State identification of home appliance with transient features in residential buildings
作者机构：Electrical and Computer Engineering DepartmentIllinois Institute of TechnologyChicagoIL 60616USA School of Electrical EngineeringNortheast Electric Power UniversityJilin 132012China
出 版 物：《Frontiers in Energy》 (能源前沿（英文版）)
年 卷 期：2022年第16卷第1期
页 面：130-143页
核心收录：
学科分类：0820[工学-石油与天然气工程] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：nonintrusive load monitoring(NILM) load disaggregation online load disaggregation Kalman filtering factorial hidden Markov model(FHMM) Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)
摘 要：Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is *** 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 *** 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 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 *** verification step involves comparing the total measured power consumption with the total estimated power *** use of transient features can achieve fast state inference and it is suitable for online load *** proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.
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