A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction
Weipeng Li,
Yuting Chong,
Xin Guo,
Jun Liu
Affiliations
Weipeng Li
School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China
Yuting Chong
School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China
Xin Guo
School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China; Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China; Corresponding author at: School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China.
Jun Liu
School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China; Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China
Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.