Short term cloud nowcasting for a solar power plant based on irradiance historical data
In one sentence. An LSTM forecasts 5-minute solar irradiance minutes ahead from one year of plant radiometer data, beating standard time-series baselines.
Abstract
This work considers the problem of forecasting the normal solar irradiance with high spatial and temporal resolution (5 minutes). The forecasting is based on a dataset registered during one year from the high resolution radiometric network at a operational solar power plan at Almeria, Spain. In particular, we show a technique for forecasting the irradiance in the next few minutes from the irradiance values obtained on the previous hour. Our proposal employs a type of recurrent neural network known as LSTM, which can learn complex patterns and that has proven its usability for forecasting temporal series. The results show a reasonable improvement with respect to other prediction methods typically employed in the studies of temporal series.
Labels
- Topic
- Local & station forecasting, Applications & impacts
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- 0–6 h
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.24215/16666038.18.e21 OpenAlex:W2904583180
- Added
- 2026-10-22
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