Yi Wang


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.


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./Research Assistant/Postdoc position and have 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) all as pdfs to me (jhma@connect.hku.hk). Usually, potential candidates will be contacted in one week. Otherwise, your application is not being considered.

Book Release


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.


  • June 2024, Our project titled “Trustworthy Probabilisctic Load Forecasting in Local Energy Systems” was granted by Research Grants Council (RGC), Hong Kong-General Reseach Fund (GRF), as PI.

  • June 2024, I was awarded the Best Teacher Award for the academic year 2022-2023 by Faculty Teaching and Learning Quality Committee.

  • Jan. 2024, I was appointed as an associate editor of Journal of Modern Power Systems and Clean Energy.

  • Jan. 2024, Our project titled “Electrification and Decarbonization: Multi-port Wireless Dock and Charge for Waterborne Transportation” was granted by Research Grants Council (RGC), Hong Kong-Young Collaborative Research Grant, as Co-PI.

  • Dec. 2023, Our project titled “Operational Risk Management of Low-carbon Urban Energy Systems under Digital Transformation” was granted by Guang-dong Basic and Applied Basic Research Foundation-General Research Fund, as PI.

  • Dec. 2023, Our paper titled “Electricity Consumer Characteristics Identification: A Federated learning Approach” received IEEE Transactions on Smart Grid Top-5 Papers Award.

  • Nov. 2023, I was recognized as an Outstanding Young Energy Science and Technology Worker by the China Energy Research Society.

  • Oct. 2023, I was recognized as one of the HKU Scholars in the Top 1% worldwide in 2022.

  • Oct. 2023, I was listed in the database of the top 2% cited researchers two consecutive years (starting from 2022). The list is compiled by Professor John P. A. Ioannidis and his team from Stanford University and is available online here.

  • Oct. 2023, I was selected for the 9th China Association for Science and Technology's “Young Talent Promotion Project”.

  • August 2023, I was offered an association with Imperial College London as Honorary Lecturer in the Department of Electrical and Electronic Engineering.

  • June 2023, Our project titled “Privacy-Preserving Data-Driven Operation of a Local Energy Community” was granted by Research Grants Council (RGC) of Hong Kong-Early Career Scheme (ECS), as PI.

  • May. 2023, Our paper titled “Trading and Valuation of Day-Ahead Load Forecasting in an Ensemble Model” received 2023 Ralph Lee Prize Paper Award.

  • May 2023, We signed a research contract with DAMO Academy, Alibaba Group for “Electrical Load Forecasting Under Complex Scenarios”.

  • Apr. 2023, the kick-off meeting of the China-Belgium joint project was successfully held.

  • Feb 2023, Our project titled “Low-carbon Operation of Green Buildings and Urban Energy Systems in Joint Electricity and Carbon Markets” was granted by the Ministry of Science and Technology of China.