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.

United States Electricity Consumption data was reported at 10.243 kWh/Day bn in Mar 2025. This records a decrease from the previous number of 11.765 kWh/Day bn for Feb 2025. United States Electricity Consumption data is updated monthly, averaging 9.940 kWh/Day bn from Jan 1991 (Median) to Mar 2025, with 411 observations. The data reached an all-time high of 13.179 kWh/Day bn in Jul 2024 and a record low of 7.190 kWh/Day bn in Apr 1991. United States Electricity Consumption data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s United States – Table US.RB004: Electricity Supply and Consumption. [COVID-19-IMPACT]

Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

Note: Find data at source; 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.

These data were collected in Greece through an anonymous survey. They contain information about the electricity consumption of households, their property type and size and, the number of occupants, their age, their educational and financial status. Moreover, the dataset contains information about some energy-related behaviors of the occupants along with some extracted indices. An article that provides more specific information is under review in a scientific journal.

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 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 330 terawatt-hours in 2022 to over one petawatt-hour in 2030. This would represent around 3.7 percent of the total electricity consumption worldwide by the end of the period under consideration. Artificial intelligence accounted for around 4.5 percent of the data centers' electricity consumption in 2023. This figure is projected to grow over the next five years.

China Total Energy Consumption data was reported at 161.897 BTU qn in 2023. This records an increase from the previous number of 153.520 BTU qn for 2022. China Total Energy Consumption data is updated yearly, averaging 44.216 BTU qn from Dec 1980 (Median) to 2023, with 44 observations. The data reached an all-time high of 161.897 BTU qn in 2023 and a record low of 18.508 BTU qn in 1981. China Total Energy Consumption data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s China – Table CN.EIA.IES: Energy Production and Consumption: Annual.

China Energy Consumption: Daily Average: Electricity data was reported at 24,210.000 kWh mn in 2022. This records an increase from the previous number of 23,340.000 kWh mn for 2021. China Energy Consumption: Daily Average: Electricity data is updated yearly, averaging 4,270.541 kWh mn from Dec 1980 (Median) to 2022, with 42 observations. The data reached an all-time high of 24,210.000 kWh mn in 2022 and a record low of 82.000 kWh mn in 1980. China Energy Consumption: Daily Average: Electricity data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCB: Electricity Summary.

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.

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 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

Canada Energy Consumption data was reported at 4,101.967 PJ in 2016. This records an increase from the previous number of 4,068.417 PJ for 2015. Canada Energy Consumption data is updated yearly, averaging 3,732.117 PJ from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 4,101.967 PJ in 2016 and a record low of 3,002.459 PJ in 1990. Canada Energy Consumption data remains active status in CEIC and is reported by Natural Resources Canada. The data is categorized under Global Database’s Canada – Table CA.RB001: Energy Consumption and Intensity.

Renewable energy consumption (% of total final energy consumption) in United States was reported at 10.9 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Renewable energy consumption (% of total final energy consumption) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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 Commercial Buildings Energy Consumption Survey (CBECS) is a national sample survey that collects information on the stock of U.S. commercial buildings, their energy-related building characteristics, and their energy consumption and expenditures. Commercial buildings include all buildings in which at least half of the floorspace is used for a purpose that is not residential, industrial, or agricultural, so they include building types that might not traditionally be considered "commercial," such as schools, correctional institutions, and buildings used for religious worship. The CBECS was first conducted in 1979; the eighth, and most recent survey, was conducted in 2003. CBECS is currently conducted on a quadrennial basis.

The existing information in this data set comes from the databases of the advanced energy monitoring service carried out within the scope of the Framework Agreement on Energy Services in Facilities of the City of Madrid and its Autonomous Bodies. In this context, and without prejudice to the monitoring and control of energy billing in all municipal buildings, energy consumption sensors are installed in some of them - those that record higher consumption, in which it presents greater variability, those that are included in specific energy certification programs or those whose technical or operational characteristics so advise - for continuous supervision and immediate action in the event of an incident. You can obtain more related information on the Energy Efficiency website at the Madrid City Council . Through the Visualization Portal ' Visualize Madrid with Open Data', the Madrid City Council puts at your disposal a visualization made with open energy data. Access the Energy visualization: Energy Consumption and Generation

In 2022, the global electricity consumption from data centers, artificial intelligence, and cryptocurrencies amounted to 460 terawatt-hours. By 2026, this figure will range between 620 and 1,050 terawatt-hours, depending on the future deployment of these technologies. Data centers, AI, and crypto will then account for a large share of the global electricity consumption, up from only some two percent in 2022.