The widespread adoption of electric vehicles (EVs) is creating new challenges for electrical distribution networks, particularly at public charging hubs. These sites vary in usage patterns, infrastructure types, and grid impact, making it critical to understand and model their electrical demand. This paper presents a data-driven framework to classify electrical demand profiles across multiple EV charging hubs, using real-world arrival and departure data. The approach is demonstrated using 2024 data from three ChargePlace Scotland (CPS) locations: a park-and-ride site, a stadium-based destination charger, and a town-centre multi-storey car park, each with varied charger types and utilisation characteristics.

The study uses probabilistic modelling and analytics to characterise key metrics, including occupancy factor, capacity factor, and half-hourly peak demand. Demand visualisations are developed using time-series heatmaps and density plots to capture temporal and spatial variations in hub usage. The analysis reveals significant variation in usage by site type and charger class. AC units show notably lower utilisation than DC chargers, with AC capacity factors ranging from 1.2% to 3.13%, while DC chargers peak at 11.9%. Overstay tariffs, user behaviour, and site context also strongly influence utilisation patterns.

Site A (park-and-ride) exhibits weekday morning peaks aligned with commuter behaviour; Site B (stadium) shows sharp weekend demand spikes linked to event schedules; and Site C (town centre) demonstrates long dwell times and consistently high occupancy but low energy throughput, suggesting inefficient use of charger capacity. These distinctions highlight the limitations of one-size-fits-all planning for EV infrastructure.

The classification framework developed supports stakeholders, including distribution network operators and site owners, in evaluating the effectiveness of existing infrastructure and planning for future demand. It also contributes to compliance with UK data transparency regulations for public charging networks. By providing the means to align infrastructure investment with actual usage patterns, the framework helps reduce the risk of stranded assets, manage peak loads, and support more flexible, resilient energy systems. This work provides a practical framework for designing smarter, scalable EV charging solutions grounded in real-world usage.