Forecasting space weather risks on power grids

At a glance End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States. Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity. Advance warning: The pipeline detected nearly 80% of major space-weather events during the evaluation period and can warn grid operators 30 to 60 minutes before a specific risk appears. Forecasting a threat to critical infrastructure During the May 2024 geomagnetic storm, utilities across North America prepared for possible impacts as auroras extended far beyond their usual range. The storm would degrade GPS accuracy and satellite operations, impacting farm operations in North America and Europe. Modern society depends on reliable electric power, yet extreme space-weather events can induce currents in transmission networks that damage equipment and increase operational risk. The challenge is not only knowing that a storm is approaching, but estimating when and where its effects could be most severe with enough warning for grid operators to respond. During my summer internship at Microsoft Research, I developed a machine learning system that forecasts space-weather risk across 66,935 substations in the continental United States. The system measures how the sun’s activity can affect the earth’s magnetic field and, ultimately, the power grid. It combines solar-wind observations, forecasts of the Auroral Electrojet

AI improves forecasting of space-weather risks

This AI system provides a significant advancement in predicting space weather impacts on power grids, offering specific, actionable warnings. It directly contributes to enhancing the resilience of critical infrastructure against natural threats, mitigating potential disruptions to human flourishing. The benefit is well-defined and supported by clear performance metrics.

SOURCE     Microsoft Research (tier 1)
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DOCUMENT   9f4a53a4-b7f1-4668-a4d3-69ceea37b9f9
           https://microsoft.com/en-us/research/blog/forecasting-space-weather-risks-on-power-grids
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EVIDENCE   2 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      0  (Mixed or uncertain)
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CONFIDENCE 77%