Spatiotemporal Attention Networks for Wind Power Forecasting

Understand

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.