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Predicting Power Outages Before They Happen: Machine Learning Approaches to Grid Vulnerability

2026-05-19 · 18:35 UTC ·NARUC ·0 min read

Brief

The presentation/demo by Dr. Feng Qiu (Argonne) showcased an interactive machine-learning tool that fuses real-time outage data with weather and environmental signals to forecast disruptions and highlight vulnerable assets. The tool generates actionable risk scores and visualizations to inform investment decisions, prioritize mitigations, and move utility planning toward proactive, data-driven resilience.

Why it matters

Dr. Feng Qiu (Argonne National Laboratory) demonstrated an interactive tool on 2026-05-19 that uses machine learning and big data to predict power outages by combining real-time outage records with weather and environmental inputs.

Key details

  • The models forecast disruptions, map system vulnerabilities, and produce data-driven risk evaluations to guide targeted investments and shift utilities from reactive restoration to proactive grid resilience planning.
Source evidence

The session will feature a demonstration of an interactive tool that showcases how machine learning and big data are transforming outage prediction and grid resilience planning. By combining real-time outage data with weather and environmental inputs, advanced models can forecast disruptions, identify system vulnerabilities, this shifts the focus from reactive to proactive resilience. Data-driven insights aims to provide better evaluations to risk, guide investments, and strengthen grid reliability.

Featured Speaker: Dr. Feng Qiu, PhD, Principal Computational Scientist and Group Manager, Energy Systems and Infrastructure Analysis Division
Argonne National Laboratory

Channel: NARUC
Published: 2026-05-19
Video URL: https://www.youtube.com/watch?v=gz4Hwp8uCzg