微软研究院用机器学习预测电网空间天气风险

AI 摘要

微软研究院实习生开发了一套端到端机器学习系统，利用太阳风观测、AE/Dst指数预报、地质电导率及电网基础设施数据，为美国本土66,935个变电站生成30-60分钟前的空间天气风险预测。该系统在2020-2026年评估期内检测到近80%的重大空间天气事件，Dst预测在62.2%的高活跃时段优于Burton方程，并借助50个AI代理探索特征与模型配置。

正文节选

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 gri

发布时间：2026-10-01 00:00

抓取时间：2026-10-01 00:43

来源机构：Microsoft Research