SolarBench: A global solar energy nowcasting benchmark

2026-09-05

AI Summary

为解决太阳能预测问题，本文通过创建SolarBench基准，整合了来自11个不同地点的图像和数据，支持模型开发与评估，以提高全球范围内太阳能预测的准确性和可靠性。

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.

Problem

• solar nowcasting
• reliable energy system operation
• fragmented datasets
• inconsistent evaluation
• climate and cloud regimes

Innovation

• solar nowcasting
• image-based forecasting
• global benchmark
• data harmonization
• predictability across cloud regimes