Reveling Internal-External Causality for Short-Term Wind Power Prediction: A Temporal Causal Attention Network

An accurate short-term prediction of wind power is crucial for grid reliability and optimized power generation allocation. Currently, deep learning-based methods for wind power prediction (WPP) focus excessively on exploiting spatial-temporal correlations of multivariate time series and neglect potential confounding factors. However, these factors can introduce spurious correlations, diminishing the reliability and robustness of the methods solely based on correlations. Specifically, traditional deep learning methods lack generalizability across varied power output scenarios due to the interference from confounding factors, such as spatial distribution differences and turbine disturbances. Therefore, this paper proposes a novel method, called Temporal Causal Attention Network (TCAN) for short-term WPP, leveraging the physically meaningful internal-external causality to model interactions among factors, and enhancing generalizability across power output scenarios. In this method, a multiscale temporal transformer is proposed to capture the internal causality of a dynamic time series, and a causal graphs-based mechanism is introduced to identify the external causal path among different factors. In addition, a causal attention-based information updating method is proposed to measure the impact of cause nodes on effect nodes. The real data of eight wind turbines from Asia's largest wind power producer are used to evaluate the proposed method. Results show that compared to the second-best method in the experiment, the average RMSE, MAAPE, and R-Squared for the proposed method for 10-min WPP are enhanced by 35.34%, 32.73%, and 4.73%, respectively.