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 ZIP Code level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the city, town, village, and county 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-ZIP-Code-Energy-Us/g2x3-izm4. 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 accelerate economic growth. 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.

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.

Hong Kong energy end-use data 2023

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 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 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.

The Broader Public Sector (BPS) plays an important role in helping Ontario meet its conservation targets and reduce greenhouse gas emissions. Under O. Reg. 25/23 (Broader Public Sector: Energy Reporting and Conservation and Demand Management Plans), made under the Electricity Act, 1998, BPS organizations are required to: 1. Report annually to the Ministry of Energy and Electrification, on their energy use and greenhouse gas (GHG) emissions and publish the reports on their websites (as of July 1, 2013); 2. Develop a five-year conservation and demand management plan and publish the plan on their websites. Plans must be updated every five years (as of July 1, 2014). This data includes the annual amount of energy used and GHG emitted by BPS organizations. BPS organizations that are required to report include municipalities, municipal service boards, school boards, universities, colleges and hospitals. Several BPS organizations also voluntarily reported on facilities that are not required by regulation. Posted data files contain the following information: * raw energy consumption and GHG emission data * data that has been normalized to account for weather conditions and GHG emissions * the names of those BPS organizations that did not report their 2013-2017 energy* consumption data. Missing or incorrect data is the sole responsibility of the BPS organization. The ministry has attempted to remove duplicate data from this data set to improve data integrity. The Ministry of Energy and Electrification developed O. Reg. 25/23 to help BPS organisations better understand how and where they use energy and demonstrate government leadership by developing conservation plans to guide energy savings. *The Ministry of Energy and Electrification is aware that COVID-19 may have restricted some BPS organizations’ ability to submit their annual energy reports using the 2018 and 2019 data.

United States US: Energy Imports: Net: % of Energy Use data was reported at 7.309 % in 2015. This records a decrease from the previous number of 9.214 % for 2014. United States US: Energy Imports: Net: % of Energy Use data is updated yearly, averaging 15.610 % from Dec 1960 (Median) to 2015, with 56 observations. The data reached an all-time high of 29.659 % in 2005 and a record low of 4.253 % in 1967. United States US: Energy Imports: Net: % of Energy Use data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Energy Production and Consumption. Net energy imports are estimated as energy use less production, both measured in oil equivalents. A negative value indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

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 38 percent of energy use.

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

Note: Find data at source. ・ 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.

This data set contains energy use data from 2009-2014 for 139 municipally operated buildings. Metrics include: Site & Source EUI, annual electricity, natural gas and district steam consumption, greenhouse gas emissions and energy cost. Weather-normalized data enable building performance comparisons over time, despite unusual weather events.

Industrial Energy End Use in the U.S

Facility-Level Combustion Energy Data

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains in-depth facility-level information on industrial combustion energy use in the United States. It provides an essential resource for understanding consumption patterns across different sectors and industries, as reported by large emitters (>25,000 metric tons CO2e per year) under the U.S. EPA's Greenhouse Gas Reporting Program (GHGRP). Our records have been calculated using EPA default emissions factors and contain data on fuel type, location (latitude, longitude), combustion unit type and energy end use classified by manufacturing NAICS code. Additionally, our dataset reveals valuable insight into the thermal spectrum of low-temperature energy use from a 2010 Energy Information Administration Manufacturing Energy Consumption Survey (MECS). This information is critical to assessing industrial trends of energy consumption in manufacturing sectors and can serve as an informative baseline for efficient or renewable alternative plans of operation at these facilities. With this dataset you're just a few clicks away from analyzing research questions related to consumption levels across industries, waste issues associated with unconstrained fossil fuel burning practices and their environmental impacts

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How to use the dataset

This dataset provides detailed information on industrial combustion energy end use in the United States. Knowing how certain industries use fuel can be valuable for those interested in reducing energy consumption and its associated environmental impacts.

• To make the most out of this dataset, users should first become familiar with what's included by looking at the columns and their respective definitions. After becoming familiar with the data, users should start to explore areas of interest such as Fuel Type, Report Year, Primary NAICS Code, Emissions Indicators etc. The more granular and specific details you can focus on will help build a stronger analysis from which to draw conclusions from your data set.

• Next steps could include filtering your data set down by region or end user type (such as direct related processes or indirect support activities). Segmenting your data set further can allow you to identify trends between fuel type used in different regions or compare emissions indicators between different processes within manufacturing industries etc. By taking a closer look through this lens you may be able to find valuable insights that can help inform better decision making when it comes to reducing energy consumption throughout industry in both public and private sectors alike.

• if exploring specific trends within industry is not something that’s of particular interest to you but rather understanding general patterns among large emitters across regions then it may be beneficial for your analysis to group like-data together and take averages over larger samples which better represent total production across an area or multiple states (timeline varies depending on needs). This approach could open up new possibilities for exploring correlations between economic productivity metrics compared against industrial energy use over periods of time which could lead towards more formal investigations about where efforts are being made towards improved resource efficiency standards among certain industries/areas of production compared against other more inefficient sectors/regionsetc — all from what's already present here!

By leveraging the information provided within this dataset users have access to many opportunities for finding all sorts of interesting yet practical insights which can have important impacts far beyond understanding just another singular statistic alone; so happy digging!

Research Ideas

• Analyzing the trends in combustion energy uses by region across different industries.
• Predicting the potential of transitioning to clean and renewable sources of energy considering the current end-uses and their magnitude based on this data.
• Creating an interactive web map application to visualize multiple industrial sites, including their energy sources and emissions data from this dataset combined with other sources (EPA’s GHGRP, MECS survey, etc)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons...

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).

PK: Alternative and Nuclear Energy: % of Total Energy Use data was reported at 4.521 % in 2014. This records a decrease from the previous number of 4.621 % for 2013. PK: Alternative and Nuclear Energy: % of Total Energy Use data is updated yearly, averaging 3.574 % from Dec 1971 (Median) to 2014, with 44 observations. The data reached an all-time high of 4.621 % in 2013 and a record low of 1.964 % in 1972. PK: Alternative and Nuclear Energy: % of Total Energy Use data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Energy Production and Consumption. Clean energy is noncarbohydrate energy that does not produce carbon dioxide when generated. It includes hydropower and nuclear, geothermal, and solar power, among others.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

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.

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 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

[v2 update] weather data correction

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)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

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/