A Spatiotemporal Directed Graph Convolution Network for Ultra-Short-Term Wind Power Prediction

The expansion of wind generation and the advance in deep learning have provided feasibility for multisite wind power prediction motivated by spatiotemporal dependencies. This paper introduces a novel spatiotemporal directed graph convolution neural network to sufficiently represent spatiotemporal prior knowledge and simultaneously generate ultra-short-term multisite wind power prediction. At first, a spatial dependency-based directed graph is established to learn the intrinsic topology structure of wind farms taking sites as graph nodes and Granger causality-defined spatial relation as directed edges. Subsequently, a unified spatiotemporal directed graph learning model is presented by embedding the multi-scale temporal convolution network as a sub-layer into the improved graph convolution operator, where the temporal features of each node are extracted by the above sub-layer to capture time patterns with different lengths, and the improved graph convolution layer is introduced by redefining K-order adjacent nodes to further share and integrate the deep spatiotemporal knowledge on the graph containing temporal features. Finally, under a comprehensive training loss function, this method is capable of improving the accuracy of each site for 4h-ahead prediction along with decent robustness and generalization. Experiment results verify the superiority of the proposed model in spatiotemporal correlation representation compared with classic and advanced benchmarks.