Power outages have become a significant concern across the United States, with the most incidents reported in the state of Texas. Between 2000 and 2023, the Lone Star State experienced *** major power outages, followed closely by California with ***. This high frequency of outages applies pressure on the country’s electrical grid system, requiring improvements to infrastructure and greater resilience measures. Causes and consequences of power outages The primary causes of power outages in the U.S. are equipment failures and weather-related incidents, accounting for **** percent and **** percent of outages, respectively. These disruptions can lead to substantial economic losses, with property damage being the most costly consequence. In some cases, property damage from power outages can reach up to ****** U.S. dollars. The financial impact of outages extends beyond immediate repairs, as businesses and households must also account for expenses related to emergency supplies. Regional disparities in outage frequency The frequency and impact of power outages vary significantly across different regions and metropolitan areas. In 2023, Detroit was the most affected U.S. metropolitan area, with nearly ** percent of households experiencing at least one complete power outage. On a single day in May 2025, the Mid-Atlantic region reported over ****** electric outages, demonstrating the vulnerability of certain areas to widespread blackouts. California, despite ranking second in the number of major outages from 2000 to 2023, had the highest number of customers affected by power outages in 2023, with over ** million people impacted.

This dataset includes an aggregated and event-correlated analysis of power outages in the United States, synthesized by integrating three data sources: the Environment for the Analysis of Geo-Located Energy Information (EAGLE-I), the Electric Emergency Incident Disturbance Report (DOE-417), and Annual Estimates of the Resident Population for Counties 2024 (CO-EST2024-POP). The EAGLE-I dataset, spanning from 2014 to 2023, encompasses over 146 million customers and offers county-level outage information at 15-minute intervals. The data has been processed, filtered, and aggregated to deliver an enhanced perspective on power outages, which are then correlated with DOE-417 data based on geographic location as well as the start and end times of events. For each major disturbance documented in DOE-417, essential metrics are defined to quantify the outages associated with the event. This dataset supports researchers in examining outages triggered by major disturbances like extreme weather and physical disruptions, thereby aiding studies on power system resilience. Links to the raw data for generating the correlated dataset are included below as "DOE-417", "EAGLE-I", and "CO-EST2024-POP" resources. Acknowledgement: This work is funded by the Laboratory Directed Research and Development (LDRD) at the Pacific Northwest National Laboratory (PNNL) as part of the Resilience Through Data-Driven, Intelligently Designed Control (RD2C) Initiative.

Contains aggregated power outage data by ZIP Code. Limited historical record inventory. Designed for and consumed by the MEMA Power Outage web application.

This DOI contains information on the EAGLE-I power outage dataset and serves as a blanket DOI for all EAGLE-I historic data. Historic power outage data from specific years have been linked to this DOI.

On average, roughly ****** people were affected per power outage in Florida in the years between 2008 to 2017. States that experience powerful tropical storms such as Hawaii or heavy blizzards such as Maine often experience widespread blackouts as a result of the disruptive weather patterns.

In 2024, over *** minutes per customer were lost to power outages in the U.S., the most in the period under consideration. The year 2019 saw the least power outage minutes, amounting to just over *** minutes per customer.

The Outage Data Initiative Nationwide (ODIN) seeks to establish a comprehensive digital reporting standard for power outage data and to enable utilities and others to exchange data freely with designated stakeholders. The program is led by Oak Ridge National Laboratory and the U.S. Department of Energy's Office of Electricity. This dataset contains power outage information provided by utilities in near-real time at county level.

Note: Find data at source. ・ This dataset includes the major outages witnessed by different states in the continental U.S. Besides major outages, this data contains information on geographical location of the outages, regional climatic information, land-use characteristics, electricity consumption patterns and economic characteristics of the states affected by the outages.https://docs.lib.purdue.edu/civeng/36/

This is a historical dataset from PowerOutage.US (https://poweroutage.us/products). The original data file was converted from TSV (tab separated values) format to Microsoft Excel by American University Library for easier usability; the contents were not altered. Both of those files are made available for downloading here as a ZIP file. For 2016, only data for 14 states is available. Meaning of columns in this dataset: Record Hours- Total number of hours recorded (most of the time it will be 365 * 24) / Customer Hours - Total number of hours recorded by customer (customers * 24 * 365) / Outage Hours - Total number of hours without power per customer / Percent Hours Out - Percent of hours without power per customer / AVG Customer - average amount of tracked customers / Max Outage Count - Max number of customers without power at one time.

