Articles | Volume 10, issue 1
https://doi.org/10.5194/wes-10-143-2025
https://doi.org/10.5194/wes-10-143-2025
Research article
 | 
15 Jan 2025
Research article |  | 15 Jan 2025

Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation

Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall

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Cited articles

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In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
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