paper
2022
Applications & impacts
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2022-02-13
012
Feb 2022
Cem Özen, Ali Deniz
Wind Engineering
Couples ERA5 and ECMWF HRES inputs with a CatBoost model and grid selection to forecast Turkey's day-ahead national wind power, reaching 7.6% NRMSE.
A comprehensive country-based day-ahead wind power generation forecast model by coupling numerical weather prediction data and CatBoost with feature selection methods for Turkey
In one sentence. Couples ERA5 and ECMWF HRES inputs with a CatBoost model and grid selection to forecast Turkey's day-ahead national wind power, reaching 7.6% NRMSE.
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
- Regional
- Data
- ERA5 / reanalysis, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1177/0309524x221078536 OpenAlex:W4213322063
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.