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A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting

Author

Listed:
  • Wang, Jianzhou
  • An, Yining
  • Li, Zhiwu
  • Lu, Haiyan

Abstract

Accurate wind speed prediction has become increasingly important in wind power generation. However, the lack of efficient data preprocessing techniques and integration strategies has been a big obstacle to the development of wind power forecasting system. Therefore, a novel and advanced combined forecasting system comprising a data preprocessing, an integration strategy and several single models is designed in this study. The proposed model not only eliminates the impact of noise, but also integrates several single-model forecasting results through a weight optimization operator. In addition, the uncertain prediction of wind speed is also discussed in detail. The results show that: (a) The MAPE values of the proposed model are 2.8645%, 2.1843% and 2.8727% respectively for the point prediction. (b) The FICP values of the proposed model are 85.1697, 89.5410 and 88.0111 respectively at the significant level α = 0.05 for the uncertainty forecasting. The AWD values are 0.0559, 0.0400 and 0.0361 and the FINAW values are 0.0478, 0.0404 and 0.0390. It is reasonable to conclude that the proposed system can effectively boost the precision and stability of wind speed forecasting and provide a new approach for the exploitation of wind energy.

Suggested Citation

  • Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008635
    DOI: 10.1016/j.energy.2022.123960
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    3. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
    4. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    5. Ju-Yeol Ryu & Bora Lee & Sungho Park & Seonghyeon Hwang & Hyemin Park & Changhyeong Lee & Dohyeon Kwon, 2022. "Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models," Energies, MDPI, vol. 15(24), pages 1-14, December.
    6. Wang, Chao & Lin, Hong & Yang, Ming & Fu, Xiaoling & Yuan, Yue & Wang, Zewei, 2024. "A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    7. Xing, Qianyi & Huang, Xiaojia & Wang, Kang & Wang, Jianzhou & Wang, Shuai, 2025. "MIG-EWPFS: An ensemble probabilistic wind speed forecasting system integrating multi-dimensional feature extraction, hybrid quantile regression, and Knee improved multi-objective optimization," Energy, Elsevier, vol. 324(C).
    8. Wang, Chao & Lin, Hong & Hu, Heng & Yang, Ming & Ma, Li, 2024. "A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction," Energy, Elsevier, vol. 293(C).
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