dc.contributorPR
dc.contributorUniversidade Estadual de Campinas (UNICAMP)
dc.contributorUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-05-01T13:57:30Z
dc.date.accessioned2022-12-20T03:49:30Z
dc.date.available2022-05-01T13:57:30Z
dc.date.available2022-12-20T03:49:30Z
dc.date.created2022-05-01T13:57:30Z
dc.date.issued2022-04-01
dc.identifierAdvanced Engineering Informatics, v. 52.
dc.identifier1474-0346
dc.identifierhttp://hdl.handle.net/11449/234180
dc.identifier10.1016/j.aei.2022.101556
dc.identifier2-s2.0-85125149045
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5414281
dc.description.abstractThe 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.
dc.languageeng
dc.relationAdvanced Engineering Informatics
dc.sourceScopus
dc.subjectElectric consumption management
dc.subjectFeatures quality
dc.subjectLoad disaggregation
dc.subjectNonintrusive load monitoring
dc.subjectSmart meters
dc.titleSelection of features from power theories to compose NILM datasets
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución