A deep reinforcement learning approach for wind speed forecasting

A deep reinforcement learning approach for wind speed forecasting

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 EW...

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Bibliographic Details
Main Authors: Shahab S. Band, Ting Jia Lin, Sultan Noman Qasem, Rasoul Ameri, Danyal Shahmirzadi, Muhammad Shamrooz Aslam, Hao-Ting Pai, Ely Salwana, Amir Mosavi
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
Renewable energy
deep reinforcement learning
wind speed forecasting
empirical wavelet transform
long short-term memory
artificial intelligence
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2025.2498355
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https://www.tandfonline.com/doi/10.1080/19942060.2025.2498355

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