Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation

dc.contributor.author Yükseltan, Ergün
dc.contributor.author Bilge, Ayşe Hümeyra
dc.contributor.author Yücekaya, Ahmet
dc.contributor.author Ağca Aktunç, Esra
dc.contributor.author Bilge, Ayşe Hümeyra
dc.contributor.author Yücekaya, Ahmet Deniz
dc.contributor.author Ağca Aktunç, Esra
dc.contributor.other Industrial Engineering
dc.date.accessioned 2020-12-24T09:10:23Z
dc.date.available 2020-12-24T09:10:23Z
dc.date.issued 2020
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü en_US
dc.description.abstract Due to expensive infrastructure and the difficulties in storage, supply conditions of natural gas are different from those of other traditional energy sources like petroleum or coal. To overcome these challenges, supplier countries require take-or-pay agreements for requested natural gas quantities. These contracts have many pre-clauses; even if they are not met due to low/high consumption or other external factors, buyers must completely fulfill them. A similar contract is then imposed on distributors and wholesale consumers. It is, thus, important for all parties to forecast their daily, monthly, and annual natural gas demand to minimize their risk. In this paper, a model consisting of a modulated expansion in Fourier series, supplemented by deviations from comfortable temperatures as a regressor is proposed for the forecast of monthly and weekly consumption over a one-year horizon. This model is supplemented by a day-ahead feedback mechanism for the forecast of daily consumption. The method is applied to the study of natural gas consumption for major residential areas in Turkey, on a yearly, monthly, weekly, and daily basis. It is shown that residential heating dominates winter consumption and masks all other variations. On the other hand, weekend and holiday effects are visible in summer consumption and provide an estimate for residential and industrial use. The advantage of the proposed method is the capability of long term projections, reflecting causality, and providing accurate forecasts even with minimal information. en_US
dc.identifier.citationcount 6
dc.identifier.doi 10.1016/j.seps.2020.100937 en_US
dc.identifier.issn 0038-0121 en_US
dc.identifier.issn 0038-0121
dc.identifier.scopus 2-s2.0-85090215008 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://hdl.handle.net/20.500.12469/3640
dc.identifier.uri https://doi.org/10.1016/j.seps.2020.100937
dc.identifier.volume 2020 en_US
dc.identifier.wos WOS:000630128700001 en_US
dc.identifier.wosquality Q1
dc.institutionauthor Yükseltan, Ergün en_US
dc.institutionauthor Yücekaya, Ahmet en_US
dc.institutionauthor Bilge, Ayşe Hümeyra en_US
dc.institutionauthor Ağca Aktunç, Esra en_US
dc.language.iso en en_US
dc.publisher Elsevier Ltd en_US
dc.relation.journal Socio-Economic Planning Sciences en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 10
dc.subject Feedback en_US
dc.subject Forecasting en_US
dc.subject Fourier series en_US
dc.subject Modulation en_US
dc.subject Natural gas consumption en_US
dc.subject Time series analysis en_US
dc.title Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation en_US
dc.type Article en_US
dc.wos.citedbyCount 7
dspace.entity.type Publication
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