Forecasting electricity prices: A machine learning approach

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2 Citations (Scopus)
60 Downloads (Pure)


The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique-namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.

Original languageEnglish
Article number119
Pages (from-to)1-16
Number of pages16
Issue number5
Publication statusPublished - 8 May 2020


  • Based programming
  • Electricity prices
  • Energy sector
  • Forecasting
  • Geometric semantic
  • Machine learning


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