Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 6
Articles | Volume 10, issue 6
https://doi.org/10.5194/wes-10-1137-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-1137-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 6
Research article
 | 
25 Jun 2025
Research article |  | 25 Jun 2025

Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data

Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen

Viewed

Total article views: 3,181 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,389 512 280 3,181 82 156
  • HTML: 2,389
  • PDF: 512
  • XML: 280
  • Total: 3,181
  • BibTeX: 82
  • EndNote: 156
Views and downloads (calculated since 23 Sep 2024)
Cumulative views and downloads (calculated since 23 Sep 2024)

Viewed (geographical distribution)

Total article views: 3,181 (including HTML, PDF, and XML) Thereof 3,140 with geography defined and 41 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Dec 2025
Download
Short summary
This study presents a novel model for predicting wind turbine power output at a high temporal resolution in wind farms using a hybrid graph neural network (GNN) and long short-term memory (LSTM) architecture. By modeling the wind farm as a graph, the model captures both spatial and temporal dynamics, outperforming traditional power curve methods. Integrated with a normal behavior model (NBM) framework, the model effectively identifies and analyzes power loss events.
Read more
Share
Altmetrics
Final-revised paper
Preprint