  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.
