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Dergiler / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 3

Development of Türkiye's Electricity Generation and Installed Capacity Mix by Source and 2040 Outlook: ILR–RegARIMA, Diversity Indices, and Exact LMDI Analysis

Faruk Kürker(ADIYAMAN ÜNİVERSİTESİ)0000-0003-1544-9743
Sayfa
—
Yayın Tarihi
2026-09-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The study provides a transparent, reproducible, and policy-compatible forecasting framework in terms of energy diversity, supply security, and sustainability.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study focuses on analyzing the transition in the sources of electricity generation and installed capacity mix in Türkiye between 2000 and 2023 and presents a forecast of its probable development until 2040.
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Temel Bulgular & Veri:Accordingly, the results indicated an average MAPE of 0.37% and a coverage of 0.78 in the 95% confidence interval.
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Sonuç & Katkı:The study provides a transparent, reproducible, and policy-compatible forecasting framework in terms of energy diversity, supply security, and sustainability.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2000 Verileri
⚙️Değişkenler:Türkiye’s electricity production · ILR–regARIMA · Diversity indices (Shannon HHI)

Abstract

This study focuses on analyzing the transition in the sources of electricity generation and installed capacity mix in Türkiye between 2000 and 2023 and presents a forecast of its probable development until 2040. The ILR transformation and regARIMA models were used in the framework of compositional data analysis, while model reliability was tested using the rolling backtest method. Accordingly, the results indicated an average MAPE of 0.37% and a coverage of 0.78 in the 95% confidence interval. Resource-based efficiency trends were determined by the capacity factor proxy, and diversity and structural breaks were analyzed using indicators of Shannon entropy, HHI, and efficient source number. The REF and SCN scenarios show that fossil fuels will decline while renewables will increase significantly in the 2035–2040 period. The study provides a transparent, reproducible, and policy-compatible forecasting framework in terms of energy diversity, supply security, and sustainability.

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Bu Makaleye Atıf Yap

Kürker, F. (2026). Development of Türkiye's Electricity Generation and Installed Capacity Mix by Source and 2040 Outlook: ILR–RegARIMA, Diversity Indices, and Exact LMDI Analysis. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 3. https://doi.org/10.17798/bitlisfen.1921295

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Kaynakça (40)

