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/).

In 2022, PG&E and Avangrid were the investor-owned electric utilities with the highest decarbonization index score in the United States, with overall scores of 4.6 and 4.2 points. They were closely followed by Public Service Enterprise Group (PSEG), which scored 4.1 that year. The U.S.'s power sector carbon intensity stood at 760 pounds of CO2 per megawatt-hour in 2023.

Comprehensive dataset containing 12,191 verified Electric utility company businesses in United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 10,100 verified Solar energy company businesses in United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 246 verified Electric utility company businesses in Iowa, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 25 verified Electric utility company businesses in Vermont, United States with complete contact information, ratings, reviews, and location data.

A comprehensive dataset of average residential, commercial, and combined electricity rates in cents per kWh for all 50 U.S. states and Washington D.C.

Comprehensive dataset containing 76 verified Electric utility company businesses in Idaho, United States with complete contact information, ratings, reviews, and location data.

PUDL v2025.2.0 Data Release

This is our regular quarterly release for 2025Q1. It includes updates to all the datasets that are published with quarterly or higher frequency, plus initial verisons of a few new data sources that have been in the works for a while.

One major change this quarter is that we are now publishing all processed PUDL data as Apache Parquet files, alongside our existing SQLite databases. See Data Access for more on how to access these outputs.

Some potentially breaking changes to be aware of:

In the EIA Form 930 – Hourly and Daily Balancing Authority Operations Report a number of new energy sources have been added, and some old energy sources have been split into more granular categories. See Changes in energy source granularity over time.

We are now running the EPA’s CAMD to EIA unit crosswalk code for each individual year starting from 2018, rather than just 2018 and 2021, resulting in more connections between these two datasets and changes to some sub-plant IDs. See the note below for more details.

Many thanks to the organizations who make these regular updates possible! Especially GridLab, RMI, and the ZERO Lab at Princeton University. If you rely on PUDL and would like to help ensure that the data keeps flowing, please consider joining them as a PUDL Sustainer, as we are still fundraising for 2025.

New Data

EIA 176

Add a couple of semi-transformed interim EIA-176 (natural gas sources and dispositions) tables. They aren’t yet being written to the database, but are one step closer. See #3555 and PRs #3590, #3978. Thanks to @davidmudrauskas for moving this dataset forward.

Extracted these interim tables up through the latest 2023 data release. See #4002 and #4004.

EIA 860

Added EIA 860 Multifuel table. See #3438 and #3946.

FERC 1

Added three new output tables containing granular utility accounting data. See #4057, #3642 and the table descriptions in the data dictionary:

out_ferc1_yearly_detailed_income_statements

out_ferc1_yearly_detailed_balance_sheet_assets

out_ferc1_yearly_detailed_balance_sheet_liabilities

SEC Form 10-K Parent-Subsidiary Ownership

We have added some new tables describing the parent-subsidiary company ownership relationships reported in the SEC’s Form 10-K, Exhibit 21 “Subsidiaries of the Registrant”. Where possible these tables link the SEC filers or their subsidiary companies to the corresponding EIA utilities. This work was funded by a grant from the Mozilla Foundation. Most of the ML models and data preparation took place in the mozilla-sec-eia repository separate from the main PUDL ETL, as it requires processing hundreds of thousands of PDFs and the deployment of some ML experiment tracking infrastructure. The new tables are handed off as nearly finished products to the PUDL ETL pipeline. Note that these are preliminary, experimental data products and are known to be incomplete and to contain errors. Extracting data tables from unstructured PDFs and the SEC to EIA record linkage are necessarily probabalistic processes.

See PRs #4026, #4031, #4035, #4046, #4048, #4050 and check out the table descriptions in the PUDL data dictionary:

out_sec10k_parents_and_subsidiaries

core_sec10k_quarterly_filings

core_sec10k_quarterly_exhibit_21_company_ownership

core_sec10k_quarterly_company_information

Expanded Data Coverage

EPA CEMS

Added 2024 Q4 of CEMS data. See #4041 and #4052.

EPA CAMD EIA Crosswalk

In the past, the crosswalk in PUDL has used the EPA’s published crosswalk (run with 2018 data), and an additional crosswalk we ran with 2021 EIA 860 data. To ensure that the crosswalk reflects updates in both EIA and EPA data, we re-ran the EPA R code which generates the EPA CAMD EIA crosswalk with 4 new years of data: 2019, 2020, 2022 and 2023. Re-running the crosswalk pulls the latest data from the CAMD FACT API, which results in some changes to the generator and unit IDs reported on the EPA side of the crosswalk, which feeds into the creation of core_epa_assn_eia_epacamd.

