This dataset, compiled by NREL using data from ABB, the Velocity Suite (http://energymarketintel.com/) and the U.S. Energy Information Administration dataset 861 (http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database (https://openei.org/apps/USURDB/).

These data provide the 2022 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

In 2022, traditional data centers accounted for a power demand of *** terawatt-hours, while the electricity used by artificial intelligence data centers was close to zero. By 2026, AI data centers demand is forecast to grow to ** terawatt-hours. By 2026, the overall electricity demand from traditional and AI data centers and cryptocurrencies is forecast to range between *** and **** terawatt-hours, depending on the scenario.

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.

Energy capacity data and map are from the California Energy Commission. Map depicts geothermal energy capacity by county. Unshaded counties had no commercial geothermal energy capacity. Data is from 2022 and is current as of May 14, 2024. Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact John Hingtgen at 916 510-9747 or Jessica Lin at 415 990-8392.

In 2022, data centers in China, the United States, and the European Union consumed approximately *** terawatt-hours of electricity. By 2026, data centers in China will account for the largest electricity consumption, with an estimate of *** terawatt-hours.

Energy generation data and map are from the California Energy Commission. Map depicts commercial geothermal energy generation by county. Unshaded counties had no commercial geothermal energy generation. Data is from 2022 and is current as of May 14, 2024. Projection: NAD 1983 (2011) California Teale) Albers (Meters). For more information, contact John Hingtgen at 916 510-9747 or Jessica Lin at 415 990-8392

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treat grid assets as fixed. Long-run marginal emissions rates are generally appropriate to use when trying to comprehensively estimate the impact of a long-lived (i.e., more than several years) intervention. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2022 project at https://scenarioviewer.nrel.gov/. For more details on input assumptions and methodology see the associated report (Cambium 2022 Scenario Descriptions and Documentation, https://www.nrel.gov/docs/fy23osti/84916.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.

In 2022, the global electricity consumption from data centers, artificial intelligence, and cryptocurrencies amounted to *** terawatt-hours. By 2026, this figure will range between *** and ***** 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.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.

This is Taiwan's electricity generation/demand data from 2017 January to 2022 July, in 10-min resolution.

Two files are included so far:

loadarea: the electricity demand in four areas (i.e. north, central, south, east) in Taiwan. The information of the area can be found on Taipower's web page.

powerRatio: the power of each type of electricity generation. The data include both the generation from Taipower company and IPP (independent power plant); 'lng' is refer to gas power plant.

Source:

the original data can be obtained from the Taiwanese government's open-data platform data.gov.tw.

The link to the corresponding dataset is https://data.gov.tw/dataset/37331. (please note this link can only download 3 months of data that be collected a half year ago)

live data can be obtained here (update very 10 min) https://data.gov.tw/dataset/8931 with Chinese characters

More information can be found on the Taipower webpage of Information Disclosure.

License:

Open Government Data License, version 1.0 (Taiwan)

The core of the provided dataset includes eight years of power outage information at the county level from 2014 to 2024 at 15-minute intervals collected from utility’s public outage maps on their websites by the EAGLE-I program at ORNL. Three supplementary files are included to augment the power outage data files. The first file includes the customer coverage rate of each state from 2018-2022. The second file provides the modeled number of electric customers per county as of 2022. The third presents our Data Quality Index and the four sub-components by year by FEMA Region for 2018-2022. UPDATE 2/16/2023: Added 2023 outage data and Uri_Map.R and DQI_processing.R files have been added. They were used to create graphics in associated works.UPDATE 4/10/2025: Added 2024 outage data.

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.

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/

Dataset of electricity and heat demand in 2022 in a city district in Belgium (similar data for 2021 is available on Zenodo as well, https://doi.org/10.5281/zenodo.5155659). For the time period of the data, the district was still under construction and no full inhabitation of the buildings was present. Electricity data include electricity demand for: Individual households EV charging stations Decentralised waste water treatment Heat pump District heating pumps Vacuum network pumps Miscellaneous 'Total' in the electricity dataset (ElectricPower) refers to the sum of the separate time series. 'Total measured' is a measurement of the total electricity use (in W). Data is averaged out over 15 minutes and expressed in Watt. The electricity demand for the individual households (ElectricPowerPrivateUnits) is expressed in Watt for the complete period. Column names have the form x.y in which x is a random number assigned to an apartment and y refers to the electricity consumption during the day (1) or at night (2). The heat demand data (HeatDemand) describes the heat demand of the complete district, i.e. all private living units as well as common areas, office buildings, sports hall... Like electricity demand data, heat demand data is averaged out over 15 minutes and expressed in Watt.

Energy capacity data and map are from the California Energy Commission. Map depicts geothermal energy capacity by county. Unshaded counties had no geothermal energy capacity. Data is from 2022 and is current as of May 14, 2024. Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact John Hingtgen at (916) 510-9747 or Jessica Lin at (415) 990-8392.

The provided EAGLE-I historic dataset includes eight years of power outage information at the county level from 2014 to 2022 at 15-minute intervals collected by the EAGLE-I program at ORNL. The data has been collected from utility’s public outage maps using an ETL process. The dataset details FIPS code, county name, state name, total number of customers without power, and a date/timestamp. Also included is the EAGLE-I coverage of each state for each year. For detailed metadata, refer to the metadata DOI.