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A new approach based on scenario planning and prediction methods for the estimation of gasoil consumption

  • M. Rezaei
  • S. K. ChaharsooghiEmail author
  • A. H. Kashan
  • R. Babazadeh
Original Paper
  • 19 Downloads

Abstract

Due to the increase in Iran’s gasoil consumption, it is necessary to present an appropriate model for predicting it. This can help the policy-makers to properly manage the consumption and make effective decisions. In this research, different approaches are represented and compared for estimation of Iran’s gasoil consumption. Prediction models are compared with a scenario planning method. Predictive methods focus on the most likely event and lead to a point estimate of the future, while scenario planning considers uncertainties and possible future occurrences. A comparison is made among multiple linear regression (MLR) as a linear method, artificial neural network (ANN) as a nonlinear method, and scenario planning as a method for exploring uncertainty. The prediction methods (MLR and ANN) are quantitative approaches which use historical data in developing the models, while the presented scenario planning method is a qualitative–quantitative approach. The proposed scenario planning is based on cross-impact and visualization methods. By these methods, and according to the country’s status in gasoil production and consumption, four scenarios are developed for Iran’s 2025 vision: polluted environment, reduction of oil reserves, reduction of fuel subsidies, and culturalization. The results show that the gasoil consumption growth in 2025 for these scenarios is 46%, 17.8%, − 5.4%, and 3.4%, respectively, while these changes for MLR and ANN methods are 20.5%, and 24%, respectively. Moreover, the presented approach indicates that the “polluted environment” and “reduction of oil reserves” are the most likely scenarios and are much more similar to those predicted by MLR and ANN methods.

Keywords

Energy Sustainable development Scenario analysis Prediction 

Notes

Acknowledgements

The authors would like to thank all organizations and specialists that helped to prepare this research.

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Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  • M. Rezaei
    • 1
  • S. K. Chaharsooghi
    • 1
    Email author
  • A. H. Kashan
    • 1
  • R. Babazadeh
    • 2
  1. 1.School of Industrial and Systems EngineeringTarbiat Modares UniversityTehranIran
  2. 2.Faculty of EngineeringUrmia UniversityUrmiaIran

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