An Automated Clustering Process for Helping Practitioners to Identify Similar EV Charging Patterns across Multiple Temporal Granularities

René Richard, Hung Cao, Monica Wachowicz

Abstract: Electric vehicles (EVs) are part of the solution towards cleaner transport and cities. Clustering EV charging events has been useful for ensuring service consistency and increasing EV adoption. However, clustering presents challenges for practitioners when first selecting the appropriate hyperparameter combination for an algorithm and later when assessing the quality of clustering results. Ground truth information is usually not available for practitioners to validate the discovered patterns. As a result, it is harder to judge the effectiveness of different modelling decisions since there is no objective way to compare them. In this work, we propose a clustering process that allows for the creation of relative rankings of similar clustering results. The overall goal is to support practitioners by allowing them to compare a cluster of interest against other similar clusters over multiple temporal granularities. The efficacy of this analytical process is demonstrated with a case study