Forecasting Electricity Prices: A Machine Learning Approach

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@Article{castelli:2020:Algorithms,

• author = "Mauro Castelli and Ales Groznik and Ales Popovic",
• title = "Forecasting Electricity Prices: A Machine Learning Approach",
• journal = "Algorithms",
• year = "2020",
• volume = "13",
• number = "5",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1999-4893",
• URL = "https://www.mdpi.com/1999-4893/13/5/119",
• DOI = "doi:10.3390/a13050119",
• 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.",
• notes = "also known as \cite{a13050119}",

}

Genetic Programming entries for Mauro Castelli Ales Groznik Ales Popovic

Citations