Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Zhiyan Zhang, Aobo Deng (), Zhiwen Wang, Jianyong Li, Hailiang Zhao and Xiaoliang Yang
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Zhiyan Zhang: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Aobo Deng: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Zhiwen Wang: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Jianyong Li: CGN New Energy Anhui Co., Ltd., Hefei 230011, China
Hailiang Zhao: CGN New Energy Anhui Co., Ltd., Hefei 230011, China
Xiaoliang Yang: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Energies, 2024, vol. 17, issue 11, 1-15

Abstract: In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and kernel principal component analysis (KPCA), and a prediction model combining bidirectional long short-term memory (BiLSTM) and an attention mechanism (ATT). Firstly, the influencing factors of wind power are analyzed. The quartile method is used to identify and eliminate the original abnormal data of wind power, and the linear interpolation method is used to replace the abnormal data. Secondly, EMD is used to decompose the preprocessed wind power data into Intrinsic Mode Function (IMF) components and residual components, revealing the changes in data signals at different time scales. Subsequently, KPCA is employed to screen the key components as the input of the BiLSTM-ATT prediction model. Finally, a prediction is made taking an actual wind farm in Anhui Province as an example, and the results show that the EMD-KPCAM-BiLSTM-ATT combined model has higher prediction accuracy compared to the comparative model.

Keywords: wind power; power prediction; empirical mode decomposition; kernel principal component analysis; bidirectional long short-term memory neural network; attention mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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