A demand-side management assessment of residential consumers by a clustering approach

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dc.contributor.authorOğuz, Eray
dc.contributor.authorTekdemir, İbrahim Gürsu
dc.contributor.authorGözel, Tuba
dc.date.accessioned2024-06-13T20:17:49Z
dc.date.available2024-06-13T20:17:49Z
dc.date.issued2023
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Elektrik Programı
dc.description.abstractResidential consumers have a significant share in total energy demand today. Demand-side management is a collection of processes which makes providing large amounts of energy less problematic. Identifying demand characteristics of energy consumers is a remarkable part of this process. Data clustering methods have recently been proposed as beneficial tools at that point. In this study, a novel parametric representation of residential energy consumption data is proposed. For that purpose, eleven specific parameters are proposed first for extraction of features in data. Next, principal component analysis is used for dimension reduction. Finally, k-means algorithm is applied for clustering. Two residential energy consumption datasets are used for validation. Analyses are carried out in MATLAB and R. Data clustering is realized on a monthly basis by using daily load curves and clustering performance is compared with another study. It is found that the proposed approach leads to the formation of meaningful clusters of residential consumers. It is also possible to observe demand tendency on a daily basis since daily consumption data is used during the process. Performance evaluation scores show that energy consumption data fit better into clusters when it is compared with another study in the literature.
dc.identifier.doi10.1007/s00202-022-01681-7
dc.identifier.endpage508
dc.identifier.issn0948-7921
dc.identifier.issn1432-0487
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85141977090
dc.identifier.scopusqualityQ2
dc.identifier.startpage493
dc.identifier.urihttps://doi.org/10.1007/s00202-022-01681-7
dc.identifier.urihttps://hdl.handle.net/11501/1095
dc.identifier.volume105
dc.identifier.wosWOS:000884155700001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofElectrical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDemand-Side Management
dc.subjectResidential Energy Consumption
dc.subjectData Clustering
dc.subjectIdentification of Energy Demand Characteristics
dc.subjectElectricity Usage Profiles
dc.subjectIdentification
dc.subjectStrategies
dc.titleA demand-side management assessment of residential consumers by a clustering approach
dc.typeArticle

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