statistics on residential and commercial building energy consumption. Data tables contain statistics related to construction, building technologies, energy consumption, and building characteristics. The Building Technologies Office (BTO) within the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy developed this resource to provide a comprehensive set of buildings- and energy-related data.

The Data Book has not been updated since 2011.

The data sets comprising the Data Book are now publicly available in user-friendly formats and you can use them to find data relevant to your questions. Please find below a list of Energy Information Administration (EIA) data sets that BTO consults:

• Annual Energy Outlook

  Projections in the Annual Energy Outlook 2015 (AEO2015) focus on the factors expected to shape U.S. energy markets through 2040.

• Monthly Energy Review

  A publication of recent and historical energy statistics.

• Residential Energy Consumption Survey (RECS)

  A nationally representative sample of housing units that specially trained interviewers collect energy characteristics, usage patterns, and household demographics.

• Commercial Buildings Energy Consumption Survey (CBECS)

  A national sample survey that collects information on the stock of U.S. commercial buildings, including their energy-related building characteristics and energy usage data.

Questions about the above resources can be directed to the relevant EIA subject matter expert.

Recent data from the United States (US) Energy Information Administration reveals that nearly one in three households in the US report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We produce a novel machine learning approach based on sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. Our analysis confirms that average household energy burdens increased, and the range of households suffering energy poverty broadened. We provide an optimized allocation structure to urge policy makers to revise the distribution of funds to better match assistance needs. Methods We use machine learning to determine how various demographic and physical characteristics are correlated with household energy burdens across the US. Energy burden estimates allow us to identify where energy poverty may be concentrated at the census-tract level. Our analysis extends and improves upon the Low-income Energy Affordability Data (LEAD) tool, developed by the US Department of Energy’s National Renewable Energy Laboratory to estimate energy expenditures and burdens in several ways (28). The LEAD tool is designed to help local and state governments with decisions for addressing energy poverty; however, it is static in time and uses self-reported energy expenditures given only for one month of the year, which is not reported publicly. The reliance on one month implies that the estimation of annual values is not guaranteed to account for the seasonal variation in energy costs throughout the months. The sampling done by the survey must sufficiently cover all months of the year, and this is not verifiable from the publicly available data. In addition, which month is used varies across respondents. Different from LEAD, we use household-level sociodemographic and geographic data, detailed in the following subsection, from the Energy Information Administration’s (EIA) Residential Energy Consumption Survey (RECS) to estimate the annual energy burden. This survey is completed every five years, enabling us to track changes in energy burden over time. To develop our projections at a census-tract level, we use an adaptive least absolute shrinkage and selection operator (LASSO) technique to select important variables from the RECS data to be applied to census-tract level information from the US Census Bureau’s American Community Survey (ACS).