基于贝叶斯网络的非侵入式家庭负荷动态监测模型  

Dynamic monitoring model of non-intrusive household load based on Bayesian network

作　　者：张恒[1] 邓其军[1] 周东国[1] Zhang Heng;Deng Qijun;Zhou Dongguo(School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学电气与自动化学院,武汉430072

出　　处：《电测与仪表》2020年第23期63-70,共8页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(61873195)。

摘　　要：智能量测技术是智能电网的重要组成部分,为了实现非侵入式负荷低频监测并进一步提升负荷辨识准确率,文中结合居民用电行为与外界环境相关的特点,提出一种基于贝叶斯网络的非侵入式家庭负荷动态监测模型,该模型选取电气特征和外部数据为特征量,综合考虑居民负荷的时间特性和对外部数据的关联特性,对居民用电行为采用贝叶斯网络模型进行建模分析,并随时间推移对特征库进行动态更新,从而实现对家庭负荷的监测作用。文中采用AMPds2公开数据集数据进行算法验证,证明文章算法的准确性和有效性,同时对外部数据和用电行为进行互信息分析,结果表明时段特征对用电行为相关性最强。Intelligent measurement technology is an important part of smart grid.In order to realize non-intrusive load low frequency monitoring and further improve the accuracy rate of load identification,a non-intrusive dynamic load monitoring model based on Bayesian network is proposed in this paper,which combines the characteristics of residential electricity consumption behavior and external environment.Gas characteristics and external data are selected as characteristic quantities.Considering the time characteristics of residential load and the correlation characteristics of external data,Bayesian network model is adopted to model and analyze residential electricity consumption behavior,and the feature database is updated dynamically over time,so as to realize the monitoring function of household load.This paper adopts AMPds2 public data set to verify the algorithm,which proves the accuracy and validity of the proposed algorithm.Meanwhile,the mutual information analysis of external data and electricity consumption behavior shows that the time-period characteristics have the strongest correlation with electricity consumption behavior.

关 键 词：贝叶斯网络 非侵入式负荷监测 用电行为分析 非电量特征 动态监测 

分 类 号：TM714[电气工程—电力系统及自动化]