Prof. Zhang received his Ph.D. and M.S. degrees in Industrial Engineering from the University of Iowa, Iowa City, IA, USA, in 2012 and 2009, respectively, and B.Eng. degree in Systems Engineering and Engineering Management from the Chinese University of Hong Kong, Hong Kong, China, in 2008.
Prof. Zhang's research focuses on data mining and computational intelligence with applications in modeling, monitoring, optimization and operations of systems in the renewable energy, energy saving, and intelligent transportation.
Awards and Achievements
- 2023 “Silver Medal” International Exhibition of Inventions Geneva.
- 2022 “Stanford's top 2% most highly cited scientists” The Stanford's list of top 2% most cited scientists.
- 2022 “Outstanding Associate Editor 2022” Editorial Board of IEEE Transactions on Sustainable Energy.
- 2022 “CityU Long Service Award” City University of Hong Kong.
- 2019 “CityU Outstanding Supervisor Award” City University of Hong Kong.
- 2016 “CityU The President's Award” City University of Hong Kong.
Previous Experience
- Jul 2018 - Jun 2023, Associate Professor, School of Data Science, City University of Hong Kong.
- Aug 2012 - Jun 2018, Assistant Professor, Department of Systems Engineering and Engineering Management, City University of Hong Kong.
Research Grants
- Development of An AI-Powered Software System for Forecasting the Full Sequence of Day-ahead Wind Power, ITF-Midstream Research Programmes, Hong Kong Innovation and Technology Commission, Jun 2023 - May 2025, Zijun Zhang (PC), Xiangyu Zhao (Co-I).
- Development of the Advanced Machine Vision Inspection Algorithm for Metro Infrastructure Condition Autonomous Inspection Platform, Shenzhen-Hong Kong-Macau Science & Technology Project, May 2023 - Apr 2025, Zijun Zhang (PI).
- Data Science Methods for Offshore Wind Power Forecasting with Considering Multi-source Data, Guangdong Basic and Applied Basic Research Joint Fund, Oct 2022 - Sep 2025, Zijun Zhang (PI).
- Renewable Energy Power Production Forecasting Based on Deep Features of Multi-dimensional Array Input Data, National Natural Science Foundation of China, 1 Jan 2021 - 31 Dec 2023, Zijun Zhang (PI).
- A Collaborative Data-driven Methodology for Improving Wind Farm Operations and Maintenance, General Research Fund, Hong Kong Research Grants Council, 1 Jan 2019 - 31 Dec 2021, Zijun Zhang (PI).
- Robust Scheduling of Wind Farm Power Generation Considering System Reliability, France/Hong Kong Joint Research Scheme, Hong Kong Research Grants Council, 1 Dec 2014 - 31 Dec 2016, Zijun Zhang (PI).
- Scheduling Power Production of Hybrid Power Systems with Data Mining and Computational Intelligence, Early Career Scheme, Hong Kong Research Grants Council, 1 Jul 2013 - 31 Dec 2016, Zijun Zhang (PI).
Patents
- X. Liu and Z. Zhang, A System And Method for Monitoring A Device, US Patent No. 11306705, 2022.
- L. Yang and Z. Zhang, Wind Turbine Performance Determination And Control, US Patent No. 63/289,306, 2021.
External Services
Professional Activity
- 2022 - Now, Advisory Board Member, Patterns: Cell Press.
- 2021 - Now, Associate Editor, IEEE Transactions on Sustainable Energy.
- 2021 - Now, Associate Editor, IEEE Power Engineering Letters.
- 2015 - Now, Associate Editor, Journal of Intelligent Manufacturing.
Five Selected Publications
- Z. Zheng and Z. Zhang*, "A Stochastic Recurrent Encoder Decoder Network for Multi-step Probabilistic Wind Power Predictions," IEEE Transactions on Neural Networks and Learning Systems, 2023, In press.
- Z. Mo, Z. Zhang*, Q. Miao, and K. Tsui, "Sparsity-Constrained Invariant Risk Minimization for Domain Generalization with Application to Machinery Fault Diagnosis Modeling," IEEE Transactions on Cybernetics, 2022, In press.
- H. Liu and Z. Zhang*, "A Bi-party Engaged Modeling Framework for Renewable Power Predictions with Privacy-preserving," IEEE Transactions on Power Systems, 2022, In press.
- L. Yang, L. Wang, and Z. Zhang*, "Generative Wind Power Curve Modeling Via Machine Vision: A Deep Convolutional Network Method with Data-Synthesis-Informed-Training," IEEE Transactions on Power Systems, 2022, In press.
- L. Yang, Z. Zheng, and Z. Zhang*, "An Improved Mixture Density Network via Wasserstein Distance Based Adversarial Learning for Probabilistic Wind Speed Predictions," IEEE Transactions on Sustainable Energy, Vol. 13, No. 2, pp. 755-766, 2022.
Last update date :
24 Jul 2023
