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dc.contributor.authorGhosh, S
dc.contributor.authorPanda, DK
dc.contributor.authorDas, S
dc.contributor.authorChatterjee, D
dc.date.accessioned2021-03-17T14:25:00Z
dc.date.issued2021-03-16
dc.description.abstractOver the last few decades, residential electrical load classification and identification have been one of the most challenging research in the area of non-intrusive load monitoring (NILM) for home energy management system. The application of NILM technique in the smart grid has gained enormous attention in recent years. Several methods, including information from the given domains into NILM, have been proposed. Recently, among these methods, machine learning techniques are shown to be significantly better based on large-scale data for load monitoring. In this paper, machine learning techniques are utilized for residential load classification on novel cross-correlation based features, which are extracted from the synthetic time series data. We also present a t-distributed stochastic neighbour embedding (t SNE) based dimensionality reduction from the high dimensional feature set so that the classification can be implemented on a general-purpose microcontroller for near real-time monitoring. Our experimental results show that the extracted features are suitable for reliable identification and classification of different and the combination of residential loads.en_GB
dc.description.sponsorshipVisvesvaraya PhD scheme, Government of Indiaen_GB
dc.identifier.citation2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), 21 - 23 January 2021, Hyderabad, Indiaen_GB
dc.identifier.doi10.1109/sefet48154.2021.9375687
dc.identifier.grantnumberVISPHD-MEITY-1431en_GB
dc.identifier.urihttp://hdl.handle.net/10871/125144
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rightsCopyright © 2021, IEEEen_GB
dc.subjectNon-intrusive load monitoringen_GB
dc.subjectsmart meteringen_GB
dc.subjectclassificationen_GB
dc.subjectsupervised machine learningen_GB
dc.titleCross-correlation based classification of electrical appliances for non-intrusive load monitoringen_GB
dc.typeConference paperen_GB
dc.date.available2021-03-17T14:25:00Z
dc.identifier.isbn978-1-7281-5681-1
dc.descriptionThis is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via the DOI in this recorden_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2021-03-16
rioxxterms.typeConference Paper/Proceeding/Abstracten_GB
refterms.dateFCD2021-03-17T14:22:08Z
refterms.versionFCDAM
refterms.dateFOA2021-03-17T14:25:05Z
refterms.panelBen_GB


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