Evolving Systems

, Volume 8, Issue 4, pp 271–285 | Cite as

Calculating production by using short term demand forecasting models: a case study of fuel supply system

  • Amir Abbas Shojaie
  • Ali Dolatshahi Zand
  • Shahrzad Vafaie
Original Paper


In today’s world, energy is one of the key elements of growth and economic development. Because of the critical role of energy in production costs and social security and environmental issues, it is very important to forecast and to optimize energy consumption. This study investigates the influential factors in urban energy (petrol) consumption. Artificial neural network was used to forecast fuel consumption in one of fuel stations in Tehran. A neural network was trained by Levenberg method and genetic algorithm. Results obtained by this method were compared with results obtained by neural network and regression. This comparison showed that neural network and training by genetic algorithm was more efficient than Levenberg and regression. All of the data used in this study were collected from fuel distribution system in Tehran.


Fuel consumption Forecasting Neural network Genetic algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Amir Abbas Shojaie
    • 1
  • Ali Dolatshahi Zand
    • 1
  • Shahrzad Vafaie
    • 2
  1. 1.School of Industrial Engineering, South Tehran BranchIslamic Azad UniversityTehranIran
  2. 2.School of Industrial Engineering, North Tehran BranchIslamic Azad UniversityTehranIran

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