Microsoft Research developed a machine learning pipeline that forecasts space-weather risk for 66,935 substations in the continental United States, using solar-wind data and local geological factors to provide location-specific risk estimates 30–60 minutes ahead of potential impact.

During evaluation, the system detected 76.5% of major events (≥10 nT/min), 81.2% of severe events (≥20 nT/min), and 64.1% of extreme events (≥50 nT/min).