Self-supervised dynamic stochastic graph network for spatio-temporal wind speed forecasting

Spatio-temporal Forecasting has been implemented in diverse fields, such as energy, traffic, and weather. As one typical paradigm of intelligent power systems, spatio-temporal wind speed forecasting has attracted increasing attention. Recently, some graph-based methods have revealed the great potential in the spatio-temporal wind speed forecasting task. Those methods are usually built on a static graph which is incapable of discovering the dynamic characteristics and the uncertainty of temporal wind patterns. To this end, we propose a novel Dynamic Stochastic Graph Network (DSGN) for spatio-temporal wind speed forecasting. Specifically, we characterize each wind turbine of regional power grids with a dynamic stochastic distribution to incorporate the uncertainty into networks. Then Wasserstein distance is adopted to build the stochastic graph, which can effectively represent the spatial dependencies between wind turbines. Additionally, we devise a reconstruction task to preserve the geographical distribution information and regularize the learning of the stochastic graph. To further exploit global spatial dependencies and enhance graph modeling, we augment the stochastic graph view by diffusion operations and integrate a self-supervised learning framework, serving as an auxiliary task, to maximize mutual information between the wind latent states from different graph views. Empirical experiments on four real-world wind speed datasets demonstrate the superiority of DSGN over some state-of-the-art methods. Some ablation studies further validate the effectiveness of the stochastic graph modeling and the self-supervised framework.