Using machine learning tools for forecasting natural gas consumption in the province of Istanbul

Omer Faruk Beyca, Beyzanur Cayir Ervural, Ekrem Tatoglu, Pinar Gokcin Ozuyar and Selim Zaim

Energy Economics, 2019, vol. 80, issue C, 937-949

Abstract: Commensurate with unprecedented increases in energy demand, a well-constructed forecasting model is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods.

Keywords: Natural gas forecasting; Machine learning; Artificial neural network; Support vector regression; Emerging countries; Istanbul (search for similar items in EconPapers)
JEL-codes: C45 C53 E17 Q47 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (40)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:80:y:2019:i:c:p:937-949

DOI: 10.1016/j.eneco.2019.03.006

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