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]

Electricity consumption in the United States totaled 4,000 terawatt-hours in 2023, one of the highest values in the period under consideration. Figures represent energy end use, which is the sum of retail sales and direct use of electricity by the producing entity. Electricity consumption in the U.S. is expected to continue increasing in the next decades. Which sectors consume the most electricity in the U.S.? Consumption has often been associated with economic growth. Nevertheless, technological improvements in efficiency and new appliance standards have led to a stabilizing of electricity consumption, despite the increased ubiquity of chargeable consumer electronics. Electricity consumption is highest in the residential sector, followed by the commercial sector. Equipment used for space heating and cooling account for some of the largest shares of residential electricity end use. Leading states in electricity use Industrial hub Texas is the leading electricity-consuming U.S. state. In 2022, the Southwestern state, which houses major refinery complexes and is also home to nearly 30 million people, consumed over 470 terawatt-hours. California and Florida trailed in second and third, each with an annual consumption of approximately 250 terawatt-hours.

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe have further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels determine the amount of electricity the average person requires in each country.

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.

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data were interpreted and transcribed manually from utility tariff sheets, which are often complex. It is a certainty that these data contain errors, and therefore should only be used as a reference. Actual utility tariff sheets should be consulted if an action requires this type of data. (2) These data only contains tariffs that were entered into the Utility Rate Database. Since not all tariffs are designed in a format that can be entered into the Database, this list is incomplete - it does not contain all tariffs in the United States. (3) These data may have changed since this list was developed (4) Many of the underlying tariffs have additional restrictions or requirements that are not represented here. For example, they may only be available to the agricultural sector or closed to new customers. (5) If there are multiple demand charge elements in a given tariff, the maximum demand charge is the sum of each of the elements at any point in time. Where tiers were present, the highest rate tier was assumed. The value is a maximum for the year, and may be significantly different from demand charge rates at other times in the year. Utility Rate Database: https://openei.org/wiki/Utility_Rate_Database

Electricity Demand Dataset

This dataset compiles and harmonizes multiple open smart meter datasets.

Curated by: Attila Balint License: BSD 3-clause "New" or "Revised" licence

  Uses

This smart meter dataset facilitates primarily electricity demand forecasting.

  Dataset Structure

The dataset contains three main files.

data/demand.parquet data/metadata.parquet data/weather.parquet

  data/demand.parquet

This file contains the electricity consumption… See the full description on the dataset page: https://huggingface.co/datasets/EDS-lab/electricity-demand.

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.

This is the first data release from the Public Utility Data Liberation (PUDL) project. It can be referenced & cited using https://doi.org/10.5281/zenodo.3653159

For more information about the free and open source software used to generate this data release, see Catalyst Cooperative's PUDL repository on Github, and the associated documentation on Read The Docs. This data release was generated using v0.3.1 of the catalystcoop.pudl python package.

Included Data Packages

This release consists of three tabular data packages, conforming to the standards published by Frictionless Data and the Open Knowledge Foundation. The data are stored in CSV files (some of which are compressed using gzip), and the associated metadata is stored as JSON. These tabular data can be used to populate a relational database.

• pudl-eia860-eia923:
  Data originally collected and published by the US Energy Information Administration (US EIA). The data from EIA Form 860 covers the years 2011-2018. The Form 923 data covers 2009-2018. A large majority of the data published in the original data sources has been included, but some parts, like fuel stocks on hand, and EIA 923 schedules 6, 7, & 8 have not yet been integrated.
• pudl-eia860-eia923-epacems:
  This data package contains all of the same data as the pudl-eia860-eia923 package above, as well as the Hourly Emissions data from the US Environmental Protection Agency's (EPA's) Continuous Emissions Monitoring System (CEMS) from 1995-2018. The EPA CEMS data covers thousands of power plants at hourly resolution for decades, and contains close to a billion records.
• pudl-ferc1:
  Seven data tables from FERC Form 1 are included, primarily relating to individual power plants, and covering the years 1994-2018 (the entire span of time for which FERC provides this data). These tables are the only ones which have been subjected to any cleaning or organization for programmatic use within PUDL. The complete, raw FERC Form 1 database contains 116 different tables with many thousands of columns of mostly financial data. We will archive a complete copy of the multi-year FERC Form 1 Database as a file-based SQLite database at Zenodo, independent of this data release. It can also be re-generated using the catalystcoop.pudl Python package and the original source data files archived as part of this data release.

