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Estimating petroleum exergy production and consumption using vehicle ownership and GDP based on genetic algorithm approach

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  • Ozturk, Harun Kemal
  • Ceylan, Halim
  • Hepbasli, Arif
  • Utlu, Zafer

Abstract

This study deals with exergy estimation of petroleum using genetic algorithm (GA) approach. The exergy estimation is carried out based on the gross domestic product (GDP) and the percentage of vehicle ownership figures in Turkey. Genetic Algorithm EXergy Production and Consumption (GAPEX) is developed. During the estimation of petroleum exergy, the GA is combined with time-series approach. For exergy consumption, three forms of the GAPEX are developed, of which one is linear, the second is exponential and the third is quadratic form of the equations. Among them, the best fit models in terms of average relative errors for the testing period are selected for future estimation. It may be concluded that the models proposed here can be used as an alternative solution and estimation techniques for available estimation techniques.

Suggested Citation

  • Ozturk, Harun Kemal & Ceylan, Halim & Hepbasli, Arif & Utlu, Zafer, 2004. "Estimating petroleum exergy production and consumption using vehicle ownership and GDP based on genetic algorithm approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(3), pages 289-302, June.
    • Handle: RePEc:eee:rensus:v:8:y:2004:i:3:p:289-302
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    References listed on IDEAS

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    Cited by:

    1. Ediger, Volkan S. & Camdali, Unal, 2007. "Energy and exergy efficiencies in Turkish transportation sector, 1988-2004," Energy Policy, Elsevier, vol. 35(2), pages 1238-1244, February.
    2. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    3. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    4. Haldenbilen, Soner & Ceylan, Halim, 2005. "The development of a policy for road tax in Turkey, using a genetic algorithm approach for demand estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(10), pages 861-877, December.
    5. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    6. Sumer, Kutluk Kagan & Goktas, Ozlem & Hepsag, Aycan, 2009. "The application of seasonal latent variable in forecasting electricity demand as an alternative method," Energy Policy, Elsevier, vol. 37(4), pages 1317-1322, April.
    7. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    8. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    9. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    10. Ozturk, Harun Kemal & Ceylan, Halim & Canyurt, Olcay Ersel & Hepbasli, Arif, 2005. "Electricity estimation using genetic algorithm approach: a case study of Turkey," Energy, Elsevier, vol. 30(7), pages 1003-1012.

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