  The widespread diffusion of electric mobility requires a contextual expansion
of the charging infrastructure. An extended collection and processing of
information regarding charging of electric vehicles may turn each electric
vehicle charging station into a valuable source of streaming data. Charging
point operators may profit from all these data for optimizing their operation
and planning activities. In such a scenario, big data and machine learning
techniques would allow valorizing real-time data coming from electric vehicle
charging stations. This paper presents an architecture able to deal with data
streams from a charging infrastructure, with the final aim to forecast electric
charging station availability after a set amount of minutes from present time.
Both batch data regarding past charges and real-time data streams are used to
train a streaming logistic regression model, to take into account recurrent
past situations and unexpected actual events. The streaming model performs
better than a model trained only using historical data. The results highlight
the importance of constantly updating the predictive model parameters in order
to adapt to changing conditions and always provide accurate forecasts.
