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

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

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    Bibliographic Info

    Article provided by Elsevier in its journal Applied Energy.

    Volume (Year): 87 (2010)
    Issue (Month): 1 (January)
    Pages: 268-274

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    Handle: RePEc:eee:appene:v:87:y:2010:i:1:p:268-274

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    Related research

    Keywords: Natural gas consumption prediction Natural gas consumption forecast Logistic equation Nonlinear programming Genetic algorithm;

    References

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    1. Liu, Lon-Mu & Lin, Maw-Wen, 1991. "Forecasting residential consumption of natural gas using monthly and quarterly time series," International Journal of Forecasting, Elsevier, vol. 7(1), pages 3-16, May.
    2. Aoki, Masanao & Yoshikawa, Hiroshi, 2002. "Demand saturation-creation and economic growth," Journal of Economic Behavior & Organization, Elsevier, vol. 48(2), pages 127-154, June.
    3. Meade, Nigel & Islam, Towhidul, 1995. "Forecasting with growth curves: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 11(2), pages 199-215, June.
    4. Sarak, H & Satman, A, 2003. "The degree-day method to estimate the residential heating natural gas consumption in Turkey: a case study," Energy, Elsevier, vol. 28(9), pages 929-939.
    5. Kejak, Michal & Seiter, Stephan & Vavra, David, 2004. "Accession trajectories and convergence: endogenous growth perspective," Structural Change and Economic Dynamics, Elsevier, vol. 15(1), pages 13-46, March.
    6. Siemek, Jakub & Nagy, Stanislaw & Rychlicki, Stanislaw, 2003. "Estimation of natural-gas consumption in Poland based on the logistic-curve interpretation," Applied Energy, Elsevier, vol. 75(1-2), pages 1-7, May.
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    Cited by:
    1. 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.
    2. 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.
    3. 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.

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