Over the past two decades, the average wind speed in the United Kingdom has remained relatively stable. In 2024, the average wind speed in the UK was 8.4 knots. Speeds peaked during this period in 2015 at 9.4 knots, before falling to 8.4 knots the following year. One knot is equivalent to one nautical mile per hour. Overall, wind speeds have mostly remained between eight and nine knots, dropping to a low of 7.8 in 2010. The first and fourth quarters were the windiest Since 2010, the first and fourth quarters of each year generally recorded the highest wind speeds. The highest quarterly wind speed averages occurred in the first quarter of 2020, with speeds of approximately 11.5 knots. Between 2015 and 2023, the most noticeable deviation from the 10-year mean was recorded in February 2020. In this month wind speeds were 4.2 knots higher than normal. Optimal wind conditions for wind energy The United Kingdom has some of the best wind conditions in Europe for wind power, so it is no surprise that it plays an important role in the country's energy mix. As of 2023, there were 39 offshore wind farms operating in the UK, by far the most in Europe. Furthermore, in the same year, offshore wind power additions in the UK reached 1.14 gigawatts.

The three digital maps provided in this product aim to assess the degree of Offshore windfarm siting suitability existing over the geographical area extent with a focal point where waters of France, Ireland and UK meet. The maps display respectively the spatial distribution of the average and lowest windfarm siting suitability scores along with the average wind speed distribution over a time period of 10 years. They are part of a process set up to assess the fit for use quality of the currently available datasets to support a preliminary selection of potential offshore sites for wind energy development. To build these maps, GIS tools were applied to several key spatial datasets from the 5 data type domains considered in the project: Air, Marine Water, Riverbed/Seabed, Biota/Biology and Human Activities, collated during the initial stages of the project. Initially, each selected dataset was formatted and clipped to the study area extent and spatially classified according to suitability scores, to define raster layers with the variables depicting levels of current anthropogenic and environmental spatial occupation of activities, seabed depth and slope, distances to shoreline, shipping intensity, mean significant wave height, and substrate type. These pre-processed layers were employed as inputs for applying a spatial multi-criteria model using a wind farming suitability classification based on a discrete 5 grades index, ranging from Very Low up to Very High suitability. In adition to suitability maps, an average wind speed spatial distribution map for a 10 years period, at 10 m height, was obtained over the study area from the raster processing of a wind speed time series of monthly means available from daily wind analysis data. The characteristics of the datasets used in this exercise underwent an appropriateness evaluation procedure based on a comparison between their measured quality and those specified for the product.
All the spatial information made available in these maps and from the subsequent appropriateness analysis of the datasets, contributes to a clearer overview of the amount of public-access baseline knowledge currently existing for the North Atlantic basin area.

This dataset provides results of mean offshore wind speed measured in metres per second at a height 100m above sea level. Wind speed is the rate of the movement of wind in distance per unit of time. It is the rate of the movement of air flow. The geographic coverage of wind speed includes an area including the Irish Internal Waters and the Irish Territorial Sea up to 12 nautical miles from the baseline. Wind speed measurements modelled during 2003. The Sustainable Energy Authority of Ireland (SEAI) Wind Atlas 2003 was a digital map of Ireland's wind energy resource. The SEAI is Ireland's national sustainable energy authority tasked with making Ireland’s energy sustainable, secure, affordable, and clean. Wind speed measurements were created to support wind energy resource potential to assist all those concerned with the wind planning process and be of great use to developers and policy makers alike. Data completed during 2003 for geographic area coverage.

This dataset provides results on mean offshore wind speed measured in metres per second at a height 75m above sea level. Wind speed is the rate of the movement of wind in distance per unit of time. It is the rate of the movement of air flow. The geographic coverage of wind speed includes an area including the Irish Internal Waters and the Irish Territorial Sea up to 12 nautical miles from the baseline. Wind speed measurements modelled in 2003. The Sustainable Energy Authority of Ireland (SEAI) Wind Atlas 2003 was a digital map of Ireland's wind energy resource. The SEAI is Ireland's national sustainable energy authority tasked with making Ireland’s energy sustainable, secure, affordable, and clean. Wind speed measurements were created to support wind energy resource potential to assist all those concerned with the wind planning process and be of great use to developers and policy makers alike. Data completed for geographic area coverage.

This dataset provides results on mean offshore wind speed measured in metres per second at a height 50m above sea level. Wind speed is the rate of the movement of wind in distance per unit of time. It is the rate of the movement of air flow. The geographic coverage of wind speed includes an area including the Irish Internal Waters and the Irish Territorial Sea up to 12 nautical miles from the baseline. Wind speed measurements modelled in 2003. The Sustainable Energy Authority of Ireland (SEAI) Wind Atlas 2003 was a digital map of Ireland's wind energy resource. The SEAI is Ireland's national sustainable energy authority tasked with making Ireland’s energy sustainable, secure, affordable, and clean. Wind speed measurements were created to support wind energy resource potential to assist all those concerned with the wind planning process and be of great use to developers and policy makers alike. Data completed for geographic area coverage.

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 50 % lowest mean wind speed locations in each region, i.e., in resource grade (RG) C. 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