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dc.contributor.authorDash, S.-
dc.contributor.authorGandhi, K.-
dc.contributor.authorSodhi, R.-
dc.date.accessioned2021-08-12T23:03:06Z-
dc.date.available2021-08-12T23:03:06Z-
dc.date.issued2021-08-13-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2387-
dc.description.abstractKnowing the power consumption of individual household appliances is useful for end-user as well as utilities. There are two ways for appliance load monitoring (ALM), namely intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). This paper focuses on the NILM approach, and discusses a simple yet effective method to improve its accuracy by constructing a better knowledge-base. The proposed methodology is initially verified with the simulation using the Reference Energy Disaggregation Data (REDD) dataset, and later tested on a lab-scale hardware setup as well. Test results reveal that careful construction of knowledge-base can increase the performance of NILM algorithms. MATLAB is used as the programming platformen_US
dc.language.isoen_USen_US
dc.subjectAutomatic State Detectionen_US
dc.subjectNon-intrusive Load Monitoringen_US
dc.subjectNon-intrusive Load Monitoringen_US
dc.titleAn automatic state detection algorithm for Non-intrusive load monitoringen_US
dc.typeArticleen_US
Appears in Collections:Year-2019

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