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Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye

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  • Yasemin Ayaz Atalan
  • Hasan Şahin
  • Abdulkadir Keskin
  • Abdulkadir Atalan

Abstract

Providing electricity needs from renewable energy sources is an important issue in the energy policies of countries. Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. The electricity usage rate must be estimated accurately to make reliable decisions in strategic planning and future investments in renewable energy. This study aims to accurately estimate the renewable energy production rate to meet Türkiye’s electricity needs from renewable energy sources. For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. In obtaining forecast data, 15 variables were considered under the oil resources, environmental parameters, and economic factors which are the main parameters affecting renewable energy usage rates. The RF algorithm performed best with the lowest mean absolute percentage error (MAPE, 0.084%), mean absolute error (MAE, 0.035), root mean square error (RMSE, 0.063), and mean squared error (MSE, 0.004) values in the test dataset. The R2 value of this model is 0.996% and the MAPE value is calculated lower than 10%. The AB model, on the other hand, has the highest error values in the test data set, but still provides an acceptable prediction accuracy. The R2 value was 0.792% and the MAPE value (0.371%) of this model was calculated to be in the range of 20%

Suggested Citation

  • Yasemin Ayaz Atalan & Hasan Şahin & Abdulkadir Keskin & Abdulkadir Atalan, 2025. "Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye," PLOS ONE, Public Library of Science, vol. 20(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0328290
    DOI: 10.1371/journal.pone.0328290
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    1. Griffiths, Steven, 2017. "A review and assessment of energy policy in the Middle East and North Africa region," Energy Policy, Elsevier, vol. 102(C), pages 249-269.
    2. Heinermann, Justin & Kramer, Oliver, 2016. "Machine learning ensembles for wind power prediction," Renewable Energy, Elsevier, vol. 89(C), pages 671-679.
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