Seamless short- to mid-term probabilistic wind power forecasting
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913693936123904 |
|---|---|
| author | Dantas, Gabriel Browell, Jethro |
| author_facet | Dantas, Gabriel Browell, Jethro |
| contents | This paper presents a method for probabilistic wind power forecasting that quantifies and integrates uncertainties from weather forecasts and weather-to-power conversion. By addressing both uncertainty sources, the method achieves state-of-the-art results for lead times of 6 to 162 hours, eliminating the need for separate models for short- and mid-term forecasting. It also improves short-term forecasts during high weather uncertainty periods, which methods based on deterministic weather forecasts fail to capture. The study reveals that weather-to-power uncertainty is more significant for short-term forecasts, while weather forecast uncertainty dominates mid-term forecasts, with the transition point varying between wind farms. Offshore farms typically see this shift at shorter lead times than onshore ones. The findings are supported by an extensive, reproducible case study comprising 73 wind farms in Great Britain over five years. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11960 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seamless short- to mid-term probabilistic wind power forecasting Dantas, Gabriel Browell, Jethro Applications This paper presents a method for probabilistic wind power forecasting that quantifies and integrates uncertainties from weather forecasts and weather-to-power conversion. By addressing both uncertainty sources, the method achieves state-of-the-art results for lead times of 6 to 162 hours, eliminating the need for separate models for short- and mid-term forecasting. It also improves short-term forecasts during high weather uncertainty periods, which methods based on deterministic weather forecasts fail to capture. The study reveals that weather-to-power uncertainty is more significant for short-term forecasts, while weather forecast uncertainty dominates mid-term forecasts, with the transition point varying between wind farms. Offshore farms typically see this shift at shorter lead times than onshore ones. The findings are supported by an extensive, reproducible case study comprising 73 wind farms in Great Britain over five years. |
| title | Seamless short- to mid-term probabilistic wind power forecasting |
| topic | Applications |
| url | https://arxiv.org/abs/2502.11960 |