Autor(es): Castelli, Mauro ; Groznik, Aleš ; Popovič, Aleš
Data: 2020
Identificador Persistente: http://hdl.handle.net/10362/99133
Origem: Repositório Institucional da UNL
Projeto/bolsa: info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT;
Assunto(s): Based programming; Electricity prices; Energy sector; Forecasting; Geometric semantic; Machine learning; Theoretical Computer Science; Numerical Analysis; Computational Theory and Mathematics; Computational Mathematics
Castelli, M., Groznik, A., & Popovič, A. (2020). Forecasting electricity prices: A machine learning approach. Algorithms, 13(5), 1-16. [119]. https://doi.org/10.3390/A13050119
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.
