  Understanding electric vehicle (EV) charging on the distribution network is
key to effective EV charging management and aiding decarbonization across the
energy and transport sectors. Advanced metering infrastructure has allowed
distribution system operators and utility companies to collect high-resolution
load data from their networks. These advancements enable the non-intrusive load
monitoring (NILM) technique to detect EV charging using load measurement data.
While existing studies primarily focused on NILM for EV charging detection in
individual households, there is a research gap on EV charging detection at the
feeder level, presenting unique challenges due to the combined load measurement
from multiple households. In this paper, we develop a novel and effective
approach for EV detection at the feeder level, involving sliding-window feature
extraction and classical machine learning techniques, specifically models like
XGBoost and Random Forest. Our developed method offers a lightweight and
efficient solution, capable of quick training. Moreover, our developed method
is versatile, supporting both offline and online EV charging detection. Our
experimental results demonstrate high-accuracy EV charging detection at the
feeder level, achieving an F-Score of 98.88% in offline detection and 93.01% in
online detection.
