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Wind speed forecasting based on hybrid model with model selection and wind energy conversion

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  • Wang, Chen
  • Zhang, Shenghui
  • Liao, Peng
  • Fu, Tonglin

Abstract

As an important part of a power system, the usage of wind power is increasing rapidly and playing an indispensable role in energy planning. Therefore, efforts are needed to find and improve the accuracy of wind speed forecasting and the reliability of wind energy conversion, which play a vital role in the development of wind farms. In this paper, a novel multi-objective optimization algorithm is proposed to optimize the parameters of different models, a model selection strategy is used to select the optimal hybrid models for different datasets, to improve the accuracy and stability of the forecasting model. Wind power conversion is examined based on the wind speed forecasting, and found to be a feasible method for wind farms. The numerical results show that compared with the mean absolute percentage error values of the multi-hybrid models, that of the optimal model is reduced about 3%. Moreover, the standard deviation of the absolute percentage error is decreased about 3% for wind speed forecasting. In addition, the effectiveness of the model selection is verified using the onsite wind speed data of four wind farms, and the selected model is shown to be more reliable and accurate than other models.

Suggested Citation

  • Wang, Chen & Zhang, Shenghui & Liao, Peng & Fu, Tonglin, 2022. "Wind speed forecasting based on hybrid model with model selection and wind energy conversion," Renewable Energy, Elsevier, vol. 196(C), pages 763-781.
    • Handle: RePEc:eee:renene:v:196:y:2022:i:c:p:763-781
    DOI: 10.1016/j.renene.2022.06.143
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    Cited by:

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    2. Manoharan Madhiarasan & S. N. Deepa & N. Yogambal Jayalakshmi, 2025. "Hyperparameter optimization of a deep radial basis neural learning approach for wind speed forecasting," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(9), pages 3053-3074, September.
    3. Dong, Zhaochen & Tian, Zhirui & Lv, Shuang, 2025. "A novel paradigm for multi-step wind speed prediction: A hybrid system based on decomposition and weighted ensemble approach enhanced by Gaussian Kernel Function," Renewable Energy, Elsevier, vol. 253(C).
    4. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    5. Sinhara M. H. D. Perera & Ghanim Putrus & Michael Conlon & Mahinsasa Narayana & Keith Sunderland, 2022. "Wind Energy Harvesting and Conversion Systems: A Technical Review," Energies, MDPI, vol. 15(24), pages 1-34, December.
    6. Gao, Junyao & Huang, Weiqing & Qian, Yu, 2026. "Efficient evaluation of wind energy and carbon mitigation potential under land resource constraints via deep learning," Energy, Elsevier, vol. 345(C).
    7. Ai, Xueyi & Feng, Tao & Gan, Wei & Li, Shijia, 2025. "An innovative memory-enhanced Elman neural network-based selective ensemble system for short-term wind speed prediction," Applied Energy, Elsevier, vol. 380(C).
    8. Liang, Yang & Zhang, Dongqin & Zhang, Jize & Hu, Gang, 2024. "A state-of-the-art analysis on decomposition method for short-term wind speed forecasting using LSTM and a novel hybrid deep learning model," Energy, Elsevier, vol. 313(C).
    9. Elshafei, Basem & Peña, Alfredo & Popov, Atanas & Giddings, Donald & Ren, Jie & Xu, Dong & Mao, Xuerui, 2023. "Offshore wind resource assessment based on scarce spatio-temporal measurements using matrix factorization," Renewable Energy, Elsevier, vol. 202(C), pages 1215-1225.

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