Overview
Prof. Zhong Fan has just joined Exeter University as the professor of net zero energy systems. Previously he was a Professor at Keele University and the Academic Director of SEND (Smart Energy Network Demonstrator). Before that, he was Chief Research Fellow with Toshiba Research Europe, Bristol, U.K., leading research on IoT, smart grid, data analytics, and 5G communications. Earlier in his career, he was a Research Fellow with Cambridge University, a Lecturer with Birmingham University, and a Researcher with Marconi Laboratories, Cambridge. He also received a BT Short-Term Fellowship for his work at BT Laboratories. His research interests are smart energy, IoT, and machine learning applications.
For prospective students and visiting scholars:
I am always keen to recruit talented PhD and MSc students as well as host visiting scholars that work in related areas. I also welcome self-funded or government-funded (e.g., CSC) students. There are various funding opportunities for postdocs, e.g., EPSRC Fellowships, Newton International Fellowships, Newton Research Collaboration Programme, the Leverhulme Trust Early Career Fellowships, RAEng Research Fellowships, and Marie Curie Research Fellowships.
Publications
Copyright Notice: Any articles made available for download are for personal use only. Any other use requires prior permission of the author and the copyright holder.
2023
- Samende C, Fan Z, Cao J, Fabián R, Baltas GN, Rodriguez P. (2023) Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning, Energies, volume 16, no. 19, pages 6770-6770, DOI:10.3390/en16196770. [PDF]
2022
- Briggs C, Fan Z, Andras P. (2022) Federated Learning for Short-Term Residential Load Forecasting, IEEE Open Access Journal of Power and Energy, volume 9, pages 573-583, DOI:10.1109/oajpe.2022.3206220. [PDF]
- Harrold DJB, Cao J, Fan Z. (2022) Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning, Applied Energy, volume 318, pages 119151-119151, article no. 119151, DOI:10.1016/j.apenergy.2022.119151. [PDF]
- Samende C, Cao J, Fan Z. (2022) Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints, Applied Energy, volume 317, pages 119123-119123, article no. 119123, DOI:10.1016/j.apenergy.2022.119123. [PDF]
- Fan Z, Cao J, Jamal T, Fogwill C, Samende C, Robinson Z, Polack F, Ormerod M, George S, Peacock A. (2022) The role of 'living laboratories' in accelerating the energy system decarbonization, ENERGY REPORTS, volume 8, pages 11858-11864, DOI:10.1016/j.egyr.2022.09.046. [PDF]
