  Wind power is one of the most important renewable energy sources and accurate
wind power forecasting is very significant for reliable and economic power
system operation and control strategies. This paper proposes a novel framework
with spatiotemporal attention networks (STAN) for wind power forecasting. This
model captures spatial correlations among wind farms and temporal dependencies
of wind power time series. First of all, we employ a multi-head self-attention
mechanism to extract spatial correlations among wind farms. Then, temporal
dependencies are captured by the Sequence-to-Sequence (Seq2Seq) model with a
global attention mechanism. Finally, experimental results demonstrate that our
model achieves better performance than other baseline approaches. Our work
provides useful insights to capture non-Euclidean spatial correlations.
