{
  "id": 10987736,
  "title": "Forecasting space weather risks on power grids",
  "url": "https://urgent.news/2026/09/30/forecasting-space-weather-risks-on-power-grids",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-30T16:00:00.000Z",
  "source": {
    "name": "Microsoft Research",
    "slug": "microsoft-research",
    "url": "https://www.microsoft.com/en-us/research/blog/forecasting-space-weather-risks-on-power-grids/"
  },
  "original_language": "en",
  "account": "A machine learning pipeline has been developed to forecast space weather risks on power grids across the continental United States. This system combines solar-wind forecasts with local factors such as latitude, geology, and ground conductivity to estimate risk for 66,935 substations. During testing, the pipeline detected nearly 80% of major space weather events and provided 30-60 minutes of warning before specific risks occurred. The challenge lies in predicting when and where a storm's effects could be most severe, as extreme space weather can damage power equipment and increase operational risk. The pipeline employs three stages: first, it generates forecasts of the Auroral Electrojet and Disturbance Storm Time indices using solar-wind measurements; second, it employs a gradient-boosting model to estimate the rate of magnetic-field change associated with GIC risk; and finally, it converts these predictions into location-specific risk estimates and aggregates them into a continental risk assessment. The system was evaluated using public data sources like NASA's OMNI and Kyoto World Data Center data, INTERMAGNET observations, and GridSFM-derived grid data. The AE predictor outperformed existing empirical solar-wind-based approaches in forecasting rare, high-intensity geomagnetic activity that drives infrastructure risk. The Dst predictor provided an additional signal for large-scale geomagnetic storm strength. The GIC risk stage, which calculates the rate of magnetic-field change (GIC), was compared to simple linear regression and achieved detection rates of 76.5% for major events, 81.2% for severe events, and 64.1% for extreme events.",
  "summary": "Extreme space-weather events can damage power systems on Earth and degrade GPS accuracy and satellite operations. A new machine learning system can predict where damage is likely to occur 30-60 minutes before a storm arrives. The post Forecasting space weather risks on power grids appeared first on Microsoft Research .",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}