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dc.creatorPanapakidis I.P., Kechagias C.-A., Bargiotas D.en
dc.date.accessioned2023-01-31T09:41:37Z
dc.date.available2023-01-31T09:41:37Z
dc.date.issued2021
dc.identifier10.1109/UPEC50034.2021.9548166
dc.identifier.isbn9781665443890
dc.identifier.urihttp://hdl.handle.net/11615/77477
dc.description.abstractClustering-based load profiling utilizes unsupervised machine learning algorithms to form homogenous clusters composed by electricity load curves with similar characteristics. Due to the importance of load profiling in modern power systems, a variety of clustering algorithms of different type and complexity has been proposed in the technical literature. Nevertheless, no attention has been placed in metaheuristics-based clustering. The present paper proposes a novel hybrid algorithm that is composed by two clustering algorithms, namely a hierarchical algorithm and a meta-heuristic one. A comparison takes place with the most common algorithms of the literature. Experimental results indicate the robustness of the proposed approach to the electricity load curves profiling research problem. © 2021 IEEE.en
dc.language.isoenen
dc.source2021 56th International Universities Power Engineering Conference: Powering Net Zero Emissions, UPEC 2021 - Proceedingsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116708730&doi=10.1109%2fUPEC50034.2021.9548166&partnerID=40&md5=c1900914652a457a2d134ff99a57aa73
dc.subjectClustering algorithmsen
dc.subjectCurve fittingen
dc.subjectLearning algorithmsen
dc.subjectMachine learningen
dc.subjectOptimizationen
dc.subjectClusteringsen
dc.subjectElectricity loaden
dc.subjectHybrid metaheuristicsen
dc.subjectLoad curvesen
dc.subjectLoad profilesen
dc.subjectLoad profilingen
dc.subjectMachine learning algorithmsen
dc.subjectMetaheuristicen
dc.subjectOptimisationsen
dc.subjectUnsupervised machine learningen
dc.subjectHeuristic algorithmsen
dc.subjectInstitute of Electrical and Electronics Engineers Inc.en
dc.titleA hybrid metaheuristics-based algorithm for electricity load curves profilingen
dc.typeconferenceItemen


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