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Forecasting Iran’s Energy Demand Using Cuckoo Optimization Algorithm

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  • Sayyed Abdolmajid Jalaee
  • Amin GhasemiNejad
  • Mehrdad Lashkary
  • Maryam Rezaee Jafari

Abstract

This study deals with the modeling of the energy consumption in Iran to forecast future projections based on socioeconomic and demographic variables (GDP, population, import and export amounts, and employment) using the cuckoo optimization algorithm. For this purpose, four diverse models including different indicators were used in the analyses. Linear and power forms of equations are developed for each model. The related data between 1972 and 2013 were used, partly for installing the models. The result of the models shows that the obtained demand estimation linear models are in closer agreement with the observed data, particularly the linear model with five independent variables including GDP, population, import, export, and employment, which outperformed other linear models. Finally, the future energy demand of Iran is forecasted up to the year 2030 using these models under three scenarios.

Suggested Citation

  • Sayyed Abdolmajid Jalaee & Amin GhasemiNejad & Mehrdad Lashkary & Maryam Rezaee Jafari, 2019. "Forecasting Iran’s Energy Demand Using Cuckoo Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, September.
    • Handle: RePEc:hin:jnlmpe:2041756
    DOI: 10.1155/2019/2041756
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    Cited by:

    1. Mahdis sadat Jalaee & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Naeeme Amani Zarin & Reza Derakhshani, 2022. "A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 15(7), pages 1-14, April.

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