Short-to-Medium Term Ocean Wind Speed Prediction via Sparse Grid Dynamic Spatial Modeling and DAI-LSTM-AT Hybrid Framework.
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| Title: | Short-to-Medium Term Ocean Wind Speed Prediction via Sparse Grid Dynamic Spatial Modeling and DAI-LSTM-AT Hybrid Framework. |
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| Authors: | Guo, Qiaoying1,2 (AUTHOR), Chen, Rengyu1,2 (AUTHOR), Dong, Dibo1,2,3 (AUTHOR), Feng, Feiyu1 (AUTHOR), Sun, Qian1,2 (AUTHOR), Ning, Liqiao1,3 (AUTHOR), Xie, Xiaojie1 (AUTHOR), Li, Jinlin3 (AUTHOR) jimlinlee@fzu.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1405. 27p. |
| Subjects: | Wind forecasting, Deep learning, Spatiotemporal processes, Wind power, Maritime safety |
| Abstract: | Highlights: What are the main findings? A novel hybrid deep learning model named DAI-LSTM-AT has been successfully developed, which can effectively predict wind speed at target positions over the sea surface using coarse-grid reanalysis data. In areas with sparse deep-sea observation data, the model utilizes multi-factor coupling of grid reanalysis data to accurately predict wind speed data on the sea surface within the target area. What are the implications of the main findings? Provides insights for accurate wind speed prediction in scenarios with sparse marine observation data, and the results can offer suggestions for intelligent navigation and ocean wind power operation and maintenance. The effectiveness of the hybrid deep learning architecture integrating ConvLSTM, dynamic attention, LSTM, and Transformer in meteorological and oceanic spatiotemporal sequence prediction has been verified, providing a new high-performance solution for similar problems. This study addresses the critical need for accurate sea wind speed predictions to support ocean wind farm operations, equipment maintenance, and maritime navigation safety. To enhance prediction accuracy for any location within target sea areas, we propose a short-to-medium-term wind speed prediction method that effectively explores spatiotemporal correlations in ocean reanalysis grid data. The method involves collecting and reanalyzing data, as well as spatial processing, to reconstruct the historical wind speed sequence at the target point. Finally, a future wind speed time series is generated using an LSTM network and a Transformer encoder. Test results validated against NOAA buoy data demonstrate the effectiveness of our spatiotemporal prediction model, achieving RMSE values of 1.161 m/s, 1.500 m/s, and 1.854 m/s for 1 h, 6 h, and 12 h predictions, respectively, outperforming comparative methods. The conclusions are threefold: (1) The proposed hybrid model effectively captures spatiotemporal dependencies and achieves more accurate spatiotemporal predictions compared to the benchmark model; (2) taking into account seasonal factors and forecasting time periods, the method proposed in this paper maintains good stability; (3) this framework provides a reliable technical approach for generating operational references in maritime navigation and wind power maintenance, with potential applications in wind farm siting and resource assessment. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel hybrid deep learning model named DAI-LSTM-AT has been successfully developed, which can effectively predict wind speed at target positions over the sea surface using coarse-grid reanalysis data. In areas with sparse deep-sea observation data, the model utilizes multi-factor coupling of grid reanalysis data to accurately predict wind speed data on the sea surface within the target area. What are the implications of the main findings? Provides insights for accurate wind speed prediction in scenarios with sparse marine observation data, and the results can offer suggestions for intelligent navigation and ocean wind power operation and maintenance. The effectiveness of the hybrid deep learning architecture integrating ConvLSTM, dynamic attention, LSTM, and Transformer in meteorological and oceanic spatiotemporal sequence prediction has been verified, providing a new high-performance solution for similar problems. This study addresses the critical need for accurate sea wind speed predictions to support ocean wind farm operations, equipment maintenance, and maritime navigation safety. To enhance prediction accuracy for any location within target sea areas, we propose a short-to-medium-term wind speed prediction method that effectively explores spatiotemporal correlations in ocean reanalysis grid data. The method involves collecting and reanalyzing data, as well as spatial processing, to reconstruct the historical wind speed sequence at the target point. Finally, a future wind speed time series is generated using an LSTM network and a Transformer encoder. Test results validated against NOAA buoy data demonstrate the effectiveness of our spatiotemporal prediction model, achieving RMSE values of 1.161 m/s, 1.500 m/s, and 1.854 m/s for 1 h, 6 h, and 12 h predictions, respectively, outperforming comparative methods. The conclusions are threefold: (1) The proposed hybrid model effectively captures spatiotemporal dependencies and achieves more accurate spatiotemporal predictions compared to the benchmark model; (2) taking into account seasonal factors and forecasting time periods, the method proposed in this paper maintains good stability; (3) this framework provides a reliable technical approach for generating operational references in maritime navigation and wind power maintenance, with potential applications in wind farm siting and resource assessment. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091405 |
