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Future projection of the energy dependency of Turkey using artificial neural network

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  • Sözen, Adnan

Abstract

Energy dependency (ED) implies the extent to which an economy relies upon imports in order to meet its energy needs. The ED is calculated as net imports divided by the sum of gross inland energy consumption plus bunkers. This study aims at obtaining numerical equations to estimate of Turkey's energy dependency based on basic energy indicators and sectoral energy consumption by using artificial neural network (ANN) technique. It seeks to contribute to the strategies necessary to preserve the supply-demand balance of Turkey. For this purpose, two different models were used to train the ANN approach. In Model 1, main energy indicators such as total production of primary energy per capita, total gross electricity generation per capita and final energy consumption per capita were used in the input layer of the ANN while sectoral energy consumption per capita was used in Model 2. The ED was in the output layer for both models. Different models were employed to estimate the ED with a high confidence for future projections. The R2 values of ED were found to be 0.999 for both models. In accordance with the analysis results, ED is expected to increase from 72% to 82% within 14 years of period. Consequently, the utilization of renewable energy sources and nuclear energy is strictly recommended to ensure the ED stability in Turkey.

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  • Sözen, Adnan, 2009. "Future projection of the energy dependency of Turkey using artificial neural network," Energy Policy, Elsevier, vol. 37(11), pages 4827-4833, November.
  • Handle: RePEc:eee:enepol:v:37:y:2009:i:11:p:4827-4833
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    Cited by:

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    2. Seyithan Ahmet Ate, 2013. "A Novel Approach to Development of Renewable Heating Support Policies in Turkey," International Journal of Energy Economics and Policy, Econjournals, vol. 3(Special), pages 115-126.
    3. Toklu, E., 2013. "Overview of potential and utilization of renewable energy sources in Turkey," Renewable Energy, Elsevier, vol. 50(C), pages 456-463.
    4. Özge Dolunay, 2020. "Geostrategic Renewable Energy Transition in Turkey: Organizational Strategies Towards an Energy Autonomous Future," Politics and Governance, Cogitatio Press, vol. 8(3), pages 199-210.
    5. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    6. Altinay, Galip & Karagol, Erdal, 2004. "Structural break, unit root, and the causality between energy consumption and GDP in Turkey," Energy Economics, Elsevier, vol. 26(6), pages 985-994, November.

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