Abstract:

This work investigated several ways to forecast with the available charging rate data from the current PEV charging stations in the Netherlands. To enable better smart charging solutions, this paper investigates the day-ahead probabilistic forecasting of the availability and the charging rate at charging stations for plug-in electric vehicles. Generalized linear models with logistic link functions are at the core of both forecast scenarios. Moreover, the availability forecast at a charging point is simply a binomial problem, whereas the charging rate forecast is handled via an ordered logistic model after categorizing the feasible range of values. These two scenarios are evaluated on real data collected from two representatives of the most occupied charging points in the Netherlands, with the focus of the analysis kept at the selection of essential regressors. Based on the ranked probability scores associated with the day-ahead forecasts generated for the last nine months of 2015, it is concluded that the usefulness of predictive models depends highly on the charging station. When contributing substantially to performance, such models possess a simple structure with a few basic lagged and indicator variables.