SIE Seminar: Feng Qiu
September 22, 2022
2:00 p.m. (MST)
Feng Qiu
Principal Computational Scientist and a Section Leader
Energy Systems Division
Argonne National Laboratory
"Learning to Solve Energy Challenges"
ENGR 301 | Zoom link
Abstract: Machine Learning has been extensively used in power system applications, mostly focusing on prediction, detection, etc. This talk will focus on three emerging topics on ML applications in power systems, each of them represents a perspective on how machine learning can renovate and help build a stronger power system application. The first topic is "learning to optimize", which covers how to use machine learning to expedite the solution of power system mixed-integer optimization problems; the second topic is "learning to model", which talks about how machine learning can be used to formulate sophisticated constraints and develop computationally tractable models; the third topic is "learning to predict", which presents a spatial-temporal model that combines stochastic process and deep neural network. We hope the three topics can present audience multiple emerging perspectives on using machine learning to address challenges in energy field.
Bio: Feng Qiu received his Ph.D. from the School of Industrial and Systems Engineering at the Georgia Institute of Technology in 2013. He is a principal computational scientist and a section leader with the Energy Systems Division at Argonne National Laboratory. His current research interests include power system modeling and optimization, electricity markets, power grid resilience, machine learning and data analytics.