Siva Sankari Subbiah1, Senthil Kumar Paramasivan2,*, Karmel Arockiasamy3, Saminathan Senthivel4, Muthamilselvan Thangavel2
Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3829-3844, 2023, DOI:10.32604/iasc.2023.030480
Abstract Wind speed forecasting is important for wind energy forecasting. In the modern era, the increase in energy demand can be managed effectively by forecasting the wind speed accurately. The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty, the curse of dimensionality, overfitting and non-linearity issues. The curse of dimensionality and overfitting issues are handled by using Boruta feature selection. The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory (Bi-LSTM). In this paper, Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed… More >
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