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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Dec 2025
Research article |
| 05 Dec 2025
| 05 Dec 2025
Data-driven probabilistic surrogate model for floating wind turbine lifetime damage equivalent load prediction
Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Erik Haugen
Siemens Gamesa Renewable Energy, Tonsbakken 16, 2740 Skovlunde, Denmark
Kasper Laugesen
Siemens Gamesa Renewable Energy, Tonsbakken 16, 2740 Skovlunde, Denmark
Richard P. Dwight
Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Axelle Viré
Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
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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.
We developed a fast, probabilistic surrogate model to predict fatigue loads on floating wind...
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