A Weather Foundation Model for the Power Grid

• URL: http://arxiv.org/abs/2509.25268v1
• Date: Sun, 28 Sep 2025 08:05:46 GMT
• Title: A Weather Foundation Model for the Power Grid
• Authors: Cristian Bodnar, Raphaël Rousseau-Rizzi, Nikhil Shankar, James Merleau, Stylianos Flampouris, Guillem Candille, Slavica Antic, François Miralles, Jayesh K. Gupta,
• Abstract summary: We fine-tune Silurian AI's WFM, Generative Forecasting Transformer (GFT)<n>It delivers hyper-local, asset-level forecasts for five grid-critical variables.<n>It attains an average precision score of 0.72 for day-ahead rime-ice detection.
• Score: 4.060631090375762
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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-Qu\'ebec 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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