Electric vehicle charging load prediction based on graph attention networks and autoformer

With the widespread popularity of electric vehicles in the domestic market, large-scale electric vehicle user data has been collected and stored. Highly accurate user-level charging load prediction has a wide range of application scenarios and great business value. However, most existing EV load prediction methods are modelled from the charging station perspective, ignoring the user's travel habits and charging demand. Therefore, this paper proposes a temporal spatial neural network based on graph attention and Autoformer to predict electric vehicle charging load. Firstly, the urban map of Wuhan is rasterized. Then, driving and charging data from the user level are aggregated into the raster module according to the time sequence, and a spatio-temporal graph data structure of user travel trajectory is constructed. Finally, the temporal spatial neural network is used to construct the EV charging load prediction model from the user's perspective. The experimental results show that, compared with other baseline prediction methods, the proposed method effectively improves the accuracy of the EV charging load prediction model by fully exploiting the distribution of EV user clusters in time and geographic space. This paper aims to tackle the critical challenge of EV charging load prediction. In this study, we integrate EV spatial trajectory data with user charging data, leading to the development of a novel temporal spatial neural network model based on graph attention and Autoformer for accurate EV charging load forecasting. Moreover, a comprehensive spatiotemporal graph dataset is constructed based on user travel trajectory. The experimental results show that the proposed method can fully tap the distribution of user clusters in time and geographical space, effectively improving the accuracy of charging load prediction. image