Electricity price estimation using deep learning approaches: An empirical study on Turkish markets in normal and Covid-19 periods

KAYA M., KARAN M. B., Telatar E.

Expert Systems with Applications, vol.224, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 224
  • Publication Date: 2023
  • Doi Number: 10.1016/j.eswa.2023.120026
  • Journal Name: Expert Systems with Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Deep learning, Electricity price prediction, Transformer encoder-decoder with self-attention, Turkish electricity market
  • Hacettepe University Affiliated: Yes


This study aims to estimate the prices in the next 24 h with deep learning methods in the Turkish electricity market. The model is based on hourly data for the period 2017–2021 using electricity prices. The model's Root Mean Square Error (RMSE) value is 3.14, and the explanatory power R2 is 0.94. Since this model also considers the subgroups in the database, it can make price predictions for the pandemic period. To test the robustness and consistency of the model, twelve RNN-based models were re-estimated with the same data set. Although all models successfully predict the prices, The TEDSE Model performs better than the others. This study will be especially beneficial to electricity market players and policymakers. In further studies, the TEDSE model can be used for price prediction in intraday energy markets. This study's most important contribution is methodology innovation, using the Transformer Encoder-Decoder with Self-Attention (TEDSE) model for the first time to estimate electricity prices.