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

Hasan Murat Ertuğrul, Mustafa Tevfik Kartal, Serpil Kılıç Depren, Uğur Soytaş*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

62 Downloads (Pure)

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.

Original languageEnglish
Article number7512
JournalEnergies
Volume15
Issue number20
Number of pages17
ISSN1996-1073
DOIs
Publication statusPublished - 2022

Keywords

  • Electricity prices
  • Global factors
  • Machine learning
  • National factors
  • Prediction
  • Time series econometrics
  • Turkey

Fingerprint

Dive into the research topics of 'Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models'. Together they form a unique fingerprint.

Cite this