ǂa ǂmachine learning approach
Mauro Castelli (Author), Aleš Groznik (Author), Aleš Popovič (Author)

Abstract

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.

Keywords

slv;energetika;cena;informatika;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL EF - Faculty of Economics
UDC: 659.2:004
COBISS: 14509571 Link will open in a new window
ISSN: 1999-4893
Views: 645
Downloads: 288
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: eng;energetics;price;informatics;
Type (COBISS): Article
Pages: 16 str.
Volume: ǂVol. ǂ13
Issue: ǂiss. ǂ5 (art. 119)
Chronology: 2020
DOI: 10.3390/a13050119
ID: 11763969