A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting

V. Majazi Dalfard, M. Nazari Asli, S. M. Asadzadeh, S. M. Sajjadi, A. Nazari-Shirkouhi

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


In some countries that energy prices are low, price elasticity of demand may not be significant. In this case, large increase or hike in energy prices may impact energy consumption in a way which cannot be drawn from historical data. This paper proposes an integrated adaptive fuzzy inference system (FIS) to forecast long-term natural gas (NG) consumption when prices experience large increase. To incorporate the impact of price hike into modeling, a novel procedure for construction and adaptation of Takagi-Sugeno fuzzy inference system (TS-FIS) is suggested. Linear regressions are used to construct a first order TS-FIS. Furthermore, adaptive network-based FIS (ANFIS) is used to forecast NG consumption in power plants. To cope with random uncertainty in small historical data sets, Monte Carlo simulation is utilized to generate training data for ANFIS. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual NG consumption in Iran where removing energy subsidies has resulted in a hike in NG prices.

Original languageEnglish
Pages (from-to)5664-5679
Number of pages16
JournalApplied Mathematical Modelling
Issue number8
Early online date13 Dec 2012
Publication statusPublished - 15 Apr 2013
Externally publishedYes


The authors would like to thank the referees for their useful comments. The work on this paper has been funded by Iran National Science Foundation (INSF) . The authors are grateful for financial support provided by INSF.


  • Adaptive neuro-fuzzy system
  • Energy price
  • Linear regressions
  • Natural gas forecasting

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics


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