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

Bowen Yan, Ruifang Shen, Ke Li, Zhenguo Wang, Qingshan Yang, Xuhong Zhou and Le Zhang

Energy, 2023, vol. 284, issue C

Abstract: 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 × 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.

Keywords: Wind speed prediction; Multi-locations; Spatio-temporal correlation; Convolutional long-short memory neural network; Residual network (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:284:y:2023:i:c:s0360544223018121

DOI: 10.1016/j.energy.2023.128418

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