List of renewable energy power stations. This Data Package contains a list of renewable energy power plants in lists of renewable energy-based power plants of Czechia, Denmark, France, Germany, Poland, Sweden, Switzerland and United Kingdom. Czechia: Renewable-energy power plants in Czech Republic. Denmark: Wind and phovoltaic power plants with a high level of detail. France: Renewable-energy power plants of various types (solar, hydro, wind, bioenergy marine, geothermal) in France. Germany: Individual power plants, all renewable energy plants supported by the German Renewable Energy Law (EEG). Poland: Summed capacity and number of installations per energy source per municipality (Powiat). Sweden: Wind power plants in Sweden. Switzerland: All renewable-energy power plants supported by the feed-in-tariff KEV (Kostendeckende Einspeisevergütung). United Kingdom: Renewable-energy power plants in the United Kingdom. Due to different data availability, the power plant lists are of different accurancy and partly provide different power plant parameter. Due to that, the lists are provided as seperate csv-files per country and as separate sheets in the excel file. Suspect data or entries with high probability of duplication are marked in the column 'comment'. Theses validation markers are explained in the file validation_marker.csv. Additionally, the Data Package includes daily time series of cumulated installed capacity per energy source type for Germany, Denmark, Switzerland, the United Kingdom and Sweden. All data processing is conducted in Python and pandas and has been documented in the Jupyter Notebooks linked below.

The Renewable Energy Planning Database (REPD) tracks the progress of renewable energy projects from planning application, through to consent, construction and operation.

Load, wind and solar, prices in hourly resolution. This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020. See previous versions for historical data from a broader range of sources. All data processing is conducted in Python/pandas and has been documented in the Jupyter notebooks linked below.

This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 199 W/m2 turbines at hub height (HH) of 100 m for the regions shown in the attached map. The analysed wind power plants are sited at the 10...50 % highest mean wind speed locations in each region, i.e., in resource grade (RG) B. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. 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 is the aggregated onshore 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 simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581

Wind Energy Index increased 0.79 USD or 5.34% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Wind Energy Index.

This dataset contains:

• A kmz file for Penmanshiel wind farm in the UK (for opening in e.g. Google Earth)
• Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)
• 10-minute SCADA and events data from the 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period, and there is no turbine WT03
• Data mappings from primary SCADA to csv signal names
• Site substation/PMU meter data where available for the same period
• Site fiscal/grid meter data where available for the same period

The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required.

Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.

If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help.

I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.

For contact my email address is charlie.plumley@cubicoinvest.com.

You can also access data from Kelmarsh wind farm here.

This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 335 W/m2 turbines at hub height (HH) of 100 m for the regions shown in the attached map. The analysed wind power plants are sited at the 10...50 % highest mean wind speed locations in each region, i.e., in resource grade (RG) B. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. 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 is the aggregated onshore 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 simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581

This dataset contains:

A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. Google Earth)

Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)

10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period

Data mappings from primary SCADA to csv signal names

Site substation/PMU meter data where available for the same period

Site fiscal/grid meter data where available for the same period

MERRA2 and ERA5 since 2000 up to the same period

The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required.

Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.

If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help.

I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.

For contact my email address is charlie.plumley@cubicoinvest.com.

You can also access data from Penmanshiel wind farm here.

The United Kingdom's offshore wind power capacity has experienced remarkable growth. Between 2009 and 2023, the cumulative installed capacity of offshore wind power in the UK rose from 951 megawatts to a peak of 14.7 gigawatts. The rapid expansion of offshore wind farms has positioned the UK as a global leader in harnessing wind energy from its coastal waters. In 2023, the country ranked as one of the countries with the highest offshore wind energy capacity worldwide, second only to China, which had roughly 37.8 gigawatts of offshore wind installations.
Onshore and offshore wind power production in the UK While offshore wind capacity has grown significantly, onshore wind installations have also seen substantial development. The United Kingdom’s onshore wind capacity reached 15.4 gigawatts in 2023. Although onshore wind installations surpass that of offshore wind, the amount of energy generated at offshore wind energy sites is considerably higher. In 2023, offshore wind power production in the UK reached 49.7 terawatt hours, while onshore wind generation stood at 32.6 terawatt hours. This is due to the higher and more consistent wind speeds found at sea. Wind energy's growing role in UK electricity generation The expansion of wind power capacity has led to a significant increase in the share of electricity generated from wind energy in the UK. From less than three percent in 2010, wind energy's contribution to the country’s electricity mix surpassed ten percent in 2016 and reached a record high of 24.9 percent in 2022. This growth underscores the increasing importance of wind power in the UK's energy landscape. By 2030, the UK aims to reach a wind energy capacity of at least 69 gigawatts.

