The 2015 study represents the 14th 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 more than 5,600 households selected at random using a complex multistage, area-probability sample design. The sample represents 118.2 million U.S. households. The 1st version of the 2015 RECS microdata file, released in April 2017, reflected preliminary household characteristics data. The file was updated in October 2017 (Version 2) to include additional square footage and household energy insecurity data. The file was updated again in May 2018 (Version 3) and included final household characteristics data, as well as final consumption and expenditures data. The final version of the microdata file was updated in December 2018 (Version 4) and contains wood consumption variables, as well as additional weather and climate-related variables used in the end-use modeling process.

The U.S. Residential Energy Consumption Survey, administered by the U.S. Energy Information Administration (EIA), uses a nationally representative sample to collect information about home characteristics, household energy usage, and energy cost. The microdata at the household level from 2020, 2015, 2009, 2005, 2001, 1997, 1993,1990, and 1987, made available by the EIA for public use, were curated by Carnegie Mellon University Libraries to make it more accessible for data analysis.

The Residential Energy Consumption Surveys were designed by the Energy Information Administration (EIA) to provide information concerning energy consumption within the residential sector. Information about a housing unit is collected through personal interviews with adult residents of a representative national sample of households. Questions are asked about energy consumption of household appliances, energy use qualities of structural improvements such as heating and air conditioning, windows and doors, insulation as well as the time and circumstances of their installation. Data about actual energy consumption (excluding transportation fuel) are obtained from fuel records maintained by the households’ fuel suppliers. Each record in the survey represents a single household. The finest geographic identification available on each household record is Census division. Sample households from Alaska and Hawaii were removed from the public use file. Therefore, these data represent only the contiguous United States.

The table your_home is part of the dataset Residential Energy Consumption Survey (RECS) 2020 **, available at https://redivis.com/datasets/ac9w-3263twez2. It contains 18496 rows across 94 variables.

Context

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)

The csv data file is accompanied by a corresponding "Layout file", which contains descriptive labels and formats for each data variable. The "Variable and response codebook" file contains descriptive labels for variables, descriptions of the response codes, and indicators for the variables used in each end-use model.

description: The Residential Energy Consumption Survey (RECS) is a national area-probability sample survey that collects energy-related data for occupied primary housing units. First conducted in 1978, the 2005 version is the 12th RECS. The survey collected data from 4,382 households sampled at random using a complex multistage, area-probability design to represent 111.1 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2005. Data were obtained from residential energy suppliers for each unit in the sample to produce the Consumption & Expenditures data.; abstract: The Residential Energy Consumption Survey (RECS) is a national area-probability sample survey that collects energy-related data for occupied primary housing units. First conducted in 1978, the 2005 version is the 12th RECS. The survey collected data from 4,382 households sampled at random using a complex multistage, area-probability design to represent 111.1 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2005. Data were obtained from residential energy suppliers for each unit in the sample to produce the Consumption & Expenditures data.

Representative sample on the energy consumption of private households in Germany in cooperation with forsa. These households are comprehensively surveyed on their consumption of the energy sources used, their living conditions and the characteristics of the building they live in. Meter readings were also collected. Data is available for the years 2003, 2005 as a cross-section and for 2006-2008, 2008-2011 and 2010-2013 as a panel. This data can be matched using an ID.

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

The table lighting is part of the dataset Residential Energy Consumption Survey (RECS) 2020 **, available at https://redivis.com/datasets/ac9w-3263twez2. It contains 18496 rows across 27 variables.

Abstract

Countries collect official statistics on energy use due to its vital role in the infrastructure, economy and living standards of any given country.

In Palestine, additional attention is warranted for energy statistics due to scarce natural resources, the high cost of energy and high population density. These factors demand comprehensive and high quality statistics.

Due to high residential consumption of energy, PCBS decided to conduct a special Household Energy Survey to provide high quality data about energy consumption by type of energy, the different energy consuming devices used by households, and energy consumption behavior. To this end, a questionnaire was attached as a module within the Area Statistics Survey.

PCBS conducted the Household Energy Survey to cover the month of January 2015 to ascertain energy consumption behavior.

The survey aimed to provide data on energy consumption by households and also on public energy consumption behavior and patterns by type of energy.

The survey covered data on energy indicators in households in Palestine, including statistical data on electricity and other types of fuel consumption in activities like cooking, baking, heating, lighting and water heating.

The report of the Household Energy Survey (January 2015) comprises three chapters: the first chapter briefly describes the main findings; the second chapter presents the methodology used in the survey, including the questionnaire design, sampling design, field work operations, data processing, data quality and technical notes; while the third chapter describes the concepts and definitions.

Geographic coverage

Palestine.

Analysis unit

Households

Universe

It consists of all Palestinian households who are staying normally in Palestine during 2015

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame was based on master sample which was update in 2013-2014 for (Expenditure and Consumption Survey (PECS) and Multiple Indicator Cluster Survey (MICS)) surveys, and the frame consists from enumeration areas. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size: The sample size is 7,690 households for Palestine level, 6,609 households responded.

