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.

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IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases

D I S C L A I M E R: Normalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter.

Home Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf.

This dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Residential Home Energy Efficiency

Evaluated Meter Project Data 2007-2012

By State of New York [source]

About this dataset

This dataset provides energy efficiency meter evaluated data from 2007-2012 for residential existing homes (one to four units) in New York State. It includes the following data points: Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units .Year Home Built , Total Project Cost , Contractor Incentive , Total Incentives , Amount Financed through Program , Estimated Annual Electric Savings (kWh), Estimated Annual Gas Savings (MMBtu), Estimated First Year Energy Bill Savings ($) Baseline Electric (kWh), Baseline Gas (MMBtu), Reporting Electric (kWh), Reporting Gas (MMBtu ),Evaluated Annual Electric Savings( kWh ), Evaluated Annual gas Savings( MMBTU )Central Hudson LIPA National Fuel gas NYSEG Orange and Rockland Rochester Gas and electric Location 1. This dataset backcasts estimated modeled savings for a subset of 2007 -2012 completed projects in the Home Performance with ENERGY STARprogram against normalized savings calculated by an open source energy efficiency meter. The open source code uses utility grade metered consumption to weather normalize the pre -and post consumption data using standard methods with no discretionary independent variables. It is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector and help inform stakeholders interested in Pay For Performance programs where providers are paid for realizing measurable weather normalized results. Please make sure you read the Disclaimer included before using this data; it contains important information about evaluating savings from contractor reported modeling estimates as well as evaluating Normalized Savigns using Open Source OEE meter

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How to use the dataset

• Last updated information: The last update for this dataset was 2019-11-15

• Data Elements Overview:This dataset includes a variety of data points that provide valuable insights into residential energy efficiency projects undertaken between 2007 TO 2012 in New York State; including project ID, county, city zip code, climate zone, weather station used for normalization methods, completion date customer type size and volume of home number of units year home was built total project cost contractor incentive total incentives amount financed through program etc.

• Definitions Overview: There are several acronyms included in this datasets such as Central Hudson (a utility company), LIPA (the Long Island Power Authority), National Fuel Gas (National Fuel Gas Utility Company), NYSEG (New York State Electric & Gas Utility Company) and Rochester Gas & Electric (Rochester Gas & Electric Utility Company). Additionally “Climate Zone” are numbered 1 through 5 representing regions from coolest north/northwest regions to warmest south/southeast regions across New York; these correspond with Warm-Humid, Marine VBZc&De2VBladium Marine Subtropical HotSummer ColdWinter ColdSummer Moderate Winter regions respectively. A Weather Station is used for normalizing Savings Data which a location like described Niagara Falls International Airport that obtains historical average temperature values from various temperatures sources . Weather Stations Normalization compares day-of vs seasonal temperature difference outside homes against model prediction retrofit reduction predictions inside home without weather normalizing watt reduction products can be over or under estimated depending on current season vs expected seasons which this model accounts The estimated annual electric savings are calculated using factors such as pre-retrofit baseline electric kWh post-retrofit usage electric kWh evaluated annual electric savings calculated by open source library software installed by customers neighborhood ? measured GHG emission reductions determined with assumptions provided input device SDK so on life cycle greenhouse gas emission reductions also tracked documented impact studies have been conducted verify conclusion accuracy projected values reported nyserda industry rebate programs benchmarking standardized meter data allowing future compare patterns? measurements document capture utilities grid management initiated demand response events companies target focus market . Moving forward Total Project Cost is figure analyzed depending estimates provided

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The percentage of homes that use utility gas for heat and cooking out of all homes. Source: American Community Survey Years Available: 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

This table shows regional figures on the average consumption of energy (natural gas and electricity) of private dwellings broken down by type of dwelling and ownership for Nederland, group of provinces, provinces and municipalities. Besides, for total dwellings only, the share of heat distribution (district heating) has been added, because this is relevant for the interpretation of the height of the average consumption of natural gas.