The changes only result in the addition of new units and generators in the EPA data, with no changes to matches at the plant level. However, the updates to generator and unit IDs have resulted in changes to the subplant IDs - some EIA boilers and generators which previously had no matches to EPA data have now been matched to EPA unit data, resulting in an overall reduction in the number of rows in the core_epa_assn_eia_epacamd_subplant_ids table. See issues #4039 and PR #4056 for a discussion of the changes observed in the course of this update.

EIA 860M

Added EIA 860m through December 2024. See #4038 and #4047.

EIA 923

Added EIA 923 monthly data through September 2024. See #4038 and #4047.

EIA Bulk Electricity Data

Updated the EIA Bulk Electricity data to include data published up through 2024-11-01. See #4042 and PR #4051.

EIA 930

Updated the EIA 930 data to include data published up through the beginning of February 2025. See #4040 and PR #4054. 10 new energy sources were added and 3 were retired; see Changes in energy source granularity over time for more information.

Bug Fixes

Fix an accidentally swapped set of starting balance / ending balance column rename parameters in the pre-2021 DBF derived data that feeds into core_ferc1_yearly_other_regulatory_liabilities_sched278. See issue #3952 and PRs #3969, #3979. Thanks to @yolandazzz13 for making this fix.

Added preliminary data validation checks for several FERC 1 tables that were missing it #3860.

Fix spelling of Lake Huron and Lake Saint Clair in out_vcerare_hourly_available_capacity_factor and related tables. See issue #4007 and PR #4029.

Quality of Life Improvements

We added a sources parameter to pudl.metadata.classes.DataSource.from_id() in order to make it possible to use the pudl-archiver repository to archive datasets that won’t necessarily be ingested into PUDL. See this PUDL archiver issue and PRs #4003 and #4013.

Other PUDL v2025.2.0 Resources

PUDL v2025.2.0 Data Dictionary

PUDL v2025.2.0 Documentation

PUDL in the AWS Open Data Registry

PUDL v2025.2.0 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2025.2.0/

PUDL v2025.2.0 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2025.2.0/

Zenodo archive of the PUDL GitHub repo for this release

PUDL v2025.2.0 release on GitHub

PUDL v2025.2.0 package in the Python Package Index (PyPI)

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. Here's a bunch of different ways to get in touch:

Follow us on GitHub

Use the PUDL Github issue tracker to let us know about any bugs or data issues you encounter

GitHub Discussions is where we provide user support.

Watch our GitHub Project to see what we're working on.

Email us at hello@catalyst.coop for private communications.

On Mastodon: @CatalystCoop@mastodon.energy

On BlueSky: @catalyst.coop

On Twitter: @CatalystCoop

Connect with us on LinkedIn

Play with our data and notebooks on Kaggle

Combine our data with ML models on HuggingFace

Learn more about us on our website: https://catalyst.coop

Subscribe to our announcements list for email updates.

Change-To-Inventory Time Series for Entergy Corporation. Entergy Corporation, together with its subsidiaries, engages in the production and retail distribution of electricity in the United States. It generates, transmits, distributes, and sells electric power in portions of Arkansas, Louisiana, Mississippi, and Texas, including the City of New Orleans; and distributes natural gas. It also engages in the ownership of interests in non-nuclear power plants that sell electric power to wholesale customers, as well as provides decommissioning services to other nuclear power plant owners. It generates electricity through gas, nuclear, coal, hydro, and solar power sources. The company sells energy to retail power providers, utilities, electric power co-operatives, power trading organizations, and other power generation companies. The company's power plants have approximately 25,000 megawatts of electric generating capacity. It delivers electricity to 3 million utility customers in Arkansas, Louisiana, Mississippi, and Texas. Entergy Corporation was founded in 1913 and is headquartered in New Orleans, Louisiana.

Released to the public as part of the Department of Energy's Open Energy Data Initiative, this is the highest resolution publicly available long-term wave hindcast dataset that – when complete – will cover the entire U.S. Exclusive Economic Zone (EEZ).

