Selection of features from power theories to compose NILM datasets

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorPR-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSouza, Wesley A.-
Autor(es): dc.creatorAlonso, Augusto M.S.-
Autor(es): dc.creatorBosco, Thais B.-
Autor(es): dc.creatorGarcia, Fernando D.-
Autor(es): dc.creatorGonçalves, Flavio A.S.-
Autor(es): dc.creatorMarafão, Fernando P.-
Data de aceite: dc.date.accessioned2025-08-21T16:06:01Z-
Data de disponibilização: dc.date.available2025-08-21T16:06:01Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.aei.2022.101556-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/234180-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/234180-
Descrição: dc.descriptionThe load disaggregation concept is gaining attention due to the increasing need for optimized energy utilization and detailed characterization of electricity consumption profiles, especially through Nonintrusive Load Monitoring (NILM) approaches. This occurs since knowledge about individualized consumption per appliance allows to create strategies striving for energy savings, improvement of energy efficiency, and creating energy awareness to consumers. Moreover, by using feature extraction to devise energy disaggregation, one can achieve accurate identification of electric appliances. However, even though several literature works propose distinct features to be utilized, no consensus exists in the literature about the most appropriate set of features that ensure high accuracy on load disaggregation. Thus, beyond presenting a critical analysis of some significant features often selected in the literature, this paper proposes identifying the most relevant ones considering collinearity and machine learning algorithms. The results show that high-performance metrics can be achieved with fewer features than usually adopted in the literature. Moreover, it is demonstrated that the Conservative Power Theory can offer the most representative features for appliance identification, leading to efficient power consumption disaggregation.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Electrical Engineering Federal University of Technology - Parana (UTFPR) Cornélio Procópio PR-
Descrição: dc.descriptionSchool of Electrical and Computer Engineering University of Campinas (UNICAMP) Campinas SP-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (UNESP) Sorocaba SP-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (UNESP) Sorocaba SP-
Descrição: dc.descriptionFAPESP: 2016/08645-9-
Idioma: dc.languageen-
Relação: dc.relationAdvanced Engineering Informatics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectElectric consumption management-
Palavras-chave: dc.subjectFeatures quality-
Palavras-chave: dc.subjectLoad disaggregation-
Palavras-chave: dc.subjectNonintrusive load monitoring-
Palavras-chave: dc.subjectSmart meters-
Título: dc.titleSelection of features from power theories to compose NILM datasets-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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