Accepting PhD Students

PhD projects

Power and Energy System Optimisation; AI for Energy: Energy Data Analytics, Applied Machine Learning for Power and Energy Systems.

20092020

Research output per year

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Personal profile

Biography

Hao Wang is a Lecturer (US equivalent Assistant Professor) at the Department of Data Science and Artificial Intelligence, Monash University. He was a Postdoctoral Research Fellow at Stanford University in Palo Alto and a Washington Research Foundation (WRF) Innovation Fellow. 

His research interests are in the optimization of power & energy systems, machine learning and big data analytics in smart grids and smart cities, and business models for incentivizing participation of electric vehicles and prosumers. He has been awarded the best paper award at IEEE PECON 2016 and the best paper run-up at IEEE ICC 2017. He has served as an Organization Committee member of ACM e-Energy 2019, a TPC member for conferences including IEEE SmartGridComm, Globecom, ICC, WCNC, and a referee for leading journals: IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, IEEE Transactions on Industrial Informatics, IEEE Internet of Things Journal, Applied Energy, Renewable Energy, etc.

He is looking for Ph.D. students with self-motivation and strong interests in interdisciplinary research of smart energy systems, big data, and machine learning. Visiting scholars and students are also welcome.

Research interests

  • Optimisation for smart grids, microgrids, and transactive energy
  • Energy economics and business models, e.g., game-theoretic analysis and sharing economy
  • Machine learning (e.g., reinforcement learning, online learning) for energy systems
  • Energy big data analytics for distributed energy resources, e.g., residential prosumers and electric vehicles

Research area keywords

  • Optimisation
  • Machine learning
  • Data Analytics
  • Power Systems
  • Smart grid
  • Energy management
  • Distributed energy resources
  • Renewable Energy
  • Demand response

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Research Output

Storage or no storage: duopoly competition between renewable energy suppliers in a local energy market

Zhao, D., Wang, H., Huang, J. & Lin, X., Jan 2020, In : IEEE Journal on Selected Areas in Communications. 38, 1, p. 31-47 17 p.

Research output: Contribution to journalArticleResearchpeer-review

Virtual energy storage sharing and capacity allocation

Zhao, D., Wang, H., Huang, J. & Lin, X., Mar 2020, In : IEEE Transactions on Smart Grid. 11, 2, p. 1112-1123 12 p.

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

A distributed online pricing strategy for demand response programs

Li, P., Wang, H. & Zhang, B., Jan 2019, In : IEEE Transactions on Smart Grid. 10, 1, p. 350-360 11 p.

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)

Controllable vs. random: renewable generation competition in a local energy market

Zhao, D., Wang, H., Huang, J. & Lin, X., 2019, 2019 IEEE International Conference on Communications (ICC) - Proceedings. Wang, J. (ed.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 6 p. 8761733

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Leveraging socioeconomic information and deep learning for residential load pattern prediction

Tang, W-J., Lee, X-L., Wang, H. & Yang, H. T., 2019, Proceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). Dumbrava, V. (ed.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 5 p. 8905483

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review