Published January 22, 2023 | Version 1
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Enriched Data of Wind Farms (EDWin)

Authors/Creators

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:

Wind Turbine data

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

 

Wind Farm data

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.
 

Sources

[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] Dunnett, S., Sorichetta, A., Taylor, G. et al. Harmonised global datasets of wind and solar farm locations and power. Sci Data 7, 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

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