Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure

Abstract:Uncertainty quantification for electric vehicle (EV) charging demand is essential to turn the increasing pressure of EV adoption on power systems into a source of flexibility. We study a large-scale dataset of public charging stations in Scotland, where demand exhibits a hierarchical structure, organized across multiple aggregation levels, from individual charging points to regional and national scales. The charging infrastructure is continuously evolving, with new stations deployed and existing ones occasionally removed, which calls for forecasting and uncertainty quantification methods tailored to this evolving structure. To address these challenges, we combine hierarchical forecasting with recent advances in conformal prediction with missing data. The conformal procedures considered produce valid prediction intervals at every node of the hierarchy and the algorithm CP-MNR-Nested successfully accounts for the time-varying hierarchical structure of the data.

Applications (stat.AP); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2610.13588 [stat.AP]

(or arXiv:2610.13588v1 [stat.AP] for this version)

https://doi.org/10.48550/arXiv.2610.13588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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