About me
Profile
I am a Postdoctoral Research Associate at Cardiff University, UK, and a research member of the CLEETS Global Centre. I received my PhD in Computer Science from Newcastle University in 2025. I was an academic visitor at the University of Essex from September to December 2019 and a PhD researcher at the University of East Anglia from October 2021 to September 2022.
My work has contributed to large-scale UKRI and US NSF projects, including the REMeDY project (approximately £2.34M in total) and the CLEETS project (approximately £6M + $5M in total), developing data-centric modelling, predictive, and optimisation capabilities for operational intelligence across sectors.
Research interests
My research interests include data-driven artificial intelligence and machine learning algorithms, with applications in multi-energy microgrids and clean transportation. Current work connects short-term demand and renewable forecasting, deep reinforcement learning for microgrid control, multi-energy dispatch, and AI-assisted decision support for electricity, hydrogen, heat, and transport networks.
- Data-driven AI
- Energy forecasting
- Microgrid optimisation
- Multi-energy systems
- Clean transportation
- Human-centred AI
Background
Education
PhD in Computer Science
Newcastle University, UK
MSc in Control Science and Engineering
Shanghai University, China
BEng in Electrical Engineering and Automation
Ludong University, China
Appointments and Research Experience
Projects
Selected large-scale research projects in AI-enabled clean energy systems, microgrids, and integrated transportation networks.
Clean Energy and Equitable Transportation Solutions (£6.0M + $5.0M)
International collaboration across the UK, US, Japan, Mexico, and India. This work contributes AI-enabled operational intelligence for integrated electricity-hydrogen-transport systems.
- Hierarchical optimisation and deep reinforcement learning for coordinated vehicle scheduling and multi-energy dispatch.
- Regulation-aware multi-agent reinforcement learning for electric heavy goods vehicle fleet operation.
- Decision-support dashboard concepts for monitoring energy flows and AI-assisted dispatch decisions.
Revolution in Energy Market Design (£2.34M)
A UKRI-funded project on energy market design and district-scale energy operation. This work focused on data-driven modelling and predictive capability for electricity and heat demand.
- Machine learning and deep learning models for short-term electricity and heat demand forecasting.
- Predictive energy-management support for a district energy network serving more than 1,100 residential properties.
- Optimisation support for coordinated operation of electricity-heat systems, storage, batteries, and heat pumps.
Publications
Selected papers and recent preprints in energy forecasting, microgrid optimisation, and AI-enabled systems.
Selected Publications
Peer-Reviewed Journal Papers
- F. Yao, W. Zhao, M. Forshaw, and W. Zhou, "A Unified Data-Driven Approach Under Deep Reinforcement Learning with Direct Control Responses for Microgrid Operations," Knowledge-Based Systems, 325, 113844, 2025.
- F. Yao, W. Zhao, M. Forshaw, and Y. Song, "A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning," Neurocomputing, 654, 131375, 2025.
- F. Yao, W. Zhao, M. Forshaw, and Y. Song, "A New Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction," Applied Soft Computing, 177, 113221, 2025.
- F. Yao, W. Zhou, M. Al Ghamdi, Y. Song, and W. Zhao, "An integrated D-CNN-LSTM approach for short-term heat demand prediction in district heating systems," Energy Reports, 8, 98-107, 2022.
- W. Zhou, F. Yao, S. Luan, J. C. Ndubuisi, and X. Wu, "A Textural Distributions-Based Detection of Hazelnut Axial Direction," International Journal of Computational Intelligence Systems, 14(1), 358-366, 2021.
- W. Zhou, F. Yao, W. Feng, and H. Wang, "Real-Time Height Measurement for Moving Pedestrians," Complexity, 2020, 5708593, 2020.
Selected Conference Papers
- M. Islam, F. Yao, W. Zhao, R. C. Cardoso, and M. Xu, "Neuro-Symbolic Pump Scheduling for Safe and Cost-efficient Water Distribution Networks," in Proc. 14th Int. Workshop on Engineering Multi-Agent Systems (EMAS), co-located with AAMAS, Paphos, Cyprus, May 25-26, 2026.
- F. Yao, W. Zhao, and M. Forshaw, "A New Error Temporal Difference for Deep Reinforcement Learning in Microgrid Optimisation," in Proc. 9th Int. Conf. on Renewable Energy and Conservation (ICREC), Rome, Italy, Nov. 22-24, 2024.
