Forecasting daily natural gas consumption with regression, time series and machine learning based methods

Basit Öğe Kaydı
dc.authoridYUCESAN, Melih / 0000-0001-6148-4959
dc.authoridGul, Muhammet / 0000-0002-5319-4289
dc.authoridCelik, Erkan / 0000-0003-4465-0913
dc.authoridSerin, Faruk / 0000-0002-1458-4508
dc.authorwosidYUCESAN, Melih / AAP-4014-2021
dc.authorwosidGul, Muhammet / H-8881-2015
dc.authorwosidCelik, Erkan / O-1075-2013
dc.contributor.authorYucesan, Melih
dc.contributor.authorPekel, Engin
dc.contributor.authorCelik, Erkan
dc.contributor.authorGul, Muhammet
dc.contributor.authorSerin, Faruk
dc.date.accessioned2021-11-01T15:05:52Z
dc.date.available2021-11-01T15:05:52Z
dc.date.issued2021
dc.department[Belirlenecek]
dc.description.abstractAn effective short-term natural gas forecasting method contributes to social contributions and allows industrial chain elements to function effectively and minimize economic losses. We dealt with a comparative framework on the applicability of different methods in daily natural gas service (NGS) consumption forecasting. In this context, time series, machine learning, evolutionary and population-based approaches, and their hybrid versions are applied to the NGS data. Hybridized approaches are tested in the scope of NGS consumption forecasting for the first time in the literature in this study. The case of Turkey is handled, and its NGS data is used to demonstrate the comparative framework's applicability. The comparative study is assessed in the lights of common forecasting accuracy measures of mean absolute percentage error (MAPE), R-squared (R-2), and mean squared error (MSE). According to each method's results, the seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and artificial neural network (ANN) hybrid model provides the most dominant performance with respect to MAPE. The lowest error was obtained with a MAPE value of 0.357 in this hybrid model constructed under seven neurons in its ANN structure. This model is followed by another hybrid model, autoregressive integrated moving average (ARIMA)-ANN, with a MAPE value of 0.5 under nine neurons in terms of accuracy performance. The worst performance value belongs to the Genetic algorithm-ANN hybrid model with a MAPE value of approximately 26%.
dc.identifier.doi10.1080/15567036.2021.1875082
dc.identifier.issn1556-7036
dc.identifier.issn1556-7230
dc.identifier.scopus2-s2.0-85099744653
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1080/15567036.2021.1875082
dc.identifier.urihttps://hdl.handle.net/11491/7427
dc.identifier.wosWOS:000609600900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[Belirlenecek]
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofEnergy Sources Part A-Recovery Utilization And Environmental Effects
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectNatural gas consumptionen_US
dc.subjectforecasting methodsen_US
dc.subjectregressionen_US
dc.subjecttime seriesen_US
dc.subjectmachine learningen_US
dc.subjectTurkeyen_US
dc.titleForecasting daily natural gas consumption with regression, time series and machine learning based methods
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu