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Volume 16
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Article

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

by
Jiawei Zhang
1,
Rongquan Zhang
2,*,
Yanfeng Zhao
3,
Jing Qiu
1,
Siqi Bu
4,
Yuxiang Zhu
5 and
Gangqiang Li
5
1
School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia
2
College of Transportation, Nanchang JiaoTong Institute, Nanchang 330100, China
3
School of Information Science and Technology, Northwest University, Xi’an 710069, China
4
Department of Electrical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong
5
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
*
Author to whom correspondence should be addressed.
Energies 2023, 16(10), 4237; https://doi.org/10.3390/en16104237
Submission received: 29 April 2023 / Revised: 14 May 2023 / Accepted: 15 May 2023 / Published: 22 May 2023
(This article belongs to the Special Issue Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems)
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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.
Keywords: wind power forecasting; wavelet transform; categorical boosting; probabilistic predictor wind power forecasting; wavelet transform; categorical boosting; probabilistic predictor

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MDPI and ACS Style

Zhang, J.; Zhang, R.; Zhao, Y.; Qiu, J.; Bu, S.; Zhu, Y.; Li, G. Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model. Energies 2023, 16, 4237. https://doi.org/10.3390/en16104237

AMA Style

Zhang J, Zhang R, Zhao Y, Qiu J, Bu S, Zhu Y, Li G. Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model. Energies. 2023; 16(10):4237. https://doi.org/10.3390/en16104237

Chicago/Turabian Style

Zhang, Jiawei, Rongquan Zhang, Yanfeng Zhao, Jing Qiu, Siqi Bu, Yuxiang Zhu, and Gangqiang Li. 2023. "Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model" Energies 16, no. 10: 4237. https://doi.org/10.3390/en16104237

APA Style

Zhang, J., Zhang, R., Zhao, Y., Qiu, J., Bu, S., Zhu, Y., & Li, G. (2023). Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model. Energies, 16(10), 4237. https://doi.org/10.3390/en16104237

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