The Canadian Wind Turbine Database contains the geographic location and key technology details for wind turbines installed in Canada. This dataset was jointly compiled by researchers at CanmetENERGY-Ottawa and by the Centre for Applied Business Research in Energy and the Environment at the University of Alberta, under contract from Natural Resources Canada. Additional contributions were made by the Department of Civil & Mineral Engineering at the University of Toronto. Note that total project capacity was sourced from publicly available information, and may not match the sum of individual turbine rated capacity due to de-rating and other factors. The turbine numbering scheme adopted for this database is not intended to match the developer’s asset numbering. This database will be updated in the future. If you are aware of any errors, and would like to provide additional information, or for general inquiries, please use the contact email address listed on this page.

This data provides locations and technical specifications of legacy versions (ver. 1.0 - ver. X.X) of the United States Wind Turbines database. Each release, typically done quarterly, updates the database with newly installed wind turbines, removes wind turbines that have been identified as dismantled, and applies other verifications based on updated imagery and ongoing quality-control. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), the American Wind Energy Association (AWEA), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single data set. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated and a confidence is recorded for both. None of the data are field verified. The current version is available for download at https://doi.org/10.5066/F7TX3DN0. The USWTDB Viewer, created by the USGS Energy Resources Program, lets you visualize, inspect, interact, and download the most current USWTDB version only, through a dynamic web application. https://eerscmap.usgs.gov/uswtdb/viewer/

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a homeowner or business.

The data formats downloadable from the Minnesota Geospatial Commons contain just the Minnesota turbines. Data, maps and services accessed from the USWTDB website provide nationwide turbines.

The regularly updated database has wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), the American Wind Energy Association (AWEA), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single data set.

Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in Esri ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated and a confidence is recorded for both. None of the data are field verified.

The U.S. Wind Turbine Database website provides the national data in many different formats: shapefile, CSV, GeoJSON, web services (cached and dynamic), API, and web viewer. See: https://eerscmap.usgs.gov/uswtdb/

The web viewer provides many options to search; filter by attribute, date and location; and customize the map display. For details and screenshots of these options, see: https://eerscmap.usgs.gov/uswtdb/help/

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This metadata record was adapted by the Minnesota Geospatial Information Office (MnGeo) from the national version of the metadata. It describes the Minnesota extract of the shapefile data that has been projected from geographic to UTM coordinates and converted to Esri file geodatabase (fgdb) format. There may be more recent updates available on the national website. Accessing the data via the national web services or API will always provide the most recent data.

This dataset is a unique compilation of field-based meteorological observations and wind power generation data, collected directly from one of our company's operational sites. The dataset represents a detailed hourly record, starting from January 2, 2017. This rich dataset provides real-world insights into the interplay between various weather conditions and wind energy production.

Context and Inspiration: The dataset was conceived out of the necessity to understand the dynamic relationship between meteorological variables and their impact on wind power generation. By collecting data directly from the field and the wind turbine installations, we aim to provide a comprehensive and authentic dataset that can be instrumental for industry-specific research, operational optimization, and academic purposes.

Data Collection: Data was meticulously gathered using state-of-the-art equipment installed at the site. The meteorological instruments measured temperature, humidity, dew point, and wind characteristics at different heights, while power generation data was recorded from the wind turbines' output. This dataset is a unique compilation of field-based meteorological observations and wind power generation data, collected directly from one of our company's operational sites. The dataset represents a detailed hourly record, starting from January 2, 2017. This rich dataset provides real-world insights into the interplay between various weather conditions and wind energy production.

Potential Uses: This dataset is ideal for industry experts, researchers, and data scientists exploring renewable energy, especially wind power. It can aid in developing predictive models for power generation, studying environmental impacts on renewable energy sources, and enhancing operational efficiency in wind farms.

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a house or business. The regularly updated database contains wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), American Clean Power (ACP) Association (formerly American Wind Energy Association (AWEA)), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single dataset. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A ...

The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. Wind turbine records are collected and compiled from various public and private sources, digitized and position-verified from aerial imagery, and quality checked.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. The creation of this database was jointly funded by the U.S. Department of Energy (DOE) Wind Energy Technologies Office (WETO) via the Lawrence Berkeley National Laboratory (LBNL) Electricity Markets and Policy Group, the U.S. Geological Survey (USGS) Energy Resources Program, and the American Clean Power Association (ACP). The database is being continuously updated through collaboration among LBNL, USGS, and ACP. Wind turbine records are collected and compiled from various public and private sources, digitized or position-verified from aerial imagery, and quality checked. Technical specifications for turbines are obtained directly from project developers and turbine manufacturers, or they are based on data obtained from public sources.

