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Article: A Hierarchical Hidden Markov Model Framework for Home Appliance Modeling

TitleA Hierarchical Hidden Markov Model Framework for Home Appliance Modeling
Authors
KeywordsHidden Markov models
Home appliances
Fitting
Load modeling
Smart meters
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411
Citation
IEEE Transactions on Smart Grid, 2018, v. 9 n. 4, p. 3079-3090 How to Cite?
AbstractCorrectly anticipating load characteristics of low voltage level is getting increased interest by distribution network operators. Energy disaggregation could be one of the potential approaches to exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modeling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward-backward algorithm, which is based on the framework of expectation maximization, is formalized for the HHMM fitting process. Tests on publically available data show that the HHMM and proposed algorithm can effectively handle the modeling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation.
Persistent Identifierhttp://hdl.handle.net/10722/278934
ISSN
2017 Impact Factor: 7.365
2015 SCImago Journal Rankings: 4.784

 

DC FieldValueLanguage
dc.contributor.authorKong, W-
dc.contributor.authorDong, ZY-
dc.contributor.authorHill, DJ-
dc.contributor.authorMa, J-
dc.contributor.authorZhao, JH-
dc.contributor.authorLuo, FJ-
dc.date.accessioned2019-10-21T02:16:37Z-
dc.date.available2019-10-21T02:16:37Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Smart Grid, 2018, v. 9 n. 4, p. 3079-3090-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/278934-
dc.description.abstractCorrectly anticipating load characteristics of low voltage level is getting increased interest by distribution network operators. Energy disaggregation could be one of the potential approaches to exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modeling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward-backward algorithm, which is based on the framework of expectation maximization, is formalized for the HHMM fitting process. Tests on publically available data show that the HHMM and proposed algorithm can effectively handle the modeling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.rightsIEEE Transactions on Smart Grid. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectHidden Markov models-
dc.subjectHome appliances-
dc.subjectFitting-
dc.subjectLoad modeling-
dc.subjectSmart meters-
dc.titleA Hierarchical Hidden Markov Model Framework for Home Appliance Modeling-
dc.typeArticle-
dc.identifier.emailHill, DJ: dhill@eee.hku.hk-
dc.identifier.authorityHill, DJ=rp01669-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TSG.2016.2626389-
dc.identifier.scopuseid_2-s2.0-85035357042-
dc.identifier.hkuros307207-
dc.identifier.volume9-
dc.identifier.issue4-
dc.identifier.spage3079-
dc.identifier.epage3090-
dc.publisher.placeUnited States-

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