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