A deep reinforcement learning approach for wind speed forecasting

Band S.S. Lin T.J. Qasem S.N. Ameri R. Shahmirzadi D. Aslam M.S. Pai H.-T. Salwana E. Mosavi A.
2025 Taylor and Francis Ltd.

Engineering Applications of Computational Fluid Mechanics
2025 #19 Issue 1

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. The EWT method transforms the original wind speed series into several independent modes and a residual series. In addition, the DRL method is utilised to optimise the weights associated with three distinct supervised deep learning models, i.e., Long Short-Term Memory (LSTM), Convolutional Neural Networks with LSTM (CNN-LSTM), and CNN with Gated Recurrent Units (CNN-GRU). The performance of the proposed EWT-DRL is evaluated against deep learning models, including LSTM, CNN-LSTM, CNN-GRU, and their coupling with EWT. The combination of EWT and the DRL (EWT-DRL) method achieves a Mean Absolute Error (MAE) of 0.151, a Mean Squared Error (MSE) of 0.060, a Root Mean Squared Error (RMSE) of 0.192, and a correlation coefficient (R) of 0.9913. These results indicate the effectiveness of EWT-DRL in improving accuracy for wind speed modeling.

artificial intelligence , deep reinforcement learning , empirical wavelet transform , long short-term memory , machine learning , Renewable energy , wind speed forecasting

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International Graduate School of AI, National Yunlin University of Science and Technology, Douliu, Taiwan
Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan
Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, Taiwan
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, China
Department of Big Data Business Analytics, National Pingtung University, Pingtung, Taiwan
Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Selangor, Bangi, Malaysia
Institute of the Information Society, Ludovika University of Public Service, Budapest, Hungary
John Von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
Department of Information and Computing Systems, Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan

International Graduate School of AI
Department of Information Management
Computer Science Department
Computer Science Department
Department of Information Management
Graduate School of Engineering Science and Technology
Artificial Intelligence Research Institute
Department of Big Data Business Analytics
Institute of Visual Informatics
Institute of the Information Society
John Von Neumann Faculty of Informatics
Department of Information and Computing Systems

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