EVStationSIM: An end-to-end platform to identify and interpret similar clustering patterns of EV charging stations across multiple time slices. (15th September 2022) - Richard, René; Cao, Hung; Wachowicz, Monica
Abstract: Transport electrification introduces new opportunities in supporting sustainable mobility. Fostering Electric Vehicle (EV) adoption integrates vehicle range and infrastructure deployment concerns. An understanding of EV charging patterns is crucial for optimizing charging infrastructure placement and managing costs. 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 hyper-parameter combination for an algorithm and later when assessing the quality of clustering results. In a clustering process, the ground truth data is normally not available for practitioners to validate different modeling decisions. Consequently, it is difficult to judge the effectiveness of the discovered patterns because there is no objective method to compare them. This work proposes an end-to-end platform prototype named "EVStationSIM" that allows for the creation of relative rankings of similar clustering results. The ultimate goal is to support users/practitioners by allowing them to identify and interpret similar clustering patterns of EV charging stations using multiple time slices. The performance of this proposed platform is demonstrated with a case study using real-world EV charging event data from charging station operators in New Brunswick, Canada. The case study illustrates how generated results can assist in downstream analytical tasks such as planning infrastructure allocation expansions. Graphical abstract: Highlights: A platform that facilitates the comparison of clustering results by practitioners. Enables the identification of similar clustering results across temporal partitions. Highlights utilization patterns, assisting in downstream analytical tasks. Leverages multiple data sources to describe EV charging station clustering results.