GCNInformer: A combined deep learning model based on GCN and Informer for wind power forecasting

Wind power energy is green, clean, and renewable, which is random and volatile. The integration of unstable wind energy severely threatens the security and constant operation of the power system. The need to enhance the reliability of wind power grid integration, mitigate the impact of wind power uncertainty, and develop a robust prediction model has become a pressing issue. However, only some people have considered the correlations among the power of multiple adjacent wind turbine arrays. In this paper, we propose GCNInformer to construct these relationships. Furthermore, we analyze the relationships among multiple features of individual wind turbines. GCNInformer is composed of two main components. The first component employs a graph convolutional network (GCN) to establish relationships among multiple wind turbine arrays, enhancing the correlation of the data. The second part employs Informer to extract the time information from the data and predict long-term sequences. For training and testing, GCNInformer utilizes two data sets: Data_CQ and Data_DL. The evaluation of the model's performance is conducted using various metrics such as mean absolute percentage error, mean absolute error, root mean square error, and mean square error. Numerous experimental findings have validated the effectiveness of the GCNInformer. GCNInformer is designed to enhance the accuracy and reliability of wind power forecasting. Graph convolutional network is utilized to capture correlations among the power of multiple adjacent wind turbine arrays, while the Informer module is used for long-term sequence prediction. Numerous experimental findings have validated the effectiveness of the model.image