The safe and stable operation of power systems is greatly challenged by the high variability and randomness of wind power in large-scale wind-power-integrated grids. Wind power forecasting is an effective solution to tackle this issue, with wind speed forecasting being an essential aspect. In this paper, a Graph-attentive Frequency-enhanced Spatial-Temporal Wind Speed Forecasting model based on graph attention and frequency-enhanced mechanisms, i.e., GFST-WSF, is proposed to improve the accuracy of short-term wind speed forecasting. The GFST-WSF comprises a Transformer architecture for temporal feature extraction and a Graph Attention Network (GAT) for spatial feature extraction. The GAT is specifically designed to capture the complex spatial dependencies among wind speed stations to effectively aggregate information from neighboring nodes in the graph, thus enhancing the spatial representation of the data. To model the time lag in wind speed correlation between adjacent wind farms caused by geographical factors, a dynamic complex adjacency matrix is formulated and utilized by the GAT. Benefiting from the effective spatio-temporal feature extraction and the deep architecture of the Transformer, the GFST-WSF outperforms other baselines in wind speed forecasting for the 6-24 hours ahead forecast horizon in case studies.

翻译：电力系统的安全稳定运行面临大规模风电并网中风电高波动性与随机性的巨大挑战。风电预测是解决这一问题的有效手段，而风速预测是其关键环节。本文提出一种基于图注意力与频率增强机制的时空风速预测模型GFST-WSF，旨在提升短期风速预测精度。该模型融合Transformer架构进行时序特征提取，并采用图注意力网络（GAT）实现空间特征提取。为精准捕捉风速监测站间的复杂空间依赖关系，GAT通过有效聚合图中邻接节点信息增强数据的空间表征能力。针对地理因素导致相邻风电场风速相关性存在时滞的问题，构建动态复数邻接矩阵供GAT使用。得益于高效的时空特征提取与Transformer的深度架构，在案例研究中，GFST-WSF对6-24小时超前预测区间的风速预测效果超越其他基线模型。