Spatiotemporal Attention Networks for Wind Power Forecasting
This work addresses accurate forecasting for wind power systems, which is crucial for reliable and economic power grid operations, but it appears incremental as it combines existing attention mechanisms.
The paper tackled wind power forecasting by proposing a spatiotemporal attention network (STAN) that captures spatial correlations among wind farms and temporal dependencies in time series, achieving better performance than baseline approaches.
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.