Note: data is continuously updated・ PG&E provides non-confidential, aggregated usage data that are available to the public and updated on a quarterly basis. These public datasets consist of monthly consumption aggregated by ZIP code and by customer segment: Residential, Commercial, Industrial and Agricultural. The public datasets must meet the standards for aggregating and anonymizing customer data pursuant to CPUC Decision 14-05-016, as follows: a minimum of 100 Residential customers; a minimum of 15 Non-Residential customers, with no single Non-Residential customer in each sector accounting for more than 15% of the total consumption. If the aggregation standard is not met, the consumption will be combined with a neighboring ZIP code until the aggregation requirements are met.

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 county level level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the city, town, and village, 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-County-Energy-Use-/46pe-aat9. 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.

The Modernising Energy Data Access (MEDA) competition was set up by Innovate UK and the Modernising Energy Data group to help develop the concept of a Common Data Architecture (CDA) for the Energy Sector. One of the main goals of the Common Data Architecture is to improve data sharing across the energy sector and make data more interoperable across organisations. Energy Consumption is one of the most sought after datasets needed by the organisations that we have worked with throughout a variety of the Modernising Energy Data projects, and although getting a household level of this information comes against GDPR challenges and is therefore non-accessible for the vast majority of organisations, breaking consumption down into smaller areas can be hugely beneficial for gaining insights into how energy is consumed within the UK. We have amalgamated Gas and Electricity consumption per Lower Layer Super Output Area (LSOA) which is available to download via file transfer, or via API

According to a 2024 forecast, global electricity consumption of data centers was projected to grow from *** terawatt-hours in 2022 to over one petawatt-hour in 2030. This would represent around *** percent of the total electricity consumption worldwide by the end of the period under consideration. Artificial intelligence accounted for around *** percent of the data centers' electricity consumption in 2023. This figure is projected to grow over the next five years.

United States US: Renewable Energy Consumption: % of Total Final Energy Consumption data was reported at 8.717 % in 2015. This records a decrease from the previous number of 8.754 % for 2014. United States US: Renewable Energy Consumption: % of Total Final Energy Consumption data is updated yearly, averaging 5.454 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 8.754 % in 2014 and a record low of 4.089 % in 1994. United States US: Renewable Energy Consumption: % of Total Final Energy Consumption data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Energy Production and Consumption. Renewable energy consumption is the share of renewables energy in total final energy consumption.; ; World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.; Weighted Average;

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.

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects.

BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

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.

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 energy balance is the most complete statistical accounting of energy products and their flow in the economy. The energy balance allows users to see the total amount of energy extracted from the environment, traded, transformed and used by different types of end-users. It also allows seeing the relative contribution of each energy carrier (fuel, product). The energy balance allows studying the overall domestic energy market and monitoring impacts of energy policies. The energy balance offers a complete view on the energy situation of a country in a compact format, such as on energy consumption of the whole economy and of individual sectors. The energy balance presents all statistically significant energy products (fuels) of a country and their production, transformation and consumption by different type of economic actors (industry, transport, etc.). Therefore, an energy balance is the natural starting point to study the energy sector.

Annual data collection cover in principle the EU Member States, EFTA, EU candidate countries, and potential candidate countries. Time series starts mostly in year 1990.

All data in energy balances are presented in terajoules, kilotonnes of oil equivalent and gigawatt hours.

Renewable energy consumption (% of total final energy consumption) in Indonesia was reported at 20.2 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Indonesia - Renewable energy consumption (% of total final energy consumption) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Indonesia Energy: Consumption: Energy Sector: Electricity data was reported at 50,643.000 TJ in 2017. This records an increase from the previous number of 49,098.000 TJ for 2016. Indonesia Energy: Consumption: Energy Sector: Electricity data is updated yearly, averaging 22,552.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 50,643.000 TJ in 2017 and a record low of 13,860.000 TJ in 2006. Indonesia Energy: Consumption: Energy Sector: Electricity data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA004: Energy Statistics: Consumption.

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.

Final energy consumption by sector (industry, transport, commercial & public services, households). Expressed in thousand tonnes of oil equivalent. Excludes (1) consumption of the energy sector itself and losses occurring during transformation and distribution of energy, (2) all non-energy use of energy carriers (e.g. natural gas used for producing chemicals, oil based lubricants, bitumen used for road surface), (3) quantities delivered to international aviation and international marine bunkers.

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.

🇺🇸 미국 English Users can generate reports showing the amount of energy consumed by geographical area, sector (residential, commercial, industrial) classifications. The database also provides easy downloading of energy consumption data into the comma-separated values (CSV) file format.

The California Electricity Consumption dashboard illustrates the state’s historical electricity consumption by agency, sector, and county. Data is sourced from Quarterly Fuel and Energy Report (QFER) California Energy Commission (CEC) Form 1306A Schedule 1, which requires utility distribution companies (UDCs) to report the amount of electricity they deliver monthly to end-use customers, along with self-generation datasets. This electricity consumption data is used to analyze electricity demand for local planning and California’s energy demand forecasts. Electricity sales and delivery data are collected quarterly under the authority of the California Code of Regulations, Title 20, Section 1306(a).

Annual statewide electricity consumption can be explored by sector, agency, and county. Each sector consists of several codes defined by the North American Industry Classification System (NAICS). Some forecast models, such as those for agriculture & water pumping, commercial building, “transportation, communications, & utilities” (TCU), industrial, and mining, are based on sector-level data subdivided by NAICS categories. These categories consist of census-defined NAICS subsectors and Energy Commission-defined category codes. The data presented in this dashboard was previously available through the California Energy Consumption Database Management (ECDMS).

Data last updated: September 5, 2025