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Volume 12
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Article

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

by
Luca Di Persio
1,
Nicola Fraccarolo
2,* and
Andrea Veronese
1
1
Department of Computer Science, University of Verona, 37134 Verona, Italy
2
Department of Mathematics, University of Trento, 38123 Trento, Italy
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(13), 2105; https://doi.org/10.3390/math12132105
Submission received: 19 May 2024 / Revised: 26 June 2024 / Accepted: 3 July 2024 / Published: 4 July 2024
(This article belongs to the Special Issue Statistical Data Modeling and Machine Learning with Applications, 3rd Edition)
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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 energy forecasting; generative adversarial networks; machine learning; renewable energies; stochastic differential equations

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MDPI and ACS Style

Di Persio, L.; Fraccarolo, N.; Veronese, A. Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks. Mathematics 2024, 12, 2105. https://doi.org/10.3390/math12132105

AMA Style

Di Persio L, Fraccarolo N, Veronese A. Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks. Mathematics. 2024; 12(13):2105. https://doi.org/10.3390/math12132105

Chicago/Turabian Style

Di Persio, Luca, Nicola Fraccarolo, and Andrea Veronese. 2024. "Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks" Mathematics 12, no. 13: 2105. https://doi.org/10.3390/math12132105

APA Style

Di Persio, L., Fraccarolo, N., & Veronese, A. (2024). Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks. Mathematics, 12(13), 2105. https://doi.org/10.3390/math12132105

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