Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge

Abstract

The transportation sector is a major contributor to greenhouse gas emissions in Europe. Shifting to electric vehicles (EVs) powered by a low-carbon energy mix could reduce carbon emissions. To support electric mobility, a better understanding of EV charging behaviours at different spatial and temporal resolutions is required, resulting in more accurate forecasting models. For instance, it would help users getting real-time parking recommendations, networks operators planning maintenance schedules, and investors deciding where to build new stations. In this context, the Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy. This challenge involved analysing a dataset of 91 charging stations across four geographical areas over seven months in 2020-2021. The forecasts were evaluated at three spatial levels (individual stations, areas regrouping stations by neighborhoods and the global level of all the stations in Paris), thus capturing the different spatial information relevant to the various use cases. The results uncover meaningful patterns in EV usage and highlight the potential of this dataset to accurately predict EV charging behaviors. This open dataset addresses many real-world challenges associated with time series, such as missing values, non-stationarity and spatio-temporal correlations. Access to the dataset, code and benchmarks are available at https://gitlab.com/smarter-mobility-data-challenge/tutorials to foster future research.