Heterogeneous Spatiotemporal Graph Convolution Network for Multi-Modal Wind-PV Power Collaborative Prediction

Accurate and generalized collaborative prediction of multi-cluster renewable energy power generation is both an inevitable trend and urgent demand as the growth of multi-region interconnected power grids with wind and photovoltaic (PV) power. In this paper, a novel heterogeneous spatiotemporal graph convolution network (HSTGCN) is proposed for ultra-short-term multi-modal prediction oriented to wind-PV power, which sufficiently considers spatiotemporal correlations in tens of wind farms or PV stations of each neighboring region and effectively coordinates the heterogeneities of different power generation types in different regions. This approach first designs a dynamical heterogeneous graph structure including modes, nodes, and edges to give a unified framework evolving over time for different interdependencies in the multi-cluster wind and PV sites, and then develops a hierarchical spatiotemporal learning mechanism to enhance representation power for multi-cluster prior information from temporal and spatial dimensions, integrating 2-D CNN with different sizes of filters and GCN embedded a specially designed lightweight graph convolution attention module (GCAM). Experiments including 57 operating wind farms and PV stations from 4 regions distributed over a broad spatial scale demonstrate the generalization and interpretation of HSTGCN compared with other commonly considered benchmarks.