The utility sector is currently experiencing transformative changes driven by unprecedented investments in renewable energy infrastructure, particularly in solar and wind projects. In 2023, the share of renewable energy in the US electric power generation has surged to over 20%, a significant climb from less than 10% just a few years ago. This shift is primarily supported by favorable tax credits to boost electricity production from sustainable sources. The sector is witnessing a resurgence in nuclear energy, exemplified by the commissioning of Vogtle Unit 3 in Georgia—marking the first new nuclear reactor in the US since 2016. Such developments signal a growing commitment to clean energy solutions and a strengthened focus on reducing environmental footprints across the nation. Overall, revenue has swelled at a CAGR of 2.7% through 2025, reaching $1.1 trillion, including a 2.9% uptick in 2025 alone. This sector's growth can also be attributed to the significant climb in electric power prices and consumption. The remarkable spike in natural gas prices following the pandemic has also bolstered revenue. The higher costs of natural gas flowing through the distribution networks enhance profitability for natural gas distributors, contributing to the overall financial performance. Key policy initiatives aimed at accelerating growth in the renewable energy sector while reducing utility costs and addressing climate change are expected to significantly impact the sector. As the transition to cleaner energy sources continues, coal's prominence will diminish, with natural gas will to remain a major energy option even as renewable energy captures a larger share of the market. The sector will also benefit from ongoing infrastructure investments to ensure access to clean drinking water and address drought challenges. The rising popularity of AI will also bolster nuclear power as large corporations ramp up reactors to power data centers. Nonetheless, dwindling electricity prices may hinder overall growth for power generators and transmitters. Despite these challenges, revenue is set to creep up at a CAGR of 0.5%, reaching $1.1 trillion by 2030.

United States US: Time Required to Get Electricity data was reported at 89.600 Day in 2017. This stayed constant from the previous number of 89.600 Day for 2016. United States US: Time Required to Get Electricity data is updated yearly, averaging 89.600 Day from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 89.600 Day in 2017 and a record low of 89.600 Day in 2017. United States US: Time Required to Get Electricity data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Company Statistics. Time required to get electricity is the number of days to obtain a permanent electricity connection. The measure captures the median duration that the electricity utility and experts indicate is necessary in practice, rather than required by law, to complete a procedure.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

As of May 2025, ************** was the company that owned the most data centers certified by Energy Star in the United States. While the surface area of Energy Star-certified data centers owned by Digital Realty amounted to ** million square feet, those figures were **** times higher than those for the second company in the ranking.

Comprehensive dataset containing 161 verified Electric utility company businesses in Nebraska, United States with complete contact information, ratings, reviews, and location data.

The United States is experiencing a surge in data center construction, with the power supply under construction reaching *** gigawatts in 2024. This marks a *** percent increase from previous years, reflecting the growing demand for data storage and processing capabilities across the country. The rapid expansion of data centers underscores their crucial role in supporting the digital infrastructure that powers businesses and consumers alike. Northern Virginia leads the charge Northern Virginia has emerged as the epicenter of data center growth in the United States. In 2023, the region boasted the highest existing data center power capacity, solidifying its position as the market with the largest data center inventory in the country. Furthermore, Northern Virginia continues to dominate new construction efforts, with data centers under construction in the second half of 2024 set to add a staggering *** gigawatts of power capacity. This far outpaces other major markets such as Dallas, Austin, and NYC-NJ combined. Cloud infrastructure fuels growth The expansion of data centers is closely tied to the increasing adoption of cloud infrastructure services. Enterprise spending on cloud infrastructure services has soared in the past decade, fueled by organizations' growing demand for modern networking, storage, and database solutions. As companies continue to migrate their operations to the cloud, the need for robust data center facilities is expected to rise, further propelling the construction boom.

Success.ai’s LinkedIn Company Data for the Renewable Energy Sector Worldwide provides a powerful and accurate dataset tailored for businesses and organizations aiming to connect with renewable energy companies and professionals globally. Covering roles and firms involved in solar, wind, hydro, and other renewable energy solutions, this dataset offers verified LinkedIn profiles, firmographic insights, and detailed decision-maker data.

With access to over 700 million verified global profiles, Success.ai ensures your marketing, outreach, and strategic initiatives are driven by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to succeed in the fast-evolving renewable energy industry.

Why Choose Success.ai’s LinkedIn Company Data?

1. Verified Profiles for Precision Engagement

   • Access verified LinkedIn profiles, employee counts, and decision-maker data for renewable energy companies worldwide.
   • AI-driven validation ensures 99% accuracy, reducing inefficiencies and boosting outreach effectiveness.

2. Comprehensive Global Coverage

   • Includes renewable energy businesses from North America, Europe, Asia-Pacific, and other key markets.
   • Gain insights into regional energy trends, innovative technologies, and company expansions in the renewable sector.

