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Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods

İsim	Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods
Yazar	Demirel, Ö. F., Zaim, S., Çalışkan, A., Özuyar, Pınar Gökçin
Basım Tarihi:	2012
Basım Yeri	- TÜBİTAK
Konu	Forecasting, Neural networks, Natural gas, Time series
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1303-6203
Kayıt Numarası	51631f67-ff4c-41b4-b445-d3ddf14b813f
Lokasyon	Entrepreneurship
Tarih	2012
Örnek Metin	The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a result, if the forecasts are not done correctly, either unused natural gas amounts must be paid or there will be shortages of natural gas in the planning periods.
DOI	10.3906/elk-1101-1029
Cilt	20

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Özyeğin Üniversitesi

Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods

Yazar Demirel, Ö. F., Zaim, S., Çalışkan, A., Özuyar, Pınar Gökçin

Basım Tarihi 2012

Basım Yeri - TÜBİTAK

Konu Forecasting, Neural networks, Natural gas, Time series

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1303-6203

Kayıt Numarası 51631f67-ff4c-41b4-b445-d3ddf14b813f

Lokasyon Entrepreneurship

Tarih 2012

Örnek Metin The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a result, if the forecasts are not done correctly, either unused natural gas amounts must be paid or there will be shortages of natural gas in the planning periods.

DOI 10.3906/elk-1101-1029

Cilt 20