The power outages in this layer are pulled directly from the utility public power outage maps and is automatically updated every 15 minutes. This dataset represents only the most recent power outages and does not contain any historical data. The following utility companies are included:Pacific Gas and Electric (PG&E)Southern California Edison (SCE)San Diego Gas and Electric (SDG&E)Sacramento Municipal Utility District (SMUD)Los Angeles Water & Power (LAWP)Layers included in this dataset:Power Outage Incidents - Point layer that shows data from all of the utilities and is best for showing a general location of the outage and driving any numbers in dashboards.Power Outage Areas - Polygon layer that shows rough power outage areas from PG&E only (They are the only company that feeds this out publicly). With in the PG&E territory this layer is useful to show the general area out of power. The accuracy is limited by how the areas are drawn, but is it good for a visual of the impacted area.Power Outages by County - This layer summaries the total impacted customers by county. This layer is good for showing where outages are on a statewide scale.If you have any questions about this dataset please email GIS@caloes.ca.gov

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.

On May 15th, 2025, the highest number of power outages in the U.S. was reported in the Mid-Atlantic region. The region recorded over ****** electric outages that day.

The dataset will include the simulation results from the WNTR analysis of power outages on the US Virgin Islands water distribution system networks. The results include the resilience metrics: modified resilience index, the water service availability, the difference in water, and the tank capacity. The results are provided for three different power outage scenarios (system-wide, source, and distribution) and are averaged over the entire network as well as three different regions. The values of the resilience metrics are provided over time. This dataset is associated with the following publication: Klise, K., R. Moglen, J. Hogge, D. Eisenberg, and T. Haxton. Resilience analysis of potable water service after power outages in the U.S. Virgin Islands. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 148(12): 05022010, (2022).

PowerOutage.US is polled every 10 minutes using GeoEvent Server to update county power outage statistics for the State of Georgia.

The provided EAGLE-I historic dataset includes 1 year of power outage information at the county level for 2023 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.

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

The highest number of weather related power outages in the United States between 2000 and 2023 was in the year 2020, with *** incidents reported. The year 2011 followed closely with *** incidents reported. Since the pandemic, there was a decrease of ** percent in incidents in 2023 with respect to the year 2020.

Results are on the 1,657 counties with 3 years of reliable power outage data (1,799,208 total county-days). Proportion of county-days with multiple simultaneous severe weather events are relative to 1,799,208 county-days in the study. Only multiple simultaneous severe weather combinations observed in the data are presented.

This dataset includes the long form of the time series from 2019/01/01 to 2019/12/31, showing the number of customers who are experiencing a power outage at the city and county level in the State of California, USA. The time series is divided into 10-min intervals. The original data source is poweroutage.us, a data vendor of power outage data across the United States.

Power outage long-form time series at the county level are provided in the form of CSV files. Each file contains data for the entire county.

This dataset is described and explored in Rice et al. 2025, "Projected Increases in Tropical Cyclone-induced U.S. Electric Power Outage Risk", published in Environmental Research Letters (doi.org/10.1088/1748-9326/adad85)

This dataset collects peak outage levels modeled for 900,000 synthetic tropical cyclones (TCs; also commonly known as hurricanes) representative of a modeled historical (1980-2015) and future (2066-2100) period under SSP5-8.5 warming. Synthetic TCs are generated with the Risk Analysis Framework for Tropical Cyclones (RAFT; see Xu et al. 2024 and Balaguru et al. 2023), forced by climate simulation data from the Coupled Model Intercomparison Project phase 6 (CMIP6; see Eyring et al. 2016). Outages are modeled with the newly introduced Electric Power Outages from Cyclone Hazards (EPOCH) model, which was trained on county-level outage data from 23 historical TC events in the EAGLE-I dataset (Brelsford et al. 2024).

The EPOCH model predicts outages based on county population and the maximum wind speed and rainfall rate experienced during the TC. Predicted outage levels are provided in the form of peak outage fraction: the maximum fraction of electricity customers expected to experience an outage at any one time during the storm's lifetime. Although we do not model outage duration, other research suggests peak outage level is strongly correlated with duration (Jamal and Hasan, 2023).

Data Format

The data is provided in NetCDF4 files, one for each CMIP6 model and time period. Each NetCDF4 files has the following:

Dimensions:

ncounties = 2715. The counties in the study domain

ntracks = 50000. The number of storms

Variables:

int pseudofips(ncounties). The FIPS code for each county. Puerto Rico data is not available at county level, but instead for six utility-defined regions. We assign "pseudo-FIPS" codes to these region starting at 100000

double centroid_lons(ncounties). Longitude of approximate center of county, in the range [-180, 0].

double centroid_lats(ncounties). Latitude of approximate center of county, in the range [0, 90].

float outage_prediction(ntracks, ncounties). The predicted peak outage fraction for each county, for each storm. Due to the particularities of ensemble models, some predictions may be slightly below zero or above one; we clip these values to the range [0,1] before any analysis in our study.

ubyte prediction_complete_flag(ntracks). A verification flag used during dataset generation. This flag should equal 1 everywhere for complete data.

Each file also contains the raw predictors at a county level for every storm, inside the 'predictors' group, for feature analysis.

Also provided for convenience is 'counties_pseudofips.csv', which maps the pseudo-FIPS codes to the the name and spatial extent (WKT format) of each county. It can be read easily by Python GeoPandas, or other software.