Yi Wang
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Yi Wang

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I am currently an Assistant Professor of the Department of Electrical and Electronic Engineering, The University of Hong Kong.

I received my Bachelor's degree in electrical engineering from Huazhong University of Science and Technology (HUST) in June 2014, and my Ph.D. degree in electrical engineering from Tsinghua University (supervised by Prof. Chongqing Kang) in Jan. 2019. From March 2017 to April 2018, I was an exchange student researcher at the University of Washington (supervised by Prof. Daniel Kirschen). From Feb. 2019 to Aug. 2021, I served as a Postdoctoral Researcher in the Power Systems Laboratory, ETH Zurich (supervised by Prof. Gabriela Hug).

I currently serve as the Secretary of IEEE Customer Systems & Smart Buildings Subcommittee, the Vice-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. I also serve as the Associate Editor for IEEE Transactions on Smart Grid, IEEE Systems Journal, and IET Renewable Power Generation.

My research interests include data analytics in the smart grid, energy forecasting, multi-energy systems, Internet-of-things, cyber-physical-social energy systems.

Find me on: Google Scholar, ORCiD, and ResearchGate. Here is my CV.

Contact

Address:  Room 604, Chow Yei Ching Building, Pokfulam Road, Hong Kong
Email:  yiwang@eee.hku.hk
Telephone:  (852)3917-8095

If you are interested in a Ph.D./Postdoc position and have a solid background in power systems, optimization theory, machine learning, or wireless communication, please send me your latest CV, BSc transcript (with ranking), and publications (if any) as PDFs (yiwang@eee.hku.hk). Potential candidates are usually contacted in one week. Otherwise, your application is not being considered.

Normally, we only consider Ph.D. applicants who have obtained or are expected to obtain a 1st class honors Bachelor's degree from a top university (Project 985 or Top 80 in QS ranking) based on the latest university ranking with a high GPA (e.g., 3.7 out of 4 or equivalent).

GitHub repositories for EDL@HKU have been established. You are welcome to refer to the code and data of our works here.

Explore our new data sharing website at here to access a wealth of public datasets and collaborate with experts in the energy sector. Join us in shaping the future of energy systems through data-driven insights and collaboration.

We are excited to announce a Special Issue on “Trustworthy Machine Learning for Power and Energy Systems” in Applied Energy. This issue focuses on advancing reliable AI/ML solutions for critical energy infrastructure, addressing challenges like interpretability, robustness, and security. Submissions are open until November 30, 2025. Learn more and submit your work here.

Book Release

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We are pleased to announce the release of our book, titled “Smart Meter Data Analytics” published by Springer.

This 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. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scales. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.

Feature of the book:

  • First book of its kind to introduce electrical smart meter data analytics in the power systems;

  • In-depth analysis and modeling of electricity consumer behavior from data-driven perspective;

  • Various applications and comprehensive case studies.

Updates

  • Jun. 2025, Our project titled “Lightweight Learning for the Coordination of Behind-the-meter Resources” was granted by Research Grants Council (RGC), Hong Kong-General Research Fund (GRF), as PI.

  • May. 2025, I was awarded the 2025 IEEE PES Outstanding Young Engineer Award by IEEE Power & Energy Society.

  • Apr. 2025, I was awarded the Young Scientist Award by 2025 IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering (AAIEE 2025).

  • Jan. 2025, I was invited to join the IEEE PES Long Range Planning Subcommittee on Emerging Technologies.