Data available from: 2010

Status of the figures: All figures from 2010 - 2021 are definite. Figures of 2022 and 2023 are revised provisional. Figures for 2024 are provisional.

Changes as of September 2025: Figures added for 2024. Figures for 2022 and 2023 have been revised based on smart-meter data. These figures are more accurate than figures based on standard yearly consumption data.

Changes as of October 2023: Provisional figures of 2022 have been added. Figures of 2021 have been updated. The category “Average consumption of electricity” is replaced by “Average supply of electricity” and a category “Average net supply of electricity” has been added.

When will new figures be published? A revision to the method of this statistic is currently underway, causing the table to be delayed. New figures will come in the 3rd quarter of the folowing year.

The IDEAL Household Energy Dataset comprises data from 255 UK homes. Alongside electric and gas data from each home the corpus contains individual room temperature and humidity readings and temperature readings from the boiler. For 39 of the 255 homes more detailed data is available, including individual electrical appliance use data, and data on individual radiators. Sensor data is augmented by anonymised survey data and metadata including occupant demographics, self-reported energy awareness and attitudes, and building, room and appliance characteristics. The 00README.txt download summarizes the contents of the other files.

Natural Gas consumption data (MJ) by suburb within the City of Sydney local government area from baseline 2005/06 to data available for the latest financial year, derived from utility data sets.

These datasets provide information collected through EnerGuide Rating System (ERS) residential energy efficiency evaluations. Data is provided by calendar year, at the Forward Sortation Area level (FSA, the first 3 digits of the postal code) for files since 2004. Home energy efficiency evaluations are performed by independent service organizations (SOs) and their registered Energy Advisors (EAs) using Natural Resources Canada’s (NRCan) HOT2000 software to simulate the annual energy consumption of a home, as well as the impact of recommended upgrades. EAs perform tests and collect data about the home to populate an energy model and create a HOT2000 file, which is submitted to NRCan. The ERS database includes existing housing assessments (pre retrofit (D files), post retrofit (E files)), and evaluations for new homes (plan files (P file) and as-built houses (N files)). This dataset includes over 400 fields of home specific information (e.g. heating equipment fuel type, number of doors, etc.) available at the FSA level. Note that NRCan initiatives (ERS for existing and new homes) are voluntary, and may be affected by self-selection bias. Data is based on homes that received an EnerGuide Rating System evaluation and may not be representative of the entire Canadian housing stock. Some areas may be better represented due to participation in local incentive programs. In accordance with Statistics Canada policies, FSA’s with data from less than 10 homes have been removed from the dataset to protect against re-identification. Calculation results (such as energy consumption, heat losses and greenhouse gas emissions) are based on standardized operating conditions and long-term climate data. Data from a home energy evaluation is collected and entered manually and is subject to human error, despite some validations being done on each file. This dataset is provided to support research on residential energy efficiency. It is not intended to report on participation in energy retrofit incentive programs. The HOT2000 software calculations and data collection procedures have changed over time. Refer to the Data Dictionary for details. Natural Resources Canada is not responsible for the accuracy or completeness of the information contained in the reproduced material. Natural Resources Canada shall at all times be indemnified and held harmless against any and all claims whatsoever arising out of negligence or other fault in the use of the information contained in this publication or product.

Community Natural Gas Consumption | Squamish Community DashboardThis indicator measures all community natural gas consumption, which includes residential and commercial use but not industrial and institutional uses. This indicator is affected by population growth and also year-to-year weather variability.About this target:45% reduction in community natural gas consumption by 2030.Analysis:This information is tracked by Fortis BC and has been made available to the District of Squamish. Despite the increase in Squamish's population to 24,000, there has not been a significant rise in natural gas consumption as might have been expected, largely due to shifts in behavior and a preference for electrifying homes.Reason for monitoring:Buildings account for 28% of community emissions, and two of the six Big Moves in the Community Climate Action Plan (CCAP) are 'Construct Better Buildings' and 'Decarbonize Existing Buildings'. Significant growth is anticipated in Squamish, and it is important to meet this ambitious target while accommodating population growth. There are important opportunities to lower or avoid natural gas consumption in new buildings.

