Short-to-Medium Term Ocean Wind Speed Prediction via Sparse Grid Dynamic Spatial Modeling and DAI-LSTM-AT Hybrid Framework

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.