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

Name: 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.

İsim	Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
Yazar	Beyca, Ö. F., Ervural, B. C., Tatoglu, E., Özuyar, Pınar Gökçin, Zaim, S.
Basım Tarihi:	2019-05
Basım Yeri	- Elsevier
Konu	Natural gas forecasting, Machine learning, Artificial neural network, Support vector regression, Emerging countries, Istanbul
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0140-9883
Kayıt Numarası	10dcca8b-08e4-45f4-b02e-a0b31581fd7f
Lokasyon	Entrepreneurship
Tarih	2019-05
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

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

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

Basım Tarihi 2019-05

Basım Yeri - Elsevier

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0140-9883

Kayıt Numarası 10dcca8b-08e4-45f4-b02e-a0b31581fd7f

Lokasyon Entrepreneurship

Tarih 2019-05

Örnek Metin 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