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.

Simulated capacity factors in Finland for six wind turbine models, Vestas V90-3.0 MW, V90-2.0 MW, V112-3.3 MW, V126-3.3 MW, V117-3.45 MW and V136-3.45 MW at four turbine hub heights 75, 100, 125, 150 m. Wind speed data are from Finnish Wind Atlas [1, 2], from which the Weibull distribution shape and scale parameters (labelled ‘Weibull all data k’ and ‘Weibull all data A’, respectively) and the frequencies of the wind sectors (‘Frequency all data’) were used.

File FWA_coordinates_2500m.csv holds the geographical coordinates (WGS 84) of the Wind Atlas in 2.5×2.5 km2 resolution.

To simulate a wind farm where each turbine experiences a slightly different wind speed, we used a normal distribution with variance (\sigma^2(v) = 0.2v + 0.6\,\mathrm{m/s}), (where v is wind speed) to smooth (convolute) the original power curves [3, 4].

The calculation of capacity factor cf at wind atlas grid point k is described by the formula

(\mathit{CF}_k = \mathop{\mathbb{E}}_{i, s} g(v_i) \approx \sum_{s=1}^{12} f_{k,s} \sum_{i=1}^N p_{k,s}(v_i) g(v_i) \Delta v),

where g(v) is the power curve function for current wind turbine model, vi the mean wind speed of bin i, fk,s the frequency of occurrence of wind direction s at point k, N the number of wind speed bins, pk,s(v) the Weibull probability density function for sector s at point k at the hub height and Δv the width of the wind speed bin.

References

1. Finnish Meteorological Institute, “Finnish Wind Atlas,” 2008. [Online]. Available: http://www.windatlas.fi. [Accessed: 28-Jun-2016]
2. B. Tammelin, T. Vihma, E. Atlaskin, J. Badger, C. Fortelius, H. Gregow, M. Horttanainen, R. Hyvönen, J. Kilpinen, J. Latikka, K. Ljungberg, N. G. Mortensen, S. Niemelä, K. Ruosteenoja, K. Salonen, I. Suomi, and A. Venäläinen, “Production of the Finnish Wind Atlas,” Wind Energy, vol. 16, no. 1, pp. 19–35, Jan. 2013.
3. Staffell, Iain, and Richard Green. 2014. “How Does Wind Farm Performance Decline with Age?” Renewable Energy 66. Elsevier Ltd: 775–86. doi:10.1016/j.renene.2013.10.041.
4. Staffell, Iain, and Stefan Pfenninger. 2016. “Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output.” Energy 114 (November): 1224–39. doi:10.1016/j.energy.2016.08.068.

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/

In 2023, around 425.2 terawatt hours of wind electricity were generated in the United States. Wind has advanced to become the main source of renewable power generation in the U.S., ahead of conventional hydropower.

Clean energy on the rise

Recent years have seen significant increases in U.S. clean energy investments, specially the years between 2020 and 2022. In 2022, renewable investments rose to 141 billion U.S. dollars, an increase of almost 25 percent compared to the previous year. Larger investments in clean energy in the past decade have brought higher generation of wind and solar power.

The globalized U.S. wind market

Based in Copenhagen, the Danish company Vestas holds a large portion of the global wind manufacturer market share. In 2923, Vestas electricity deliveries were the highest to the U.S. Though the U.S. has generated increasing amounts of wind power, it continues to source much of its wind power turbines and equipment from international companies such as Vestas.

The Global Wind Power Tracker (GWPT) is a worldwide dataset of utility-scale wind facilities. It includes wind farm phases with capacities of 10 megawatts (MW) or more. A wind project phase is generally defined as a group of one or more wind turbines that are installed under one permit, one power purchase agreement, and typically come online at the same time. The GWPT catalogs every wind farm phase at this capacity threshold of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each wind farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Wind Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual wind farm phases and includes information such as project owner, status, installation type, and location. A wiki page for each wind farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Wind Power Tracker data set draws on various public data sources, including:

• Government data on individual power wind farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking wind farm permits and applications;
• Reports by power companies (both state-owned and private);
• News and media reports;
• Local non-governmental organizations tracking wind farms or permits.