Methodology

The USWTDB combines a 2014 USGS data set (48,956 wind turbines, including decommissioned and duplicate turbines) with a 2017 LBNL data set (43,827 wind turbines) and includes regular updates from ACP's WindIQ as well as the Federal Aviation Administration (FAA) Digital Obstacle File (DOF) and Obstacle Evaluation Airport Airspace Analysis (OE-AAA). The USWTDB is updated as frequently as quarterly as new data become available and will lag installations by approximately one quarter.

All turbine points in the data set are visually verified using high-resolution aerial imagery in ESRI ArcGIS Desktop, and X/Y locations are manually moved to the base of the turbine with an estimated locational tolerance of 10 meters. Visual verification also enables identification and removal of duplicate turbine points and decommissioned turbines from the database, although some decommissioned turbines likely have not yet been identified and thus remain in the data set. Moreover, because of a lag in obtaining up-to-date aerial imagery, some turbine locations have not been visually verified.

Technical specifications for turbines are assigned based on turbine make and model as described in literature, specifications listed in the FAA DOF, and collected via ACP, LBNL, and turbine manufacturer websites. Because some make and model information does not exist or is difficult to obtain, uncertainty may exist for certain turbine specifications.

The uncertainties associated with location and attribute data quality are rated, and a confidence level is recorded. None of the data in the USWTDB are field verified.

Related Literature

Walker, C. (2020). Using the United States Wind Turbine Database to identify increasing turbine size, capacity and other development trends. https://doi.org/10.4236/epe.2020.127025

Miller, L. M., & Keith, D. W. (2018). Observation-based solar and wind power capacity factors and power densities. Environmental Research Letters, 13(10), 104008. https://doi.org/10.1088/1748-9326/aae102

Preziuso, D., Orrell, A., & Lantz, E. (2022). Categorizing distributed wind energy installations in the United States to inform research and stakeholder priorities. Energy, Sustainability and Society, 12(1), 31. https://doi.org/10.1186/s13705-022-00357-1

Diffendorfer, J. E., Vanderhoof, M. K., & Ancona, Z. H. (2022). Wind turbine wakes can impact down-wind vegetation greenness. Environmental Research Letters, 17(10), 104025. https://doi.org/10.1088/1748-9326/ac8da9

In this dataset the anther's analysis is based on data from NREL about Solar & Wind energy generation by operation areas.

NASA Prediction of Worldwide Energy Resources

Solar

Monthly averages for global horizontal radiation over 22-year period (Jul 1983

• Jun 2013)

Wind

Monthly average wind speed at 50m above the surface of earth over a 30-year period (Jan 1984 - Dec 2013)Year: Averaged Over 10 to 15 years

COA = central operating area.

EOA = eastern operating area.

SOA = southern operating area.

WOA = western operating area. Source: NRELSource Link

The database created encompasses a comprehensive collection of operational and environmental parameters from over seventy offshore wind farms situated in the Baltic, North, and Irish Seas. This dataset was compiled to analyze the impact of wind farm density on their efficiency and capacity factors. The objective was to support the development of a robust analytical framework capable of assessing the limitations and optimization potentials of offshore wind energy under varying geographic and climatic conditions.

Database Structure

The database consists of several key components structured to facilitate both broad and detailed analyses:

1. Wind Farm Characteristics:

• Name and location.
• Technical Specifications: Turbine power, hub height, rotor diameter, installed capacity (in MW), and total area (in km²).

1. Operational Data:

• Annual Production Data: Historical yearly energy output recorded, including sources.
• Capacity Factor Data: Real-world capacity factor based on the actual output compared to the theoretical maximum output.

1. Environmental and Technical Parameters:

• Wind Data: Average wind speed at given height, Weibull distribution parameters.

1. Model Inputs and Outputs:

• Finite wind-farm effect scaling parameter
• Predicted Capacity Factors: Results from the closed-form analytical model applied to each wind farm, estimating potential capacity factors under ideal, isolated, and infinite array conditions.

Data Collection Methodology

Data were primarily sourced from publicly available databases, technical reports and other sources. These are listed in a report.

A machine readable collection of documented wind siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled from several sources including, DOE's Wind Exchange Ordinance Database (Linked in the submission), National Conference of State and Legislatures Wind Energy Siting (also linked in the submission), and scholarly legal articles. The citations for each ordinance are included in the Wind Ordinances spreadsheet resource below. This data is an updated to a previously developed database of wind ordinances found in OEDI Submission 1932: "U.S. Wind Siting Regulation and Zoning Ordinances"

This dataset is a series of wind turbine data collected for the Wind for Schools project. The U.S. Department of Energy funded the Wind for Schools project, which helped develop a future wind energy workforce by encouraging students at higher education institutions to join Wind Application Centers and serve as project consultants for small wind turbine installations at rural elementary and secondary schools. The data are collected from the school wind turbine installations. The Wind for Schools OpenEI data project was archived at the end of July 2024. The data that was collected up that point is provided here as the resource "Wind For Schools Dataset".

