用天气大模型提升电网关键设备的局部预报精度，提前预警冰灾风险。
A Weather Foundation Model for the Power Grid
- 基于电网资产数据微调天气大模型，实现设备级精细化预报。
- 温度、降水和风速预测误差降低15%~35%，冰灾检测准确率达0.72。
- 可提前数小时预警导线覆冰，适合电网运维与防灾决策使用。
天气基础模型（WFMs）虽在全局预报上达到新基准，但其对现代社会依赖的气象敏感基础设施的实际价值仍不明确。本文将Silurian AI的15亿参数天气大模型GFT，在包含输电线路气象站、风电场测风塔数据及覆冰传感器等丰富历史观测数据上进行微调，实现对电网五大关键变量的超本地化预测：地表温度、降水、叶轮高度风速、风力机覆冰风险以及架空导线上的霜冰积累。在6-72小时预报时效内，该定制模型超越现有最优数值天气预报（NWP）基准，温度平均绝对误差（MAE）降低15%，总降水量MAE下降35%，风速MAE减少15%。尤为重要的是，其日前瞻霜冰检测平均精确率达0.72，是当前运行系统所不具备的能力，为可能引发严重停电事件提供数小时的可行动预警。结果表明，通过少量高保真数据微调，天气基础模型可成为下一代电网韧性智能的实用基石。
原文摘要 · Abstract (English)
Weather foundation models (WFMs) have recently set new benchmarks in global forecast skill, yet their concrete value for the weather-sensitive infrastructure that powers modern society remains largely unexplored. In this study, we fine-tune Silurian AI's 1.5B-parameter WFM, Generative Forecasting Transformer (GFT), on a rich archive of Hydro-Québec asset observations--including transmission-line weather stations, wind-farm met-mast streams, and icing sensors--to deliver hyper-local, asset-level forecasts for five grid-critical variables: surface temperature, precipitation, hub-height wind speed, wind-turbine icing risk, and rime-ice accretion on overhead conductors. Across 6-72 h lead times, the tailored model surpasses state-of-the-art NWP benchmarks, trimming temperature mean absolute error (MAE) by 15%, total-precipitation MAE by 35%, and lowering wind speed MAE by 15%. Most importantly, it attains an average precision score of 0.72 for day-ahead rime-ice detection, a capability absent from existing operational systems, which affords several hours of actionable warning for potentially catastrophic outage events. These results show that WFMs, when post-trained with small amounts of high-fidelity, can serve as a practical foundation for next-generation grid-resilience intelligence.
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