Spatio-temporal correlation for simultaneous ultra-short-term wind speed prediction at multiple locations

Wind, as a fluid, has continuity in both space and time. Coupling spatial and temporal information to build prediction models can improve wind speed prediction accuracy. This paper proposes a method that predicts wind speed at multiple locations using both spatial and temporal data. Three deep learning models are introduced: Convolutional Residual Spatial-Temporal Long Short-Term Memory neural network (CoReSTL), Convolutional Spatial-Temporal-3D neural network (CoST-3), and Convolutional Spatial-Temporal Long Short-Term Memory neural network (CoST-L). These models combine Convolutional Long Short-Term Memory (ConvLSTM), Residual Network (ResNet), and 1 x 1 3D convolution to extract spatial and temporal correlations between multi-site wind speeds. The spatio-temporal prediction of wind fields under two terrains was carried out to screen out neural network models with higher accuracy. The results show that CoReSTL, CoST-3, and CoST-L all achieved better prediction results. However, the performance of the CoReSTL model was better than that of CoST-3 and CoST-L, with stronger applicability in complex real terrain.

Automated summary

This paper proposes a method that predicts wind speed at multiple locations using both spatial and temporal data, and introduces three deep learning models. These models combine ConvLSTM, ResNet, and 3D convolution to extract spatial and temporal correlations between multi-site wind speeds. The experiments show that the CoReSTL model achieves the best prediction results.