Wednesday, April 15, 2026 | 1:00 to 2:30 p.m. ET | Open to public utility commissions, state energy offices, and relevant state agencies | Register now
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 shifting 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
**This session is part of the broader Technical Foundations of Grid Resilience Cohort. Learn more and join the full cohort to access the full technical learning series and contribute to a final resource and explore NASEO's upcoming Wildfire Resilience Cohort for additional opportunities to engage on emerging resilience challenges