LAUSR

Abstract We develop a novel iterative clustering method for classifying time series of EV charging rates based on their “tail features”. Our method first extracts tails from a diversity of charging time series that have different lengths, contain missing data, and are distorted by scheduling algorithms and measurement noise. The charging tails are then clustered into a small number of types whose representatives are then used to improve tail extraction. This process iterates until it converges. We apply our method to ACN-Data, a fine-grained EV charging dataset recently made publicly available, to illustrate its effectiveness and potential applications.