Application of Machine Learning in Power Systems


Presented by:
Qiuhua Huang, Pacific Northwest National Laboratory 

In Part I, this webinar will begin with a short tutorial of machine learning, then provide an overview of application of machine learning in power generation, transmission and distribution systems, including the history, recent applications and lessons learned. Lastly, future work and research directions will be discussed.

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




Mark SiiraQiuhua 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.

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


For any questions, please contact Phyllis Caputo at


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