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

Povzetek

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.

Ključne besede

slv;energetika;cena;informatika;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL EF - Ekonomska fakulteta
UDK: 659.2:004
COBISS: 14509571 Povezava se bo odprla v novem oknu
ISSN: 1999-4893
Št. ogledov: 645
Št. prenosov: 288
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: eng;energetics;price;informatics;
Vrsta dela (COBISS): Članek v reviji
Strani: 16 str.
Letnik: ǂVol. ǂ13
Zvezek: ǂiss. ǂ5 (art. 119)
Čas izdaje: 2020
DOI: 10.3390/a13050119
ID: 11763969