Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images
In one sentence. CNN on infrared all-sky images forecasts clear-sky index ranges 5 to 15 min ahead, reaching about 65% accuracy at 5 min for solar irradiance.
Abstract
This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.
Labels
- Topic
- Nowcasting, Applications & impacts
- Type
- New model or method
- Method
- CNN / U-Net
- Time scale
- 0–6 h
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3390/forecast4010019 OpenAlex:W4221010848
- Added
- 2026-10-22
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