Correlation analysis of factors affecting wind power based on machine learning and Shapley value. Issue 3 (2nd May 2021)

• Record Type: Journal Article
• Authors: Pang, Chuanjun; Yu, Jianming; Liu, Yan
• Other Names: Jiang Tao guestEditor.; Bai Linquan guestEditor.; Mu Yunfei guestEditor.; Venayagamoorthy Kumar guestEditor.; Zhang Yingchen guestEditor.; Teng Fei guestEditor.; Chen Peiyuan guestEditor.; Zhong Haiwang guestEditor.; Yao Wei guestEditor.; Wan Can guestEditor.
• Is Part Of: IET energy systems integration. Volume 3:Issue 3(2021)
• Page Start: 227
• Page End: 237
• Publication Date: 2021-05-02
• Subjects: Power resources -- Periodicals; Energy conservation -- Periodicals; Power resources; Energy conservation; Periodicals; 333.79
• DOI: 10.1049/esi2.12022
• Languages: English
• ISSNs: 2516-8401

Abstract

Abstract: An analysis of the impact of various factors on wind power can help grid dispatchers understand the characteristics of wind power output and improve the accuracy of wind power forecasting. A correlation analysis method of factors affecting wind power is proposed based on machine learning and the Shapley value. First, factors affecting wind power and the method of constructing wind power models based on machine learning are introduced. Then, to measure the influence of factors on wind power, the Shapley value is proposed based on the wind power model. In addition, calculation methods, properties, and application scenarios of the Shapley value are introduced. Finally, based on the actual data of a wind farm, the method is used to analyse environmental factors affecting wind power, and the main factors affecting the wind farm are determined. The experimental results show that the method can identify important factors affecting wind power and measure the complex non‐linear relation between each environmental factor and wind power.