Customized Uncertainty Quantification of Parking Duration Predictions
for EV Smart Charging
Abstract
As Electric Vehicle (EV) demand increases, so does the demand for
efficient Smart Charging (SC) applications. How- ever, SC is only
acceptable if the EV user’s mobility requirements and risk preferences
are fulfilled, i.e. their respective EV has enough charge to make their
planned journey. To fulfill these requirements and risk preferences, the
SC application must consider the predicted parking duration at a given
location and the uncertainty associated with this prediction. However,
certain regions of uncertainty are more critical than others for user-
centric SC applications, and therefore, such uncertainty must be
explicitly quantified. Therefore, the present paper presents multiple
approaches to customize the uncertainty quantification of parking
duration predictions specifically for EV user-centric SC applications.
We decompose parking duration prediction errors into a critical
component which results in undercharging, and a non-critical component.
Furthermore, we derive quantile- based security levels that can minimize
the probability of a critical error given a user’s risk preferences. We
evaluate our customized uncertainty quantification with four different
proba- bilistic prediction models on an openly available semi-synthetic
mobility data set and a data set consisting of real EV trips. We show
that our customized uncertainty quantification can regulate critical
errors, even in challenging real-world data with high fluctuation and
uncertainty