现代电子技术2025，Vol.48Issue(9)：167-172,6.DOI:10.16652/j.issn.1004-373x.2025.09.025
基于NILM的电动自行车室内充电监测
Non-intrusive load monitoring(NILM)for indoor electric bicycle charging monitoring
摘要
Abstract
The increase in fires caused by indoor charging of electric bicycles(EBs)has made effective monitoring a challenging issue.By analyzing common characteristics of charging loads of the EBs with various brands and models,a non-intrusive real-time EB indoor charging monitoring system based on wavelet event detection and a two-stage feature selection process is proposed.In the system,the sensitivity of multi-scale wavelet transform to signal mutation is used to detect EB charging events based on transient characteristics when the load connects to the circuit,improving detection accuracy and reducing computational load.Then,on the basis of steady-state characteristics during operation,a two-stage feature selection is employed.In the first stage,the MDMR(maximum discrimination and minimum redundancy)filter is used to rank the importance of 14 load features.In the second stage,the OCSVM(one-class support vector machine)is used as a wrapper to select the optimal feature subsets.The actual user monitoring experiments verify that the proposed method can achieve fast response,high precision identification and low calculation cost,so it provides an economical and efficient solution for indoor charging monitoring of EBs.关键词
非侵入式负荷监测/电动自行车/室内充电/小波变换/特征选择/一类支持向量机Key words
NILM/EB/indoor charging/wavelet transform/feature selection/OCSVM分类
电子信息工程引用本文复制引用
陈志高,李琰,徐天奇..基于NILM的电动自行车室内充电监测[J].现代电子技术,2025,48(9):167-172,6.基金项目
国家自然科学基金项目(62062068) （62062068）
云南省青年学术和技术带头人计划(202305AC160077) （202305AC160077）
