Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917086266130432 |
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| author | Nikhal, Kshitij Ackerknecht, Lucas Riggan, Benjamin S. Stahlfeld, Phillip |
| author_facet | Nikhal, Kshitij Ackerknecht, Lucas Riggan, Benjamin S. Stahlfeld, Phillip |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26910 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study Nikhal, Kshitij Ackerknecht, Lucas Riggan, Benjamin S. Stahlfeld, Phillip Machine Learning 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. |
| title | Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.26910 |