A method for planning a layout of a renewable energy site is disclosed. Correlated sets of historical meteorological data (1) and terrain data (2) are obtained for at least one geographical area. A data model (4) is derived based on the basis of the correlated sets of historical meteorological data (1) and terrain data (2), by training the data model (4) using a deep learning algorithm. The trained data model (4) is adapted to identify coherence between meteorological data and terrain data relating to the same geographical area. Meteorological data (5) and terrain data (6) related to the renewable energy site are fed to the trained data model (4), the terrain data (6) having a higher resolution than the meteorological data (5). Based on the data (5, 6) fed to the trained data model (4), meteorological data related to the renewable energy site with increased resolution is estimated by downscaling the meteorological data (5), using the trained data model (4), and based on the data (5, 6) fed to the trained data model (4). The estimated meteorological data with increased resolution for the renewable energy site is then used for planning the layout of the renewable energy site.
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