{"ID":2864214,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25268","arxiv_id":"2509.25268","title":"A Weather Foundation Model for the Power Grid","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-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.","short_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...","url_abs":"https://arxiv.org/abs/2509.25268","url_pdf":"https://arxiv.org/pdf/2509.25268v1","authors":"[\"Cristian Bodnar\",\"Raphaël Rousseau-Rizzi\",\"Nikhil Shankar\",\"James Merleau\",\"Stylianos Flampouris\",\"Guillem Candille\",\"Slavica Antic\",\"François Miralles\",\"Jayesh K. Gupta\"]","published":"2025-09-28T08:05:46Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"physics.ao-ph\"]","methods":"[\"Transformer\"]","has_code":false}