Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 1
Articles | Volume 10, issue 1
https://doi.org/10.5194/wes-10-143-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-143-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 1
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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Bodini, N. and Optis, M.: How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?, J. Phys.: Conf. Ser., 1618, 062037, https://doi.org/10.1088/1742-6596/1618/6/062037, 2020a. a
Bodini, N. and Optis, M.: The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds, Wind Energ. Sci., 5, 489–501, https://doi.org/10.5194/wes-5-489-2020, 2020b. a, b, c, d, e, f
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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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