Data Analytics for Resilient Energy Infrastructure and Communities
DescriptionResilience of our energy infrastructure under weather extremes is an important problem to the US and the world as shown by large-scale power outages occurred in the past 10 years. Resilience is to reduce the impact of such power disruptions to people, which is particularly important in a changing climate. A fundamental question is how resilient is our energy infrastructure to weather extremes? Further, how does enhancing the resilience of the energy infrastructure benefit people? We describe how data analytics plays an important role in studying resilience. We collect field data from the operational distribution grid. The data are at the large scale, spanning multiple service regions across the US states. We then describe how computational algorithms based on spatial temporal models are derived to learn knowledge from the data. The analytics disseminates knowledge, i.e., insights on vulnerability and potential improvements of the energy infrastructure. We discuss a future direction on extending resilience study to communities through Georgia EnergyShed (G-Shed)*.
*Joint work with 12 co-authors from Georgia Tech, New York State utilities and Public Service Commission.
*G-Shed: a DoE team-project led by Richard Simmons at Georgia Tech.
TimeWednesday, June 2812:00 - 12:30 CEST
Computer Science, Machine Learning, and Applied Mathematics