Abstract:short term forecasting (STF) of power load aims to guide the formulation of power dispatch plans and achieve dynamic balance between supply and demand. Deep learning (DL) has been widely applied in power load forecasting. However, existing short-term forecasting models mainly rely on historical load data and rarely consider the mutual influence between multiple user electric power consumption behaviors. This paper proposes an integrated GCN-Informer prediction model, which combines the efficiency of graph convolution network (GCN) in processing non Euclidean data and the accuracy of Informer model in predicting long-term sequences. The proposed model first constructs a graph convolutional network based on the correlation between user groups, extracts the user"s electric power consumption patterns, and then uses the Informer model to extract the user"s historical load characteristics. These two features are used to learn the local hidden correlation of historical load sequences and the global potential correlation between different user’s electric power consumption patterns, reducing the processing complexity while improving prediction accuracy. An experimental comparison was conducted between the GCN-Informer model and six benchmark models using power load data from a certain district in Chongqing. The results show that in terms of RSME, the GCN-Informer model reduces by 2% -10% compared to the Transformer model and by 2% -8% compared to the LSTM model. In terms of regression performance, the average R-squared value of the GCN-Informer model is 95.42%, indicating a better fit.