This submission includes publicly available data extracted in its original form. If you have questions about the underlying data stored here, please contact WINDExchange (windcommunitybenefits@nrel.gov). "This searchable database reflects agreements, funds, donations, and other forms of benefits offered to communities by land-based and offshore wind energy developments in the U.S. compiled by the National Renewable Energy Laboratory (NREL) from 2022 to 2024. What Forms of Community Benefits Does This Database Include? Benefits to communities for wind energy projects can be structured in many ways, but the following categories are the most common and are the focus of this database: Formal Agreements signed by Developers, Local Governments, Tribal Governments, and/or Community Organizations: Developers and representatives of a government or community may sign an agreement stating the benefits that will be provided from a project and detailing the mechanisms and timelines for delivering benefits. Terminology may vary, depending on factors like the type of infrastructure or who the signatories are. Common names or types include community benefit agreement, host community agreement, good neighbor agreement, and Tribal benefit agreement. Payments to Local Governments Outside of a Formal Agreement: Developers may provide payments, donations, or other financial benefits to a local or Tribal government outside of the bounds of a formal agreement; these are often one-time payments. Funds Established by Developers: Developers may establish funds that distribute funding to different causes or recipients in the community over time, often through the form of grants. Terminology and structure may vary, with common names or types including community benefit fund, community fund, or scholarship fund. Direct Contributions to Local Priorities or Programs: Developers may directly donate or contribute to local organizations, programs, or causes in the community (e.g., schools, fire departments, community service organizations). What Forms of Community Benefits Are Not Included in This Database? Agreements and related forms of benefits may be provided alongside other agreements or economic impacts that serve different purposes, such as: Land lease payments to landowners that host wind turbines. Project labor agreements for construction of wind energy projects. Taxes or tax agreements like payment in lieu of taxes (PILOTs). Direct compensation to impacted stakeholders, such as commercial fishermen. This database does not include these other types of wind energy benefits, as they differ from agreements and related benefit mechanisms in several key ways; namely, the data included in this database are unrelated to taxation, are intended to provide benefits to the community as a whole rather than a specific group of people, like landowners, and are separate from impact mitigation measures required by permitting agencies." Quote from https://windexchange.energy.gov/projects/community-benefit-agreements

Overview

The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data include power, loads, and meteorological information from the turbine during startup, operation, and shutdown, and when it was parked and idle.

Data Details

Additional files are attached:
sumr_d_5-Min_Database.mat - a database file in MATLAB format of this dataset, which can be used to search for desired data files; sumr_d_5-Min_Database.xlsx - a database file in Microsoft Excel format of this dataset, which can be used to search for desired data files; loadcartU.m - this script loads in a CART data file and puts it in your workspace as a Matlab matrix (you can call this script from your own Matlab scripts to do your own analysis); charts.mat - this is a dependency file needed for the other scripts (it allows you to make custom preselections for cartPlotU.m); cartLoadHdrU.m - this script loads in the header file information for the data file (the header is embedded in each data file at the beginning); cartPlotU.m - this is a graphic user interface (GUI) that allows you to interactively look at different channels (to use it, run the script in Matlab, and load in the data file(s) of interest; from there, you can select different channels and plot things against each other; note that this script has issues with later versions of MATLAB; the preferred version to use is R2011b).

Data Quality

Wind turbine blade loading data were calibrated using blade gravity calibrations prior to data collection and throughout the data collection period. Blade loading was also checked for data quality following data collection as strain gauge measurements drifted throughout the data collection. These drifts in the strain gauge measurements were removed in post processing.

This data (csv file) provides simulated hourly time series of existing offshore wind generation for the regions shown in the attached map. Only regions with existing (by the time of modeling) offshore wind power plants are simulated (otherwise the data are NaN). The map shows the resulting capacity factors (annual mean). Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00_OFF is the aggregated offshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the ERA5-based simulation methodology. It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the modeling of wake losses and storm shutdown behaviour for the offshore wind power plants. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581

The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States. The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page. For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below. No filters have been applied to the raw WRF output.

Snapshot img

Wind Energy Market Size 2025-2029

The wind energy market size is forecast to increase by USD 70.9 billion at a CAGR of 8.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing awareness of environmental pollution and the global push towards renewable energy sources. However, the market faces substantial hurdles, with high upfront costs and investments required to establish wind energy projects. Energy policy and climate policy are shaping the market, pushing for grid parity and energy efficiency. Turbine efficiency is a key focus, with advancements in yaw control, torque control, and blade pitch enhancing power curve performance.
These financial constraints necessitate strategic planning and innovative financing models for companies seeking to capitalize on this market's potential. Navigating these challenges will be crucial for stakeholders looking to succeed in the market. Land use and turbine installation are also essential considerations, with power transmission infrastructure playing a crucial role in integrating wind power into the grid. Research and development in sustainable energy have led to the integration of battery energy storage and hydrogen storage for improved energy storage capabilities.

