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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhiyan Zhang & Aobo Deng & Zhiwen Wang & Jianyong Li & Hailiang Zhao & Xiaoliang Yang, 2024. "Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model," Energies, MDPI, vol. 17(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2568-:d:1402180
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    References listed on IDEAS

    as
    1. Shuling Zhao & Sishuo Zhao, 2023. "Wind Power Interval Prediction via an Integrated Variational Empirical Decomposition Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-14, April.
    2. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 11(9), pages 1-18, August.
    3. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    4. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
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