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Energy Demand Forecasting and Policy Development in Turkey

Author

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  • Ercan Köse

    (Electrical-Electronics Engineering Department, Tarsus University, Tarsus, 33400 Mersin, Turkey)

  • Sevil Kutlu Kaynar

    (Energy Systems Engineering ABD, Graduate Education Institute, Tarsus University, Tarsus, 33400 Mersin, Turkey)

Abstract

As Turkey’s energy demand surges due to industrialization, population growth, and economic development, precise forecasting of electricity demand has become crucial for ensuring energy security and facilitating sustainable planning. This study undertakes an analysis of Turkey’s current energy landscape and develops long-term electricity demand forecasts utilizing a diverse array of statistical and machine learning models, including linear regression, polynomial regression, and artificial neural networks (ANNs). By incorporating economic indicators, demographic trends, and historical consumption data, this research projects Turkey’s electricity demand up to 2045. Among the various influencing factors, industrial production stands out as the most significant driver. The findings offer strategic insights into infrastructure investments, the integration of renewable energy, and policies aimed at enhancing efficiency. This research presents a data-driven, policy-oriented framework to assist decision-makers in reducing import dependence while steering Turkey towards a sustainable energy transition.

Suggested Citation

  • Ercan Köse & Sevil Kutlu Kaynar, 2025. "Energy Demand Forecasting and Policy Development in Turkey," Energies, MDPI, vol. 18(13), pages 1-31, June.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3301-:d:1686091
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    References listed on IDEAS

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    1. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    2. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
    3. AkpInar, Adem & Kömürcü, Murat Ihsan & Kankal, Murat & Özölçer, Ismail HakkI & Kaygusuz, Kamil, 2008. "Energy situation and renewables in Turkey and environmental effects of energy use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2013-2039, October.
    4. Carley, Sanya, 2009. "State renewable energy electricity policies: An empirical evaluation of effectiveness," Energy Policy, Elsevier, vol. 37(8), pages 3071-3081, August.
    5. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
    6. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    7. Jie Wang & Yuqiang Li & Junfeng Mai & Minmin Yuan & Zhiqiang Liu, 2025. "Highway Typical Scenario Operation and Maintenance Energy Demand Forecasting," Sustainability, MDPI, vol. 17(5), pages 1-17, February.
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