- X. Ren, F. Yao, T. Zhou, X. Gu, and W. Zhou, "Obstacle Avoidance Based on Component Separation-Refusion Algorithm for Blind People," in Proc. Chinese Control Conf. (CCC), Tianjin, China, Jul. 27-30, 2021, pp. 7168-7173.
- F. Yao, T. Zhou, M. Xia, H. Wang, W. Zhou, and J. C. Ndubuisi, "Dynamic Pedestrian Height Detection Based on TOF Camera," in Proc. LSMS & ICSEE Workshops, in Recent Featured Applications of Artificial Intelligence Methods, Singapore: Springer, 2020, pp. 442-455.
Recent Preprints
- F. Yao, Y. Xu, M. Albano, L. Cipcigan, N. Hamed, N. Valizadeh, and O. Rana, "A Hierarchical Optimization Framework for Integrated Electric-Hydrogen Transportation Systems," arXiv:2607.25776, 2026.
- F. Yao, W. Zhao, C. Zheng, and X. Han, "Trend-Aware Multi-Task Learning for Short-Term Energy Forecasting," arXiv:2511.09789, 2025.
- F. Yao, W. Zhou, and H. Hu, "A Review of Vision-Based Assistive Systems for Visually Impaired People: Technologies, Applications, and Future Directions," arXiv:2505.14298, 2025.
Presentations
Selected invited and oral presentations on data-driven energy-system optimisation and AI-enabled systems.
Conference Presentations
- Invited Speaker, "Data-Driven Strategies for Optimizing Microgrid Operations in Clean Energy Systems," 10th International Conference on Power and Renewable Energy (ICPRE), Hangzhou, China, Sep. 20, 2025.
- Oral Presentation, "A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization," 9th International Conference on Renewable Energy and Conservation (ICREC), Rome, Italy, Nov. 22, 2024.
- Oral Presentation, "An Integrated D-CNN-LSTM Approach for Short-Term Heat Demand Prediction in District Heating Systems," 5th International Conference on Electrical Engineering and Green Energy (ICEEGE), Berlin, Germany, Jun. 11, 2022.
- Oral Presentation, "Dynamic Pedestrian Height Detection Based on TOF Camera," 6th International Conference on Life System Modeling and Simulation & Intelligent Computing for Sustainable Energy and Environment (LSMS & ICSEE), Hangzhou, China, Oct. 25, 2020.
Service
Teaching, selected awards, and professional service activities.
Teaching, Awards, and Service
Teaching
- Teaching Assistant, Newcastle University: 700+ hours of laboratory teaching and dissertation supervision.
- Academic Tutor, HighMark Education Group: 100+ hours of tutoring in computer science and data science.
Selected Awards
- Chinese National Scholarship, Ministry of Education, China, 2020.
- Outstanding Graduate, Shanghai University, China, 2021.
- Outstanding Graduate, Ludong University, China, 2018.
- Chinese National Encouragement Scholarship, Ministry of Education, China, 2016 and 2017.
- PhD Scholarship, Newcastle University, UK, 2022-2024.
- PhD Scholarship, University of East Anglia, UK, 2021-2022.
Professional Service
- Journal reviewer for Applied Energy, Engineering Applications of Artificial Intelligence, Applied Soft Computing, Knowledge-Based Systems, Neurocomputing, Energy Reports, and IEEE Access.
- Editorial Board Member, Journal of Electrical and Computational Innovations.
- Technical Program Committee Member, 2026 International Conference on Mechatronics and Intelligent Control.
- IEEE PES Task Force Member: Cross-Sector Energy System Resilience Under Climate Change.
Open Science
Open datasets, source code, and interactive demonstrations supporting reproducible research.
Open Science Contributions
Open Datasets
- Representative District Microgrid Dataset (GitHub).
- Representative Integrated Energy System Dataset (GitHub).
Open-Source Code
- Multi-task Energy Forecasting Framework (GitHub).
- Fuzzy Neural Network for energy forecasting (GitHub).
Interactive Demonstration
- AI-enabled decision-support dashboard for monitoring system operation, energy flows, and dispatch decisions (GitHub).