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Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

Author

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  • Hasan Murat Ertuğrul

    (Department of Economics, Faculty of Economics and Administrative Sciences, Anadolu University, 26470 Eskişehir, Turkey)

  • Mustafa Tevfik Kartal

    (Borsa İstanbul Financial Reporting and Subsidiaries Directorate, 34467 Istanbul, Turkey)

  • Serpil Kılıç Depren

    (Department of Statistics, Yildiz Technical University, 34349 Istanbul, Turkey)

  • Uğur Soytaş

    (Department of Technology, Management, and Economics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

Abstract

The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed.

Suggested Citation

  • Hasan Murat Ertuğrul & Mustafa Tevfik Kartal & Serpil Kılıç Depren & Uğur Soytaş, 2022. "Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models," Energies, MDPI, vol. 15(20), pages 1-17, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7512-:d:940113
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