Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space. (15th March 2019)

• Record Type: Journal Article
• Authors: Yu, Ruiguo; Liu, Zhiqiang; Li, Xuewei; Lu, Wenhuan; Ma, Degang; Yu, Mei; Wang, Jianrong; Li, Bin
• Is Part Of: Applied energy. Volume 238(2019)
• Page Start: 249
• Page End: 257
• Publication Date: 2019-03-15
• Subjects: Wind -- Embedding -- Spatio-temporal feature -- Prediction -- Convolutional networks; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.01.010
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The spatio-temporal feature is proposed, instead of traditional feature, time series. Convolutional network is used to predict wind power based on spatio-temporal feature. Much higher accuracy is achieved within much less training time than existing works. Abstract: Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed. But in fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In this paper, a new kind of feature that can describe the process of temporal and spatial variation is proposed, namely, spatio-temporal feature. We first map the data collected at each moment from the wind turbines to the plane to form the state map, namely, the scene, according to the relative positions. The scene time series over a period of time is a multi-channel image, i.e. the spatio-temporal feature. Based on the spatio-temporal features, the deep convolutional network is applied to predict the wind power, achieving a far better accuracy than the existing methods. Compared with the state-of-the-art methods, the mean-square error in our method is reduced by 49.83%, and the average time cost for training models can be shortened by a factor of more than 150.