Short-Term Wind Speed Prediction based on Deep Learning

Content Provider	Scilit
Author	Chu, Jingchun Yuan, Ling Wang, Wenliang Pan, Lei Wei, Jie
Copyright Year	2019
Description	Journal: Iop Conference Series: Earth and Environmental Science 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.
Related Links	https://iopscience.iop.org/article/10.1088/1755-1315/233/5/052007/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/233/5/052007
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	5
Volume Number	233
Language	English
Publisher	IOP Publishing
Publisher Date	2019-02-26
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Industrial Engineering Wind Speed Deep Learning Fuzzy Rough Rough Set
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science