Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
Yükleniyor...
Dosyalar
Tarih
2019
Yazarlar
Beyca, Ömer Faruk
Ervural, Beyzanur Çayır
Tatoğlu, Ekrem
Özuyar, Pınar Gökçin
Zaim, Selim
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
Commensurate with unprecedented increases in energy demand, awell-constructed forecastingmodel 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.
Açıklama
Anahtar Kelimeler
Natural Gas Forecasting, Machine Learning, Artificial Neural Network, Support Vector Regression, Emerging Countries Istanbul
Kaynak
Energy Economics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
80
Sayı
Künye
Beyca, Ö. F., Ervural, B. Ç., Tatoğlu, E., Özuyar, P. G., Zaim, S. (2019). Using machine learning tools for forecasting natural gas consumption in the province of Istanbul. Energy Economics, 80, pp. 937-949.