3. Continuously Updated Datasets

   • Real-time updates capture changes in organizational structures, employee roles, and firm locations.
   • Stay aligned with market shifts to capitalize on emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access LinkedIn company data for renewable energy professionals and organizations worldwide.
• Firmographic Insights: Gain employee counts, business locations, and operational details to refine targeting.
• Decision-maker Profiles: Connect with executives, project managers, and engineers shaping renewable energy innovations.
• Industry Trends: Leverage actionable data to understand growth areas, company expansions, and adoption of renewable solutions.

Key Features of the Dataset:

1. Comprehensive Company Profiles in Renewable Energy

   • Identify and connect with renewable energy companies specializing in solar, wind, geothermal, and other sustainable solutions.
   • Target professionals driving green initiatives, energy storage technologies, and project development.

2. Advanced Filters for Precision Campaigns

   • Filter companies and professionals by industry focus, geographic location, or operational size.
   • Tailor campaigns to align with specific needs, such as promoting energy technologies or forming strategic partnerships.

3. Regional and Industry-specific Insights

   • Leverage data on renewable energy adoption, project pipelines, and investment trends across key global regions.
   • Refine strategies to align with market priorities and regional demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote renewable energy technologies, services, or consulting solutions to businesses and decision-makers in the sector.
   • Use verified contact data for multi-channel outreach, including email, phone, and LinkedIn.

2. Partnership Development and Collaboration

   • Build relationships with renewable energy companies and stakeholders exploring project funding, technology partnerships, or market entry.
   • Foster collaborations that advance green energy adoption and operational efficiencies.

3. Market Research and Competitive Analysis

   • Analyze trends in the renewable energy market to refine product offerings, marketing strategies, and investment plans.
   • Benchmark against competitors to identify growth opportunities and emerging market needs.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for roles in renewable energy project management, engineering, and sustainability leadership.
   • Provide workforce optimization platforms or talent development tools tailored to the renewable energy industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality LinkedIn company data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified LinkedIn company data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, enhancing productivity and simplifying workflows.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions...

Payables-Turnover Time Series for Entergy Corporation. Entergy Corporation, together with its subsidiaries, engages in the production and retail distribution of electricity in the United States. It generates, transmits, distributes, and sells electric power in portions of Arkansas, Louisiana, Mississippi, and Texas, including the City of New Orleans; and distributes natural gas. It also engages in the ownership of interests in non-nuclear power plants that sell electric power to wholesale customers, as well as provides decommissioning services to other nuclear power plant owners. It generates electricity through gas, nuclear, coal, hydro, and solar power sources. The company sells energy to retail power providers, utilities, electric power co-operatives, power trading organizations, and other power generation companies. The company's power plants have approximately 25,000 megawatts of electric generating capacity. It delivers electricity to 3 million utility customers in Arkansas, Louisiana, Mississippi, and Texas. Entergy Corporation was founded in 1913 and is headquartered in New Orleans, Louisiana.

Working-Capital-Turnover Time Series for Entergy Corporation. Entergy Corporation, together with its subsidiaries, engages in the production and retail distribution of electricity in the United States. It generates, transmits, distributes, and sells electric power in portions of Arkansas, Louisiana, Mississippi, and Texas, including the City of New Orleans; and distributes natural gas. It also engages in the ownership of interests in non-nuclear power plants that sell electric power to wholesale customers, as well as provides decommissioning services to other nuclear power plant owners. It generates electricity through gas, nuclear, coal, hydro, and solar power sources. The company sells energy to retail power providers, utilities, electric power co-operatives, power trading organizations, and other power generation companies. The company's power plants have approximately 25,000 megawatts of electric generating capacity. It delivers electricity to 3 million utility customers in Arkansas, Louisiana, Mississippi, and Texas. Entergy Corporation was founded in 1913 and is headquartered in New Orleans, Louisiana.

Hourly Electricity Demand by State

This archive contains the output of the Public Utility Data Liberation (PUDL) Project state electricity demand allocation analysis, as of the v0.4.0 release of the PUDL Python package. Here is the script that produced this output. It was run using the Docker container and processed data that are included in PUDL Data Release v2.0.0.

The analysis uses hourly electricity demand reported at the balancing authority and utility level in the FERC 714 (data archive), and service territories for utilities and balancing authorities inferred from the counties served by each utility, and the utilities that make up each balancing authority in the EIA 861 (data archive), to estimate the total hourly electricity demand for each US state.

