Optimising Electric Charging Station Management for Green Transportation with Cloud-Based E-GraphSage Models

The rapid expansion of EVs in recent years has caught the attention of markets, policymakers, the general public, and academia. Issues continue to arise despite the progress. The numerous complaints regarding the low battery life and long charging periods contribute to the problem of electric vehicle charging being an annoyance that discourages more people from purchasing these vehicles. The proposed procedure consists of three stages, which are preprocessing, feature extraction, and training the model. To ensure that the input data can be loaded into the developed model more effectively, preprocessing is an essential step. We begin by extracting three types of charging station features-geographic, traffic, and area-in order to address the data sparsity challenge. We used E-GraphSAGE to train the model. This cutting-edge method outperforms both AdaBoost and Random Forest in terms of average accuracy, coming in at 93.55%.