Global Energy Monitor researchers perform data validation by comparing our dataset against proprietary and public data such as Platts World Energy Power Plant database and the World Resource Institute’s Global Power Plant Database, as well as various company and government sources.

Wiki Pages

For each wind farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a wind farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

The data in this repository consists of 4 files. This includes a readme file [readme.txt], a file summarizing the wind speed [All_Windspeed_Data.csv], a file for the resulting power outputs [All_Power_Data.csv],and a zip-file including detailed data for each wind farm [Data_Per_Wind_Farm.zip]. Each file can be downloaded seperatly or colectivly by clicking the "Download all"-Button.The structure of this repository is as follows:├── readme.txt (this file)├── All_Power_Data.csv (Power time series of wind farms)├── All_Windspeed_Data.csv (Windspeed time series of wind farms)├── Data_Per_Wind_Farm (folder including csv-files for each wind farm) ├── Baie_de_Saint_Brieuc ├── Baltic_Eagle ├── Beatrice ├── Borkum_Riffgrund ├── Borssele_(Phase_1,2) ├── Borssele_(Phase_3,4) ├── Dieppe_et_Le_Treport ├── Dogger_Bank_(Phase_A,B) ├── East_Anglia_One ├── Gemini ├── Gode_Wind ├── Greater_Gabbard ├── Gwynt_y_Mor ├── Hautes_Falaises ├── Hohe_See ├── Hollandse_Kust_Noord ├── Hollandse_Kust_Zuid ├── Horns_Rev ├── Hornsea_(Project_1) ├── Hornsea_(Project_2) ├── Iles_dYeu_et_de_Noirmoutir ├── Kriegers_Flak ├── London_Array ├── Moray_Firth ├── Race_Bank ├── Seagreen ├── Seamade ├── Triton_Knoll ├── WalneyIn the 29 files included in the zip-file [Data_Per_Wind_Farm.zip], we report detailed data for each wind farm. Therein, each column includs one variable while each row represents one point in time. Namely, the columns contain:- time- u-component of wind 100m above ground- v-component of wind 100m above ground- forecasted surface roughness (fsr)- scaled windspeed at hub heigts (heigt given in parentheses - multiple time series possible)- Wind direction in degrees- Power of wind turbines (type given in parentheses - multiple time series possible)- Turn_off (0: turbine turned off because of strong winds, 1: turbines active)- Power (resulting power output of wind farm over all turbine types).Starting from January 1, 1980, 00:00 am UTC in the first row, the data set ranges up to December 31, 2019, 11:00 pm in the last of 350640 rows.Similar to the detailed files per wind farm, each row in the two csv files [All_Power_Data.csv , All_Windspeed_Data.csv] reporting wind speed at hub height and total power represent one point in time for the same period.In the [All_Power_Data.csv] each row gives the sythetic resulting power outout in MW of one wind farm. I.e., the dataset includes 29 columns one for each wind farm. In the [All_Windspeed_Data.csv] each row gives the calculated windspeed im 100m above ground in m/s at the position of each wind farm. I.e., the dataset includes 29 columns one for each wind farm. Data generated using Copernicus Climate Change Service information [1980-2019] and containing modified Copernicus Climate Change Service information [1980-2019].

