Modeling and Forecasting Residential Natural Gas Demand in IRAN

Fatemeh Daei Jafari, Raissi Sadigh


The main focus of this paper is to provide an appropriate mathematical model to predict the natural gas demand for the next six months in the household sector using technical method regardless of influential variables. For this purpose, the most important and most widely used modeling methods of natural gas demands were used as modeling options and four different candidate families were analyzed based on 234 months of historical data of actual consumption and model parameters were estimated using appropriate methods. Then, by using the precision indicators such as average absolute error, average absolute percentage error, waste diffraction model and inequality coefficient of Thiel time series, predicted for a period SARIMA (1,1,2) (1,1,0)12 accepted as the most appropriate fitness function were Identified and of the next 6 months. The results showed that the proposed method has minimum residual in terms of MAE, MAPE, TIC and error variance.


Natural Gas Demand, Forecasting, Box-Jenkins’ Time Series, Fourier Series, Generalized Autoregressive Conditional Heteroscedasticity (GARCH)

Texto completo:

PDF (English)



Oil Ministry, the Institution of International Energy Studies, a twenty-year master plan for natural gas of Iran.


Akmal, M., & David, S. (2001). Reseidential energy demand in Australia: An application of dynamic OLS. Australian Bureau of Agricultural and Resource Economics,Australian National Univesity, WP 0101.

Arac, N., & Aras, H. (2004). Forecasting residential natural gas gemand. Energy Source 26, 463-476.

Azadeh, A., Asadzadeh, S., Saberi, M., Nadimi, V., Tajvidi, A., & Sheikalishahi, A. (2011). A Neuro-fuzzy-Stochastic frontier analysis approach for Long-term natural gas consumption forecasting and behavior Analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE. Applied Energy 88, 3850-3859.

Box G.E.P., Jenkins G.M. (2015). Time series analysis: forecasting and control (3rd ed.). USA: Englewood Cliffs, Prentice-Hall.

Dagher, L. (2011). Natural gas demand at the utility level: An application of dynamic elasticities. Energy Economics 34, 961-969.

Dilaver, O., Dilaver, Z., & C. Hunt, L. (2014). What drives natural gas consumption in Europe? Analysis and projections. Journal of Natural Gas Science and Engineering 19, 125-136.

Ediger, V., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35, 1701-1708.

Faheemullah Shaikh, Q. J. (2016). Forecasting natural gas demand in China: Logistic modelling analysis. Electrical Power and Energy Systems, 25-32.

Faheemullah Shaikh, Qiang Ji, Pervez Hameed Shaikh,Nayyar Hussain Mirjat , Muhammad Aslam Uqaili . (2017). Forecasting China’s natural gas demand based on optimised nonlinear. Energy , 941-951.

Filik, U., Gerek, O., & Kurban, M. (2010). A novel modeling approach for hourly forecasting of long-term electric energy demand. Energy Conversion and Management 52, 199-211.

Forouzanfar, M., Doustmohammadi, A., Bagher Menhaj, M., & Hasanzadeh, S. (2009). Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran. Applied Energy 87, 268-274.

Huntington, G. (2007). Industrial natural gas consumption in the United State: An empirical model for evaluting futur trend. Energy Economics29, 743-759.

Kani, A., Abbasspour, M., & Abedi, Z. (2013). Estimation of demand function for natural gas in Iran: Evidences based on smooth transition regression models. Economic Modelling 36, 341–347.

Kiani, B., & Pourfakhraei, M. (2010). A system dynamic model for production and consumption policy in Iran oil and gas sector. Energy Policy 38, 7764-7774.

Kovacic, M., & Sarler, B. (2014). Genetic programming prediction of the natural gas consumption in a steel plant. Energy, 1-12.

Liu, Q., & Kaboudan, M. (2003). Forecasting quarterly US demand for natural gas. Energy 26, 25-31.

Majazi Dalfard, V., Nazari Asli, M., Asadzadeh, S., Sajjadi, S., & Nazari Shirkouhi, A. (2012). A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting. Applied Mathematical Modelling 37, 5664-5679.

Melikoglu, M. (2013). Vision 2023: Forecasting Turkey’s natural gas demand between 2013 and 2023. Renewable and Sustainable Energy Reviews 22, 393-400.

Pourazarm, E., & Cooray, A. (2013). Estimating and forecasting residential electricity demand in Iran. Economic Modelling 35, 546-55.

Salehnia, N., Falahi, M., Seifi, A., & Mahdavi Adeli, M. (2013). Forecasting natural gas spot prices with nonlinear modeling using Gamma test analysis. Journal of Natural Gas Science and Engineering 14, 238-249.

Szoplik, J. (2015). Forecasting of natural gas consumption with artificial neural networks. Energy, 208-220.

Taspınar, F., Celebi, N., & Tutkun, N. (2012). Forecasting of daily natural gas consumption on regional basis in Turkey using. Energy and Buildings 56, 23-31.

Wadud, Z., Dey, H., AshfanoorKabir, M., & Khan, S. (2011). Modeling and forecasting natural gas demand in Bangladesh. Energy Policy39, 7372-7380.

Wang, T., & Lin, B. (2013). China's natural gas consumption and subsidies—From a sector perspective. Energy Policy 65, 541-551.

Wei Zhang, J. Y. (2015). Forecasting natural gas consumption in China by Bayesian Model. Energy Reports, 216–220.

Xu, G., & Wang, W. (2010). Forecasting China’s natural gas consumption. Journal of Natural Gas Chemistry 19, 493-496.

Ying Chen, Wee Song Chua, Thorsten Koch. (2018). Forecasting day-ahead high-resolution natural-gas demand and supply in. Applied Energy, 1091-1110.

Yu, Y., Zheng, X., & Han, Y. (2014). On the demand for natural gas in urban China. Energy Policy 29, 1-7.


Métricas do artigo

Carregando Métricas ...

Metrics powered by PLOS ALM


  • Não há apontamentos.

Direitos autorais 2019 Revista Gestão & Tecnologia

Licença Creative Commons
Esta obra está licenciada sob uma licença Creative Commons Atribuição - NãoComercial 4.0 Internacional.