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Ultra-short-term combined prediction approach based on kernel function switch mechanism

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  • Lu, Peng
  • Ye, Lin
  • Tang, Yong
  • Zhao, Yongning
  • Zhong, Wuzhi
  • Qu, Ying
  • Zhai, Bingxu

Abstract

A novel combined prediction approach including nonlinear data analysis, data decomposition and reconstruction, and parameter optimization is proposed to improve the accuracy of ultra-short-term wind power prediction. The distance correlation coefficient (DCC) is applied to analyze the non-linear correlation between wind power and numerical weather prediction (NWP) data. The original wind power is decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique to eliminate the noise of the data, and then the decomposed wind power is reconstructed into new subsequences by weight permutation entropy (WPE) according to time series complexity. The least squares support vector machine (LSSVM) prediction model based on the kernel function switch mechanism is developed by the use of the new subsequence and key factor as input variables. Furthermore, an improved cuckoo search (ICS) strategy is established to optimize the penalty factor and kernel function parameters of the prediction model. The validation of the proposed model is tested with data from four actual wind farms at a large wind farm base in Northeast China. The results show that the proposed approach is effective with higher accuracy compared with other prediction approaches.

Suggested Citation

  • Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:842-866
    DOI: 10.1016/j.renene.2020.09.110
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    Cited by:

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    2. Lu, Peng & Yang, Jianbin & Ye, Lin & Zhang, Ning & Wang, Yaqing & Di, Jingyi & Gao, Ze & Wang, Cheng & Liu, Mingyang, 2024. "A novel adaptively combined model based on induced ordered weighted averaging for wind power forecasting," Renewable Energy, Elsevier, vol. 226(C).
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    5. Zheng, Xidong & Chen, Huangbin & Jin, Tao, 2024. "A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province," Renewable Energy, Elsevier, vol. 220(C).
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    7. Bo Wang & Tiancheng Wang & Mao Yang & Chao Han & Dawei Huang & Dake Gu, 2023. "Ultra-Short-Term Prediction Method of Wind Power for Massive Wind Power Clusters Based on Feature Mining of Spatiotemporal Correlation," Energies, MDPI, vol. 16(6), pages 1-16, March.
    8. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective," Energy, Elsevier, vol. 283(C).

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