Wind Power Forecasting Based on a Spatial-Temporal Graph Convolution Network with Limited Engineering Knowledge

Wind power forecasting is critical for ensuring the reliability of wind power systems. A wind turbine consists of several subsystems, each containing various sensors that collect multivariate time series. These subsystems can naturally classify the turbines into clusters. This clustering strongly correlates variables within the same cluster, and the correlation between two clusters can be derived from engineering knowledge. In this study, we propose a hierarchical multivariate time series forecasting method based on a spatial-temporal graph convolution network (HMTGCN) to forecast wind power by leveraging engineering knowledge. The model uses a spatial-temporal graph neural network (GNN) containing a graph learning module to extract features from each time-series cluster. These features are concatenated to form graph data at the cluster level, which are subsequently processed by a graph convolution network. We evaluated the performance using simulation experiments and a real-life wind power dataset, and the results showed that the proposed method improved the prediction performance by 8.99% on average, which demonstrated the effectiveness and superiority of our approach.