Articles | Volume 10, issue 12
https://doi.org/10.5194/wes-10-2865-2025
https://doi.org/10.5194/wes-10-2865-2025
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

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on wes-2025-24', Frank Lemmer, 24 Feb 2025
  • RC1: 'Comment on wes-2025-24', Anonymous Referee #1, 07 Mar 2025
  • RC2: 'Comment on wes-2025-24', Anonymous Referee #2, 27 May 2025
  • AC1: 'Comment on wes-2025-24', Deepali Singh, 18 Jul 2025

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Deepali Singh on behalf of the Authors (18 Jul 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (21 Jul 2025) by Yi Guo
RR by Anonymous Referee #2 (23 Jul 2025)
ED: Publish subject to minor revisions (review by editor) (01 Aug 2025) by Yi Guo
AR by Deepali Singh on behalf of the Authors (06 Aug 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (06 Aug 2025) by Yi Guo
ED: Publish as is (13 Sep 2025) by Athanasios Kolios (Chief editor)
AR by Deepali Singh on behalf of the Authors (24 Sep 2025)  Manuscript 
Download
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.
Share
Altmetrics
Final-revised paper
Preprint