Ultra-short-term / short-term wind speed prediction based on improved singular spectrum analysis. (January 2022)

• Record Type: Journal Article
• Authors: Yang, Qiuling; Deng, Changhong; Chang, Xiqiang
• Is Part Of: Renewable energy. Volume 184(2022)
• Page Start: 36
• Page End: 44
• Publication Date: 2022-01
• Subjects: Wind speed prediction (WSP) -- Improved singular spectrum analysis (ISSA) -- Singular entropy -- Artificial neural network -- Data preprocessing; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.11.044
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Aiming at the problem that the wind speed data collected by wind farms are affected by many factors and easy to introduce noise information, a wind speed prediction method based on data decomposition of improved singular spectrum analysis (ISSA) is proposed. In this paper, the ISSA is used to decompose the wind speed sequence into a series of sub-sequences. Based on the singular spectrum analysis (SSA), the ISSA introduces the singular entropy to judge the noise components of the wind speed series and remove them. Then, the artificial neural network model is used to calculate and compare the prediction results of several data preprocessing decomposition methods using EMD, EEMD, CEEMD, ISSA and the prediction results without data preprocessing. Experimental results show that the proposed method can effectively improve the prediction accuracy of the artificial neural network, and also has higher prediction accuracy than the comparison method, which verifies the effectiveness of the ISSA.