用神经算子实现全球尺度6小时太阳辐射预测，支持卫星数据微调。
Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale
- 基于NWP和AI气象模型变量，用神经算子建模全球太阳辐射。
- 可长期稳定滚动预测，微调后区域精度显著提升。
- 适合能源规划与电网调度，助力可再生能源整合。
准确的地表太阳辐射（SSI）预测对优化可再生能源系统至关重要，尤其在全局长期能源规划中。本文提出一种创新方法，利用数值天气预报（NWP）与数据驱动的机器学习气象模型的最新进展，实现长时稳定滚动预测和大规模集合预报，提升预测可靠性。所提模型基于NVIDIA Modulus构建，采用这些模型输出的变量，实现全球尺度6小时间隔的SSI估计，是首个具备自适应能力的全球框架。该模型可通过卫星数据进行微调，显著提升特定区域性能，同时保持其他地区精度。更高精度的预测有助于太阳能并网管理，推动全球能源转型。
原文摘要 · Abstract (English)
Accurate surface solar irradiance (SSI) forecasting is essential for optimizing renewable energy systems, particularly in the context of long-term energy planning on a global scale. This paper presents a pioneering approach to solar radiation forecasting that leverages recent advancements in numerical weather prediction (NWP) and data-driven machine learning weather models. These advances facilitate long, stable rollouts and enable large ensemble forecasts, enhancing the reliability of predictions. Our flexible model utilizes variables forecast by these NWP and AI weather models to estimate 6-hourly SSI at global scale. Developed using NVIDIA Modulus, our model represents the first adaptive global framework capable of providing long-term SSI forecasts. Furthermore, it can be fine-tuned using satellite data, which significantly enhances its performance in the fine-tuned regions, while maintaining accuracy elsewhere. The improved accuracy of these forecasts has substantial implications for the integration of solar energy into power grids, enabling more efficient energy management and contributing to the global transition to renewable energy sources.
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