Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks

Luca Di Persio, Nicola Fraccarolo () and Andrea Veronese
Additional contact information
Luca Di Persio: Department of Computer Science, University of Verona, 37134 Verona, Italy
Nicola Fraccarolo: Department of Mathematics, University of Trento, 38123 Trento, Italy
Andrea Veronese: Department of Computer Science, University of Verona, 37134 Verona, Italy

Mathematics, 2024, vol. 12, issue 13, 1-16

Abstract: This paper focuses on developing a predictive model for wind energy production in Italy, aligning with the ambitious goals of the European Green Deal. In particular, by utilising real data from the SUD (South) Italian electricity zone over seven years, the model employs stochastic differential equations driven by (fractional) Brownian motion-based dynamic and generative adversarial networks to forecast wind energy production up to one week ahead accurately. Numerical simulations demonstrate the model’s effectiveness in capturing the complexities of wind energy prediction.

Keywords: energy forecasting; generative adversarial networks; machine learning; renewable energies; stochastic differential equations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/13/2105/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/13/2105/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:13:p:2105-:d:1429081

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().