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This paper addresses the critical need for uncertainty quantification in electric vehicle (EV) charging demand by analyzing a large-scale dataset from Scotland's evolving public charging infrastructure. Leveraging hierarchical forecasting combined with conformal prediction techniques for missing data, the study generates valid prediction intervals across all levels of the infrastructure hierarchy. The proposed CP-MNR-Nested algorithm effectively captures the dynamic, time-varying hierarchical structure, providing robust probabilistic forecasts essential for grid management.
Conformal prediction with missing data unlocks valid probabilistic forecasts for EV charging demand across a dynamic, multi-level infrastructure.
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.