Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model - Chen, Zixuan; Zhang, Jinman; Zhou, Shuang; Zhao, Zengbao; Liu, Yushan (Frontiers in Earth Science, 2025)
This study develops a spatio-temporal forecasting model for predicting wind speeds across the Beijing-Tianjin-Hebei region over a 4-h horizon. The model, built using advanced deep learning techniques, operates with a temporal resolution of 1 hour and a spatial resolution of 9 km. The experiments were first trained based on ConvLSTM and UNet, and improved by introducing the Self-Attention (SA) mechanism module to construct two hybrid deep learning models, Conv-SA as well as UNet-SA, respectively. The results show that the spatio-temporal predictions of the UNet model are significantly better than ConvLSTM, and the TS scores show that for the prediction of high wind, the enhancement is more than 50% for the next 4 hours. The addition of the SA module significantly improves the model prediction accuracy, and Conv-SA improves significantly, compared to ConvLSTM by more than 60%. The models were more accurate in predicting wind speeds in the region of the terrestrial than the oceanic subsurface. In addition, the model produces more accurate wind speed predictions for coastal as well as plateau regions. This study provides a new research idea for the proximity prediction of wind speed.