Data-driven energy demand forecasting for electric vehicle charging infrastructure

In the era of Big Data and electric vehicles growth by market, data-driven methodologies assume a crucial role to create valuable information. The focus is on supporting the decision-making process for the development of an accurate charging infrastructure. Forecast analysis allows prediction of energy demand over the network. This supports growing trends with a consequent increase in customer satisfaction. By anticipating potential breakdowns due to infrastructure overloads, maintenance costs are reduced. In this paper, we focus on analyzing charging sessions data together with external data (weather and population information and energy/fuel prices) collected from different sources. The proposed methodology, named GEORGE (enerGy dEmand fOrecasting foR charGing infrastructurE), offers a building-blocks based approach for the monthly energy demand forecasting. The approach is both generalisable and data-specific. We discuss the results of a classification learning approach to predict a belonging range of kwh for a charge point. In particular the most promising model has good performances in predicting high utilization and is more advantageous to support the company’s decision-making process. Many possible developments are discussed to improve the prediction