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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jan 2025
Research article |
| 15 Jan 2025
| 15 Jan 2025
Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation
Farkhondeh (Hanie) Rouholahnejad
CORRESPONDING AUTHOR
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Julia Gottschall
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
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Short summary
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.
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch...
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