paper
2022
Applications & impacts
Local & station forecasting Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)RNN / LSTM / ConvLSTM6 h–3 days Journal
2022-11-30
008
Nov 2022
Hyojeoung Kim, Sujin Park, Sahm Kim
Communications for Statistical Applications and Methods
CatBoost best predicts solar radiation in Korea from temperature, humidity, cloudiness and extraterrestrial radiation, outperforming LSTM and GRU variants.
Solar radiation forecasting using boosting decision tree and recurrent neural networks
In one sentence. CatBoost best predicts solar radiation in Korea from temperature, humidity, cloudiness and extraterrestrial radiation, outperforming LSTM and GRU variants.
Labels
- Topic
- Applications & impacts, Local & station forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels), RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.29220/csam.2022.29.6.709 OpenAlex:W4311077346
- Added
- 2026-10-22
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