GCN-TRN: Efficient Transformer based Electric Vehicle Charging Demand Forecasting System

Accurate traffic prediction has a wide spectrum of benefits for the society. As Electric Vehicles (EV) are being used more frequently, an accurate EV Charging Station availability forecast becomes necessary. These forecasting models can help alleviate congestion at charging stations and maneuver EV drivers to an ideal location. There have been many data-driven models applied to similar problems related to traffic prediction [1]. Despite numerous efforts to ameliorate already established models and to innovate new models, the complex topological structure of the road networks and the variations of temporal data hinder the models from achieving a good prediction. As for the most classic Recurrent Neural Network (RNN) only extracts time-series information, its lack of information related to spatial dependencies cause a significant loss in long-term accuracy. The Graph Neural Network (GNN) can perform well in extracting spatial dependencies but cannot process time-series information well. Combining the Graph Convolutional Network (a variant of GNN) and the Transformer, we seek to efficiently capture both spatial and temporal data related EV Charging Station Availability. The model is tested for its performances using the Dundee City dataset. And the result reflects that our model surpassed an accuracy of 80% and attained more accurate predictions than the classic baselines.