Sampling Design: The sample is two stage stratified cluster sample as following:

First stage: selection of a PPS random sample of 370 enumeration areas.

Second stage: A random area sample of 20 households from each enumeration area selected in the first stage.

Sample strata: The population was divided by: 1- Governorate 2- locality type (Urban, rural, camps)

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of Palestine.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: The household energy survey questionnaire was programmed onto handheld devices and data were entered directly using these devices in the West Bank. With regard to Jerusalem J1 and the Gaza Strip, data were entered into the computer in the offices in Ramallah and Gaza. At this stage, data were entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements: · To prevent the duplication of questionnaires during data entry. · To apply checks on the integrity and consistency of entered data. · To handle errors in a user friendly manner. · The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

percent was about 89.5%.

Sampling error estimates

Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are anticipated in comparison with the real values obtained through censuses. The variance was calculated for the most important indicators: the variance table is attached with the final report. There is no problem in the dissemination of results at national and regional level (North, Middle, South of West Bank, Gaza Strip) and by locality. However, the indicator of averages of household consumption for certain fuels by region show a high variance.

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.

The table space_heating is part of the dataset Residential Energy Consumption Survey (RECS) 2020 **, available at https://redivis.com/datasets/ac9w-3263twez2. It contains 18496 rows across 48 variables.

Households in the United States consumed approximately 18.45 quadrillion British thermal units in 2024. This was a slight increase compared to the previous year. Energy consumption by the residential sector peaked in 2010, at 21 quadrillion British thermal units.

Abstract

The 2017 HECS was undertaken in order to establish a baseline for energy consumption in households in Lesotho. The last survey was done in 1985 and scenarios for energy consumption were drawn and extrapolated to the year 2010. The survey is intended to be done after a five year period. The variables collected included housing and household characteristics, economic and energy consumption of different forms of fuels such fuel wood, paraffin etc.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The survey covers all households not sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2016 census Enumaration Area (EA) were used as Primary Sample Units (PSU) and all the private households in the slected EAs were included un the sample. The sample was designed such that all the ten districts, ecological zones as well as settlement were includesd

The sample design of Household Energy Consumption Survey is a two-staged stratified sample design. The stratifying variables being the agro-ecological zones namely the lowlands, foothills, mountains and Senqu River Valley (SRV) and settlements(urban, peri-urban and rural).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed in English language and manual to help in execution of the questionnaire was also compiled. It was also reviewed by stakeholders and their comments were incorporated. The household questionnaire was administered in each selected household, which collected various information on household members including sex, age, relationship, and education level. The household questionnaire includes: 1. Household characteristics 2. Education 3. Housing and Housing Characteristics 4. Economic characteristics 5. Biomass 6. Electricity 7. Cooking and kitchen utensils 8. Heating and cooling 9. Lighting 10. Access to automobiles 11. Solar energy usage 12. Solar water heating 13. Generators 14. General energy use 15. Other energy source

Response rate

The response rate was 93%

This table contains 1320 series, with data for years 2011-2019 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...); Household income (8 items: Under $20,000 (includes income loss); $20,000 to $39,999; $40,000 to $59,999; $60,000 to $79,999; ...); Energy type (4 items: Total, all energy types; Electricity; Natural gas; Heating oil); Energy consumption (4 items: Gigajoules; Gigajoules per household; Proportion of total energy; Number of households).

This table contains 1155 series, with data for years 2011 - 2015 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...); Type of dwelling (7 items: Single-detached; Double; Row or terrace; Duplex; ...); Energy type (4 items: Total, all energy types; Electricity; Natural gas; Heating oil); Energy consumption (4 items: Gigajoules; Gigajoules per household; Proportion of total energy; Number of households).

This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also uses the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Hourly load profiles are available for over all TMY3 locations in the United States here.

Browse files in this dataset, accessible as individual files and as commercial and residential downloadable ZIP files. This dataset is approximately 4.8GiB compressed or 19GiB uncompressed.

July 2nd, 2013 update: Residential High and Low load files have been updated from 366 days in a year for leap years to the more general 365 days in a normal year.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

In 2023, fuel wood accounted for the highest share of the household sector's energy demand mix, at roughly ** percent. Fuelwood is a type of biomass used for cooking and heating purposes, especially in rural areas. Meanwhile, electricity represented the second highest energy consumption share.

This dataset includes U.S. low-temperature heating and cooling demand at the county level in major end-use sectors: residential, commercial, manufacturing, agricultural, and data centers. Census division-level end-use energy consumption, expenditure, and commissioned power database were dis-aggregated to the county level. The county-level database was incorporated with climate zone, numbers of housing units and farms, farm size, and coefficient of performance (COP) for heating and cooling demand analysis. This dataset also includes a paper containing a full explanation of the methodologies used and maps. Residential data were updated from the latest Residential Energy Consumption Survey (RECS) dataset (2015) using 2020 census data. Commercial data were baselined off the latest Commercial Building Energy Consumption Survey (CBECS) dataset (2012). Manufacturing data were baselined off the latest Manufacturing Energy Consumption Survey (MECS) dataset (2021).