Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space

TL;DR

This paper introduces a novel spatio-temporal feature representation for wind power prediction, utilizing deep convolutional networks to significantly improve accuracy and efficiency over existing methods.

Contribution

It proposes a new spatio-temporal feature extraction method that embeds turbines into grid space and applies deep CNNs for wind power prediction, outperforming prior approaches.

Findings

MSE reduced by 49.83% compared to state-of-the-art

Training time decreased by over 150 times

Achieved higher prediction accuracy with new features

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 Features. We first map the data collected at each moment from the wind turbine 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 Features. 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 starge-of-the-art method, the mean-square error (MSE) 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.