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Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model

Author

Listed:
  • Jiawei Zhang

    (School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia)

  • Rongquan Zhang

    (College of Transportation, Nanchang JiaoTong Institute, Nanchang 330100, China)

  • Yanfeng Zhao

    (School of Information Science and Technology, Northwest University, Xi’an 710069, China)

  • Jing Qiu

    (School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia)

  • Siqi Bu

    (Department of Electrical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong)

  • Yuxiang Zhu

    (Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots, Henan Provincial Key Laboratory of Smart Lighting, College of Computer and Artificial Intelligence, Huanghuai University, Zhumadian 463000, China)

  • Gangqiang Li

    (Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots, Henan Provincial Key Laboratory of Smart Lighting, College of Computer and Artificial Intelligence, Huanghuai University, Zhumadian 463000, China)

Abstract

Uncertainty in wind power is often unacceptably large and can easily affect the proper operation, quality of generation, and economics of the power system. In order to mitigate the potential negative impact of wind power uncertainty on the power system, accurate wind power forecasting is an essential technical tool of great value to ensure safe, stable, and efficient power generation. Therefore, in this paper, a hybrid intelligent model based on isolated forest, wavelet transform, categorical boosting, and quantile regression is proposed for deterministic and probabilistic wind power prediction. First, isolated forest is used to pre-process the original wind power data and detect anomalous data points in the power sequence. Then, the pre-processed original power sequence is decomposed into sub-frequency signals with better profiles by wavelet transform, and the nonlinear features of each sub-frequency are extracted by categorical boosting. Finally, a quantile-regression-based wind power probabilistic predictor is developed to evaluate uncertainty with different confidence levels. Moreover, the proposed hybrid intelligent model is extensively validated on real wind power data. Numerical results show that the proposed model achieves competitive performance compared to benchmark methods.

Suggested Citation

  • Jiawei Zhang & Rongquan Zhang & Yanfeng Zhao & Jing Qiu & Siqi Bu & Yuxiang Zhu & Gangqiang Li, 2023. "Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model," Energies, MDPI, vol. 16(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4237-:d:1152456
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    References listed on IDEAS

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    Cited by:

    1. Tariq Kamal & Syed Zulqadar Hassan, 2023. "Special Issue “Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems”," Energies, MDPI, vol. 16(22), pages 1-4, November.
    2. Dongran Song & Xiao Tan & Qian Huang & Li Wang & Mi Dong & Jian Yang & Solomin Evgeny, 2024. "Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023," Energies, MDPI, vol. 17(6), pages 1-22, March.
    3. Huang, Hao-Hsuan & Huang, Yun-Hsun, 2024. "Applying green learning to regional wind power prediction and fluctuation risk assessment," Energy, Elsevier, vol. 295(C).
    4. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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