The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.This dataset includes electricity and natural gas usage data reported at the city, town, and village level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the county and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-Community-Energy-U/4txm-py4p.Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315, and updated the protocol in a modification order on August 12, 2021. The order requires utilities and CCA administrators under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported at the city, town, and village level. Other UER datasets include energy use data reported at the county and ZIP code level.

Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Find energy data related to clean energy, energy markets, emissions, transportation and more.

Facility-level industrial combustion energy use is calculated from greenhouse gas emissions data reported by large emitters (>25,000 metric tons CO2e per year) under the U.S. EPA's Greenhouse Gas Reporting Program (GHGRP, https://www.epa.gov/ghgreporting). The calculation applies EPA default emissions factors to reported fuel use by fuel type. Additional facility information is included with calculated combustion energy values, such as industry type (six-digit NAICS code), location (lat, long, zip code, county, and state), combustion unit type, and combustion unit name. Further identification of combustion energy use is provided by calculating energy end use (e.g., conventional boiler use, co-generation/CHP use, process heating, other facility support) by manufacturing NAICS code. Manufacturing facilities are matched by their NAICS code and reported fuel type with the proportion of combustion fuel energy for each end use category identified in the 2010 Energy Information Administration Manufacturing Energy Consumption Survey (MECS, http://www.eia.gov/consumption/manufacturing/data/2010/). MECS data are adjusted to account for data that were withheld or whose end use was unspecified following the procedure described in Fox, Don B., Daniel Sutter, and Jefferson W. Tester. 2011. The Thermal Spectrum of Low-Temperature Energy Use in the United States, NY: Cornell Energy Institute.

The statistic shows an estimated breakdown of energy demand in data centers, as of 2015. As of that time, cooling consumed the greatest amount of power in the running of a data center, being responsible for ** percent of energy use.

This API provides state-level and national-level energy consumption data. Data organized by major economic sectors. EIA's State Energy Data System (SEDS) is a comprehensive data set that consists of annual time series estimates of state-level energy use by major economic sectors, energy production and and State-level energy price and expenditure data. The system provides data back from 1960. Data are presented in physical units, Btu, and dollars. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

The aggregate energy use data presented for the industrial sector are taken from Statistics Canada’s Report on Energy Supply-Demand in Canada (RESD) (Cat. No. 57-003-X). The RESD contains data derived primarily from Statistics Canada surveys of energy distributors and end-users as well as administrative records received by Statistics Canada. Such data are then supplemented with data from the National Energy Board and various energy-producing provinces. The major energy survey used for the industrial sector is the Industrial Consumption of Energy (ICE) survey (Cat. No. 57-505-X).

The data describes an electrical energy community, containing photovoltaic (PV) production profiles and end-user consumption profiles, desegregated by individual appliances used.

A dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. The data concerns a full year.

The overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles.

This work has been published in Elsevier's Data in Brief journal:
Calvin Goncalves, Ruben Barreto, Pedro Faria, Luis Gomes, Zita Vale,
Dataset of an energy community's generation and consumption with appliance allocation,
Data in Brief, Volume 45, 2022, 108590, ISSN 2352-3409,
https://doi.org/10.1016/j.dib.2022.108590
(https://www.sciencedirect.com/science/article/pii/S2352340922007971)

Reference data used to create this dataset:

• Renewable energy production profiles: https://site.ieee.org/pes-iss/data-sets/

• End-user profiles:
  • https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households
  • https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption
  • https://site.ieee.org/pes-iss/data-sets/

This dataset contains World Energy Use 1960-2014 Data from World Resources Institute. Follow datasource.kapsarc.org for timely data to advance energy economics research.Socio Economic Data (Population, GDP and Energy Use)CAIT - Historical Emissions Data (Countries, U.S. States, UNFCCC)CAIT Climate Data Explorer. 2015. Washington, DC: World Resources Institute. Available online at: http://cait.wri.org

