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Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran

Author

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  • Forouzanfar, Mehdi
  • Doustmohammadi, Ali
  • Menhaj, M. Bagher
  • Hasanzadeh, Samira

Abstract

In this paper, a logistic based approach is used to forecast the natural gas consumption for residential as well as commercial sectors in Iran. This approach is relatively simple compared with other forecasting approaches. To make this approach even simpler, two different methods are proposed to estimate the logistic parameters. The first method is based on the concept of the nonlinear programming (NLP) and the second one is based on genetic algorithm (GA). The forecast implemented in this paper is based on yearly and seasonal consumptions. In some unusual situations, such as abnormal temperature changes, the forecasting error is as high as 8.76%. Although this error might seem high, one does not need to be deeply concerned about the overall results since these unusual situations could be filtered out to yield more reliable predictions. In general, the overall results obtained using NLP and GA approaches are as well as or even in some cases better than the results obtained using some older approaches such as Cavallini's. These two approaches along with the gas consumption data in Iran for the previous 10 years are used to predict the consumption for the 11th, 12th, and 13th years. It is shown that the logistic approach with the use of NLP and GA yields very promising results.

Suggested Citation

  • Forouzanfar, Mehdi & Doustmohammadi, Ali & Menhaj, M. Bagher & Hasanzadeh, Samira, 2010. "Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran," Applied Energy, Elsevier, vol. 87(1), pages 268-274, January.
  • Handle: RePEc:eee:appene:v:87:y:2010:i:1:p:268-274
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Li, Junchen & Dong, Xiucheng & Shangguan, Jianxin & Hook, Mikael, 2011. "Forecasting the growth of China’s natural gas consumption," Energy, Elsevier, vol. 36(3), pages 1380-1385.
    2. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    3. repec:gam:jeners:v:10:y:2017:i:12:p:2047-:d:121471 is not listed on IDEAS
    4. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    5. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    6. Amin Yousefi-Sahzabi & Kyuro Sasaki & Hossein Yousefi & Yuichi Sugai, 2011. "CO 2 emission and economic growth of Iran," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 16(1), pages 63-82, January.
    7. Melikoglu, Mehmet, 2013. "Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 393-400.
    8. Forouzanfar, Mehdi & Doustmohammadi, A. & Hasanzadeh, Samira & Shakouri G, H., 2012. "Transport energy demand forecast using multi-level genetic programming," Applied Energy, Elsevier, vol. 91(1), pages 496-503.
    9. Huebner, Gesche M. & Hamilton, Ian & Chalabi, Zaid & Shipworth, David & Oreszczyn, Tadj, 2015. "Explaining domestic energy consumption – The comparative contribution of building factors, socio-demographics, behaviours and attitudes," Applied Energy, Elsevier, vol. 159(C), pages 589-600.
    10. Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Scenario analysis of nonresidential natural gas consumption in Italy," Applied Energy, Elsevier, vol. 113(C), pages 392-403.
    11. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    12. Askari, S. & Montazerin, N. & Fazel Zarandi, M.H., 2016. "Gas networks simulation from disaggregation of low frequency nodal gas consumption," Energy, Elsevier, vol. 112(C), pages 1286-1298.
    13. Spoladore, Alessandro & Borelli, Davide & Devia, Francesco & Mora, Flavio & Schenone, Corrado, 2016. "Model for forecasting residential heat demand based on natural gas consumption and energy performance indicators," Applied Energy, Elsevier, vol. 182(C), pages 488-499.
    14. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    15. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 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, Elsevier, vol. 88(11), pages 3850-3859.
    16. repec:eee:energy:v:141:y:2017:i:c:p:1269-1284 is not listed on IDEAS

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