What will be the Size of the Wind Energy Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, meteorological data plays a crucial role in optimizing wind atlas analysis for site assessment. Circular economy principles are increasingly applied, with blade recycling and material recycling reducing operational costs and promoting green technology. Sustainable investing and green finance are driving the adoption of renewable energy portfolios, including both bottom-fixed and floating wind turbines.
Wind shear and wake effect management are essential for maximizing energy output from wind farms. Offshore substations are becoming more common, enabling larger wind farms and greater grid integration. Research and development in areas like battery energy storage, control systems, and condition monitoring are also crucial to optimizing energy yield and power output.

How is this Wind Energy Industry segmented?

The wind energy industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Onshore
  Offshore


End-user

  Industrial
  Commercial
  Residential


Component

  Turbines
  Support structures
  Electrical infrastructure
  Control systems
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The onshore segment is estimated to witness significant growth during the forecast period. Wind power has experienced significant advancements in the last decade, driving down production costs by half for new onshore projects. This economic shift has positioned wind power as the most cost-effective source of electricity generation globally. Sweden, for instance, has set ambitious targets to expand onshore wind energy, with wind temporarily surpassing traditional sources in December 2024. In this record-breaking year, wind energy generated 40.8 TWh, accounting for a quarter of the nation's electricity mix, up from 22% in 2023. During this period, wind covered 35% of Sweden's electricity demand, underscoring its growing importance. Technological innovations have played a pivotal role in this progress.

For example, blade manufacturing has evolved with the use of carbon fiber, enhancing durability and energy yield. Wind turbine design has advanced, with rotor dynamics and control systems optimized for increased power output and grid integration. Environmental regulations have also influenced the wind power industry, with a focus on climate change mitigation and carbon emissions reduction. Wind energy associations have advocated for renewable portfolio standards and condition monitoring, ensuring wind farms operate efficiently and adhere to environmental guidelines.

Offshore wind has emerged as a promising sector, with offshore installation and capacity factor improvements contributing to increased power output. Despite these advancements, challenges remain. Wind direction and wind speed variability, noise pollution, and public acceptance are critical concerns.

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The Onshore segment was valued at USD 87.00 billion in 2019 and showed a gradual increase during the forecast period.

The Wind Energy Market is rapidly expanding as nations invest in sustainable pow

This is the data set behind the Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period.

This data set provides industrial-scale onshore wind turbine locations, corresponding facility information, and turbine technical specifications, in the United States to March 2014. The database has nearly 49,000 wind turbine records that have been collected, digitized, locationally verified, and internally quality assured and quality controlled. Turbines from the Federal Aviation Administration Digital Obstacle File, product date March 2, 2014, were used as the primary source of turbine data points. Verification of the position of turbines was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacle Repository System (FAA ORS) numbers were visually identified and supplemental points were added to the collection. A locational error of plus or minus 10 meters for turbine positions was estimated. Wind farm facility names were identified from publicly available facility data sets. Facility names were then used in a web search of additional industry publications and press releases to attribute additional turbine information (such as manufacturer, model, and technical specifications of wind turbines). Wind farm facility location data from various wind and energy industry sources were used to search for and digitize turbines not in existing databases. Technical specifications assigned to were based on the make and model as described in literature, in the Federal Aviation Administration Digital Obstacle File, and information from the turbine manufacturers' websites. Some facility and turbine information did not exist or was difficult to obtain. Thus, uncertainty may be present. That uncertainty was rated and a confidence was recorded for both location and attribution data quality.

The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. The creation of this database was jointly funded by the U.S. Department of Energy (DOE) Wind Energy Technologies Office (WETO) via the Lawrence Berkeley National Laboratory (LBNL) Electricity Markets and Policy Group, the U.S. Geological Survey (USGS) Energy Resources Program, and the American Clean Power Association (ACP). The database is being continuously updated through collaboration among LBNL, USGS, and ACP. Wind turbine records are collected and compiled from various public and private sources, digitized or position-verified from aerial imagery, and quality checked. Technical specifications for turbines are obtained directly from project developers and turbine manufacturers, or they are based on data obtained from public sources.Data accessed from here: https://eerscmap.usgs.gov/uswtdb/

The USGS United States Wind Turbine Database (USWTDB) holds data which provide the locations of land based and offshore wind turbines in the United States as well as corresponding wind project information and turbine technical specifications. The data are available on this page in a variety of tabular and geospatial file formats; cached and dynamic web services are available for users that which to access the USWTDB as a Representational State Transfer Services (RESTful) web service.The methods of data collection and related publications are available on this page as well to inform users of the data compilations and other related data sources.