Using Smart Meter Data and Machine Learning to Identify Residential Light-duty Electric Vehicles

Abstract

The growing adoption of electric vehicles (EVs) poses new challenges to power grids. To upgrade the grids with the increasing demand from charging EVs and from the change in customers consumption behaviors, utilities need to know where EV customers are. However, ownerships of EVs are not always known to utilities. This paper presents a methodology on how to use advanced metering infrastructure (AMI) data and apply machine learning to identify residential customers with EVs. It focuses on such aspects as how to perform sampling to reduce effects of external factors associated with other high-usage home appliances, how to create and evaluate variables for enhancing modeling, and how to apply the ensemble method to arrive at the estimation or forecasting needed for grid enhancement.