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Forecasting of Turkey s Sectoral Energy Demand by Using Fuzzy Grey Regression Model

Author

Listed:
  • Abdulkerim Karaaslan

    (Faculty of Economics and Administrative Sciences, Atat rk University, Erzurum, Turkey,)

  • Mesliha Gezen

    (Faculty of Economics and Administrative Sciences, Atat rk University, Erzurum, Turkey)

Abstract

Population growth, technological developments, economical growth and efforts to achieve a high standard of living increase the demand for energy. Satisfying this increasing demand without interruption is of vital importance for countries to ensure security of supply. Safely forecasting the energy demand of Turkey, which is about 3-4 times the world average, is important for sustainable development and improving standards of living in the country. This study seeks to forecast Turkey s total energy demand and determine the distribution of this demand among sectors and the amount of unutilized energy. In the study, the energy demand projection until 2023 was revealed with fuzzy grey regression model using the data between years 1990 and 2012.

Suggested Citation

  • Abdulkerim Karaaslan & Mesliha Gezen, 2017. "Forecasting of Turkey s Sectoral Energy Demand by Using Fuzzy Grey Regression Model," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 67-77.
    • Handle: RePEc:eco:journ2:2017-01-08
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    References listed on IDEAS

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    Cited by:

    1. Zeynep Ceylan, 2020. "Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 944-956, September.
    2. Alkan, Ömer & Albayrak, Özlem Karadağ, 2020. "Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA," Renewable Energy, Elsevier, vol. 162(C), pages 712-726.
    3. Gezen, Mesliha & Karaaslan, Abdulkerim, 2022. "Energy planning based on Vision-2023 of Turkey with a goal programming under fuzzy multi-objectives," Energy, Elsevier, vol. 261(PA).
    4. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.

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    More about this item

    Keywords

    Fuzzy Grey Prediction; Sectorial Energy Demand in Turkey; Fuzzy Grey Regression Model;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L69 - Industrial Organization - - Industry Studies: Manufacturing - - - Other

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