Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study
This work addresses the need for cost-effective and grid-resilient EV charging infrastructure, which is critical for transportation decarbonization, by providing actionable insights for operators, though it is incremental in improving forecasting methods.
The study tackled the problem of understanding EV charging behavior for infrastructure planning by proposing a framework that integrates clustering with few-shot forecasting using a large-scale dataset, resulting in archetype-specific expert models that outperform global baselines in forecasting demand at unseen sites.
The decarbonization of transportation relies on the widespread adoption of electric vehicles (EVs), which requires an accurate understanding of charging behavior to ensure cost-effective, grid-resilient infrastructure. Existing work is constrained by small-scale datasets, simple proximity-based modeling of temporal dependencies, and weak generalization to sites with limited operational history. To overcome these limitations, this work proposes a framework that integrates clustering with few-shot forecasting to uncover site archetypes using a novel large-scale dataset of charging demand. The results demonstrate that archetype-specific expert models outperform global baselines in forecasting demand at unseen sites. By establishing forecast performance as a basis for infrastructure segmentation, we generate actionable insights that enable operators to lower costs, optimize energy and pricing strategies, and support grid resilience critical to climate goals.