This is a set of aggregated data tables that underlie the key figures in the SERL statistical report "Smart Energy Research Lab: Energy use in GB domestic buildings 2022 and 2023". The report – Volume 2 of the SERL Statistical Reports Series – describes domestic gas and electricity energy use in Great Britain in 2022 and 2023 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB domestic building stock along a range of geographic, building and socio-demographic characteristics. The report provides an update to the statistics provided in Volume 1 of the SERL Statistical Report Series (Few et al., 2022), which covered 2021 data, and analyses residential energy use in GB in 2022 and 2023 (over the whole year, in each month and half-hourly over the course of the day). Statistics are presented for groups of homes with specific occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), heating systems, photovoltaics and electric vehicles, and by weather, region and IMD quintile. Unless otherwise noted, the findings in the report relate to homes in the SERL Observatory that use gas as their main heating source and do not have photovoltaic (PV) electricity generation.The report also shows how metered residential energy use in GB varies over time from 2021 to 2023.

Power plant capacity data and map are from the California Energy Commission. The CEC licenses thermal power plants 50 megawatts (MW) and greater and the infrastructure serving the plants such as electric transmission lines, fuel supply lines, and water pipelines. These licensed plants are referred to as jurisdictional plants. This map depicts the capacity of CEC-licensed(jurisdictional) natural gas power plants and non-jurisdictional natural gas plants. Counties without symbols had no natural gas power plants. Data is from 2021 and is current as of August 23, 2022. Projection: NAD 1983 (2011)California (Teale) Albers (Meters). For more information, contact Rebecca Vail at (916) 477-0738 or John Hingtgen at (916) 510-9747.

Natural Gas Service Area

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This data set includes modeled savings for specific energy efficiency measures by measure category and measure sub-category for a subset of 2007-2012 completed projects in the Residential Existing Homes (One to Four Units) Energy Efficiency Projects with Income-based Incentives by Customer Type: Beginning 2010 (https://data.ny.gov/d/assk-vu73) dataset. It is anticipated that this dataset will be most helpful when used in conjunction with the project-level dataset, Residential Existing Homes (one to Four Units) Energy Efficiency Meter Evaluated Project Data: 2007-2012 (https://data.ny.gov/d/5vqm-4rpf). When used together these datasets backcast estimated measure-level savings and project-level estimated (modeled) savings against normalized savings calculated by an open source energy efficiency meter available at: https://www.openee.io/. This dataset includes the following data points for a subset of projects completed in 2007-2012: Project ID, Measure ID, Measure Category, Measure Sub-category, Measure Cost ($), Measure Quantity, Measure Life, Measure SIR, Measure Incremental Energy Savings (MMBtu), Measure Estimated Annual Electric Savings (kWh), Measure Estimated Annual Energy Savings (MMBtu), Measure Estimated Annual Natural Gas Savings (MMBtu), Measure Estimated Oil Savings (MMBtu), Measure Estimated Propane Savings (MMBtu), Measure Estimated Coal Savings (MMBtu), Measure Estimated Kerosene Savings (MMBtu), Measure Estimated Pellets Savings (MMBtu), Measure Estimated Wood Savings (MMBtu). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

This table contains 165 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; ...) ; 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).

