Biography
Min Zhou received the B.Eng. degree in 2017 and the M.S. degree in 2020, both from the School of Management of Engineering, Nanjing University, Nanjing, China.
He is currently pursuing the Ph.D. degree with the Department of Data Science, City University of Hong Kong, supervised by Prof. Minghua Chen.
He is a recipient of the prestigious City University of Hong Kong Presidential PhD Scholarship. His research interests include machine learning and its applications to power system analysis and optimization.
News & Awards
- [2023] Outstanding Academic Performance Award for Research Degree Students, CityU.
- [2020] City University of Hong Kong Presidential PhD Scholarship.
- [2019] Outstanding Postgraduates of Nanjing University.
- [2019] Artificial Intelligence Scholarship of Nanjing.
- [2018] National Scholarship for Postgraduates.
- I serve as a Maintainer for the ACM SIGEnergy ML-OPF Wiki, which aims to provide a knowledge base for Machine Learning in Optimal Power Flow.
Publications
Working Papers / Preprints
Breaking the Iterative Barrier: Machine Learning Facilitates Real-Time Grid Optimization with Certifiable SafetyIn Preparation
M. Zhou, E. Liang and M. Chen.
M. Zhou, E. Liang and M. Chen.
Machine learning for solving multi-stage optimization problemsIn Preparation
M. Zhou, E. Liang and M. Chen.
M. Zhou, E. Liang and M. Chen.
Refereed Journals
Partially Permutation-Invariant Neural Network for Solving Two-Stage Stochastic AC-OPF Problem
M. Zhou, E. Liang, M. Chen and S. H. Low.
IEEE Transactions on Power Systems, Accepted, 2025.
M. Zhou, E. Liang, M. Chen and S. H. Low.
IEEE Transactions on Power Systems, Accepted, 2025.
DeepOPF-FT: One Deep Neural Network for Multiple AC-OPF Problems with Flexible Topology
M. Zhou, M. Chen and S. H. Low.
IEEE Transactions on Power Systems, vol. 38, no. 1, pp.964-967, 2023.
M. Zhou, M. Chen and S. H. Low.
IEEE Transactions on Power Systems, vol. 38, no. 1, pp.964-967, 2023.
Multi-objective prediction intervals for wind power forecast based on deep neural networks
M. Zhou, B. Wang, S. Guo, and J. Watada.
Information Sciences, vol. 550, pp. 207-220, 2021.
M. Zhou, B. Wang, S. Guo, and J. Watada.
Information Sciences, vol. 550, pp. 207-220, 2021.
Deep learning-based rolling horizon unit commitment under hybrid uncertainties
M. Zhou, B. Wang, and J. Watada.
Energy, vol. 186, p. 115843, 2019.
M. Zhou, B. Wang, and J. Watada.
Energy, vol. 186, p. 115843, 2019.
A data-driven approach for multi-objective unit commitment under hybrid uncertainties
M. Zhou, B. Wang, T. Li, and J. Watada.
Energy, vol. 164, pp. 722-733, 2018.
M. Zhou, B. Wang, T. Li, and J. Watada.
Energy, vol. 164, pp. 722-733, 2018.
Conference Proceedings
Optimizing Demand Response in Distribution Network with Grid Operational Constraints
T. Zhao, M. Zhou, Y. Mo, J. M. Wang, J. Luo, X. Pan, and M. Chen.
Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy), 2023.
T. Zhao, M. Zhou, Y. Mo, J. M. Wang, J. Luo, X. Pan, and M. Chen.
Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy), 2023.
Professional Activities
Reviewer for Journals
- IEEE Transactions on Power Systems
- IEEE Transactions on Smart Grid
- Energy
Teaching Experience
Teaching Assistant, City University of Hong Kong
- SDSC6014: Networked Life and Data Science
- SDSC3060: Operations Research
- SDSC3102: Quality Technologies
Research Interests
Machine Learning for Power Systems
Data-Driven Optimization
Smart Grid Operations