ESIC Zoom seminar: What we can learn from smart meter data

About the event

What We Can Learn from Smart Meter Data by Zhaoyu Wang, Assistant Professor, Iowa State University

Overview

This talk will present our research on enhancing power distribution grid observability using real utility data and machine learning techniques.

We have received a large amount of smart meter, microPMU and SCADA data and associated grid circuit models from collaborating utilities. We will begin the talk by introducing our data and one real utility dataset that we share with the research community.

By leveraging the smart meter data, we have proposed a multi-timescale learning model that enables utilities to infer hourly consumption patterns of unobservable customers using only their monthly billing information, thus significantly enhancing the grid observability.

Further, the smart meter data has been used to develop a model free framework to estimate cold load pick-up and help utilities make decisions in service restoration.

Bio

Dr. Zhaoyu Wang is the Harpole-Pentair Assistant Professor with Iowa State University. He received B.S. and M.S. degrees in electrical engineering from Shanghai Jiaotong University, and his Ph.D. degree in electrical and computer engineering from Georgia Institute of Technology.

His research interests include optimization and data analytics in power distribution systems and microgrids. He is the Principal Investigator for a multitude of projects focused on these topics and funded by the National Science Foundation, the Department of Energy, National Laboratories, PSERC, and Iowa Energy Center.

He is an editor of IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Open Access Journal of Power and Energy, and IEEE PES Letters and an associate editor of IET Smart Grid. Dr. Wang is the Secretary of IEEE Power and Energy Society (PES) Award Subcommittee, Co-Vice Chair of PES Distribution System Operation and Planning Subcommittee, and Vice Chair of PES Task Force on Advances in Natural Disaster Mitigation Methods.