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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Luca Di Persio & Nicola Fraccarolo & Andrea Veronese, 2024. "Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks," Mathematics, MDPI, vol. 12(13), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2105-:d:1429081
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    References listed on IDEAS

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