Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks

Wind speed forecasting is significant in practical applications such as energy dispatch and meteorological early warning systems. However, spatio-temporal correlations of wind speed are dynamically influenced by weather conditions, seasonal variations, and diurnal fluctuations, resulting in constantly changing spatio-temporal patterns. Meanwhile, wind speed data inherently include short-term high-frequency fluctuations and long-term low-frequency trends, which traditional methods struggle to adapt to and accurately capture multiscale features. This paper proposes a dynamic spatio-temporal graph convolutional network model (DSTGFP) for multi-station wind speed prediction to address this challenge. First, the model constructs an adaptive dynamic adjacency matrix (ADAM) by integrating geographical locations among stations, dynamic time warping (DTW), and mutual information (MI), facilitating dynamic graph modeling. Next, we introduce a novel spatio-temporal feature extraction framework, which employs residual graph convolutional networks combined with a multi-head attention mechanism to extract spatial features. We simultaneously integrate temporal-domain convolution and frequency-domain convolution to capture multiscale temporal-frequency features. Finally, the particle swarm optimization (PSO) algorithm is used for hyperparameter optimization to improve the prediction accuracy. Experimental results demonstrate that the DSTGFP model achieves reductions of 24.66% in mean absolute error (MAE) and 25.47% in root mean square error (RMSE) compared to existing deep learning methods, highlighting its superior predictive performance.