paper
2019
Applications & impacts
Application study
Classical ML (trees, GP, linear, kernels)6 h–3 days Other
2019-01-01
008
Jan 2019
Lukas Kugler
preprint
Machine learning classifiers using NWP and site data raise the economic value of wind turbine icing forecasts over logistic regression and empirical rules.
The Added Value of Machine Learning in Forecasting Wind Turbine Icing
In one sentence. Machine learning classifiers using NWP and site data raise the economic value of wind turbine icing forecasts over logistic regression and empirical rules.
Labels
- Topic
- Applications & impacts
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.13140/rg.2.2.25876.45441 OpenAlex:W3155863617
- Added
- 2026-10-22
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