Graph Neural Networks in Wind Power Forecasting
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916817832771584 |
|---|---|
| author | Castellano, Javier Villanueva, Ignacio |
| author_facet | Castellano, Javier Villanueva, Ignacio |
| contents | We study the applicability of GNNs to the problem of wind energy forecasting. We find that certain architectures achieve performance comparable to our best CNN-based benchmark. The study is conducted on three wind power facilities using five years of historical data. Numerical Weather Prediction (NWP) variables were used as predictors, and models were evaluated on a 24 to 36 hour ahead test horizon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks in Wind Power Forecasting Castellano, Javier Villanueva, Ignacio Machine Learning Systems and Control We study the applicability of GNNs to the problem of wind energy forecasting. We find that certain architectures achieve performance comparable to our best CNN-based benchmark. The study is conducted on three wind power facilities using five years of historical data. Numerical Weather Prediction (NWP) variables were used as predictors, and models were evaluated on a 24 to 36 hour ahead test horizon. |
| title | Graph Neural Networks in Wind Power Forecasting |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2507.00105 |