Data created from the combination of Wind Turbine Data created by the FAA and USGS. Duplicate turbines were removed. Below is the discription for each.FAA - Point shapefile of Wind Turbine locations in Wyoming as of December 2010. Turbine location and attribute data extracted from the FAA Obstruction Evaluation Database (https://oeaaa.faa.gov/oeaaa/external/portal.jsp). The FAA requires evaluation of any wind turbine (or structure) over 200ft. Therfore any wind turbine under 200ft would not be included in this data layer. In 2008 the FAA put a focus on wind turbines creating region categories specifically for wind turbines. With this, records for wind turbines before 2008 are less inclusive. The FAA database provides no determination of Wind Turbines that have been built and Wind Turbines that are still in planning stages. Therefore this layer includes Wind Turbines that may not be constructed yet but are in planning/development stages.USGS - The Wyoming wind turbine data set was developed for the project "Seasonal predictive habitat models for Greater Sage-grouse in Wyoming". This project is aimed at developing spatially-explicit seasonal distribution models for Sage-Grouse in Wyoming, which will provide resource managers tools for conservation planning. These specific data are being used for assessing the impact of disturbance resulting from wind energy development within Wyoming on sage-grouse populations. Additionally, this data will also support the Wyoming Landscape Conservation Initiative (WLCI). WLCI is a long-term, science-based, collaborative effort to ensure that the Southwest Wyoming's wildlife and its habitats are sustained over time with increased land-use pressures. Additional information about WLCI can be found at www.wlci.gov or in the Wyoming Landscape Conservation Initiative Science Workshop Proceedings (U.S. Geological Survey Scientific Report 2008-5073 found on-line at http://pubs.usgs.gov/sir/2008/5073/). These data represent locations of wind turbines found within Wyoming as of 08/01/2009. The attributes are estimates based on what information could be found via American Wind Energy Association (AWEA) and miscellaneous on-line reports. Caution should be used when using the data attributes. The locations are derived from NAIP August 2009 true color imagery and have a positional accuracy of approximately +/-5 meters. Because some wind turbines were under construction, under construction wind turbine locations will likely be less accurate, and therefore caution is required while using these data.

The American company, GE Wind, was the largest wind turbine manufacturer in the United States in terms of installation capacity at around 4,918 megawatts in 2022. With a total capacity of approximately 2,048 megawatts, the Danish manufacturer, Vestas, ranked second in that year.

General Electric invests in renewables

GE Wind is a branch from the General Electric division, GE Renewable Energy. Created in 2015, GE Renewable combined the wind power assets of two GE purchases, and the headquarters was moved from upstate New York to Paris, France. Though based abroad, GE Wind remains the only American company among the leading wind turbine manufacturers in the United States.

The globalized wind turbine market

With its extensive overseas operations, Copenhagen-based Vestas dominates the international wind turbine manufacturer market with over 20 percent of the market share. In 2022, Vestas delivered more electricity to the United States than to any other country, followed by deliveries to Brazil. The United States has the second highest cumulative installed capacity of wind power worldwide after China, but still relies heavily on international manufacturers such as Vestas.

Wind turbine data for completed Alaska wind energy projects. Data includes the wind farm operator, the number of turbines and model, rated power and output, commission date, project cost, and power output type. Source: Alaska Energy Authority, Alaska Industrial Development and Export Authority

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Energy Authority Wind Program Overview

Wind turbine condition monitoring (CM) can potentially help the wind industry reduce turbine downtime and operation and maintenance (O&M) cost. NREL CM research has investigated various condition-monitoring techniques such as acoustic emission (AE specifically stress wave), vibration, electrical signature, lubricant and debris monitoring based on the Gearbox Reliability Collaborative dynamometer and field tests, and other test turbines and resources accessible by NREL. During the past several years, NREL CM research has shown that there are very few validation and verification efforts on commercial wind turbine CM systems. One of the reasons might be limited benchmarking datasets accessible by stakeholders. To fill this gap, NREL executed a data collection effort. The targeted users of these datasets include those investigating vibration-based wind turbine CM research, evaluating commercially available vibration-based CM systems, or testing prototyped vibration-based CM systems. NREL collected data from a healthy and a damaged gearbox of the same design tested by the GRC. Vibration data were collected by accelerometers along with high-speed shaft RPM signals during the dynamometer testing. The healthy gearbox was only tested in the dynamometer. The damaged gearbox was first tested in the dynamometer and later sent to a wind farm close to NREL for field testing. In the field test, it experienced two loss-of-oil events that damaged its internal bearings and gear elements. The gearbox was brought back to NREL and it was retested in the dynamometer with CM systems deployed under controlled loading conditions that would not cause catastrophic failure of the gearbox. The objective of releasing these datasets to the public along with information about the real damage that occurred to the damaged gearbox is to provide the wind industry with some benchmarking datasets. These datasets will benefit research, development, validation, verification, and advancement of vibration-based wind condition-monitoring techniques. By accessing this data you acknowledge the terms outlined in the "License Information" document. Please contract Shawn Sheng (NREL) if you have any questions on the data or would like to collaborate on publications based on the datasets.

This data set provides industrial-scale onshore wind turbine locations in the United States through January 4, 2023, corresponding facility information, and turbine technical specifications, clipped to the Indiana boundaries. The database has more than 47,000 wind turbine records that have been collected, digitized, locationally verified, and internally quality controlled. Turbines from the Federal Aviation Administration Digital Obstacle File, through product release date July 22, 2013, were used as the primary source of turbine data points. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacle Repository System numbers were visually identified and point locations were added to the collection. We estimated a locational error of plus or minus 10 meters for turbine locations. 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 for turbines were assigned based on the wind turbine make and model as described in literature, specifications listed in the Federal Aviation Administration Digital Obstacle File, and information on the turbine manufacturer’s website. 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. That uncertainty was rated and a confidence was recorded for both location and attribution data quality.

In the United States, the wind energy operator, NextEra Energy, owned 12.9 gigawatts of wind power, making it the leading wind energy operator as of 2016. Regulated utilities in the United States only build a small share of wind assets but most prefer to sign power purchase agreements (PPAs) with independent generators rather than building their own projects. In recent years, the U.S. turbine market has been dominated by just a few original equipment manufacturers (OEMs) which has further supported the trend of consolidation.

Global Wind Power Industry The total wind power generated worldwide has been increasing substantially year after year since 2001. In 2017, the global cumulative installed wind power capacity amounted to 539.3 gigawatts. China, the United States, and Germany are the top three wind power producers worldwide. As of 2017, China had installed about 188.23 gigawatts of wind power cumulatively.

Wind Turbine Market in the United States The Alta Wind Energy Center in California is the largest wind power project installed in the United States as of 2018. It has the capacity to produce 1,548 megawatts of wind energy. The second largest U.S. wind power project is the Roscoe Wind Project, based in Texas, with a wind energy capacity of 781.5 megawatts. Over the last decade, wind energy has become less and less expensive. In 2009, it cost about 1.72 million U.S. dollars to produce one megawatt of wind energy and by 2019, the price index for wind energy had dropped to 790,000 U.S. dollars per megawatt.

As of June 2022, the MySE 16.0-242 from MingYang Smart Energy was the largest wind turbine with a 242 rotor diameter and a nameplate capacity of 16 megawatts. As of that time it was still under construction and was expected to be online by 2026.

Wind turbines

The main function of wind turbines is to convert the wind’s kinetic energy into electrical power. Energy generated from wind turbines is undoubtedly one of the cleanest forms of producing electrical power from a renewable source. Wind turbines can be used to generate large amounts of electricity in wind farms. Wind power is considered one of the fastest growing sources of electricity in the world. Newly installed wind power capacity worldwide reached approximately 93.6 gigawatts in 2021.

Vestas: a leader in wind turbine manufacturing

Vestas is one of the largest wind turbine manufacturers worldwide. The Danish-based company’s focus revolves primarily around the production of wind turbines. In 2020, the company had the highest revenue among wind turbine manufacturers, surpassing 18 billion U.S. dollars. Vestas has turbines installed in over 80 countries and had the largest wind commissioned capacity in 2021. Apart from turbine manufacturing, Vestas is also involved in selling, installing, and maintaining operational power plants.

