A spatiotemporal fusion network based on Mamba and GCN for wind power forecasting

Efficient and accurate wind power forecasting is beneficial for the rational distribution and stable supply of electricity. Existing deep learning-based methods are generally focused on extracting effective spatiotemporal features from historical wind power data. However, the current prediction accuracy is often constrained by three aspects: temporal feature extraction, spatial feature extraction, and spatiotemporal fusion scheme. Specifically, the temporal feature extractor fails to efficiently capture long-term temporal dependencies, the spatial feature extractor does not account for comprehensive spatial relationships among wind turbines, and there is a lack of effective spatiotemporal fusion strategies. In this work, we design a spatiotemporal fusion architecture based on Mamba and graph convolutional network, which further enhances the accuracy of wind power forecasting. Our model, which is based on an encoder–decoder framework, primarily consists of three components: temporal block, spatial block, and spatiotemporal fusion block. Specifically, a temporal block is capable of efficiently extracting long-term temporal dependencies from wind power data. Spatial block takes into account the inherent and dynamic spatial relationships among wind turbines. The spatiotemporal fusion block employs a progressive fusion strategy that effectively integrates spatiotemporal features. Multi-step predictions are conducted on a Spatial Dynamic Wind Power Forecasting dataset and a private dataset, and experimental results show that our model outperforms other models in predictive performance.