DOI: 10.5281/zenodo.7558885
title: Enriched Data of Wind Farms (EDWin)

description: EDWin (Enriched Data of Wind Farms) is a dataset developed to provide information about global wind farms. The dataset is based on OpenStreetMap (OSM) data and has been enriched with additional variables obtained from various databases. The dataset includes two separate data sets, one for global turbines and one for wind farms. As of September 2022, this dataset contains the most recent information available. The datasets have the following structures: <strong>Wind Turbine data </strong> The data for wind turbines includes 359,947 entries and 12 columns. Variable Name Description id Key value of the data point lon Longitude of the location lat Latitude of the location country Country where the turbine is located continent Continent where the turbine is located land cover The type of land on which the turbine is located landform The physical features of the land on which the turbine is located elevation The altitude of the turbine turbine spacing The distance between turbines in the wind farm WFid Wind Farm ID number of turbines The number of turbines in the wind farm shape The rough shape of the wind farm <strong>Wind Farm data </strong> The data for wind farms includes 20,608 entries and 11 columns. Variable Name Description WFid Wind Farm ID lon Longitude of the location (center of the wind farm) lat Latitude of the location (center of the wind farm) country Country where the wind farm is located continent Continent where the wind farm is located land cover The modal value of the land cover for the turbines in the wind farm landform The average value of the landform for the turbines in the wind farm elevation The average elevation of the turbines in the wind farm turbine spacing The average turbine spacing for the turbines in the wind farm number of turbines The number of turbines in the wind farm shape The rough shape of the wind farm Note that the data for "Country", "Continent", "Land Cover", "Landform", "Elevation" and "Turbine spacing" were collected turbine-specific and later added to the wind farm dataset in an aggregated form. For the categorical variables, the modulus of the respective turbine values was taken, and for numerical variables, the average was calculated. The two variables, number of turbines (i.e. wind farm size) and wind farm shape (i.e. a rough shape of the wind farm), were obtained from the wind farms data and added to the turbine dataset.<br> <strong>Sources</strong> [1] Open street map. https://openstreetmap.org/. [Online] Accessed: 2022-10-02. [2] Cutler J. Cleveland, Christopher Morris, Dictionary of Energy (Second Edition), Elsevier, 2015, Pages 638-655, ISBN 9780080968117 https://doi.org/10.1016/B978-0-08-096811-7.50023-8. [4]<em> </em>Dunnett, S., Sorichetta, A., Taylor, G. <em>et al.</em> Harmonised global datasets of wind and solar farm locations and power. <em>Sci Data</em> <strong>7</strong>, 130 (2020). https://doi.org/10.1038/s41597-020-0469-8 [5] Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Bertels, L. ; Smets, B. Copernicus Global Land Cover Layers-Collection 2. Remote Sensing 2020, 12 Volume 108, 1044. doi:10.3390/rs12061044 [6] Theobald, D. M., Harrison-Atlas, D., Monahan, W. B., & Albano, C. M. (2015). Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PloS one, 10(12), e0143619 [7] Global Multi-resolution Terrain Elevation Data 2010 courtesy of the U.S. Geological Survey