Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas

TL;DR

This paper presents a neural network-based approach to simulate EV charging profiles in urban areas, helping optimize infrastructure planning despite limited data availability.

Contribution

It introduces a novel neural network model that captures latent charging profiles influenced by spatial and temporal factors for urban EV load prediction.

Findings

Type of Basic Administrative Units significantly affects load predictions

Model effectively predicts peak power demand and daily load shapes

Provides insights for infrastructure planning and optimization

Abstract

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.