8th International Conference on Advanced Power System Automation and Protection (APAP2019)

Traditional wind speed forecast usually regards wind farm as a point to make forecast, but in a wind farm, wind speed of wind turbines in different geographical locations is not the same. For many wind turbines with wide geographical distribution in a wind farm, this paper gives a forecast method based on convolutional neural network (CNN) to forecast the wind speed at each wind turbine location. In this method, the wind speed and direction characteristics of all wind turbines at different geographical locations are input into the CNN network as variables, and local low-dimensional features of the original data are mapped to high-dimensional features through convolution operation of CNN, thereby realizing the wind speed forecast. The main advantage of this method is that by automatically studying the informative spatial correlation of wind speed, rather than artificial extracting , multi-task forecasts（MTF）are made and the wind speed forecast at different wind turbines locations is more informative and accurate.

Convolutional neural network,deep learning,multi-task prediction,wind farm,wind speed forecast