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A Hybrid Algorithm for Short-Term Wind Power Prediction

Author

Listed:
  • Zhenhua Xiong

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Yan Chen

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
    Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, China)

  • Guihua Ban

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Yixin Zhuo

    (Dispatch and Control Center of Guangxi Power Grid, Nanning 530023, China)

  • Kui Huang

    (Dispatch and Control Center of Guangxi Power Grid, Nanning 530023, China)

Abstract

Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the “best parameters” (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency.

Suggested Citation

  • Zhenhua Xiong & Yan Chen & Guihua Ban & Yixin Zhuo & Kui Huang, 2022. "A Hybrid Algorithm for Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(19), pages 1-11, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7314-:d:933880
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    References listed on IDEAS

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    6. Pavan Kumar Singh & Nitin Singh & Richa Negi, 2021. "Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 111-138, April.
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    Cited by:

    1. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.
    2. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.
    3. Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang & Hsin-Jen Chen, 2023. "One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods," Energies, MDPI, vol. 16(6), pages 1-22, March.

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