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dc.contributor.authorNalici, Mehmet Eren
dc.contributor.authorSöylemez, İsmet
dc.contributor.authorÜnlü, Ramazan
dc.date.accessioned2024-07-03T13:38:24Z
dc.date.available2024-07-03T13:38:24Z
dc.date.issued2024en_US
dc.identifier.urihttp://doi.org/10.17798/bitlisfen.1395411
dc.identifier.urihttps://hdl.handle.net/20.500.12573/2244
dc.description.abstractNatural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in vehicles and fleets. Moreover, natural gas plays a vital role in boosting energy efficiency through the development of combined heat and power systems. These systems produce electricity and useful heat concurrently. As nations move towards more sustainable energy solutions, natural gas has gained prominence as a transitional fuel. This is due to its lower carbon emissions when compared to coal and oil, thus making it an essential component of the global energy framework. In this study, monthly natural gas consumption data of 28 different European countries between 2014 and 2022 are used. Symbolic Aggregate Approximation method is used to analyse the data. Analyses are made with different numbers of segments and numbers of alphabet sizes, and alphabet vectors of each country are created. These letter vectors are used in hierarchical clustering and dendrogram graphs are created. Furthermore, the elbow method is used to determine the appropriate number of clusters. Clusters of countries are created according to the determined number of clusters. In addition, it is interpreted according to the consumption trends of the countries in the determined clusters.en_US
dc.language.isoengen_US
dc.publisherBitlis Eren Üniversitesien_US
dc.relation.isversionof10.17798/bitlisfen.1395411en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learningen_US
dc.subjectSymbolic Aggregate Approximationen_US
dc.subjectClusteringen_US
dc.subjectEnergyen_US
dc.titleSymbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumptionen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0002-7954-6916en_US
dc.contributor.authorID0000-0002-8253-9389en_US
dc.contributor.authorID0000-0002-1201-195Xen_US
dc.contributor.institutionauthorNalici, Mehmet Eren
dc.contributor.institutionauthorSöylemez, İsmet
dc.contributor.institutionauthorÜnlü, Ramazan
dc.identifier.volume13en_US
dc.identifier.issue1en_US
dc.identifier.startpage307en_US
dc.identifier.endpage313en_US
dc.relation.journalBitlis Eren Üniversitesi Fen Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US


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