Evaluation Hybrid model of Neural Networks and Genetic Algorithms in the Forecast Energy Consumption the Transportation Sector

Authors

  • Mostafa Heidari Haratemeh * Department of Economics, Naragh Branch, Islamic Azad University, Naragh, Iran‎.

https://doi.org/10.22105/tqfb.v1i1.21

Abstract

Energy  besides  Other  factors  production  is  considered  the  main  factor  in  the growth  and  economic  development  and  in  the  performance  of  different  sectors economic  can  play  beneficial  roles.  Hence,  the  country  authorities  should  try  to predict  anything  more  precise  energy  consumption  in  the  proper  planning  and guidance  consumption,  to  control  the  way  they  desired  energy  demand  and  supply parameters. The purpose of this paper  is Evaluation Hybrid model of artificial neural networks  and  genetic  algorithms  in  the  forecast  demand  energy.  for  Prediction  energy  consumption  in  the  country.  Case  study  is  energy consumption in transportation sector of Iran.  So for this review, were used the annual data energy consumption of transport as a variable output of forecast models and data from the  entire  country's annual population, GDP and the  number of  vehicle as the input variables. Evaluation results showed that   the hybrid model of neural networks and  genetic  algorithm  (ANN-GA),  compared  to  other models  with  the  highest accuracy in predicting energy demand in the transportation sector.

Keywords:

Energy consumption‎, Multivariate regression‎, Artificial neural networks‎, Genetic algorithm‎

Author Biography

  • Mostafa Heidari Haratemeh, Department of Economics, Naragh Branch, Islamic Azad University, Naragh, Iran‎.

    Associate Professor of the Department of Economics, Naragh Branch, Islamic Azad University, Naragh, Iran

     

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Published

2024-09-05

Issue

Vol. 1 No. 1 (2024): Transactions on Quantitative Finance and Beyond

Section

Articles

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How to Cite

Heidari Haratemeh, M. (2024). Evaluation Hybrid model of Neural Networks and Genetic Algorithms in the Forecast Energy Consumption the Transportation Sector. Transactions on Quantitative Finance and Beyond, 1(1), 48-57. https://doi.org/10.22105/tqfb.v1i1.21
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