Application of Machine Learning in Power Systems – Part 2

Presented by:
Qiuhua Huang, Pacific Northwest National Laboratory 
Jason Hou, Pacific Northwest National Laboratory

Part II of this webinar will focus on the-state-of-the-art applications with some R&D work from Pacific Northwest National Laboratory as examples. These include PMU data analytics, uncertainty quantification(UQ), tie-line exchange prediction, adaptive Remedial Action Scheme (RAS) settings using machine learning, power system emergency control using deep reinforcement learning, which powered AlphaGo to beat human Go champions. The ultimate goal of these projects is to leverage state-of-the-art machine learning technologies to make decision-makings in power system control centers—the “brain” of the grid— adaptive, robust and smart. 

Click Here to Stream

Download Slides

INTENDED AUDIENCE: Planning and operation engineers in ISOs, TSOs and Utilities, vendors, consultants, researchers, undergraduate and postgraduate students.     



Qiuhua HuangQiuhua Huang received his B.Eng. and M.S. degree in electrical engineering from South China University of Technology, Guangzhou, China, in 2009 and 2012, respectively, and his Ph.D. degree in electrical engineering from Arizona State University, Tempe, AZ, USA, in 2016. Qiuhua Huang is currently a power system research engineer in the Electricity Infrastructure group, Pacific Northwest National Laboratory, Richland, WA, USA. His research interests include power system modeling, simulation and control, transactive energy, and application of advanced computing and machine learning technologies in power systems. Currently, he is the principal investigator/project manager of several DOE funded projects. He is co-chair of the “Deep Learning and Smart Grid Applications” panel session at PES GM 2018. He is an Associated Editor of CSEE Journal of Power and Energy Systems

Mark Siira
Z. Jason Hou received his Ph.D. from U.C. Berkeley, and is currently a senior data scientist and statistician at Pacific Northwest National Laboratory. He has been well recognized for leading pioneering work in developing and applying advanced machine learning, uncertainty quantification, and extreme events analysis, in the areas of earth, energy, and environmental systems. Dr. Hou's research broadly cuts across areas in stochastic operation and planning of energy systems, extreme events in earth systems, carbon sequestration, oil/gas exploration, and environmental remediation.

REGISTRATION IS COMPLIMENTARY so please sign up today and join us on November 1, 2018 1:00PM EDT 

Click Here to Register TODAY

To view previous webinars on-demand, visit the IEEE Smart Grid Resource Center.


For any questions, please contact Phyllis Caputo at


Become a Presenter

If you'd like to be a presenter, please fill out this form.