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. The Residential Existing Homes Program is a market transformation program that uses Building Performance Institute (BPI) Goldstar contractors to install comprehensive energy-efficient improvements. The program is designed to use building science and a whole-house approach to reduce energy use in the State’s existing one-to-four family and low-rise multifamily residential buildings and capture heating fuel and electricity-related savings. The Program provides income-based incentives, including an assisted subsidy for households with income up to 80% of the State or Median County Income, whichever is higher to install eligible energy efficiency improvements including building shell measures, high efficiency heating and cooling measures, ENERGY STAR appliances and lighting. D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Many factors influence the degree to which estimated savings are realized, including proper calibration of the savings model and the savings algorithms used in the modeling software. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. Beginning November 2017, the Program requires the use of HPXML-compliant modeling software tools and data quality protocols have been implemented to more accurately project savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. The New York Residential Existing Homes (One to Four Units) dataset includes the following data points for projects completed during Green Jobs Green-NY, beginning November 15, 2010: Home Performance Project ID, Home Performance Site ID, Project County, Project City, Project Zip, Gas Utility, Electric Utility, Project Completion Date, Customer Type, Low-Rise or Home Performance Indicator, Total Project Cost (USD), Total Incentives (USD), Type of Program Financing, Amount Financed Through Program (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Volume of Home, Number of Units, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Energy Savings $ Estimate (USD), Homeowner Received Green Jobs-Green NY Free/Reduced Cost Audit (Y/N). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

The National Energy Efficiency Data-Framework (NEED) was set up to provide a better understanding of energy use and energy efficiency in domestic and non-domestic buildings in Great Britain. The data framework matches gas and electricity consumption data, collected for DESNZ sub-national energy consumption statistics, with information on energy efficiency measures installed in homes, from the Homes Energy Efficiency Database (HEED). It also includes data about property attributes and household characteristics, obtained from a range of sources. Accredited Official Statistics

Fossil fuels have helped our civilization get to where it is today, we’ve used them to power our homes, factories, and vehicles. Fossil fuels are plant and animal matter that died millions of years ago and have then been subjected to heat and pressure over millions of years.

Fossil fuels come in three major groups: Coal – is mined and fuels 1/3 of the world’s power (the largest consumers are China, India, and the U.S.) Crude oil – pumped up through the earth and split through refining to produce different oils we use for fuel (like gasoline, diesel, kerosene, etc.) Natural gas – this is mainly methane found near oil deposits and caused the development of the controversial fracking process.

This table shows the average prices paid for natural gas and electricity. The total prices represent the sum of energy supply prices and network prices.

The total price is the price paid by an end-user, for instance a household or an industrial company consuming energy in their production process. Natural gas used for non-energy purposes or for electricity generation is excluded from the data.

Data available from: 1st semester of 2009

Status of the figures: The figures in this table are provisional for the two most recent semesters, and the annual figures follow the status of the second semester of the relevant reporting year. The remaining figures are final.

Changes as of September 30: Figures for the first half of 2025 have been added.

The network prices for final non-household customers will from now on, and dating back to 2009, be derived from administrative data sources. This now follows the methodology for households. Consumption data can be combined with tariffs that are published on the websites of the network companies, providing the necessary data to compile the prices. The change in methodology is carried out for the full time-series, making sure the network prices are consistent and price changes are not the result of varying measurement approaches.

When will new figures be published? New provisional figures will be published three months after the semesters end, at the end of September and at the end of March.

What is contained in this dataset?

The dataset contains the most relevant information collected about the apartments and their mechanical equipment, results of the participant survey, results of air leakage and airflow measurements at the homes, pollutant concentrations measured by time-integrated passive samplers inside and outside of the home, usage of cooktop and oven, external door and window open state, and time-series or air pollutants and environmental indicators measured within and outside of the apartments

Organization of Dataset

Home_Equipment_Data

This folder contains data about the house, including basic characteristics, air leakage test results, and measured airflow rates of mechanical ventilation equipment. There is one EXCEL file containing the data for all homes. The home characteristics form used by the field team is also included in the folder to explain the data parameters used in the EXCEL file.

IAQ_Activity_Monitoring

This folder contains time-resolved indoor ...