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dc.contributor.authorÇelik, Erkan
dc.contributor.authorYucesan, Melih
dc.contributor.authorPekel, Engin
dc.contributor.authorGul, Muhammet
dc.contributor.authorSerin, Faruk
dc.date.accessioned2021-03-02T15:45:26Z
dc.date.available2021-03-02T15:45:26Z
dc.identifier.citationYucesan M., Pekel E., Çelik E., Gul M., Serin F., "Forecasting daily natural gas consumption with regression, time series and machine learning based methods", Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 2021
dc.identifier.issn1556-7036
dc.identifier.othervv_1032021
dc.identifier.otherav_45998df3-9401-438f-86c4-2d6acf12abfa
dc.identifier.urihttp://hdl.handle.net/20.500.12627/1661
dc.identifier.urihttps://doi.org/10.1080/15567036.2021.1875082
dc.description.abstract© 2021 Taylor & Francis Group, LLC.An 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 (R2), 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.language.isoeng
dc.subjectEndüstri Mühendisliği
dc.subjectMühendislik ve Teknoloji
dc.subjectMÜHENDİSLİK, ENDÜSTRİYEL
dc.subjectFizik
dc.subjectMühendislik
dc.subjectTemel Bilimler (SCI)
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.titleForecasting daily natural gas consumption with regression, time series and machine learning based methods
dc.typeMakale
dc.relation.journalEnergy Sources, Part A: Recovery, Utilization and Environmental Effects
dc.contributor.departmentMunzur University , ,
dc.contributor.firstauthorID2513039


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