  Wind power is attracting increasing attention around the world due to its
renewable, pollution-free, and other advantages. However, safely and stably
integrating the high permeability intermittent power energy into electric power
systems remains challenging. Accurate wind power forecasting (WPF) can
effectively reduce power fluctuations in power system operations. Existing
methods are mainly designed for short-term predictions and lack effective
spatial-temporal feature augmentation. In this work, we propose a novel
end-to-end wind power forecasting model named Hierarchical Spatial-Temporal
Transformer Network (HSTTN) to address the long-term WPF problems.
Specifically, we construct an hourglass-shaped encoder-decoder framework with
skip-connections to jointly model representations aggregated in hierarchical
temporal scales, which benefits long-term forecasting. Based on this framework,
we capture the inter-scale long-range temporal dependencies and global spatial
correlations with two parallel Transformer skeletons and strengthen the
intra-scale connections with downsampling and upsampling operations. Moreover,
the complementary information from spatial and temporal features is fused and
propagated in each other via Contextual Fusion Blocks (CFBs) to promote the
prediction further. Extensive experimental results on two large-scale
real-world datasets demonstrate the superior performance of our HSTTN over
existing solutions.
