Spatiotemporal Attention Networks for Wind Power Forecasting

2019/09/14 by Xingbo Fu, Fu, Xingbo, Feng Gao +7
Engineering · #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1909.07369

openalex publication_date 2019/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Related

• Enhancing Short-Term Wind Speed Forecasting using Graph Attention and Frequency-Enhanced Mechanisms
• Deep Spatio-Temporal Wind Power Forecasting
• STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting
• Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting
• Multistream Graph Attention Networks for Wind Speed Forecasting