The WHO Household energy database monitors household energy use. The database contains nationally representative data from surveys and censuses on cooking, heating and lighting fuels. A proportion of the available surveys also include questions on stove type, time spent collecting fuel, and incidence of acute lower respiratory infection. The database is used to calculate national, rural and urban estimates for use of clean fuels and technologies (as well as the population affected), which WHO reports for Sustainable Development Goal Indicator 7.1.2.Similarly, the database is used as input for estimating the percent of the population who use polluting fuels and stoves (as well as the number of people), such as those that burn wood, charcoal, animal dung, coal and kerosene – this serves a proxy for exposure to household air pollution. It also forms the basis for further assessment of the burden of disease attributable to household air pollution, which is associated with increased mortality and morbidity from acute lower respiratory infections among children, as well as from cardiovascular disease, chronic obstructive pulmonary disease and lung cancer among adults.The database is regularly updated with new data from national censuses and large-scale household surveys such as the World Bank’s Living Standard and Measurement Survey and UNICEF’s Multiple Cluster Indicator Survey (MICS). The database is currently being upgraded in association with international and national surveys and censuses to include more data on heating and lighting fuels and technologies and emission rates, and to disaggregate data by sex and age whenever possible.

The primary greenhouse gas (GHG) sources for agriculture are nitrous oxide (N2O) emissions from cropped and grazed soils, methane (CH4) emissions from ruminant livestock production and rice cultivation, and CH4 and N2O emissions from managed livestock waste. The management of cropped, grazed, and forestland has helped offset GHG emissions by promoting the biological uptake of carbon dioxide (CO2) through the incorporation of carbon into biomass, wood products, and soils, yielding a U.S. net emissions of 5,903 MMT CO2 eq (million metric tonnes of carbon dioxide equivalents). Net emissions equate to total greenhouse gas emissions minus CO2 sequestration in growing forests, wood products, and soils. The report 'U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2018' serves to estimate U.S. GHG emissions for the agricultural sector, to quantify uncertainty in emission estimates, and to estimate the potential of agriculture to mitigate U.S. GHG emissions. This dataset contains tabulated data from the figures and tables presented in Chapter 5, Energy Use in Agriculture, of the report. Data are presented for carbon dioxide emissions from on-farm energy use. Please refer to the report for full descriptions of and notes on the data. Resources in this dataset:Resource Title: Table 5-1. File Name: Table5_1.csvResource Description: Energy Use and Carbon Dioxide Emissions by Fuel Source on U.S. Farms, 2018. Energy consumed is shown in the table as QBTU (quadrillion British thermal units). Carbon content is displayed as MMT C/QBTU (million metric tons carbon per quadrillion British thermal units). Emissions are shown as Tg CO2 eq. (teragrams carbon dioxide equivalent). Resource Title: Data for Figure 5-1. File Name: Figure5_1.csvResource Description: CO2 Emissions From Energy Use in Agriculture, by State, 2018 in MMT CO2 eq. (million metric tons carbon dioxide equivalent).Resource Title: Data for Figure 5-2. File Name: Figure5_2.csvResource Description: Energy use in agriculture, by source, 1965–2018 in QBTU (quadrillion British thermal units).Resource Title: Data for Figure 5-3. File Name: Figure5_3.csvResource Description: CO2 Emissions from Energy Use in Agriculture, by Fuel Source, 2001, 2005, 2008, 2013, and 2018 in MMT CO2 eq. (million metric tons carbon dioxide equivalent).Resource Title: Chapter 5 tables and figures. File Name: Chapter 5 data.zip

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf.

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2022, figures for 2023 are revised provisional figures and figures for 2024 are provisional.

Changes as of July 2025: Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the outdated data from June 2025. The most recent figures are 5 percent higher for 2024 solar electricity production. These figures are in these two tables (in Dutch): - StatLine - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - StatLine - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update is scheduled in November 2025. From that moment all figures will be fully consistent again. We apologize for the inconvenience.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

The Build Smart NY Program aims to increase energy efficiency of New York State government buildings. Build Smart NY was established by Executive Order 88 and mandates a reduction in energy consumption by 20% in government owned and operated buildings by 2020. Site utility data has been collected for all government buildings larger than 20,000 square feet and this has been converted to Source Energy Use Intensity (EUI) which is a ratio of Source Energy Use to gross square footage. The Source EUI will be used as a performance metric to achieve the 20% reduction targets. The dataset represents a baseline of Source EUI for New York State government buildings at the baseline year of SFI 2010/2011; subsequent reports will demonstrate a progression to achieving a 20% energy reduction target.

These tables provide a statistical overview of Canada's sectoral energy markets. The data in these tables were taken from the Comprehensive Energy Use Database.