# Spatiotemporal Attention Networks for Wind Power Forecasting

arXiv: https://arxiv.org/abs/1909.07369 · HTML: https://mlsanity.com/paper/1909.07369/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Future](https://mlsanity.com/paper/2204.02360/) (2022)
- [Deep Spatio-Temporal Wind Power Forecasting](https://mlsanity.com/paper/2109.14530/) (2021)
- [Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses](https://mlsanity.com/paper/2201.03229/) (2022)

## Abstract (arXiv)

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.