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The widespread diffusion of electric mobility requires a contextual expansionof the charging infrastructure. An extended collection and processing ofinformation regarding charging of electric vehicles may turn each electricvehicle charging station into a valuable source of streaming data. Chargingpoint operators may profit from all these data for optimizing their operationand planning activities. In such a scenario, big data and machine learningtechniques would allow valorizing real-time data coming from electric vehiclecharging stations. This paper presents an architecture able to deal with datastreams from a charging infrastructure, with the final aim to forecast electriccharging station availability after a set amount of minutes from present time.Both batch data regarding past charges and real-time data streams are used totrain a streaming logistic regression model, to take into account recurrentpast situations and unexpected actual events. The streaming model performsbetter than a model trained only using historical data. The results highlightthe importance of constantly updating the predictive model parameters in orderto adapt to changing conditions and always provide accurate forecasts.

Short-term forecast of EV charging stations occupancy probability using big data streaming analysis