paper
2022
Local & station forecasting
Applications & impacts Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 days Journal
2022-05-19
008
May 2022
Amaris Dalton, Bernard Bekker
Applied Energy
Study of exogenous atmospheric variables as machine learning predictors of wind speed for energy applications; abstract unavailable, so scope is inferred from the title.
Exogenous atmospheric variables as wind speed predictors in machine learning
In one sentence. Study of exogenous atmospheric variables as machine learning predictors of wind speed for energy applications; abstract unavailable, so scope is inferred from the title.
Labels
- Topic
- Local & station forecasting, 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
- Other
- Status
- Research
- Identifiers
- DOI:10.1016/j.apenergy.2022.119257 OpenAlex:W4280630919
- Added
- 2026-10-22
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