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dc.contributor.authorde Souza, Wesley Angelino
dc.contributor.authorGarcia, Fernando Deluno [UNESP]
dc.contributor.authorMarafão, Fernando Pinhabel [UNESP]
dc.contributor.authorDa Silva, Luiz Carlos Pereira
dc.contributor.authorSimões, Marcelo Godoy
dc.date.accessioned2019-10-06T17:14:42Z
dc.date.available2019-10-06T17:14:42Z
dc.date.issued2019-01-01
dc.identifierhttp://dx.doi.org/10.3390/en12142641
dc.identifier.citationEnergies, v. 12, n. 14, 2019.
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/11449/190485
dc.description.abstractA new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.en
dc.language.isoeng
dc.relation.ispartofEnergies
dc.sourceScopus
dc.subjectArtificial intelligence
dc.subjectCognitive meters
dc.subjectLoad disaggregation
dc.subjectMachine learning
dc.subjectNILM
dc.subjectState machine
dc.titleLoad disaggregation using microscopic power features and pattern recognitionen
dc.typeArtigo
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionColorado School of Mines
dc.description.affiliationDepartment of Computer Science Federal University of São Carlos (UFSCar)
dc.description.affiliationInstitute of Science and Technology of Sorocaba São Paulo State University (UNESP)
dc.description.affiliationSchool of Electrical and Computer Engineering (FEEC) University of Campinas (UNICAMP)
dc.description.affiliationDepartment of Electrical Engineering Colorado School of Mines
dc.description.affiliationUnespInstitute of Science and Technology of Sorocaba São Paulo State University (UNESP)
dc.identifier.doi10.3390/en12142641
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-85068766271
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