paper
2022
Post-processing & calibration
Benchmark or dataset
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2022-04-21
008
Apr 2022
Hadrien Verbois, Yves‐Marie Saint‐Drenan, Alexandre H. Thiéry et al.
Solar Energy
A benchmark of statistical learning methods for post-processing NWP solar irradiance forecasts; abstract not available, so scope is inferred from the title.
Statistical learning for NWP post-processing: A benchmark for solar irradiance forecasting
In one sentence. A benchmark of statistical learning methods for post-processing NWP solar irradiance forecasts; abstract not available, so scope is inferred from the title.
Labels
- Topic
- Post-processing & calibration
- Type
- Benchmark or dataset
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1016/j.solener.2022.03.017 OpenAlex:W4224291524
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.