Energy Demand Forecasting and Policy Development in Turkey

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.

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Publication Details

Journal
Energies
Published
2025-06-24
DOI
https://doi.org/10.3390/en18133301
Citations
4
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
2.92
Controls
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article

Energy Demand Forecasting and Policy Development in Turkey

Ercan Köse, Sevil Kutlu Kaynar
4 citations
Energies
Energy Load and Power Forecasting
2.92
article

Energy Demand Forecasting and Policy Development in Turkey

Ercan Köse, Sevil Kutlu Kaynar
article en
4 citations

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.

EnergiesVol. 18(13)
Tarsus University (TR)
Openalex Percentile: Top 9%
Energy Load and Power Forecasting
2.92
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