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

Hasan Ertugrul (), Mustafa Kartal, Serpil Kılıç Depren and Ugur Soytas
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Serpil Kılıç Depren: Department of Statistics, Yildiz Technical University, 34349 Istanbul, Turkey

Energies, 2022, vol. 15, issue 20, 1-17

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.

Keywords: electricity prices; global factors; national factors; prediction; machine learning; time series econometrics; Turkey (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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