paper
2021
Applications & impacts
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2021-03-01
008
Mar 2021
Hao Chen, Yngve Birkelund, Stian Normann Anfinsen et al.
Journal of Renewable and Sustainable Energy
Comparison of nine machine learning methods for short-term wind power forecasting at five Arctic farms; NWP wind inputs give an edge over univariate models for 1 to 6 hours.
Comparative study of data-driven short-term wind power forecasting approaches for the Norwegian Arctic region
In one sentence. Comparison of nine machine learning methods for short-term wind power forecasting at five Arctic farms; NWP wind inputs give an edge over univariate models for 1 to 6 hours.
Labels
- Topic
- Applications & impacts
- Type
- Evaluation or intercomparison of existing models
- 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:10.1063/5.0038429 OpenAlex:W3159458251
- Added
- 2026-10-22
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