Validation of Charging Demand Modeling with Real-World Data

Abstract

With the increasing uptake of electric vehicles, accurate prediction of public charging demand is critical for effective planning of charging and energy infrastructure. However, there are often discrepancies between modeled predictions and actual usage. Therefore, the following research questions are addressed: First, how big is the difference between model output and real-world data? Second, how can we improve the accuracy of charging demand modeling to make forecasts more robust? By comparing real world data on charging demand with modeled charging demand, we identify inconsistencies in the modeled results. Therefore, model assumptions are refined until the desired level of accuracy is achieved. This paper contributes to a better understanding of real-world charging behavior, exemplifying an approach for enhanced charging demand predictions. Furthermore, this work establishes a basis for optimized resource allocation, which could potentially lead to greater utilization of charging points compared to a model state that inadequately reflects user behavior.