Contact Us

If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. You can also:

• Subscribe to our announcements list for email updates.
• Use the Github issue tracker to file bugs, suggest improvements, or ask for help.
• Email the project team at pudl@catalyst.coop for private communications.
• Follow @CatalystCoop on Twitter.

Using the Data

The data packages are just CSVs (data) and JSON (metadata) files. They can be used with a variety of tools on many platforms. However, the data is organized primarily with the idea that it will be loaded into a relational database, and the PUDL Python package that was used to generate this data release can facilitate that process. Once the data is loaded into a database, you can access that DB however you like.

Make sure conda is installed

None of these commands will work without the conda Python package manager installed, either via Anaconda or miniconda:

• Install Anaconda
• Install miniconda

Download the data

First download the files from the Zenodo archive into a new empty directory. A couple of them are very large (5-10 GB), and depending on what you're trying to do you may not need them.

• If you don't want to recreate the data release from scratch by re-running the entire ETL process yourself, and you don't want to create a full clone of the original FERC Form 1 database, including all of the data that has not yet been integrated into PUDL, then you don't need to download pudl-input-data.tgz.
• If you don't need the EPA CEMS Hourly Emissions data, you do not need to download pudl-eia860-eia923-epacems.tgz.

Load All of PUDL in a Single Line

Use cd to get into your new directory at the terminal (in Linux or Mac OS), or open up an Anaconda terminal in that directory if you're on Windows.

If you have downloaded all of the files from the archive, and you want it all to be accessible locally, you can run a single shell script, called load-pudl.sh:

bash pudl-load.sh

This will do the following:

• Load the FERC Form 1, EIA Form 860, and EIA Form 923 data packages into an SQLite database which can be found at sqlite/pudl.sqlite.
• Convert the EPA CEMS data package into an Apache Parquet dataset which can be found at parquet/epacems.
• Clone all of the FERC Form 1 annual databases into a single SQLite database which can be found at sqlite/ferc1.sqlite.

Selectively Load PUDL Data

If you don't want to download and load all of the PUDL data, you can load each of the above datasets separately.

Create the PUDL conda Environment

This installs the PUDL software locally, and a couple of other useful packages:

conda create --yes --name pudl --channel conda-forge \
  --strict-channel-priority \
  python=3.7 catalystcoop.pudl=0.3.1 dask jupyter jupyterlab seaborn pip
conda activate pudl

Create a PUDL data management workspace

Use the PUDL setup script to create a new data management environment inside this directory. After you run this command you'll see some other directories show up, like parquet, sqlite, data etc.

pudl_setup ./

Extract and load the FERC Form 1 and EIA 860/923 data

If you just want the FERC Form 1 and EIA 860/923 data that has been integrated into PUDL, you only need to download pudl-ferc1.tgz and pudl-eia860-eia923.tgz. Then extract them in the same directory where you ran pudl_setup:

tar -xzf pudl-ferc1.tgz
tar -xzf pudl-eia860-eia923.tgz

To make use of the FERC Form 1 and EIA 860/923 data, you'll probably want to load them into a local database. The datapkg_to_sqlite script that comes with PUDL will do that for you:

datapkg_to_sqlite \
  datapkg/pudl-data-release/pudl-ferc1/datapackage.json \
  datapkg/pudl-data-release/pudl-eia860-eia923/datapackage.json \
  -o datapkg/pudl-data-release/pudl-merged/

Now you should be able to connect to the database (~300 MB) which is stored in sqlite/pudl.sqlite.

Extract EPA CEMS and convert to Apache Parquet

If you want to work with the EPA CEMS data, which is much larger, we recommend converting it to an Apache Parquet dataset with the included epacems_to_parquet script. Then you can read those files into dataframes directly. In Python you can use the pandas.DataFrame.read_parquet() method. If you need to work with more data than can fit in memory at one time, we recommend using Dask dataframes. Converting the entire dataset from datapackages into Apache Parquet may take an hour or more:

tar -xzf pudl-eia860-eia923-epacems.tgz
epacems_to_parquet datapkg/pudl-data-release/pudl-eia860-eia923-epacems/datapackage.json

You should find the Parquet dataset (~5 GB) under parquet/epacems, partitioned by year and state for easier querying.

Clone the raw FERC Form 1 Databases

If you want to access the entire set of original, raw FERC Form 1 data (of which only a small subset has been cleaned and integrated into PUDL) you can extract the original input data that's part of the Zenodo archive and run the ferc1_to_sqlite script using the same settings file that was used to generate the data release:

tar -xzf pudl-input-data.tgz
ferc1_to_sqlite data-release-settings.yml

You'll find the FERC Form 1 database (~820 MB) in sqlite/ferc1.sqlite.

