SolarBench: A global solar energy nowcasting benchmark
As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply deep learning to sky camera and geostationary satellite observations, but fragmented datasets and inconsistent evaluation make it difficult to determine whether reported improvements generalize across climates, cloud regimes, and photovoltaic (PV) systems. Here we introduce SolarBench, an open global benchmark for image-based solar nowcasting. SolarBench harmonizes more than six million sky and satellite images from 11 diverse sites spanning a decade, together with irradiance or PV output and auxiliary atmosphe...
| Authors | Yuhao Nie, Stephen Campbell, Quentin Paletta et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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