paper
2020
Applications & impacts
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2020-01-06
008
Jan 2020
Antonio Bracale, Pierluigi Caramia MS, Guido Carpinelli MS et al.
International Transactions on Electrical Energy Systems
Ranking and combining spatially distributed NWPs with gradient boosting and quantile regression improves day-ahead probabilistic wind farm power forecasts by up to 1.8%.
Day‐ahead probabilistic wind power forecasting based on ranking and combining NWPs
In one sentence. Ranking and combining spatially distributed NWPs with gradient boosting and quantile regression improves day-ahead probabilistic wind farm power forecasts by up to 1.8%.
Labels
- Topic
- Applications & impacts, Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1002/2050-7038.12325 OpenAlex:W2999034066
- Added
- 2026-10-22
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