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

Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection