文献类型：期刊文献

英文题名：Incremental principal component analysis based depthwise separable Unet model for complex wind system forecasting

作者：Zhang, Zeguo[1,2,3];Yin, Jianchuan[1,2,3]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China;[3]Guangdong Prov Engn Res Ctr Ship Intelligence & Sa, Zhanjiang 524088, Peoples R China

年份：2025

卷号：334

外文期刊名：ENERGY

收录：SCI-EXPANDED(收录号：WOS:001545454400006)、、EI(收录号：20253118913321)、Scopus(收录号：2-s2.0-105012104206)、WOS

基金：Acknowledgements This work was supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under

语种：英文

外文关键词：2D wind pattern prediction; Renewable wind energy; Depthwise separable convolution; Incremental principal component analysis; Deep learning; Fine-grid wind variability

外文摘要：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 largescale 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.