Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space

Ruiguo Yu, Zhiqiang Liu, Xuewei Li, Wenhuan Lu, Degang Ma, Mei Yu, Jianrong Wang and Bin Li

Applied Energy, 2019, vol. 238, issue C, 249-257

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.

Keywords: Wind; Embedding; Spatio-temporal feature; Prediction; Convolutional networks (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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DOI: 10.1016/j.apenergy.2019.01.010

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