Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph

Accurate wind speed forecasting can help ensure power-system stability. Many previous studies often neglect spatio-temporal dependence. Therefore, effectively modeling the complex and dynamic spatio-temporal correlations (STCs) between spatially distributed wind speeds and extracting informative spatio-temporal features is very important for boosting forecast accuracy. This study proposes a novel sparse and dynamic graph-based spatio-temporal wind speed forecasting method with local–global features (LGFs). First, a dynamic STC modeling block is designed to learn the dynamic STC degree based on the similarity of wind temporal characteristics. To reduce computational costs, a threshold is set to select the most highly correlated neighboring sites, resulting in a sparse graph. Then, a parallel-structured LGF extraction block including a local feature extraction module and a global feature extraction module is developed. It can capture local features for a single site and global features representing spatio-temporal dependence among neighbor sites according to the obtained graph. The obtained features are fused into the comprehensive LGFs. Finally, accurate wind speed forecasts for multiple sites are generated simultaneously. The proposed model is tested using numerous benchmark models, including temporal, spatio-temporal, static graph-based, and complete graph-based models. The results show that it can effectively learn dynamic STCs and attain the highest accuracy.