Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study

• URL: http://arxiv.org/abs/2510.26910v1
• Date: Thu, 30 Oct 2025 18:16:45 GMT
• Title: Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study
• Authors: Kshitij Nikhal, Luke Ackerknecht, Benjamin S. Riggan, Phil Stahlfeld,
• Abstract summary: Decarbonization of transportation relies on the widespread adoption of electric vehicles (EVs)<n>Existing work is constrained by small-scale datasets, simple proximity-based modeling of temporal dependencies, and weak generalization to sites with limited operational history.<n>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.
• Score: 1.5866079116942815
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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