Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 12
Articles | Volume 10, issue 12
https://doi.org/10.5194/wes-10-2865-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2865-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 12
Research article
 | 
05 Dec 2025
Research article |  | 05 Dec 2025

Data-driven probabilistic surrogate model for floating wind turbine lifetime damage equivalent load prediction

Deepali Singh, Erik Haugen, Kasper Laugesen, Richard P. Dwight, and Axelle Viré

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Short summary
We developed a fast, probabilistic surrogate model to predict fatigue loads on floating wind turbines based on site conditions. Unlike traditional methods, our approach directly uses site data, avoiding complex binning or distribution fitting. A mixture density network is used to capture uncertainties and enables quick lifetime fatigue estimates.
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