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https://hdl.handle.net/20.500.13091/551
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DC Field | Value | Language |
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dc.contributor.author | Erdem, Osman Emin | - |
dc.contributor.author | Kesen, Saadettin Erhan | - |
dc.date.accessioned | 2021-12-13T10:26:55Z | - |
dc.date.available | 2021-12-13T10:26:55Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2147-1762 | - |
dc.identifier.issn | 2147-1762 | - |
dc.identifier.uri | https://doi.org/10.35378/gujs.586107 | - |
dc.identifier.uri | https://app.trdizin.gov.tr/makale/TXpZeU5UUXlNZz09 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/551 | - |
dc.description.abstract | Technological advancements coupled with growing world population require the increasing need of energy. Natural gas is one of the most important usable energy resources. Turkey is with high external dependency on energy as it has its own limited natural and underground energy resources. Thus, in order to effectively and productively use of natural gas purchased from foreign countries and to make reliable and robust energy policies for the years ahead, it is crucial to make a reasonable and plausible prediction for natural gas consumption of Turkey. In this paper, we estimate the natural gas consumption using machine learning techniques on the basis of real monthly data representing natural gas consumption of Turkey between the years 2010 and 2018. The performances of machine learning techniques involving Artificial Neural Networks, Random Forest Tree, Regression, Time Series and Multiple Seasonality Time Series are compared in predicting the natural gas consumption of Turkey. Experimental results show that among the five techniques, artificial neural networks produce the best estimation, having the lowest mean square errors, followed by regression method. Time series shows the worst performance among all the techniques. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Gazi University Journal of Science | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.title | Estimation of Turkey’s Natural Gas Consumption by Machine Learning Techniques | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.35378/gujs.586107 | - |
dc.identifier.scopus | 2-s2.0-85086758219 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü | en_US |
dc.identifier.volume | 33 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.startpage | 120 | en_US |
dc.identifier.endpage | 133 | en_US |
dc.identifier.wos | WOS:000519536100010 | en_US |
dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.trdizinid | 362542 | en_US |
dc.identifier.scopusquality | Q3 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | 02.09. Department of Industrial Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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f3688b70-4a71-45d6-adc3-fc1440015481.pdf | 930.46 kB | Adobe PDF | View/Open |
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