  1. M. Kayacı Çodur, “Ensemble machine learning approaches for prediction of Türkiye’s energy demand,” Energies, vol. 17, no. 1, 74, 2024.
  2. E. Köse and S. Kutlu Kaynar, “Energy demand forecasting and policy development in Turkey,” Energies, vol. 18, no. 13, 3301, 2025.
  3. M. S. Kıran, E. Özceylan, M. Gündüz, and T. Paksoy, “Swarm intelligence approaches to estimate electricity energy demand in Turkey,” Knowledge-Based Systems, vol. 36, pp. 93–103, 2012.
  4. M. Aslan, “Archimedes optimization algorithm-based approaches for solving energy demand estimation problem: A case study of Turkey,” Neural Computing and Applications, vol. 35, pp. 19627–19649, 2023.
  5. M. Aslan and M. Beşkirli, “Realization of Turkey’s energy demand forecast with the improved arithmetic optimization algorithm,” Energy Reports, vol. 8, pp. 18–32, 2022.
  6. D. Özdemir, S. Dörterler, and D. Aydın, “A new modified artificial bee colony algorithm for energy demand forecasting problem,” Neural Computing and Applications, vol. 34, no. 24, pp. 17455–17471, 2022.
  7. M. F. Tefek, H. Uğuz, and M. Güçyetmez, “A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey,” Neural Computing and Applications, vol. 31, no. 10, pp. 2939–2954, 2019.
  8. H. A. Issa, H. H. Çevik, A. Yılmaz, and M. Cunkas, “A genetic programming-based ensemble method for long-term electricity demand forecasting,” PeerJ Computer Science, vol. 11, e2825, 2025.
  9. S. Yu, K. Zhu, and X. Zhang, “Energy demand projection of China using a path-coefficient analysis and PSO–GA approach,” Energy Conversion and Management, vol. 53, no. 1, pp. 142–153, 2012.
  10. P. A. Verwiebe, S. Seim, S. Burges, L. Schulz, and J. Müller-Kirchenbauer, “Modeling energy demand—A systematic literature review,” Energies, vol. 14, no. 23, 7859, 2021.
  11. E. Korkmaz, “Energy demand estimation in Turkey according to modes of transportation: Bezier search differential evolution and black widow optimization algorithms-based model development and application,” Neural Computing and Applications, vol. 35, no. 10, pp. 7125–7146, 2023.
  12. U. Kumar and V. K. Jain, “Time series models (Grey-Markov, Grey model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India,” Energy, vol. 35, no. 4, pp. 1709–1716, 2010.
  13. M. Kankal and E. Uzlu, “Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey,” Neural Computing and Applications, vol. 28, Suppl. 1, pp. S737–S747, 2017.
  14. G. S. Daş, “Forecasting the energy demand of Turkey with a NN based on an improved particle swarm optimization,” Neural Computing and Applications, vol. 28, Suppl. 1, pp. S539–S549, 2017.
  15. M. Beşkirli, H. Haklı, and H. Kodaz, “The energy demand estimation for Turkey using differential evolution algorithm,” Sādhanā, vol. 42, no. 10, pp. 1705–1715, 2017.
  16. A. Ünler, “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025,” Energy Policy, vol. 36, no. 6, pp. 1937–1944, 2008.
  17. A. Beşkirli, M. Beşkirli, H. Haklı, and H. Uğuz, “Comparing energy demand estimation using artificial algae algorithm: The case of Turkey,” Journal of Clean Energy Technologies, vol. 6, no. 4, pp. 349–352, 2018.
  18. E. Guevara, F. Babonneau, T. Homem-de-Mello, and S. Moret, “A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty,” Applied Energy, vol. 271, 115005, 2020.
  19. L. Suganthi and A. A. Samuel, “Energy models for demand forecasting—A review,” Renewable and Sustainable Energy Reviews, vol. 16, no. 2, pp. 1223–1240, 2012.
  20. A. A. Mir, M. Alghassab, K. Ullah, Z. A. Khan, Y. Lu, and M. Imran, “A review of electricity demand forecasting in low- and middle-income countries: The demand determinants and horizons,” Sustainability, vol. 12, no. 15, 5931, 2020.
  21. M. Emami Javanmard and S. F. Ghaderi, “Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms,” Sustainable Cities and Society, vol. 95, 104623, 2023.
  22. I. Ghalehkhondabi, E. Ardjmand, G. R. Weckman, and W. A. Young II, “An overview of energy demand forecasting methods published in 2005–2015,” Energy Systems, vol. 8, no. 2, pp. 411–447, 2017.
  23. E. Efekemo, E. G. Saturday, and J. C. Ofodu, “Electricity demand forecasting: A review,” International Journal of Multidisciplinary and Current Educational Research, vol. 4, no. 2, pp. 279–301, 2022.
  24. K. Panklib, C. Prakasvudhisarn, and D. Khummongkol, “Electricity consumption forecasting in Thailand using an artificial neural network and multiple linear regression,” Energy Sources, Part B: Economics, Planning, and Policy, vol. 10, no. 4, pp. 427–434, 2015.
  25. A. Danandeh Mehr, F. Bagheri, and M. J. S. Safari, “Electrical energy demand prediction: A comparison between genetic programming and decision tree,” Gazi University Journal of Science, vol. 33, no. 1, pp. 62–72, 2020.
  26. V. P. Utgikar and J. P. Scott, “Energy forecasting: Predictions, reality and analysis of causes of error,” Energy Policy, vol. 34, no. 17, pp. 3087–3092, 2006.
  27. Ö. A. Karaman and Y. Bektaş, “Long-term electricity demand forecasting with machine learning and optimization methods: The case of Turkey,” Mühendislik Bilimleri ve Araştırmaları Dergisi, vol. 5, no. 2, pp. 285–292, 2023.
  28. K. Değer, M. G. Özkaya, and F. E. Boran, “Modelling and analysis of future energy scenarios on the sustainability axis,” Politeknik Dergisi, vol. 26, no. 2, pp. 665–678, 2023.
  29. C. Hamzaçebi and F. Kutay, “Yapay sinir ağları ile Türkiye elektrik enerjisi tüketiminin 2010 yılına kadar tahmini,” Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 19, no. 3, pp. 227–233, 2004.
  30. H. A. Es, F. Y. Kalender, and C. Hamzaçebi, “Yapay sinir ağları ile Türkiye net enerji talep tahmini,” Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 29, no. 3, pp. 495–504, 2014.
  31. H. Var and B. E. Türkay, “Yapay sinir ağları kullanılarak kısa dönem elektrik yükü tahmini: Short term electric load forecasting using artificial neural networks,” in ELECO 2014 Elektrik–Elektronik–Bilgisayar ve Biyomedikal Mühendisliği Sempozyumu, Bursa, Türkiye, 2014.
  32. Y. S. Murat and H. Ceylan, “Use of artificial neural networks for transport energy demand modeling,” Energy Policy, vol. 34, no. 17, pp. 3165–3172, 2006.
  33. S. Ozturk and F. Ozturk, “Forecasting energy consumption of Turkey by ARIMA model,” Journal of Asian Scientific Research, vol. 8, no. 2, pp. 52–60, 2018.
  34. E. Erdogdu, “Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey,” Energy Policy, vol. 35, no. 2, pp. 1129–1146, 2007.
  35. K. Kavaklıoğlu, “Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression,” Applied Energy, vol. 88, no. 1, pp. 368–375, 2011.
  36. J. Ferrero Bermejo, J. F. Gómez Fernández, F. Olivencia Polo, and A. Crespo Márquez, “A review of the use of artificial neural network models for energy and reliability prediction: A study of the solar PV, hydraulic and wind energy sources,” Applied Sciences, vol. 9, no. 9, 1844, 2019.
  37. K. Muralitharan, R. Sakthivel, and R. Vishnuvarthan, “Neural network-based optimization approach for energy demand prediction in smart grid,” Neurocomputing, vol. 273, pp. 199–208, 2018.
  38. A. Sözen, “Future projection of the energy dependency of Turkey using artificial neural network,” Energy Policy, vol. 37, no. 11, pp. 4827–4833, 2009.
  39. A. Nur, B. Güre, S. Rüstemli, and Ö. Bezek Güre, “Yapay Sinir Ağları ile Yenilenebilir Güneş Enerjisi Kaynağının Öngörüsü”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 12, no. 27, pp. 416–425, 2025.
  40. T. Huybrechts, P. Reiter, S. Mercelis, J. Famaey, S. Latré, and P. Hellinckx, “Automated testbench for hybrid machine learning-based worst-case energy consumption analysis on batteryless IoT devices,” Energies, vol. 14, no. 13, 3914, 2021.