Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 4
Articles | Volume 10, issue 4
https://doi.org/10.5194/wes-10-779-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-779-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 4
Research article
 | 
28 Apr 2025
Research article |  | 28 Apr 2025

Modular deep learning approach for wind farm power forecasting and wake loss prediction

Stijn Ally, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen

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Short summary
Wind farms are crucial for a sustainable energy future. However, their power can fluctuate significantly due to changing weather conditions, which complexly affect their power generation. This paper presents a novel machine-learning-based method to enhance wind farm power predictions, enabling improved power scheduling, trading and grid balancing. This makes wind power more valuable and easier to integrate into the energy system.
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