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dc.creatorSánchez-Zuleta C.C.spa
dc.creatorFernández-Gutiérrez J.P.spa
dc.creatorPiedrahita-Escobar C.C.spa
dc.date.accessioned2017-12-19T19:36:43Z
dc.date.available2017-12-19T19:36:43Z
dc.date.created2017
dc.identifier.issn1206230
dc.identifier.urihttp://hdl.handle.net/11407/4267
dc.description.abstractThe study of non-technical losses affecting energy trading companies has guided the researchers' perspective on different techniques and tools that allow them to detect, and why not, to forecast such losses. In the search for a solution to the problem, the different researchers rely on variables that, in many cases, the same marketing companies, from their practical experience, have been considered as incidents in the identification of the problem. However, most of the studies carried out do not support their solutions with the fact that each trading company retains particular data in which both, technical and socio-economic characteristics recorded, are not necessarily shared in their databases. In this work, we follow up on some of the characteristics registered by two Colombian energy trading companies, which serve two different regions of the country in terms of topography and idiosyncrasy. In particular, attention is focused on two characteristics measured in both companies, which by their nature, will always be on the data of any energy trading company: Consumption in kWh, and the period, measured in months. For this purpose, Benford curves analysis, MultiDimensional Scaling (MDS), and hierarchical cluster will be implemented. Finally, it will be studied if the incidence of the variables visualized in the studies presented is reflected in the decision tree model.eng
dc.language.isoeng
dc.publisherUniversidad de Antioquiaspa
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85029857355&doi=10.17533%2fudea.redin.n84a08&partnerID=40&md5=c7181a171c796e0ae37e81518f7379c1spa
dc.sourceScopusspa
dc.titleIdentification of the characteristics incident to the detection of non-technical losses for two Colombian energy companiesspa
dc.typeArticleeng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.contributor.affiliationSánchez-Zuleta, C.C., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, Colombiaspa
dc.contributor.affiliationFernández-Gutiérrez, J.P., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, Colombiaspa
dc.contributor.affiliationPiedrahita-Escobar, C.C., Departamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, Colombiaspa
dc.identifier.doi10.17533/udea.redin.n84a08
dc.subject.keywordBenford's Laweng
dc.subject.keywordClustereng
dc.subject.keywordDecision treeseng
dc.subject.keywordMDSeng
dc.subject.keywordNon-technical losseseng
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.abstractThe study of non-technical losses affecting energy trading companies has guided the researchers' perspective on different techniques and tools that allow them to detect, and why not, to forecast such losses. In the search for a solution to the problem, the different researchers rely on variables that, in many cases, the same marketing companies, from their practical experience, have been considered as incidents in the identification of the problem. However, most of the studies carried out do not support their solutions with the fact that each trading company retains particular data in which both, technical and socio-economic characteristics recorded, are not necessarily shared in their databases. In this work, we follow up on some of the characteristics registered by two Colombian energy trading companies, which serve two different regions of the country in terms of topography and idiosyncrasy. In particular, attention is focused on two characteristics measured in both companies, which by their nature, will always be on the data of any energy trading company: Consumption in kWh, and the period, measured in months. For this purpose, Benford curves analysis, MultiDimensional Scaling (MDS), and hierarchical cluster will be implemented. Finally, it will be studied if the incidence of the variables visualized in the studies presented is reflected in the decision tree model.eng
dc.creator.affiliationDepartamento de Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 # 30 - 65, Medellín, Colombiaspa
dc.relation.ispartofesRevista Facultad de Ingenieriaspa
dc.relation.ispartofesRevista Facultad de Ingenieria Volume 2017, Issue 84, 2017, Pages 60-71spa
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.instnameinstname:Universidad de Medellínspa


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