| Title |
Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks |
| ID_Doc |
63709 |
| Authors |
Di Persio, L; Fraccarolo, N; Veronese, A |
| Title |
Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks |
| Year |
2024 |
| Published |
Mathematics, 12, 13 |
| DOI |
10.3390/math12132105 |
| 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. |
| Author Keywords |
energy forecasting; generative adversarial networks; machine learning; renewable energies; stochastic differential equations |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001266591300001 |
| WoS Category |
Mathematics |
| Research Area |
Mathematics |
| PDF |
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