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.

翻译：电动的普及需要根据具体情况扩大电动基础设施; 扩大电动车辆收费信息的收集和处理,可以使每个电动车辆充电站成为宝贵的流数据源; 电点操作员可以从所有这些数据中获益,以优化其运行和规划活动; 在这种情况下,大数据和机器学习技术可以使电动车辆充电站的实时数据具有可估量性; 本文提供了一个能够处理电动基础设施数据流的结构,最终目的是预测电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电动电