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.

In 2024, China was the leading country in the world based on wind energy consumption, accounting for a share of almost ** percent of the global wind power consumed during that year. The United States followed as the second largest consumer with an **** percent share, highlighting the significant gap between the two leading nations in wind energy utilization. Wind energy production worldwide Global wind energy production has seen remarkable growth, reaching approximately *** petawatt hours in 2024. This significant increase represents a ****** expansion since 2010, highlighting the global adoption of wind power. Overall, the share of wind energy over the total electricity generation worldwide surpassed ***** percent in 2024, doubling since the Paris Agreement was adopted in 2015. As governments worldwide implement supportive policies, the wind energy market is expected to continue its upward trajectory, playing a crucial role in the transition away from fossil fuels. Leading wind power producers While China and the United States are the leading wind power producers worldwide, other countries have registered a higher adoption of the technology relative to their country size. Denmark stands out as the global leader in wind energy penetration, with wind power accounting for almost ** percent of its electricity mix in 2024. On a per capita basis, Sweden and Finland lead the pack, each producing over *** and *** megawatt hours of wind energy per inhabitant in 2024, respectively.

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 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].

The United Kingdom generated **** terawatt hours worth of electricity and heat through wind power in 2023. Onshore wind farms produced **** terawatt hours of power, which was less than the amount generated by farms situated offshore. Wind power capacities have steadily increased in the past year, with renewable energies taking up a greater share of the UK's energy mix, following the phase-out of coal.

Introduction

Offshore Wind Energy: The offshore wind energy industry is becoming an increasingly important player in the transition to renewable energy worldwide. It is scaling rapidly in both capacity and economic value as technology advances and increased investment comes in. By 2024, the offshore wind energy market is expected to reach extraordinary levels and very strongly address global energy needs and climate goals.

We will delve into the latest features, including offshore wind energy statistics in 2024, trends, and financial impacts.

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.

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/

Introduction

Wind Energy Statistics: The year 2024 sees wind power installation figures remaining almost at the same level as the previous year, with the contribution of wind energy being still paramount among renewable energy sources and promoting environmental sustainability considerably. In 2024, Wind Energy found its place in the renewable energy field like never before.

Governments, companies, and private individuals have turned their focus on this technology because it is efficient and economical. Towards this, wind energy statistics for 2025 look at the capacity, costs, and development of wind energy.

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.

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.

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.

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 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 confidence is recorded for both. None of the data are field verified.

Wind Energy Index rose to 19.16 USD on August 1, 2025, up 1.48% from the previous day. Over the past month, Wind Energy Index's price has risen 3.07%, and is up 15.35% compared to the same time last year, 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 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".

The DOE Planning Portal provides provisional Renewable Energy statistics based on NI planning applications. Tables are available for Renewable Energy applications and decisions by type, Local Government District and Parliamentary Constituency levels, as well as historical data going back to 2002/03.

Source agency: Environment (Northern Ireland)

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Renewable Energy Statistics

This dataset contains the potential onshore wind turbine installation sites identified in "The Future of European Onshore Wind Energy Potential: Detailed Distribution and Simulation of Advanced Turbine Designs". These placements were found following a land eligibility analysis which ensures that the placed turbines avoid ineligible locations such as being too near to settlement areas or within protected conservation zones. Explicit locations of futuristic turbines designs are then identified within the eligible areas via an explicit placement algorithm, after which hourly electricity generation simulation is performed for all locations over a 37 year time frame. In the final stage, an economic evaluation is performed to estimate the levelized cost of electricity for each potential location. In total, 3.4 million locations are found amounting to a technical capacity potential of 13.4 TW with a total average annual generation potential of 34.3 PWh.

Files are provided at the national level and are given in shapefile (".shp") format. Furthermore, the file consists of the point geometries and the following fields: "capacity" - Turbine capacity, given in kilowatts (kW) "rotordiam" - Turbine rotor diameter, given in meters (m) "hubHeight" - Turbine hub height, given in meters (m) "spPower" - Turbine specific power, given in watts per square meter (W m-2) "capex" - Estimate total turbine capital cost, given in Euros "flh_min" - Minimum annual full load hour from all weather years, given in kilowatt-hours per kilowatt (kWh kW-1) "flh_mean" - Average annual full load hour from all weather years, given in kilowatt-hours per kilowatt (kWh kW-1) "flh_max" - Maximum annual full load hour from all weather years, given in kilowatt-hours per kilowatt (kWh kW-1) "flh_std" - Standard deviation of annual full load hours from all weather years, given in kilowatt-hours per kilowatt (kWh kW-1) "lcoe" - Estimated turbine levelized cost of electricity, given in Euro-cents per kWh (ct kWh-1)

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.

Forecast: Wind Energy Consumption in Italy 2022 - 2026 Discover more data with ReportLinker!