Back to Search
Start Over
Spatio-temporal correlation for simultaneous ultra-short-term wind speed prediction at multiple locations.
- Source :
-
Energy . Dec2023, Vol. 284, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
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. • A multi-sites wind speed prediction methodology that leverages both spatial and temporal correlations. • ConvLSTM, ResNet, and 3D convolution are combined as the prediction model. • The proposed prediction method works well with ideal 3D hills and real complex mountainous terrain. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03605442
- Volume :
- 284
- Database :
- Academic Search Index
- Journal :
- Energy
- Publication Type :
- Academic Journal
- Accession number :
- 173321934
- Full Text :
- https://doi.org/10.1016/j.energy.2023.128418