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, spatiotemporal correlations of wind speed are dynamically influenced by weather conditions, seasonal variations, and diurnal fluctuations, resulting in constantl...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11078154/ |
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| author | Jianhong Gan Runqing Kang Xun Deng Chentao Mao Zhibin Li Peiyang Wei Chunjiang Wu Tongli He |
| author_facet | Jianhong Gan Runqing Kang Xun Deng Chentao Mao Zhibin Li Peiyang Wei Chunjiang Wu Tongli He |
| author_sort | Jianhong Gan |
| collection | DOAJ |
| description | Wind speed forecasting is significant in practical applications such as energy dispatch and meteorological early warning systems. However, spatiotemporal correlations of wind speed are dynamically influenced by weather conditions, seasonal variations, and diurnal fluctuations, resulting in constantly changing spatiotemporal 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 article proposes a dynamic spatiotemporal graph convolutional network (DSTGFP) model for multistation wind speed prediction to address this challenge. First, the model constructs an adaptive dynamic adjacency matrix by integrating geographical locations among stations, dynamic time warping, and mutual information, facilitating dynamic graph modeling. Next, we introduce a novel spatiotemporal feature extraction framework, which employs residual graph convolutional networks combined with a multihead attention mechanism to extract spatial features. We simultaneously integrate temporal- and frequency-domain convolutions to capture multiscale temporal-frequency features. Finally, the particle swarm optimization algorithm is used for hyperparameter optimization to improve the prediction accuracy. Experimental results demonstrate that the DSTGFP model achieves reductions of 24.66% in the mean absolute error and 25.47% in the root-mean-square error compared to existing deep learning methods, highlighting its superior predictive performance. |
| format | Article |
| id | doaj-art-428bcfa41b5a41eabff140fd60b573e5 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-428bcfa41b5a41eabff140fd60b573e52025-08-20T02:57:45ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118187891880510.1109/JSTARS.2025.358826011078154Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional NetworksJianhong Gan0Runqing Kang1https://orcid.org/0009-0001-0075-801XXun Deng2https://orcid.org/0009-0005-8173-4157Chentao Mao3https://orcid.org/0000-0002-9648-1835Zhibin Li4https://orcid.org/0000-0002-0501-2305Peiyang Wei5https://orcid.org/0009-0008-9632-8006Chunjiang Wu6Tongli He7College of Software Engineering, Chengdu University of Information Technology, Chengdu, ChinaCollege of Software Engineering, Chengdu University of Information Technology, Chengdu, ChinaCollege of Software Engineering, Chengdu University of Information Technology, Chengdu, ChinaHangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, ChinaCollege of Software Engineering, Chengdu University of Information Technology, Chengdu, ChinaCollege of Software Engineering, Chengdu University of Information Technology, Chengdu, ChinaCollege of Software Engineering, Chengdu University of Information Technology, Chengdu, ChinaCollege of Applied Mathematics, Chengdu University of Information Technology, Chengdu, ChinaWind speed forecasting is significant in practical applications such as energy dispatch and meteorological early warning systems. However, spatiotemporal correlations of wind speed are dynamically influenced by weather conditions, seasonal variations, and diurnal fluctuations, resulting in constantly changing spatiotemporal 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 article proposes a dynamic spatiotemporal graph convolutional network (DSTGFP) model for multistation wind speed prediction to address this challenge. First, the model constructs an adaptive dynamic adjacency matrix by integrating geographical locations among stations, dynamic time warping, and mutual information, facilitating dynamic graph modeling. Next, we introduce a novel spatiotemporal feature extraction framework, which employs residual graph convolutional networks combined with a multihead attention mechanism to extract spatial features. We simultaneously integrate temporal- and frequency-domain convolutions to capture multiscale temporal-frequency features. Finally, the particle swarm optimization algorithm is used for hyperparameter optimization to improve the prediction accuracy. Experimental results demonstrate that the DSTGFP model achieves reductions of 24.66% in the mean absolute error and 25.47% in the root-mean-square error compared to existing deep learning methods, highlighting its superior predictive performance.https://ieeexplore.ieee.org/document/11078154/Adjacency matrixdynamic time warping (DTW)graph convolutionparticle swarm optimization (PSO)wind speed forecasting |
| spellingShingle | Jianhong Gan Runqing Kang Xun Deng Chentao Mao Zhibin Li Peiyang Wei Chunjiang Wu Tongli He Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adjacency matrix dynamic time warping (DTW) graph convolution particle swarm optimization (PSO) wind speed forecasting |
| title | Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks |
| title_full | Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks |
| title_fullStr | Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks |
| title_full_unstemmed | Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks |
| title_short | Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks |
| title_sort | multistation wind speed forecasting based on dynamic spatiotemporal graph convolutional networks |
| topic | Adjacency matrix dynamic time warping (DTW) graph convolution particle swarm optimization (PSO) wind speed forecasting |
| url | https://ieeexplore.ieee.org/document/11078154/ |
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