Spatiotemporal Attention Networks for Wind Power Forecasting 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 multihead selfattention mechanism to extract spatial correlations among wind farms. Then, temporal dependencies are captured by the SequencetoSequence (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 nonEuclidean spatial correlations.

Probabilistic Wind Power Forecasting via Bayesian Deep Learning Based Prediction Intervals In this paper, we present a probabilistic wind power forecasting model based on Bayesian deep learning. It distinguishes and characterizes respectively inherent error and modeling error of wind and power datum to construct prediction intervals. Specifically, a fully convolutional neural network is designed as the basic model, which is trained based on Bayesian theory and by Monte Carlo Dropout to fit the best posterior distribution for probabilistic forecasting. We assess the performance of the proposed model over the Global Energy Forecasting Competition 2014 wind dataset. Comparison with existing works demonstrates a stable performance of the proposed model in providing a more precise prediction of narrow prediction intervals.