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A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

Author

Listed:
  • Mojtaba Bahmani
  • Mehdi Nejati
  • Amin GhasemiNejad
  • Fateme Nazari Robati
  • Mehrdad Lashkary
  • Naeeme Amani Zarin

Abstract

In this paper, we develop a function of population, GDP, import, and export by applying a hybrid bat algorithm (BAT) and artificial neural network (ANN). We apply these methods to forecast oil consumption in Iran. For this purpose, an improved artificial neural network (ANN) structure, which is optimized by the BAT is proposed. The variables between 1980 and 2017 were used, partly for installing and testing the method. This method would be helpful in forecasting oil consumption and would provide a level playing field for checking the energy policy authority impacts on the structure of the energy sector in an economy such as Iran with high economic interventionism by the government. The result of the model shows that the findings are in close agreement with the observed data, and the performance of the method was excellent. We demonstrate that its efficiency could be a helpful and reliable tool for monitoring oil consumption.

Suggested Citation

  • Mojtaba Bahmani & Mehdi Nejati & Amin GhasemiNejad & Fateme Nazari Robati & Mehrdad Lashkary & Naeeme Amani Zarin, 2021. "A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, February.
    • Handle: RePEc:hin:jnlmpe:6189329
    DOI: 10.1155/2021/6189329
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    Cited by:

    1. Mahdis sadat Jalaee & Alireza Shakibaei & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Reza Derakhshani, 2021. "A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran," Sustainability, MDPI, vol. 13(14), pages 1-16, July.

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