Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network.

The operations of power systems are becoming increasingly challenging due to the high penetration of renewable power generation, which is uncertain and stochastic. Highly accurate day-ahead renewable power forecasting helps operators in a dispatch center schedule power generations for conventional thermal generators. Efficient day-ahead renewable power forecasting requires information from widely separated locations. This paper proposes a novel day-ahead spatiotemporal wind speed forecasting method based on a convolutional neural network (CNN), which is an image-based deep learning method. This paper exploits a robust design based on Taguchi's orthogonal array to determine the numbers of cascade/parallel layers, convolutions in each layer, kernels and hidden layers, as well as the kernel size, zero-padding and dropout ratio in the proposed CNN to ensure that the proposed CNN is insensitive to the seasonal characteristics of wind speeds while retaining high accuracy. In this paper, the day-ahead forecasting of wind speed at an offshore wind farm (Fuhai) near Taiwan is performed using historical wind speeds at seven sites in Taiwan, China, South Korea and the Philippines. Simulation results reveal that the proposed robust design-based CNN outperforms existing methods. Image 1 • Structure parameters of the CNN are determined by robust design. • Hyperparameters of the CNN are determined by robust design without empirical tests. • Spatiotemporal wind speeds provide the 2D information applied to the CNN. • Other ancillary wind farms are determined by cross-correlation analysis. [ABSTRACT FROM AUTHOR]