融合气象大模型与卫星图像，实现公里级太阳能辐射日预测
Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting
- 分两阶段融合大模型预报与卫星云图，保留云结构细节
- 在东亚测试中误差比基线低16.08%，更好捕捉云影变化
- 已在中国东部省份实际部署，支持电网调度
精准的日前太阳能辐照度预测对太阳能并网至关重要。然而，由于明显的昼夜周期和复杂的云层动态，这一任务仍具挑战性。现有方法或缺乏细粒度分辨率（如数值天气预报、气象大模型），或在长时序预测中性能下降（如卫星外推）。本文提出Baguan-solar，一种两阶段多模态框架，将全球气象大模型Baguan的预报结果与高分辨率静止卫星图像融合，实现千米级24小时辐照度预测。其解耦式两阶段设计首先预测昼夜连续中间变量（如云覆盖），再推导辐照度；多模态融合同时保留卫星提供的精细云结构与Baguan提供的大尺度约束。在东亚地区以CLDAS为真实值评估，Baguan-solar优于强基线（包括ECMWF IFS、原生Baguan和SolarSeer），RMSE降低16.08%，更准确解析云引起的瞬态变化。自2025年7月起，该系统已在我国东部一省份投入实际运行。代码已开源：https://github.com/DAMO-DI-ML/Baguan-solar.git。
原文摘要 · Abstract (English)
Accurate day-ahead solar irradiance forecasting is essential for integrating solar energy into the power grid. However, it remains challenging due to the pronounced diurnal cycle and inherently complex cloud dynamics. Current methods either lack fine-scale resolution (e.g., numerical weather prediction, weather foundation models) or degrade at longer lead times (e.g., satellite extrapolation). We propose Baguan-solar, a two-stage multimodal framework that fuses forecasts from Baguan, a global weather foundation model, with high-resolution geostationary satellite imagery to produce 24-hour irradiance forecasts at kilometer scale. Its decoupled two-stage design first forecasts day-night continuous intermediates (e.g., cloud cover) and then infers irradiance, while its modality fusion jointly preserves fine-scale cloud structures from satellite and large-scale constraints from Baguan forecasts. Evaluated over East Asia using CLDAS as ground truth, Baguan-solar outperforms strong baselines (including ECMWF IFS, vanilla Baguan, and SolarSeer), reducing RMSE by 16.08% and better resolving cloud-induced transients. An operational deployment of Baguan-solar has supported solar power forecasting in an eastern province in China, since July 2025. Our code is accessible at https://github.com/DAMO-DI-ML/Baguan-solar.git.
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