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

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Abstract

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.

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Authors and Affiliations

  1. School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Amir Abbas Shojaie & Ali Dolatshahi Zand

  2. School of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Shahrzad Vafaie

Authors
  1. Amir Abbas Shojaie
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  2. Ali Dolatshahi Zand
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  3. Shahrzad Vafaie
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Correspondence to Amir Abbas Shojaie.

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Shojaie, A.A., Dolatshahi Zand, A. & Vafaie, S. Calculating production by using short term demand forecasting models: a case study of fuel supply system. Evolving Systems 8, 271–285 (2017). https://doi.org/10.1007/s12530-016-9173-5

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  • DOI: https://doi.org/10.1007/s12530-016-9173-5

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