Incremental principal component analysis based depthwise separable Unet model for complex wind system forecasting

Accurate spatiotemporal wind prediction is crucial for grid stability and wind farm optimization, yet existing neural network approaches encounter limitations: recurrent architectures exhibit unstable convergence, while grid-specific sampling methods neglect essential spatiotemporal correlations and nonlinear dynamics in large-scale wind systems. This study introduces the IPCA-MHA-DSRUnet to mitigate these challenges. The depthwise separable U-Net architecture efficiently reconstructs multiscale wind patterns while reducing parameter redundancy, preserving energy dynamics across spatial-temporal domains. Integrated attention modules selectively prioritize regions of nonlinear atmospheric interactions, for enhancing feature extraction precision. Residual learning blocks stabilize temporal modeling by maintaining phase coherence during abrupt meteorological transitions. Validation experiments prove superior performance, with 1-h and 12-h U-component RMSEs of 0.168 m/s and 0.606 m/s respectively, alongside spatial average RMSEs below 0.17 m/s (U) and 0.15 m/s (V) for short-term forecasts. The model achieves a 0.98 spatiotemporal correlation coefficient while resolving fine-grid wind variability, outperforming conventional approaches. These advancements establish a computationally efficient framework for renewable energy systems, enabling high-resolution wind forecasting critical for operational grid management and strategic infrastructure planning. By harmonizing multiscale atmospheric dynamics with lightweight architecture design, the proposed methodology offers a robust solution for optimizing wind energy utilization across diverse geographic and climatic conditions.