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Electric vehicle charging demand models, with charging records as input, willinherently be biased toward the supply of available chargers. These modelsoften fail to account for demand lost from occupied charging stations andcompetitors. The lost demand suggests that the actual demand is likely higherthan the charging records reflect, i.e., the true demand is latent(unobserved), and the observations are censored. As a result, machine learningmodels that rely on these observed records for forecasting charging demand maybe limited in their application in future infrastructure expansion and supplymanagement, as they do not estimate the true demand for charging. We proposeusing censorship-aware models to model charging demand to address thislimitation. These models incorporate censorship in their loss functions andlearn the true latent demand distribution from observed charging records. Westudy how occupied charging stations and competing services censor demand usingGPS trajectories from cars in Copenhagen, Denmark. We find that censorshipoccurs up to $61\%$ of the time in some areas of the city. We use the observedcharging demand from our study to estimate the true demand and find thatcensorship-aware models provide better prediction and uncertainty estimation ofactual demand than censorship-unaware models. We suggest that future chargingmodels based on charging records should account for censoring to expand theapplication areas of machine learning models in supply management andinfrastructure expansion.

Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand