Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks

Abstract

Spatio-temporal wind power forecasting is significant to the stability of electric power systems. However, the accuracy of power forecasting results is easily impaired by the insufficient capacity of sequence modeling and misleading information from distinct wind turbines. In this paper, a novel method is proposed to resolve the mentioned problem. Specifically, the reliability of wind condition knowledge is enhanced by considering the spatial information of surrounding wind turbines. Moreover, two metrics based on the distance and correlation are developed to evaluate the quality of spatial information. To learn sequential dependencies regardless of the distance, wind power modeling is achieved by transformer neural networks based on the multi-head attention mechanism. Furthermore, experiments are conducted to assess the performance of the proposed method with real-world measurements. Results show that the proposed method outperforms several baseline and state-of-the-art approaches, and the superiority is particularly prominent with large steps. In two experiments, the average values of mean absolute error of forecasting results generated by the proposed method are only 0.0914 and 0.0911, respectively, which is significantly better than other approaches. With accurate results of short-term multi-step forecasting, this work makes contributions to the effective utilization of wind energy resources.