We used the total electricity sales by state reported in the EIA 861 as a scaling factor to ensure that the magnitude of electricity sales is roughly correct, and obtains the shape of the demand curve from the hourly planning area demand reported in the FERC 714. The scaling is necessary partly due to imperfections in the historical utility and balancing authority service territory maps which we have been able to reconstruct from the data reported in the EIA 861 Service Territories and Balancing Authority tables.

The compilation of historical service territories based on the EIA 861 data is somewhat manual and could be improved, but overall the results seem reasonable. Additional predictive spatial variables will be required to obtain more granular electricity demand estimates (e.g. at the county level).

FERC 714 Respondents

The file ferc714_respondents.csv links FERC Form 714 respondents to what we believe to be their corresponding EIA utilities or balancing authorities.

• eia_code: An integer ID reported in the FERC Form 714 corresponding to the respondent's EIA ID. In some cases this is a Utility ID, and in others it is a Balancing Authority ID, but which is not specified and so we have had to infer the type of entity which is responding. Note that in many cases the same company acts as both a utility and a balancing authority, and the integer ID associated with the company is often the same in both roles, but it does not need to be.
• respondent_type: Either balancing_authority or utility depending on which type of entity we believe was responding to the FERC 714.
• respondent_id_ferc714: The integer ID of the responding entity within the FERC 714.
• respondent_name_ferc714: The name provided by the respondent in the FERC 714.
• balancing_authority_id_eia: If the respondent was identified as a balancing authority, the EIA ID for that balancing authority, taken from the EIA Form 861.
• balancing_authority_code_eia: If the respondent was identified as a balancing authority, the EIA short code used to identify the balancing authority, taken from the EIA Form 861.
• balancing_authority_name_eia: If the respondent was identified as a balancing authority, the name of the balancing authority, taken from the EIA Form 861.
• utility_id_eia: If the respondent was identified as a utility, the EIA utility ID, taken from the EIA Form 861.
• utility_name_eia: If the respondent was identified as a utility, the name of the utility, taken from the EIA 861.

FERC 714 Respondent Service Territories

The file ferc714_service_territories.csv describes the historical service territories for FERC 714 respondents for the years 2006-2019. For each respondent and year, their service territory is composed of a collection of counties, identified by their 5-digit FIPS codes. The file contains the following columns, with each row associating a single county with a FERC 714 respondent in a particular year:

• respondent_id_ferc714: The FERC Form 714 respondent ID, which is also found in ferc714_respondents.csv
• report_date: The first day of the year for which the service territory is being described.
• state: Two letter abbreviation for the state containing the county, for human readability.
• county: The name of the county, for human readability.
• state_id_fips: The 2-digit FIPS state code.
• county_id_fips: The 5-digit FIPS county code for use with other geospatial data resources, like the US Census DP1 geodatabase.

State Hourly Electricity Demand Estimates

The file demand.csv contains hourly electricity demand estimates for each US state from 2006-2019. It contains the following columns:

• state_id_fips: The 2-digit FIPS state code.
• utc_datetime: UTC time at hourly resolution.
• demand_mwh: Electricity demand for that state and hour in MWh. This is an allocation of the electricity demand reported directly in the FERC Form 714.
• scaled_demand_mwh: Estimated total electricity demand for that state and hour, in MWh. This is the reported FERC Form 714 hourly demand scaled up or down linearly such that the total annual electricity demand matches the total annual electricity sales reported at the state level in the EIA Form 861.

A collection of plots are also included, comparing the original and scaled demand time series for each state.

Acknowledgements

This analysis was funded largely by GridLab, and done in collaboration with researchers at the Lawrence Berkeley National Laboratory, including Umed Paliwal and Nikit Abhyankar.

• Ethan Welty wrote the final code and most of the algorithms.
• Yash Kumar did initial data explorations and geospatial analyses.

The data screening methods were originally designed to identify unrealistic data in the electricity demand timeseries reported to EIA on Form 930, and have been applied here to data form the FERC Form 714.

They are adapted from code published and modified by:

• Tyler Ruggles
• Greg Schivley

And described at:

• Developing reliable hourly electricity demand data through screening and imputation
• EIA Cleaned Hourly Electricity Demand Code (Zenodo)
• EIA Cleaned Hourly Electricity Demand Code (GitHub)

The imputation methods were designed for multivariate time series forecasting.

They are adapted from code published by:

• Xinyu Chen chenxy346@gmail.com

And described at:

• Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
• Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
• Tensor Learning (张量学习)

About PUDL & Catalyst Cooperative

For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• The PUDL Documentation
• Other Catalyst Cooperative data archives on Zenodo