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dc.contributor.authorSchirmer, Pascal
dc.contributor.authorMporas, Iosif
dc.contributor.authorSheikh-Akbari, Akbar
dc.date.accessioned2020-05-04T15:00:10Z
dc.date.available2020-05-04T15:00:10Z
dc.date.issued2020-05-01
dc.identifier.citationSchirmer , P , Mporas , I & Sheikh-Akbari , A 2020 , ' Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors ' , Energies , vol. 13 , no. 9 , 2148 . https://doi.org/10.3390/en13092148
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/2299/22648
dc.description© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.description.abstractA data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.en
dc.format.extent17
dc.format.extent1176042
dc.language.isoeng
dc.relation.ispartofEnergies
dc.titleEnergy Disaggregation Using Two-Stage Fusion of Binary Device Detectorsen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionBioEngineering
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.3390/en13092148
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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