State-level data on all energy sources. Data include production, consumption, reserves, stocks, prices, imports, and exports. Data are collated from state-specific data reported elsewhere on the EIA website and are the most recent values available. The system provides data back from 1960. While some SEDS data series come directly from surveys conducted by EIA, many are estimated using other available information. These estimations are necessary for the compilation of "total energy" estimates. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

State Energy Data System (SEDS): 2023 Updates by energy source

Data for 2023 energy consumption, prices, expenditures, and indicators estimates by source. From the U.S. Energy Information Administration (EIA). Original download was from this site: https://www.eia.gov/state/seds/seds-data-fuel-prev.php but the data tables were downloaded on 12-Feb-2025 and the data on the website are from 27-June-2025 (as of 19-Sep-2025).

The New York State Energy Research and Development Authority (NYSERDA) hosts a web-based Distributed Energy Resources (DER) integrated data system at https://der.nyserda.ny.gov/. This site provides information on DERs that are funded by and report performance data to NYSERDA. Information is incorporated on more diverse DER technology as it becomes available. Distributed energy resources (DER) are technologies that generate or manage the demand of electricity at different points of the grid, such as at homes and businesses, instead of exclusively at power plants, and includes Combined Heat and Power (CHP) Systems, Anaerobic Digester Gas (ADG)-to-Electricity Systems, Fuel Cell Systems, Energy Storage Systems, and Large Photovoltaic (PV) Solar Electric Systems (larger than 50 kW). Historical databases with hourly readings for each system are updated each night to include data from the previous day. The web interface allows users to view, plot, analyze, and download performance data from one or several different DER sites. Energy storage systems include all operational systems in New York including projects not funded by NYSERDA. Only NYSERDA-funded energy storage systems will have performance data available. The database is intended to provide detailed, accurate performance data that can be used by potential users, developers, and other stakeholders to understand the real-world performance of these technologies. For NYSERDA’s performance-based programs, these data provide the basis for incentive payments to these sites. 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. 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 https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Consumption data for coal, petroleum, and natural gas are multiplied by their respective thermal conversion factors, which are in units of heat energy per unit of fuel consumed (i.e., per cubic foot, barrel, or ton), to calculate the amount of heat energy derived from fuel combustion. The thermal conversion factors are given in Appendix A of each issue of Monthly Energy Review, published by the Energy Information Administration (EIA) of the U.S. Department of Energy (DOE). Results are expressed in terms of heat energy obtained from each fuel type. These energy values were obtained from the State Energy Data Report (EIA, 2003a), ( http://www.eia.doe.gov/emeu/states/sep_use/total/csv/use_csv.html), and served as our basic input. The energy data are also available in hard copy from the Energy Information Administration, U.S. Department of Energy, as the State Energy Data Report (EIA, 2003a,b).For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/trends/emis_mon/stateemis/emis_state.html

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

This dataset contains detailed information on geothermal district heating (GDH) systems across the United States, including technical specifications, costs, energy usage, and funding sources. Data includes system details such as state, site, location, operational years, temperature, flow rate, capacity, energy use, load factor, costs (original, updated, and operational), funding sources, employment impact, and project status. Additionally included are contributions to GDH systems, including federal, state, and local grants, as well as loans from agencies such as USDA and USDOE. This dataset serves as a resource for analyzing the economic and technical landscape of geothermal district heating in the United States.

Map by zip code of Green Energy Program Grants. These grants provide funding to Delmarva Power customers through the Green Grant Delaware program for purchase and installation of renewable energy sources such as photovoltaic (PV), geothermal, wind, and solar water heater systems.

Two years (2019, 2020) on-site data with time granularity of 15 minutes of six wind farms and eight solar stations in China was recorded, which includes weather conditions and power generation information.

This dataset presents the accumulated data for use in assessment of solar energy potential on industrial roofs in an insular island-state. The data were obtained with the use of two government portals, being the Department of land and surveys web portal Cyprus and national open data portal of Cyprus. The extracted data were processed in the ArcGIS Pro software. The obtained data were processed in order to be used for the calculation of the solar energy potential of industrial roofs in an insular island-state. Data regarding each investigated case for building identity, industrial zones, sheets, plan, parcel number, parcel area, roof area, orientation, inclination angle and photovoltaics installation are provided.

THE NYSERDA 2023 SOILS DATA IS TO BE USED FOR NYSERDA’S RENEWABLE ENERGY STANDARD (RES) REQUEST FOR PROPOSAL (RFP) ISSUED AFTER THE PUBLICATION OF THIS DATA OR THE NY-SUN PROGRAM AND IS NOT INTENDED TO REPRESENT ACTUAL IN SITU SOIL CONDITIONS. In order to facilitate the protection of agricultural lands, developers participating in RESRFPs or the NY-Sun program may be responsible for making an agricultural mitigation payment to a designated fund based on the extent to which the solar project’s facility area overlaps with an Agricultural District and New York’s highly productive agricultural soils, identified as Mineral Soil Groups (MSG) classifications 1 through 4 (MSG 1-4). This mitigation approach is designed to discourage solar projects from siting on MSG 1-4. Furthermore, this mitigation approach is designed to encourage retaining agricultural productivity on the project site. Instances where Proposers cannot avoid or minimize impacts on MSG 1-4 will result in a payment to a fund administered by NYSERDA. Disbursement of collected agricultural mitigation payment funds will be informed by consultation with the New York State Department of Agriculture and Markets (AGM) to support ongoing regional agricultural practices and/or soil conservation initiatives. This dataset contains a combination of soils data from multiple sources to serve participants of NYSERDA’s Large-Scale Renewable and NY-Sun programs. The NYSERDA 2023 Soils Data was created by converting the 2023 New York State Agricultural Land Classification (https://agriculture.ny.gov/system/files/documents/2023/01/masterlistofagriculturalsoils.pdf) master list of soils maintained by AGM to a tabular form and providing a corresponding unique identifier for each listed soil that enables the user to link the soils to the Natural Resources Conservation Service (NRCS) SSURGO soils database, allowing for a geographical representation. When the NYSERDA 2023 Soils Data is joined with spatial data from the Natural Resources Conservation Service (NRCS) SSURGO soils database (https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627), the corresponding soil unit can be mapped in a geographic information system software. The latest version of the SSURGO database (https://nrcs.app.box.com/v/soils) should be used to get the most accurate join. Data is updated yearly from both NRCS and from AGM, however, NYSERDA will not update this dataset and it will remain intact for future reference. NYSERDA intends on creating new soils datasets for future procurements on an annual basis. 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.

Three categories of data are included in this dataset: Measurement Datasets; Forecast Datasets; Dimension Datasets. The Measurement Datasets include the following: 1) Timeseries Database Table: t_msb1m (resolution 1-min): Sonnen data recorded from the 'MySonnenBatterie' (MSB) portal, including a range of Energy records and State of Charge data. 2) Timeseries Database Table: t_ims1m (resolution 1-min): Independent Monitoring System (IMS), consisting of Energy, Power, Grid Voltage, Grid Frequency & Grid Power. 3) Timeseries Database Table: t_ims1s (resolution 1-sec): Independent Monitoring System (IMS), consisting of Power (PV, Grid, BESS) and Grid Frequency. The Forecast Datasets include the following: 1) Timeseries Database Table: t_prodconsforecasts (resolution 1-hr): Production & Consumption Forecasts from Sonnen, via Sonnen VPP. 2) Timeseries Database Table: t_weatherforecasts (resolution 1-hr): Weather Forecasts from external provider, via the MSB portal, including precipitation, solar irradiance and sunshine hours, available at Post Code District level. The Dimension Datasets include the following: 1) Database Table: t_sites: Site List, linking the persistent site ID, to Battery Energy Storage System (BESS) type, BESS power/energy capacities, photovoltaic (PV) capacity and major load type. 2) Additional MetaData: including the following: a) Original Web Questionnaire; completed by respondents from the original recruitment exercise - results available for 66 sites; b) Desktop & Site Surveys, Design & System Costs; completed by SunGift (Installation Contractor) on behalf of Sonnen (BESS Vendor) - results available for 71 sites; c) Summary of EPC data; data retrieved from the EPC register[1] completed by TLC - results available for 75 sites.

A more complete description is provided in the Data Dictionary document.

[1] EPC Register (England & Wales), https://www.epcregister.com/, accessed October 2020

The Cornwall Local Energy Market (LEM) is a £17m programme that sought to design, build and trial an auction-based flexibility market for distribution network-connected decentralised energy resources (DER). The programme included the creation domestic and commercial scale DER ecosystems that formed part of the LEM trials. The domestic DER ecosystem was made up of 100 private homes, distributed across Cornwall. Each home received a Sonnen battery and solar PV system (where the home did not have an existing PV system). This data deposit and associated reports constitutes the research findings from the domestic DER workstream

This data is usually updated quarterly by February 1st, May 1st, August 1st, and November 1st.The CEC Power Plant geospatial data layer contains point features representing power generating facilities in California, and power plants with imported electricity from Nevada, Arizona, Utah and Mexico.The transmission line, substation and power plant mapping database were started in 1990 by the CEC GIS staffs. The final project was completed in October 2010. The enterprise GIS system on CEC's critical infrastructure database was leaded by GIS Unit in November 2014 and was implemented in May 2016. The data was derived from CEC's Quarterly Fuel and Energy Report (QFER), Energy Facility Licensing (Siting), Wind Performance Reporting System (WPRS), and Renewable Energy Action Team (REAT). The sources for the power plant point digitizing are including sub-meter resolution of Digital Globe, Bing, Google, ESRI and NAIP aerial imageries, with scale at least 1:10,000. Occasionally, USGS Topographic map, Google Street View and Bing Bird's Eye are used to verify the precise location of a facility.Although a power plant may have multiple generators, or units, the power plant layer represents all units at a plant as one feature. Detailed attribute information associated with the power plant layer includes CEC Plant ID, Plant Label, Plant Capacity (MW), General Fuel, Plant Status, CEC Project Status, CEC Docket ID, REAT ID, Plant County, Plant State, Renewable Energy, Wind Resource Area, Local Reliability Area, Sub Area, Electric Service Area, Service Area Category, California Balancing Authorities, California Air District, California Air Basin, Quad Name, Senate District, Assembly District, Congressional District, Power Project Web Link, CEC Link, Aerial, QRERGEN Comment, WPRS Comment, Geoscience Comment, Carto Comment, QFERGEN Excel Link, WPRS Excel Link, Schedule 3 Excel Link, and CEC Data Source. For power plant layer which is joined with QFer database, additional fields are displayed: CEC Plant Name (full name), Plant Alias, EIA Plant ID, Plant City, Initial Start Date, Online Year, Retire Date, Generator or Turbine Count, RPS Eligible, RPS Number, Operator Company Name, and Prime Mover ID. In general, utility and non-utility operated power plant spatial data with at least 1 MW of demonstrated capacity and operating status are distributed. Special request is required on power plant spatial data with all capacities and all stages of status, including Cold Standby, Indefinite Shutdown, Maintenance, Non-Operational, Proposed, Retired, Standby, Terminated, and Unknown.For question on power generation or others, please contact Michael Nyberg at (916) 654-5968.California Energy Commission's Open Data Portal.

This submission defines a DOI for the Great Basin Center for Geothermal Energy's (GBCGE) Subsurface Database Explorer web application and underlying data services, and acknowledges the INGENIOUS project as a major source of funding for data compilation and quality assurance.

The GBCGE Subsurface Database Explorer is an interactive web mapping application that provides public access to the GBCGE Subsurface Database, and its collection of datasets pertinent to geothermal exploration, oil and gas exploration, critical mineral exploration, and other subsurface characterization for the Great Basin Region, western US.

This is a living database, and will be continuously updated with new data and datasets as funding and motivations allow. The underlying database views that populate the web application are on an automated refresh schedule.

Data sources and acknowledgements:

We thank our partners with the Nevada Division of Minerals (NDOM), the Southern Methodist University (SMU), and Great Basin State Geological Surveys for their active efforts in data curation, schema design, and quality assurance. We also thank contributors among the USGS, Oregon Institute of Technology, State Divisions of Water Resources, State Divisions of Oil, Gas, and Minerals, and State Geological Surveys for open data availability and direct contributions made under the National Geothermal Data System (NGDS).

This data submission contains raw and near-real-time updated data from FLOATr (Fixed Location Ocean and Atmosphere Tracking) buoys, Sofar Spotter wave buoys, and Nexsens meteorological buoys at sites in the PacWave open-ocean testing facility operated by Oregon State University, located off the coast of Newport, Oregon.

There are two sites located at PacWave, aptly named PacWave North (PWN) and PacWave South (PWS). PWN is an autonomous test site located 2 nm offshore, has a water depth of 45-55 m, and is between 44.68 & 44.70 degrees North and 124.12 & 124.15 degrees West. PWS is a grid-connected test site located 6 nm offshore, has a water depth of 65-78 m, and is between 44.55 & 44.58 degrees North and 124.21 & 124.24 degrees West.

The FLOATr buoys provide meteorological measurements of wind speed and direction, air temperature and pressure, shortwave radiation (light). An onboard CTD (conductivity-temperature-depth) sensor (Seabird SBE 37-SM MicroCAT) provides measurements of water temperature, salinity, and dissolved oxygen. Down-looking ADCPs (RDI Workhorse 600 kHz) installed on the FLOATr buoys provide observations of water velocity. Buoys are named with a 3 digit number that increases for each deployment.

The wave buoys (Spotter and Nexsens) provide measurements of standard and directional wave statistics as well as additional metocean variables, depending on the firmware version installed. Data are provided in the original json file format as pulled from the cloud API, and processed data are provided in netCDF4 format. Raw Spotter CSV datafiles are uploaded sporadically as the buoys are recovered and SD cards retrieved. Nexsens buoys are named with a 3 digit number that increases for each deployment.

Bottom deployments of Nortek Signature250 ADCPs were deployed between Apr 2024 - Jul 2025 measuring water velocity and surface waves. These data are provided in the raw native ADCP format and processed files in netCDF format.

Processed data are provided in netCDF4 format based on Integrated Ocean Observing System (IOOS) standards. Note, minimal quality control has been conducted on these data.

Processed and Raw data can be accessed via the "PacWave Observation Data on AWS" resource below. For links to specific datasets see the "PacWave Data Structure Table" resource.

Raw data from the FLOATr buoys are stored in CSV files with the following filenames: - ADCP.dat (subsampling of ADCP binary data - Teledyne Sentinel Workhorse 300khz) - Airmar_buffer.dat (Airmar WX200 instrument serial data buffer) - gga.dat (gps Degree & Decimal Minutes) - hdg.dat (magnetic heading, deviation, variation) - hdt.dat (heading true) - mda.dat (meteorological composite) - Met.dat (multiple data values from various sources (instruments, nmea strings) into a single data table) - best for quick data checks - mwv_r.dat (calculated mean wind velocity_relative) - mwv_t.dat (calculated mean wind velocity_true) - Ocean.dat (CTD data - Seabird SBE16, temp, conductivity/salinity, 02) - zda.dat - (time and date)

Raw data from the FLOATr ADCPs and CTDs are made available in their native binary format. Telemetry data from the Spotter and Nexsens buoys is stored in json strings containing wave statistics and basic meteorological parameters.

Raw data from the Spotter buoys is stored in CSV files with the following filenames: - BARO.csv (barometric pressure) - FLT.csv (processed buoy displacement: surge, sway, and heave) - GMN.csv (raw GPS parameters) - HDR.csv (raw buoy position) - HTU.csv (air temperature and humidity) - LOC.csv (GPS latitude and longitude) - PWR.csv (power data) - SPC.csv (buoy-derived auto- and cross-spectra * note frequency vector is missing) - SST.csv (sea surface temperature) - *.log (various log files)

This data layer contains geothermal resource areas and their technical potential used in long-term electric system modeling for Integrated Resource Planning and SB 100. Geothermal resource areas are delineated by Known Geothermal Resource Areas (KGRAs) (Geothermal Map of California, 2002), other geothermal fields (CalGEM Field Admin Boundaries, 2020), and Bureau of Land Management (BLM) Geothermal Leasing Areas (California BLM State Office GIS Department, 2010). The fields that are considered in our assessment have enough information known about the geothermal reservoir that an electric generation potential was estimated by USGS (Williams et al. 2008) or estimated by a BLM Environmental Impact Statement (El Centro Field Office, 2007). For the USGS identified geothermal systems, any point that lies within 2 km of a field is summed to represent the total mean electrical generation potential from the entire field.

Geothermal field boundaries are constructed for identified geothermal systems that lie outside of an established geothermal field. A circular footprint is assumed with a radius determined by the area needed to support the mean resource potential estimate, assuming a 10 MW/km² power density.

Several geothermal fields have power plants that are currently generating electricity from the geothermal source. The total production for each geothermal field is estimated by the CA Energy Commission’s Quarterly Fuel and Energy Report that tracks all power plants greater than 1 MW. The nameplate capacity of all generators in operation as of 2021 were used to inform how much of the geothermal fields are currently in use. This source yields inconsistent results for the power plants in the Geysers. Instead, an estimate from the net energy generation from those power plants is used. Using these estimates, the net undeveloped geothermal resource potential can be calculated.

Finally, we apply the protected area layer for geothermal to screen out those geothermal fields that lie entirely within a protected area. The protected area layer is compiled from public and private lands that have special designations prohibiting or not aligning with energy development.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log:

Version 1.1 (January 18, 2024)

• ProtectedArea_Exclusion field was updated to correct for the changes to the Protected Area Layer. A Development Focus Area on Bureau of Land Management (BLM) land that overlays the Coso Hot Springs allows its resource potential to be considered in the statewide estimate.

Data Dictionary:

Total_MWe_Mean: The estimated resource potential from each geothermal field. All geothermal fields, except for Truckhaven, was given an estimate by Williams et al. 2008. If more than one point resource intersects (within 2km of) the field, the sum of the individual geothermal systems was used to estimate the magnitude of the resource coming from the entire geothermal field. Estimates are given in MW.

Total_QFER_NameplateCapacity: The total nameplate capacities of all generators in operation as of 2021 that intersects (within 2 km of) a geothermal field. The resource potential already in use for the Geysers is determined by Lovekin et al. 2004. Estimates are given in MW.

ProtectedArea_Exclusion: Binary value representing whether a field is excluded by the land-use screen or not. Fields that are excluded have a value of 1; those that aren’t have a value of 0.

NetUndevelopedRP: The net undeveloped resource potential for each geothermal field. This field is determined by subtracting the total resource potential in use (Total_QFER_NameplateCapacity) from the total estimated resource potential (Total_MWe_Mean). Estimates are given in MW.

Acres_GeothermalField: This is the geodesic acreage of each geothermal field. Values are reported in International Acres using a NAD 1983 California (Teale) Albers (Meters) projection.

References:

1. Geothermal Map of California, S-11. California Department of Conservation, 2002. https://www.conservation.ca.gov/calgem/geothermal/maps/Pages/index.aspx
2. CalGEM Field Admin Boundaries, 2020. https://gis.conservation.ca.gov/server/rest/services/CalGEM/Admin_Bounds/MapServer
   
3. California BLM State Office GIS Department, California BLM Verified and Potential Geothermal Leases in California, 2010. https://databasin.org/datasets/5ec77a1438ab4402bf09ef9bfd7f04d9/
   
4. Williams, Colin F., Reed, Marshall J., Mariner, Robert H., DeAngelo, Jacob, Galanis, S. Peter, Jr. 2008. "Assessment of moderate- and high-temperature geothermal resources of the United States: U.S. Geological Survey Fact Sheet 2008-3082." 4 p. https://certmapper.cr.usgs.gov/server/rest/services/geothermal/westus_favoribility_systems/MapServer/0
5. El Centro Field Office, Bureau of Land Management (2007). Final Environmental Impact Statement for the Truckhaven Geothermal Leasing Area (Publication Index Number: BLM/CA/ES-2007-017+3200). United States Department of the Interior Bureau of Land Management.
6. Lovekin, James W., Subir K. Sanyal, Christopher W. Klein. 2004. “New Geothermal Site Identification and Qualification.” Richmond, California:

GIS data for India's direct normal irradiance (DNI) and global horizontal irradiance (GHI). This dataset provides 10-kilometer (km) solar resource maps and data for India. The 10-km hourly solar resource data were developed using weather satellite (METEOSAT) measurements incorporated into a site-time specific solar modeling approach developed at the U.S. State University of New York at Albany. The data is made publicly available in geographic information system (GIS) format (shape files etc).

The new maps and data were released in June 2013. The new data expands the time period of analysis from 2002-2007 to 2002-2011 and incorporates enhanced aerosols information to improve direct normal irradiance (DNI). These products were developed by the U.S. National Renewable Energy Laboratory (NREL) in cooperation with India's Ministry of New and Renewable Energy, through funding from the U.S. Department of Energy and U.S. Department of State.

For updated solar data please see the "National Solar Radiation Database" link below.