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

Jiawei Zhang, Rongquan Zhang (), Yanfeng Zhao, Jing Qiu, Siqi Bu, Yuxiang Zhu and Gangqiang Li
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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

Energies, 2023, vol. 16, issue 10, 1-15

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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