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

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Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4–8 August 2019, Anchorage AK USA, 2623–2631, https://doi.org/10.1145/3292500.3330701, 2019. a
Bentsen, L., Warakagoda, N., Stenbro, R., and Engelstad, P.: Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses, J. Phys. Conf. Ser., 2265, 022035, https://doi.org/10.1088/1742-6596/2265/2/022035, 2022. a
Bilendo, F., Badihi, H., Lu, N., Cambron, P., and Jiang, B.: A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines, IEEE T. Instrum. Meas., 71, 1–13, https://doi.org/10.1109/TIM.2022.3196116, 2022. a
Bleeg, J.: Graph Neural Networks for Power Prediction in Offshore Wind Farms using SCADA Data, J. Phys. Conf. Ser., 1618, 062054, https://doi.org/10.1088/1742-6596/1618/6/062054, 2020. a
Daenens, S., Vervlimmeren, I., Verstraeten, T., Daems, P.-J., Nowé, A., and Helsen, J.: Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach, J. Phys. Conf. Ser., 2767, 092014, https://doi.org/10.1088/1742-6596/2767/9/092014, 2024. a
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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.
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