Machine Learning-Based Clustering of Load Profiling to Study the Impact of Electric Vehicles on Smart Meter Applications

Abstract

The data collected from advanced metering infrastructure enables the electric utilities to develop a deep insight about the energy consumption behavior of the consumer. However, the load signature and consumption pattern varies due to addition of multiple types of new loads, such as electric vehicles (EVs). Therefore, it becomes imminent to further dig down these variations. To this end, this paper investigates the impacts of insertion of EV profiles in the household level smart meter data. The Irish CER dataset and EV data from the NREL residential PEV are utilized in this study to classify the users with and without EVs' loads. The results show that change in the cluster membership can help to separate the consumers with the EV load from the stand-alone consumers without the EV load.