Yi Wang (王毅)
Scholar

Yi Wang (王毅)

Google Scholar ID: KYDSElAAAAAJ
The University of Hong Kong
data analyticssmart gridload forecastingmulti-energy systems
Citations & Impact
All-time
Citations
11,045
 
H-index
52
 
i10-index
109
 
Publications
20
 
Co-authors
47
list available
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • Released a book titled “Smart Meter Data Analytics” published by Springer. The book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporate them into consumer behavior modeling and distribution system operations.
Research Experience
  • Serves as the Secretary of IEEE Customer Systems & Smart Buildings Subcommittee, the Chair of IEEE PES Working Group on Energy Forecasting and Analytics, and the secretary of CIGRE Working Group on Application of 5G Technology to Smart Grids. Also serves as the Associate Editor for IEEE Transactions on Smart Grid, IEEE Systems Journal, and IET Renewable Power Generation.
Education
  • Received a Bachelor's degree in electrical engineering from Huazhong University of Science and Technology (HUST) in June 2014, and a Ph.D. degree in electrical engineering from Tsinghua University in Jan. 2019, supervised by Prof. Chongqing Kang. From March 2017 to April 2018, was an exchange student researcher at the University of Washington, supervised by Prof. Daniel Kirschen. Served as a Postdoctoral Researcher in the Power Systems Laboratory, ETH Zurich, from Feb. 2019 to Aug. 2021, supervised by Prof. Gabriela Hug.
Background
  • Currently an Assistant Professor of the Department of Electrical and Electronic Engineering at The University of Hong Kong. Research interests include data analytics in the smart grid, energy forecasting, multi-energy systems, Internet-of-things, and cyber-physical-social energy systems.
Miscellany
  • Interested in a Ph.D./Postdoc position with a solid background in power systems, optimization theory, machine learning, or wireless communication, please send your latest CV, BSc transcript (with ranking), and publications (if any) as PDFs. Potential candidates are usually contacted in one week.