  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.