Data Quality Control

We have performed basic sanity checks on much but not all of the data compiled in PUDL to ensure that we identify any major issues we might have introduced through our processing

This dataset, compiled by NREL using data from ABB, the Velocity Suite and the U.S. Energy Information Administration dataset 861, 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.

This dataset is about countries per year in the United States. It has 64 rows. It features 4 columns: country, electricity production from coal sources, and individuals using the Internet.

Datasets are for the US electricity grid system for eGrid regions (AKGD, AKMS, AZNM, CAMX, ERCT, FRCC, HIMS, HIOA, MROE, MROW, NEWE, NWPP, NYCW, NYLI, NYUP, RFCE, RFCM, RFCW, RMPA, SPNO, SPSO, SRMV, SRMW, SRSO, SRTV, SRVC) for 2008. The data is provided in life cycle inventory forms (xls and xml) . A module report and a detailed spreadsheet are also included.Datasets include generation and transmission of electricity for each of the eGrid regions. It is representative of the year 2008 mix of fuels used for utility generations for each of the eGrid regions and is based on the EIA electricity reports for all power plants in the US. Detailed information on the methodology is included in the module report and detailed spreadsheet.

Analysis of ‘Electric Generation By Fuel Type, GWh: Beginning 1960’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/16423e2c-6232-4aea-ac1a-03e3062afb82 on 30 September 2021.

--- Dataset description provided by original source is as follows ---

New York Electric Generation By Fuel Type, GWh dataset provides data on total electricity requirements and in-state generation for New York State in giga-watt hours. Sources of electricity include coal, natural gas, petroleum products, hydro, nuclear, waste, landfill gas, wood, wind, solar, and net imports of electricity.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

--- Original source retains full ownership of the source dataset ---

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. 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 by utilities at the county level. Other UER datasets include energy use data reported at the city, town, and village, 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.

New York Energy Prices presents retail energy price data. Energy prices are provided by fuel type in nominal dollars per million Btu for the residential, commercial, industrial, and transportation sectors. This section includes a column in the price table displaying gross domestic product (GDP) price deflators for converting nominal (current year) dollars to constant (real) dollars. To convert nominal to constant dollars, divide the nominal energy price by the GDP price deflator for that particular year. Historical petroleum, electricity, coal, and natural gas prices were compiled primarily from the Energy Information Administration. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Much of the world’s data are stored, managed, and distributed by data centers. Data centers re-quire a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center’s water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint..

The Utility Rate Database (URDB) is a free storehouse of rate structure information from utilities in the United States. Here, you can search for your utilities and rates to find out exactly how you are charged for your electric energy usage. Understanding this information can help reduce your bill, for example, by running your appliances during off-peak hours (times during the day when electricity prices are less expensive) and help you make more informed decisions regarding your energy usage.

Rates are also extremely important to the energy analysis community for accurately determining the value and economics of distributed generation such as solar and wind power. In the past, collecting rates has been an effort duplicated across many institutions. Rate collection can be tedious and slow, however, with the introduction of the URDB, OpenEI aims to change how analysis of rates is performed. The URDB allows anyone to access these rates in a computer-readable format for use in their tools and models. OpenEI provides an API for software to automatically download the appropriate rates, thereby allowing detailed economic analysis to be done without ever having to directly handle complex rate structures. Essentially, rate collection and processing that used to take weeks or months can now be done in seconds!

NREL’s System Advisor Model (formerly Solar Advisor Model or SAM), currently has the ability to communicate with the OpenEI URDB over the internet. SAM can download any rate from the URDB directly into the program, thereby enabling users to conduct detailed studies on various power systems ranging in size from a small residential rooftop solar system to large utility scale installations. Other applications available at NREL, such as OpenPV and IMBY, will also utilize the URDB data.

Upcoming features include better support for entering net metering parameters, maps to summarize the data, geolocation capabilities, and hundreds of additional rates!

United States Electricity End Use: Retail Sales data was reported at 331,923.000 kWh mn in Sep 2018. This records a decrease from the previous number of 376,416.000 kWh mn for Aug 2018. United States Electricity End Use: Retail Sales data is updated monthly, averaging 252,528.860 kWh mn from Jan 1973 (Median) to Sep 2018, with 549 observations. The data reached an all-time high of 381,192.000 kWh mn in Aug 2016 and a record low of 131,360.946 kWh mn in May 1973. United States Electricity End Use: Retail Sales data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.RB067: Electricity Overview.