Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas
This work helps Distribution System Operators optimize EV charging infrastructure planning in urban settings, but it is incremental as it applies existing neural network methods to a new domain-specific problem.
This study tackled the problem of predicting electric vehicle charging profiles in urban areas with limited data by using a neural network architecture, finding that the type of Basic Administrative Units significantly impacts predicted load curves.
This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to uncover latent charging profiles influenced by spatio-temporal factors. Our model focuses on peak power demand and daily load shapes, providing insights into charging behavior. Our results indicate significant impacts from the type of Basic Administrative Units on predicted load curves, which contributes to the understanding and optimization of EV charging infrastructure in urban settings and allows Distribution System Operators (DSO) to more efficiently plan EV charging infrastructure expansion.