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dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorAlsharif, Futoon
dc.date.accessioned2022-11-08T11:29:32Z
dc.date.available2022-11-08T11:29:32Z
dc.date.issued2020
dc.identifier55555
dc.identifier.citationQaisar, Saeed Mian, and Futoon Alsharif. “Event-Driven System For Proficient Load Recognition by Interpreting the Smart Meter Data.” Procedia Computer Science, vol. 168, 2020, pp. 210–16. DOI.org (Crossref), https://doi.org/10.1016/j.procs.2020.02.267
dc.identifier.issn1877-0509
dc.identifier.doi10.1016/j.procs.2020.02.267
dc.identifier.urihttp://hdl.handle.net/20.500.14131/124
dc.identifier.urihttp://hdl.handle.net/20.500.14131/183
dc.description.abstractThe technological advancements have evolved the deployment of smart meters. A fine-grained metering data collection and analysis is necessary to bring benefits to multiple smart grid stakeholders. The classical sensing mechanism is time-invariant. Therefore, it results in the collection, transmission, and processing of a large amount of unnecessary data. This work employs the event-driven sensing mechanism to achieve real-time data compression. Afterward, the novel adaptive rate techniques are employed for the data conditioning, segmentation, and extraction of features. The pertinent features regarding the appliances' consumption patterns are afterward used for their identification. It is realized by employing the mature Support Vector Machine and k-Nearest Neighbor classifiers. Results confirm a 3.4 times compression gain and the computational effectiveness of the suggested solution while securing 95.4% classification precision. It shows the benefits of integrating the proposed method in the realization of current energy efficiency services like enumerated consumption billing, effective load identification, and dynamic load management.
dc.language.isoen_US
dc.publisherElsevier B.V.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSmart Meter Data
dc.subjectConsumption Pattern
dc.subjectAutomatic Load Identification
dc.subjectEvent-Driven sensing
dc.subjectAdaptive Rate Processing
dc.subjectFeatures extraction
dc.subjectMachine learning
dc.titleEvent-Driven System For Proficient Load Recognition by Interpreting the Smart Meter Data
dc.typeArticle
dc.source.journalProcedia Computer Science
dc.source.volume168
refterms.dateFOA2022-11-08T11:31:05Z
dc.contributor.researcherElectrical and Computer Engineering


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