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.

Wind energy sources accounted for more than ***** percent of electricity generation worldwide in 2024, up from a *** percent share a year earlier. This was over double the share compared to 2015 values, the year Paris Agreement was adopted.

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].

In 2024, around 453 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 2022 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 2024, 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.

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 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.

Global onshore wind capacity has increased ******** since 2009, rising to over **** terawatts in 2024. Wind energy has seen significant growth in the past decade as nations around the world move further away from fossil fuels. Wind energy leader China is by far the leading country in the wind power industry, with a cumulative onshore capacity of *** gigawatts in 2024. This was more than ***** times the capacity of the United States, which ranks second in the world. In this same year ** gigawatts were added to China’s wind power capacity, making up over half of new wind power capacity installations worldwide. It comes as no surprise that China has the highest capacity in the world as it is home to the world’s largest onshore wind farm, located in Gansu Province. Turbine manufacturers Chinese companies are well represented in the industry's leading wind turbine manufacturers. Of these, Beijing based Goldwind is the nation’s biggest manufacturer and second in the world in terms of market share. The company with the largest market share globally is Denmark’s Vestas, which had a share of more than ** percent in 2018.

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.

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.

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2022, figures for 2023 are revised provisional figures and figures for 2024 are provisional.

Changes as of July 2025: Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the outdated data from June 2025. The most recent figures are 5 percent higher for 2024 solar electricity production. These figures are in these two tables (in Dutch): - StatLine - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - StatLine - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update is scheduled in November 2025. From that moment all figures will be fully consistent again. We apologize for the inconvenience.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

GIS data for Bhutan's Wind Power Density at 50m Above Ground Level. NREL developed estimates of Bhutans wind resources at a spatial resolution of 1 km^2 using NREL's Wind Resource Assessment and Mapping System (WRAMS). Wind turbine output at a given site can be predicted using wind speed data and the turbine's power curve, which describes the turbines operating power at different wind speeds. Using data found from this analysis, estimates can be made for the best potential locations for wind energy throughout Bhutan.

China is by far the largest installer of wind power in the world, more than tripling the second-ranked United States. As of the end of 2024, China had cumulatively installed over 561 gigawatts of wind energy, in comparison to 154 gigawatts of wind energy installed in the United States. Worldwide, the cumulative capacity of wind energy reached more than one terawatt in 2024. Wind energy production worldwide Electricity can be generated by harnessing the kinetic energy created by air in motion. Wind hits the blades, rotating the turbines that are connected to a generator, and shifts kinetic energy to rotational energy and eventually to electricity. This electricity is then transformed to meet the grid’s voltage levels. The amount of power that can be generated from wind turbines generally depends on the size of the turbines and length of the blades, as well as wind speeds. Over the years, wind energy technologies have developed to accommodate much higher power ratings, and wind turbines have significantly grown in size. Offshore wind Offshore wind power refers to wind farms that stand within bodies of water, often in the ocean. Offshore wind speeds tend to be faster than on land and are also steadier, thus presenting a higher generation potential as well as a more reliable energy source. However, offshore wind farms are more expensive to build and maintain than onshore wind farms due to the difficulties of building robust turbines to withstand heavy winds in deep ocean waters.

This dataset contains measured timeseries of renewable energy production and electricity consumption as well as exchange with neighboring countries/continents on hourly time resolution. The timeseries data has been divided into two xml files, one for each of the Danish price regions; DK1 (Western Denmark) and DK2 (Eastern Denmark). The data comes from the Danish TSO Energinet and was used in a flexibility study by Karen Olsen in 2018-19 leading to a paper that is to appear in the proceedings of the ICAE19 conference and is entitled: "Data-driven flexibility requirements for current and future scenarios with high penetration of renewables". A journal paper has also been submitted using the same data.The data has been extracted from a website run by Energinet at the following link where time series data is publicly available:https://www.energidataservice.dk/dataset/electricitybalanceThe present version was extracted in September 2019 and contains installation and production data from 2011 until and including the beginning of September 2019.The data is in the originally downloaded xml files, ready to be parsed by the python code written by Karen Olsen (see reference for Fanfare code).Data used for analysis:- offshore wind power generated (column: "Offshore Wind Power" in the xml file)- onshore wind power generated (column: "Onshore Wind Power" in the xml file)- solar power generated (column: "Solar Power Prod" in the xml file)- gross consumption (column: "Gross Con" in the xml file)Further information and code for analysis can be found under:https://kpolsen.github.io/FANFARE/Contains data used pursuant to 'Conditions for use of Danish public-sector data' from the Energi Data Service portal (www.energidataservice.dk).

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".

Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km. Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km. The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only.

The following variables were extracted from the WRF model data: - Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Pressure at 0, 100, 200 m - Surface Precipitation Rate - Surface Relative Humidity - Inverse Monin Obukhov Length

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 dataset comes from a single turbine on an inland wind farm. The dataset covers the duration of one year, but data at some of the time instances are missing. Two time resolutions are included in the dataset: the 10-min data and the hourly data; the latter is the further average of the former. For each temporal resolution, the data is arranged in three columns. The first column is the time stamp, the second column is the wind speed, and the third column is the wind power. This dataset is used in Chapter 2 of the Data Science for Wind Energy book.

Wind Energy Index fell to 18.54 USD on August 29, 2025, down 1.33% from the previous day. Over the past month, Wind Energy Index's price has fallen 1.96%, but it is still 9.12% higher than a year ago, 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 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.