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Wind Power Prediction Method: Support Vector Regression Optimized by Improved Jellyfish Search Algorithm

Author

Listed:
  • Dong-Dong Yuan

    (State Grid Jibei Zhangjiakou Wind and Solar Energy Storage and Transportation New Energy Co., LTD., Zhangjiakou 075000, China)

  • Ming Li

    (State Grid Jibei Zhangjiakou Wind and Solar Energy Storage and Transportation New Energy Co., LTD., Zhangjiakou 075000, China)

  • Heng-Yi Li

    (State Grid Henan Extra High Voltage Company, Zhengzhou 450052, China
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China)

  • Cheng-Jian Lin

    (Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 41170, Taiwan)

  • Bing-Xiang Ji

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China)

Abstract

To address the problems of grid connection and power dispatching caused by non-stationary wind power output, an improved Jellyfish Search algorithm optimization support vector regression (IJS-SVR) model was proposed in this study to achieve high-precision wind power prediction. The random selection of internal parameters of SVR model will affect its performance. In this study, the Jellyfish Search (JS) algorithm was selected and improved to propose an Improved Jellyfish Search (IJS) algorithm. Compared with the comparative algorithms, the optimized values of IJS algorithm are closer to 0. It exhibits good convergence ability, search stability, and optimization-seeking ability, as well as being more suitable for solving optimization problems. Therefore, IJS was used to optimize SVR, and the prediction model of IJS-SVR was established. Different weather and seasons affect wind power and model prediction accuracy. The wind power in spring and winter was selected for model prediction verification in this study. Compared with other methods, the IJS-SVR model proposed in this study could achieve better prediction results than other models in both seasons, and its prediction performance was better, which could improve the prediction accuracy of wind power. This study provides a more economical and effective method of wind power to solve its uncertainties and can be used as a reference for grid power generation planning and power system economic dispatch.

Suggested Citation

  • Dong-Dong Yuan & Ming Li & Heng-Yi Li & Cheng-Jian Lin & Bing-Xiang Ji, 2022. "Wind Power Prediction Method: Support Vector Regression Optimized by Improved Jellyfish Search Algorithm," Energies, MDPI, vol. 15(17), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6404-:d:904541
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    References listed on IDEAS

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    Cited by:

    1. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    2. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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