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/

Original Visualization

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Resources

Visualization: U.S. Wind Turbines Data Source: U.S. Wind Turbine Database | Metadata | About

By 2049, more than 6.5 million metric tons of blade material waste is estimated to be produced worldwide by existing wind turbines in operation, as they reach the end of their lifespan. Roughly 16 percent of this volume is expected to be generated in the U.S., with the European Union accounting for another 30 percent. Turbine blade recycling challenges

The lightweight and durable turbine blades are typically made of carbon fiber or glass fiber composite materials. While established recycling methods exist for the steel, cement, and wiring components used in wind turbines, the blades pose a new challenge as their composite materials require specific recycling processes. In 2021, the largest available rotor size diameter was approximately 220 meters, as larger blades are better for increased capacity. As the global cumulative installed wind capacity increases significantly, blade material waste will become a more pressing issue as more blades reach the end of their roughly 20-year life span.

Developments in global wind power

The global wind power market has undergone considerable growth in recent decades, with global installed capacity rising from 24 gigawatts in 2001 to 743 gigawatts in 2020. During that time, the value of investments in wind energy technologies has gone up while the cost of onshore and offshore wind energy has decreased. Turbine manufacturers are now producing larger, taller, and more powerful turbines, with capacities now reaching 10 gigawatts for a single unit.

This dataset including two contents. One part is the global offshore wind turbine dataset constructed by us, which provide geocoded information on global offshore wind turbines (OWTs) derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. It identified 6,924 wind turbines comprising of more than 10 nations. Data is available at 10 m spatial resolution, providing an explicit dataset for planning, monitoring, and managing marine space. The global OWTs are stored in Shapefile (.shp) format. The attributes and metadata are organized with referenced to the WGS84 datum, and each record consists of seven attributes: centroid latitude (centr_lat), centroid longitude (centr_lon), continent, country, sea area (sea_area), appearance year (occ_year) and month (occ_month).Another part is the validation set we generated that consisted of 50 random offshore wind farms, covering 2,663 wind turbines using three methods. Reference data include (1) the high-resolution aerial imagery and Google images, (2) the comparison and corroboration across multiple source datasets, and (3) a comprehensive visual examination and an extensive internal review by the authors using Sentinel 2-MSI data or Landsat 8-OLI imagery under true colour composition and Sentinel 1 data after floating or temporarily mobile object removal. The validation set is stored in the Shapefile (.shp) format for each wind farm. The attributes and metadata are organized with referenced to the WGS84 datum, and each record consists of three attributes: centroid latitude (centr_lat), centroid longitude (centr_lon) and country. Besides, one of the reference data, Sentinel 1 data after floating or temporarily mobile object removal, were attached.

The number of active wind power turbines in Denmark increased every year from 2011 until the peak in 2023, when it reached 6,974. In 2020, the number had decreased slightly, down to 6,924 active wind turbines. Most of the active wind turbines in Denmark were onshore, around 6,326 compared to 648 offshore.

Wind power coverage   

In 2022, over 53 percent of the total electricity consumption in Denmark was covered by wind power. This was an increase from the previous year, when the coverage had reached 43.7 percent. Except for a drop in 2016, 2018, and 2021, the share of wind power coverage increased markedly since 2009, when only around 19 percent of Denmark’s total electricity consumption was covered by wind power.

Wind power production   

How much wind power was produced to cover the large share of the electricity consumption? In 2022, 19 terawatt hours of wind power were produced in Denmark, which was an increase compared to the previous year. That year, the highest production was in December when it reached around 1.8 terawatt hours, and the lowest in June, when 836 gigawatt hours were produced.

The database on offshore wind farms in the EU was created in 2014 by CETMAR for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources from all across the EU. It is updated every year and is available for viewing and download on EMODnet - Human Activities web portal (www.emodnet-humanactivities.eu). The database contains points and/or (where available) polygons representing offshore wind farms in the following countries: Belgium, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Spain, Sweden and United Kingdom. Each feature has the following attributes (where available): Name, Nº of turbines, Status (Authorised, Operational, Planned, Under Construction), Country, Year, Power (MW), Distance to coast (metres) and Perimeter (kilometres), Surface (square kilometres) only for polygons. The distance to coast (EEA coastline shapefile) has been calculated using the UTM WGS84 Zone projected coordinate system where data fall in. Since the previous version 47 new data was recorded and 55 has been updated for status changes in the points dataset.