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

Title	Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
Author	Beyca, Ö. F., Ervural, B. C., Tatoglu, E., Özuyar, Pınar Gökçin, Zaim, S.
Publication Date:	2019-05
Publication Place	- Elsevier
Subject	Natural gas forecasting, Machine learning, Artificial neural network, Support vector regression, Emerging countries, Istanbul
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0140-9883
Record ID	10dcca8b-08e4-45f4-b02e-a0b31581fd7f
Library Location	Entrepreneurship
Date	2019-05
Sample Text	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.
DOI	10.1016/j.eneco.2019.03.006
Cilt	80

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Özyeğin University

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

Author Beyca, Ö. F., Ervural, B. C., Tatoglu, E., Özuyar, Pınar Gökçin, Zaim, S.

Publication Date 2019-05

Publication Place - Elsevier

Subject Natural gas forecasting, Machine learning, Artificial neural network, Support vector regression, Emerging countries, Istanbul

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0140-9883

Record ID 10dcca8b-08e4-45f4-b02e-a0b31581fd7f

Library Location Entrepreneurship

Date 2019-05

Sample Text 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.

DOI 10.1016/j.eneco.2019.03.006

Cilt 80