20.500.12556/RUL-116491 Forecasting electricity prices a machine learning approach 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. energetics price informatics energy sector electricity prices forecasting machine learning geometric semantic based programming energetika cena informatika true false true Angleški jezik Slovenski jezik Članek v reviji 2020-05-25 15:01:21 2020-05-25 15:06:22 2022-08-28 03:49:03 0000-00-00 00:00:00 2020 0 0 16 str. iss. 5, art. 119 Vol. 13 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 659.2:004 1999-4893 10.3390/a13050119 14509571 517501977 RAZ_Castelli_Mauro_2020.pdf RAZ_Castelli_Mauro_2020.pdf 1 B0B117BACD79D5B361411E4D79745D37 1362508574ee75f04d759a5c24108cde508f0e237927411a4a59816bba3835a9 7b1f7aba-58d8-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=151968 https://www.mdpi.com/1999-4893/13/5/119 0 6d4fb4f4-58d8-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=151967 Ekonomska fakulteta 0 0 0