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

arXiv: https://arxiv.org/abs/2312.01887 · HTML: https://mlsanity.com/paper/2312.01887/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning](https://mlsanity.com/paper/2106.00854/) (2021)
- [Fairness-Aware Optimization of Vehicle-to-Vehicle Interaction for Smart EV Charging Coordination](https://mlsanity.com/paper/2304.02232/) (2023)
- [Cross-Entropy-Based Approach to Multi-Objective Electric Vehicle Charging Infrastructure Planning](https://mlsanity.com/paper/2308.14117/) (2023)

## Built on by (most cited first, 3 of 3)

- [Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method](https://mlsanity.com/paper/2410.15379/) (2024)
- [Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data](https://mlsanity.com/paper/2403.13246/) (2024)
- [Uncovering Residential PV-EV Co-Adoption from Smart-Meter Data: Load Archetypes and Detection for Demand-Side Planning](https://mlsanity.com/paper/2609.28578/) (2026)

## Abstract (arXiv)

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.