典型文献
State identification of home appliance with transient features in residential buildings
文献摘要：
Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users'behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verifi-cation step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hIgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.
文献关键词：
中图分类号：
作者姓名：
Lei YAN;Runnan XU;Mehrdad SHEIKHOLESLAMI;Yang LI;Zuyi LI
作者机构：
Electrical and Computer Engineering Department,Illinois Institute of Technology,Chicago,IL 60616,USA;School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,China
文献出处：
引用格式：
[1]Lei YAN;Runnan XU;Mehrdad SHEIKHOLESLAMI;Yang LI;Zuyi LI-.State identification of home appliance with transient features in residential buildings)[J].能源前沿,2022(01):130-143
A类：
Nonintrusive,FHMM,hIgh,daTaset,LIFTED
B类：
State,identification,home,transient,features,residential,buildings,load,monitoring,NILM,crucial,extracting,patterns,electricity,consumption,household,that,can,guide,users,behavior,using,while,their,privacy,respected,This,study,proposes,online,individual,appliances,well,system,steady,characteristics,operating,states,It,determines,number,each,density,spatial,clustering,applications,noise,DBSCAN,models,transition,relationship,among,different,working,are,identified,from,aggregated,power,signals,Kalman,filtering,factorial,hidden,Markov,Thereafter,confirmed,by,verification,which,combination,step,involves,comparing,total,measured,estimated,achieve,fast,inference,suitable,disaggregation,proposed,was,tested,high,resolution,data,such,Labeled,Frequency,Electricity,Disaggregation,outperformed,other,related,methods,literature
AB值：
0.533824
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