Learning and Prediction of E-Car Charging Requirements for Flexible Loads Shifting

Abstract

The availability of distributed renewable energy sources (RES), such as photo-voltaic panels, allows to locally consume or accumulate energy, avoiding power peaks and loss along the power network. However, as the number of utilities in a household or a building increases, and the energy must be equally and intelligently shared among the utilities and devices, demand side management systems must exploit new solution for allowing such energy usage optimisation. The current trends of demand side management systems highly exploit loads shifting, as a concrete solution to align consumption to the fluctuating produced power, and to maximise the energy utilisation avoiding its wastage. Moreover the introduction, in the latest years, of e-cars has given a boost to smart charging, as it can increase the flexibility that is necessary for maximising the self-consumption. However we strongly believe that a performing demand side management system must be able to learn and predict user’s habits and energy requirements of her e-car, to better schedule the loads shifting and reduce energy wastage. This paper focuses on the e-car utilisation, investigating the exploitation of machine learning techniques to extract and use such knowledge from the power measures at charging plug.