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Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting

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  • Wu, Zhuochun
  • Xia, Xiangjie
  • Xiao, Liye
  • Liu, Yilin

Abstract

Wind power forecasting plays a significant role to ensure the safe operation of power systems. However, due to the stochastic nature and dynamic uncertainty of wind power, accurate and stable forecasting faces challenges. In this study, a modified combined structure with secondary decomposition-model selection and non-agnostic uncertainty sampling-active learning-sample selection strategy is proposed for multi-step deterministic and probabilistic wind power forecasting. Secondary decomposition-model selection strategy is used before experiments, selecting better forecasting models from the initial model space to the selected model space, and secondary decomposition methods are applied for decreasing the interference in initial data. Non-agnostic uncertainty sampling-active learning-sample selection strategy is employed to accelerate the sample selection process and enhance the testing efficiency, indirectly promoting the final performance. To further promote the forecasting precision and stability simultaneously, an advanced multi-objective optimization is applied, automatically selecting the top best models into the combined models from the selected model space. Except deterministic forecasting, uncertainty estimation is vital, offering different aspects of forecasting information for risk management, as the grid system absorbs large-scale wind power generation, therefore, probabilistic forecasting is considered too. Multi-step forecasting is also considered. Eight datasets from Elia, Belgium, are applied to assess the forecasting performance of secondary decomposition-model selection and the proposed combined models. According to results, compared with comparison models, the proposed combined models obtain higher accuracy and stability of multi-step deterministic forecasting (improving the forecasting skill by more than 90% in accuracy and stability for 1-step to 3-step forecasting) and also offer multi-step probabilistic information.

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  • Wu, Zhuochun & Xia, Xiangjie & Xiao, Liye & Liu, Yilin, 2020. "Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s030626191932032x
    DOI: 10.1016/j.apenergy.2019.114345
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    Cited by:

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    6. Zhang, Wanying & He, Yaoyao & Yang, Shanlin, 2023. "A multi-step probability density prediction model based on gaussian approximation of quantiles for offshore wind power," Renewable Energy, Elsevier, vol. 202(C), pages 992-1011.
    7. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
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    14. Sun, Zexian & Zhao, Mingyu & Zhao, Guohong, 2022. "Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform," Energy, Elsevier, vol. 261(PB).
    15. Liu, Yanli & Wang, Junyi, 2022. "Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 312(C).
    16. Wang, Ying & Wang, Jianzhou & Li, Zhiwu & Yang, Hufang & Li, Hongmin, 2021. "Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction," Energy, Elsevier, vol. 231(C).
    17. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Jin, Yu, 2022. "A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization," Energy, Elsevier, vol. 239(PE).
    18. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    19. Sun, Shaolong & Du, Zongjuan & Jin, Kun & Li, Hongtao & Wang, Shouyang, 2023. "Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy," Applied Energy, Elsevier, vol. 350(C).
    20. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    21. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).

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