构建全球太阳能短时预报基准，统一数据与评估标准
SolarBench: A global solar energy nowcasting benchmark
- 整合11个站点十年六百万张图像，统一天空与卫星数据
- 发现模型平均精度高但难捕捉快速太阳波动
- 支持新光伏系统快速适配，适合能源与气象研究者
随着太阳能占比提升，精准预测天气驱动的太阳辐射波动对电网稳定至关重要。现有深度学习方法多基于云相机和静止卫星影像，但数据分散、评估不一，难以判断性能是否跨气候、云况和光伏系统泛化。本文提出SolarBench，一个开放的全球图像型太阳能短时预报基准。该基准整合来自11个多样化站点、跨越十年的超六百万张天空与卫星图像，附带辐照度或光伏输出及辅助大气数据。配套工具箱支持可复现的数据访问、处理、建模与评估。利用SolarBench，我们评测了代表性模型，揭示平均预测精度与捕捉快速太阳波动能力之间存在差距；进一步量化不同云况下的可预测性，并实现新光伏系统的数据高效适配。SolarBench为太阳能短时预报提供了可扩展的公平比较与方法创新基础。
原文摘要 · Abstract (English)
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.
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