Natural gas consumption behavior of companies by clustering analysis

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dc.authorscopusid57202924825
dc.contributor.authorDoğan, Onur
dc.date.accessioned2022-02-15T16:57:34Z
dc.date.available2022-02-15T16:57:34Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractWe have still consumed natural gas as a restricted source of energy in our daily life. Moreover, the consumption of natural gas energy will continue to increase by the year. Although many studies have focused on electrical energy consumption, natural gas is another significant energy source that can be examined. Since companies consume much more gas, their gas consumption data are examined in this study. The study contributes to the literature by applying intuitionistic fuzzy c-means (IFCM) methodology to the natural gas industry. The main motivation and advantage of the methodology is two-fold. Because of its fuzziness, one data point can be assigned into more than one cluster, similar to real-world cases. Because of its intuitionistic side, it considers membership, non-membership and hesitant degrees. These two strengths of IFCM improves the clustering accuracy. IFCM clustering was used to arrange the companies with respect to the consumption amount to increase the understandability because 1049 companies' consumption data were collected. A calendar view was developed to visualize the consumption amounts in the clusters. The changes in consumption amounts were presented in different weather temperatures. Whereas some clustered companies were directly affected by temperature changes, others were not affected. The companies in the clusters were analyzed with respect to two main criteria: regularity and complexity. The findings showed while high levels in routine are related to manufacturing companies, high complexity level is an indicator of being active in the service industry.en_US
dc.identifier.doi10.1016/j.engappai.2021.104502
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85116942263en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2021.104502
dc.identifier.urihttps://hdl.handle.net/20.500.14034/208
dc.identifier.volume106en_US
dc.identifier.wosWOS:000711162100003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDoğan, Onur
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.journalEngineering Applications Of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCompany behaviorsen_US
dc.subjectNatural gas consumptionen_US
dc.subjectIntuitionistic fuzzy c-meansen_US
dc.subjectCalendar viewen_US
dc.subjectSCADA smart meteringen_US
dc.subjectEnergy-Consumptionen_US
dc.subjectElectricity Consumptionen_US
dc.subjectLoad Profilesen_US
dc.subjectPredictionen_US
dc.subjectAlgorithmen_US
dc.subjectClassificationen_US
dc.subjectImplementationen_US
dc.subjectIntelligenceen_US
dc.titleNatural gas consumption behavior of companies by clustering analysisen_US
dc.typeArticleen_US

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