Forecast: Wind Energy Capacity in the UK 2024 - 2028 Discover more data with ReportLinker!

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].

This dataset provides an overview of the natural heritage sensitivity to wind farms. It identifies land with the greatest opportunity for wind farm development in natural heritage terms, and areas where natural heritage sensitivities indicate a medium or high level of constraint. The intermediate levels (Zone 3 high sensitivity - wild land search areas (hatched) and Zone 2 medium sensitivity (hatched)) indicate that the sensitivity does not apply to the entirety of that area, but only to a proportion. Zone 1 - lowest natural heritage sensitivity; Zone 2 - medium natural heritage sensitivity; Zone 3 - high natural heritage sensitivity. Further information can be found in the Strategic Locational Guidance for Onshore Wind Farms in respect of the Natural Heritage Policy Statement.

This dataset was generated from the original vector data displayed in Map 5 available http://www.snh.gov.uk/planning-and-development/renewable-energy/onshore-wind/

This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 277 W/m2 turbines at hub height (HH) of 100 m for the regions shown in the attached map. The analysed wind power plants are sited at the best 10 % of locations in each region, i.e., in resource grade (RG) A. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. 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 is the aggregated onshore 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 simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses.
This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581

The UK's energy use from renewable and waste sources, by source (for example, hydroelectric power, wind, wave, solar, and so on) and industry (SIC 2007 section - 21 categories), 1990 to 2022.

The dataset on offshore wind farms in the European seas 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. It is updated every year and it is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset 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 point and polygon has the following attributes (where available): Name, Nº of turbines, Status (Approved, Planned, Dismantled, Construction, Production, Test site), Country, Year, Power (MW), Distance to coast (metres) and Area (square kilometres). The distance to coast (EEA coastline shapefile) has been calculated using the UTM WGS84 Zone projected coordinate system where data fall in.

GlobalData’s renewable energy offering, “Wind Power Market Outlook in Japan to 2020 – Capacity, Generation, Major Power Plants, Market share of Equipment Manufacturers and Regulations” gives a view of Japan’s wind energy market and provides forecasts to 2020. This report includes information on wind installed capacity and generation. It provides information on key trends, market share analysis of key component manufacturers, profiles of major industry participants, information on major wind Power Plants(wind farms) and analysis of important deals. This, along with detailed information on the regulatory framework and key policies governing the industry, provides a comprehensive understanding of the market for wind power in the country. This report is built using data and information sourced from proprietary databases, primary and secondary research and in-house analysis by GlobalData’s team of industry experts. Read More

Forecast: Wind Power Gross Available Energy in the UK 2024 - 2028 Discover more data with ReportLinker!

"Spain Wind Power Analysis – Market Outlook to 2030, Update 2021” is the latest report from GlobalData, the industry analysis specialist, that offers comprehensive information and understanding of the wind power market in Spain. The report discusses the renewable power market in the country and provides forecasts up to 2030. The report highlights installed capacity and power generation trends from 2010 to 2030 in the country's wind power market. A detailed coverage of renewable energy policy framework governing the market with specific policies pertaining to wind power is provided in the report. The report also provides company snapshots of some of the major market participants. The report is built using data and information sourced from proprietary databases, secondary research, and in-house analysis by GlobalData’s team of industry experts. Read More