paper
2019
Applications & impacts
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 daysStanford University Journal
2019-08-01
024
Aug 2019
Yuchi Sun, Vignesh Venugopal, Adam R. Brandt
Solar Energy
Stanford University
Explores deep learning input and output configurations for short-term solar power forecasts; the target is energy output rather than weather.
Short-term solar power forecast with deep learning: Exploring optimal input and output configuration
In one sentence. Explores deep learning input and output configurations for short-term solar power forecasts; the target is energy output rather than weather.
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
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1016/j.solener.2019.06.041 OpenAlex:W2955010283
- Added
- 2026-10-22
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