期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：233
期号：5
页码：052007
DOI：10.1088/1755-1315/233/5/052007
出版社：IOP Publishing
摘要：Wind speed forecasting has great significance to the improvement of wind turbine intelligent control technology and the stable operation of power system. In this paper, the Long Short-term Memory (LSTM) mode with deep learning ability combined with the fuzzy-rough set theory has been proposed to do short-term wind speed prediction. Fuzzy rough sets can reduce input and spatial characteristics. The main factors affecting wind speed were found as input of the prediction model of LSTM neural network. Deep learning conforms to the trend of big data. It has strong generalization ability on massive data learning. The experimental results show that the Fuzzy rough set Long Short-term Memory (FRS-LSTM) model has higher prediction accuracy than traditional neural network.
