Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model
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 wer...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1580945/full |
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| author | Zixuan Chen Zixuan Chen Zixuan Chen Jinman Zhang Jinman Zhang Shuang Zhou Zengbao Zhao Zengbao Zhao Yushan Liu |
| author_facet | Zixuan Chen Zixuan Chen Zixuan Chen Jinman Zhang Jinman Zhang Shuang Zhou Zengbao Zhao Zengbao Zhao Yushan Liu |
| author_sort | Zixuan Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c78beb3a88134f24af1811b5ad64b7e2 |
| institution | DOAJ |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-c78beb3a88134f24af1811b5ad64b7e22025-08-20T03:07:20ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-06-011310.3389/feart.2025.15809451580945Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning modelZixuan Chen0Zixuan Chen1Zixuan Chen2Jinman Zhang3Jinman Zhang4Shuang Zhou5Zengbao Zhao6Zengbao Zhao7Yushan Liu8China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory, Xiong’an New Area, ChinaKey Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaEnergy Meteorology Key Laboratory, China Meteorological Administration, Beijing, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaEnergy Meteorology Key Laboratory, China Meteorological Administration, Beijing, ChinaHebei Provincial Meteorology Service Center, Shijiazhuang, ChinaThis 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.https://www.frontiersin.org/articles/10.3389/feart.2025.1580945/fulldeep learningwind speedpredictionultra-short-termspatio-temporal |
| spellingShingle | Zixuan Chen Zixuan Chen Zixuan Chen Jinman Zhang Jinman Zhang Shuang Zhou Zengbao Zhao Zengbao Zhao Yushan Liu Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model Frontiers in Earth Science deep learning wind speed prediction ultra-short-term spatio-temporal |
| title | Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model |
| title_full | Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model |
| title_fullStr | Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model |
| title_full_unstemmed | Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model |
| title_short | Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model |
| title_sort | ultra short term prediction of spatio temporal wind speed based on a hybrid deep learning model |
| topic | deep learning wind speed prediction ultra-short-term spatio-temporal |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1580945/full |
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