SolarBench: A global solar energy nowcasting benchmark
Yuhao Nie Stephen Campbell Quentin Paletta Liwenbo Zhang Tao Jing Samer Chaaraoui Jonathan Giezendanner Andea Scott Tao Sun Cong Feng Max Aragon Jacques Camier Adam Jensen Florian Kotthoff Yuexing Yang Yang Ming Mengying Li Stefanie Meilinger Yupeng Wu Adam Brandt Sherrie Wang
作者信息
Abstract
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 atmospheric data. An accompanying toolbox supports reproducible data access, processing, model development, and evaluation. Using SolarBench, we benchmark representative models and reveal a gap between average forecasting accuracy and the ability to capture rapid solar fluctuations. We further quantify predictability across cloud regimes and demonstrate data-efficient adaptation to new PV systems. SolarBench provides an extensible foundation for fair comparison and methodological innovation in solar nowcasting.引用本文复制引用
Yuhao Nie,Stephen Campbell,Quentin Paletta,Liwenbo Zhang,Tao Jing,Samer Chaaraoui,Jonathan Giezendanner,Andea Scott,Tao Sun,Cong Feng,Max Aragon,Jacques Camier,Adam Jensen,Florian Kotthoff,Yuexing Yang,Yang Ming,Mengying Li,Stefanie Meilinger,Yupeng Wu,Adam Brandt,Sherrie Wang.SolarBench: A global solar energy nowcasting benchmark[EB/OL].(2026-09-05)[2026-09-20].https://arxiv.org/abs/2609.06187.学科分类
能源概论、动力工程概论
