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Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm

Author

Listed:
  • Xiangyue Wang

    (School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China)

  • Ji Li

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Shao

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Hongli Liu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Ren

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lihua Zhu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

Abstract

Considering the volatility and randomness of wind speed, this research suggests an improved hunter-prey optimization (IHPO) algorithm-based extreme learning machine (ELM) short-term wind power prediction model to increase short-term wind power prediction accuracy. The original wind power history data from the wind farm are used in the model to achieve feature extraction and data dimensionality reduction, using the partial least squares’ variable importance of projection (PLS-VIP) and normalized mutual information (NMI) methods. Adaptive inertia weights are added to the HPO algorithm’s optimization search process to speed up the algorithm’s convergence. At the same time, the initialized population is modified, to improve the algorithm’s ability to perform global searches. To accomplish accurate wind power prediction, the enhanced algorithm’s optimal parameters optimize the extreme learning machine’s weights and threshold. The findings demonstrate that the method accurately predicts wind output and can be confirmed using measured data from a wind turbine in Inner Mongolia, China.

Suggested Citation

  • Xiangyue Wang & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:991-:d:1025912
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    References listed on IDEAS

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    1. Sami Zdiri & Jaouher Chrouta & Abderrahmen Zaafouri, 2021. "An Expanded Heterogeneous Particle Swarm Optimization Based on Adaptive Inertia Weight," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-24, October.
    2. Namrye Son & Seunghak Yang & Jeongseung Na, 2019. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory," Energies, MDPI, vol. 12(20), pages 1-17, October.
    3. Zhang, Jinliang & Wei, Yiming & Tan, Zhongfu, 2020. "An adaptive hybrid model for short term wind speed forecasting," Energy, Elsevier, vol. 190(C).
    4. Feifan Wang & Baihai Zhang & Senchun Chai & Yuanqing Xia, 2018. "An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-10, August.
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    Cited by:

    1. Chao Tan & Wenrui Tan & Yanjun Shen & Long Yang, 2023. "Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network," Sustainability, MDPI, vol. 15(11), pages 1-17, June.

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