A deep reinforcement learning approach for wind speed forecasting Article Swipe

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Shahab S. Band , T. Lin , Sultan Noman Qasem , Rasoul Ameri , Danyal Shahmirzadi , Muhammad Shamrooz Aslam , Hao-Ting Pai , Ely Salwana , Amir Mousavi ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1080/19942060.2025.2498355

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.

Related Topics

Reinforcement Learning

Wind Speed

Computer Science

Artificial Intelligence

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Reinforcement learning Wind speed Computer science Artificial intelligence Engineering Simulation Meteorology Geography

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Type: article
Language: en
Landing Page: https://doi.org/10.1080/19942060.2025.2498355
OA Status: gold
Cited By: 4
References: 45
Related Works: 10
OpenAlex ID: https://openalex.org/W4410141840

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DOI: https://doi.org/10.1080/19942060.2025.2498355

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Title: A deep reinforcement learning approach for wind speed forecasting

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Language: en

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Publication year: 2025

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Publication date: 2025-05-07

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Authors: Shahab S. Band, T. Lin, Sultan Noman Qasem, Rasoul Ameri, Danyal Shahmirzadi, Muhammad Shamrooz Aslam, Hao-Ting Pai, Ely Salwana, Amir Mousavi

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Landing page: https://doi.org/10.1080/19942060.2025.2498355

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OA status: gold

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OA URL: https://doi.org/10.1080/19942060.2025.2498355

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Concepts: Reinforcement learning, Wind speed, Computer science, Artificial intelligence, Engineering, Simulation, Meteorology, Geography

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Cited by: 4

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