Ing. Jakub Zíka

As cities worldwide accelerate their transition to electrified transport, robust insights into public EV charging demand, remain pivotal, for infrastructure development and grid reliability. This paper evaluates a high-resolution dataset from Prague, integrating geospatial and demographic indicators to uncover key spatiotemporal charging behaviors. Our findings demonstrate pronounced morning, afternoon, and evening peaks in different urban zones, highlighting the interplay between land-use patterns and grid constraints. We further discuss how incorporating predictive modeling can facilitate proactive planning, reducing bottlenecks and ensuring equitable access to charging infrastructure. Ultimately, this approach can be extended to diverse urban settings, fostering more sustainable and resilient transport systems.

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.