Articles | Volume 10, issue 9
https://doi.org/10.5194/wes-10-1979-2025
https://doi.org/10.5194/wes-10-1979-2025
Research article
 | 
12 Sep 2025
Research article |  | 12 Sep 2025

Multi-task learning long short-term memory model to emulate wind turbine blade dynamics

Shubham Baisthakur and Breiffni Fitzgerald

Cited articles

Abbas, N., Zalkind, D., Pao, L., and Wright, A.: A reference open-source controller for fixed and floating offshore wind turbines, Wind Energy Science Discussions, 2021, 1–33, 2021. a
Abbas, N. J., Zalkind, D. S., Pao, L., and Wright, A.: A reference open-source controller for fixed and floating offshore wind turbines, Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, 2022. a, b
Ahmed, N., Natarajan, T., and Rao, K. R.: Discrete cosine transform, IEEE Trans. Comput., 100, 90–93, 2006. a
Bai, H., Shi, L., Aoues, Y., Huang, C., and Lemosse, D.: Estimation of probability distribution of long-term fatigue damage on wind turbine tower using residual neural network, Mech. Syst. Signal Pr., 190, 110101, https://doi.org/10.1016/j.ymssp.2023.110101, 2023. a
Baisthakur, S.: Multi-task Learning Long Short-term Memory Model to Emulate Wind Turbine Blade Dynamics, In Wind Energy Science, Zenodo [code], https://doi.org/10.5281/zenodo.13305715, 2024. a
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Short summary
Site-specific performance analysis of wind turbines is crucial but computationally prohibitive due to the high cost of evaluating numerical models. To address this, the authors propose a machine learning model combined with dimensionality reduction using principal component analysis and the discrete cosine transform, along with a long short-term memory model, to predict dynamic responses at a fraction of the computational cost.
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