Microsoft uses machine learning to predict space weather risks to the grid
What happened
Microsoft Research has developed an end-to-end machine learning pipeline to predict space weather risks facing the power grid. It uses L1 solar wind observations, forecasts of the AE and Dst indices, and local geological conductivity data to estimate local space weather risks for 66,935 substations in the continental US, with 30 to 60 minutes’ lead time. Microsoft Research is currently the primary source publishing this work, which covers substations in the continental US.
Written by AI from the reports · updated 21 h ago
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- Microsoft ResearchForecasting space weather risks on power grids
Microsoft Research has developed an end-to-end machine learning pipeline that uses solar wind observations at L1, forecasts of the AE and Dst indices, and local geological conductivity data to estimate local space weather risks 30 to 60 minutes ahead for 66,935 substations in the contiguous United States.
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