期刊
RENEWABLE ENERGY
卷 184, 期 -, 页码 36-44出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.11.044
关键词
Wind speed prediction (WSP); Improved singular spectrum analysis (ISSA); Singular entropy; Artificial neural network; Data preprocessing
In this paper, a wind speed prediction method based on data decomposition of improved singular spectrum analysis (ISSA) is proposed. The ISSA is used to decompose the wind speed sequence and remove noise components using singular entropy. The experimental results show that the proposed method can effectively improve the prediction accuracy.
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. (c) 2021 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据