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Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting

Author

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  • Yonggang Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Yue Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Binyuan Wu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

Abstract

Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is proposed. First, the initial data set is preprocessed by a box plot and gray correlation analysis. Then, a generalized method is proposed to calculate the natural gradient and the improved natural gradient boosting (NGBoost) model is proposed based on this method. Finally, blending fusion is used in order to enhance the learning effect of improved NGBoost. The model is validated with the help of measured data from Dalian Tuoshan wind farm in China. The results show that under the specified confidence, compared with the single NGBoost metamodel and other short-term direct probability prediction models, the model proposed in this paper can reduce the forecast area coverage probability while ensuring a higher average width of prediction intervals, and can be used to build new efficient and intelligent energy power systems.

Suggested Citation

  • Yonggang Li & Yue Wang & Binyuan Wu, 2020. "Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting," Energies, MDPI, vol. 13(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4629-:d:409547
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    References listed on IDEAS

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    1. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    2. Suo Li & Ling-ling Huang & Yang Liu & Meng-yao Zhang, 2021. "Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-16, February.

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