Dr Fanlin Meng

Department of Mathematical Sciences
Dr Fanlin Meng



Dr Fanlin Meng is a Lecturer in Data Science and a member of the Institute for Analytics and Data Science at the University of Essex. Prior to his current appointment, he held several research positions in Cardiff (Engineering), Edinburgh (Mathematics), Durham (ECS) and Loughborough (AAE). He received his PhD in Computer Science from the University of Manchester. His primary research interests include Energy Data Analytics, Energy Market with Demand Response and Renewable Energy Integration, Smart Energy and Mobility, Machine Learning, Game Theory and Optimisation. He has worked on several research projects supported by internal and external funds on - Smart Energy Market and Data Analytics ( "Demand Response to the Rescue: Controlling the Uncertainty of Green Energy Resources" funded by University of Essex PVC (Research) Strategic Fund as Co-I, “A Distributed and Real-time Learning Framework for Smart Meter Big Data” funded by University of Essex PhD Studentship and “Distributed Auction Design for Smart Microgrids” funded by Durham Energy Institute, both as PI) - Smart Grids and Sustainable Energy Systems (EPSRC funded projects “The Autonomic Power System”, “Energy Storage for Low Carbon Grids” and “Development and Evaluation of Sustainable Technologies for Flexible Operation of Conventional Power Plants”) - Smart/Digital Buildings and Building Energy Management (ERDF funded project "Visual Building Fire Protection System" as PI, European Commission Horizon 2020 funded projects “TABEDE: TowArds Building rEady for Demand rEsponse” and “DRiVE: Demand Response Integration tEchnologies: unlocking the demand response potential in the distribution grid”), - Autonomous Driving (EPSRC funded project “Towards More Autonomy for Unmanned Vehicles: Situational Awareness and Decision Making under Uncertainty”) - AI for Healthcare (“Non-intrusive residential energy monitoring for dementia” funded by ESRC IAA via University of Essex as PI and EPSRC funded project “SAMS - Software Architecture for Mental health Self management”). He is Fellow of British Computer Society (FBCS), member of EPSRC Peer Review College, Senior Member of IEEE, and an Academic Editor of Complexity. He is also the special session chair of the International Conference on Life System Modeling and Simulation & International Conference on Intelligent Computing for Sustainable Energy and Environment (2017, 2021), and chair of conference special sessions “Computational Intelligence in Transactive Energy Management and Smart Energy Network (CITESEN)” (WCCI-CITESEN 2020, IJCNN-CITESEN 2021).


  • PhD in Computer Science University of Manchester, (2015)

  • MSc in Systems Engineering Xiamen University, (2011)

  • BSc in Automation China University of Mining and Technology, (2008)


University of Essex

  • Lecturer in Data Science, University of Essex (15/1/2019 - present)

Other academic

  • External Examiner, Leeds University Business School, University of Leeds (1/11/2021 - 31/10/2025)

Research and professional activities

Research interests

Data Science


Machine Learning

Game Theory

Teaching and supervision

Current teaching responsibilities

  • Applied Statistics (MA321)

  • Databases and data processing with SQL (MA332)

  • Professional Placement (MA911)


Journal articles (14)

Su, J., Wang, Y., Zhai, X., Meng, F. and liu, C., (2022). Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms. Remote Sensing. 14 (3), 782-782

Niño de Zepeda, MV., Meng, F., Su, J., Zeng, X-J. and Wang, Q., (2021). Dynamic Clustering Analysis for Driving Styles Identification. Engineering Applications of Artificial Intelligence. 97, 104096-104096

Chen, X., Wang, LG., Meng, F. and Luo, Z-H., (2021). Physics-Informed Deep Learning for Modelling Particle Aggregation and Breakage Processes. Chemical Engineering Journal. 426, 131220-131220

Liu, Z., Han, J., Meng, F. and Liao, H., (2021). A cloud-based and web-based group decision support system in multilingual environment with hesitant fuzzy linguistic preference relations. International Journal of Intelligent Systems

Gong, D., Pan, F., Tian, T., Yang, S. and Meng, F., (2020). A feedback-directed method of evolutionary test data generation for parallel programs. Information and Software Technology. 124, 106318-106318

Zhang, Y., Meng, F., Wang, R., Kazemtabrizi, B. and Shi, J., (2019). Uncertainty-resistant Stochastic MPC Approach for Optimal Operation of CHP Microgrid. Energy. 179, 1265-1278

Zhang, Y., Meng, F., Wang, R., Zhu, W. and Zeng, X-J., (2018). A stochastic MPC based approach to integrated energy management in microgrids. Sustainable Cities and Society. 41, 349-362

Meng, F., Zeng, X-J., Zhang, Y., Dent, CJ. and Gong, D., (2018). An integrated optimization + learning approach to optimal dynamic pricing for the retailer with multi-type customers in smart grids. Information Sciences. 448-449, 215-232

Liu, Z., Zeng, X. and Meng, F., (2018). An Integration Mechanism between Demand and Supply Side Management of Electricity Markets. Energies. 11 (12), 3314-3314

Ma, Q., Meng, F. and Zeng, X-J., (2018). Optimal dynamic pricing for smart grid having mixed customers with and without smart meters. Journal of Modern Power Systems and Clean Energy. 6 (6), 1244-1254

Gong, D., Zhang, G., Yao, X. and Meng, F., (2017). Mutant reduction based on dominance relation for weak mutation testing. Information and Software Technology. 81, 82-96

Liu, K., Gong, D., Meng, F., Chen, H. and Wang, G-G., (2017). Gesture segmentation based on a two-phase estimation of distribution algorithm. Information Sciences. 394-395, 88-105

Meng, F-L. and Zeng, X-J., (2016). A Profit Maximization Approach to Demand Response Management with Customers Behavior Learning in Smart Grid. IEEE Transactions on Smart Grid. 7 (3), 1516-1529

Meng, F-L. and Zeng, X-J., (2013). A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid. Soft Computing. 17 (12), 2365-2380

Conferences (11)

Dai, S., Wang, Q. and Meng, F., A telehealth framework for dementia care: an ADLs patterns recognition model for patients based on NILM

Dai, S. and Meng, F., (2020). Energy Forecasting with Building Characteristics Analysis

Liu, N., Xu, Z., Wu, H., Ren, P. and Meng, F., (2020). An Inverse Prospect Theory Based-Approach for Linear Ordinal Ranking Aggregation with Its Application in Site Selection of Electric Vehicle Charging Station

Meng, F., Kazemtabrizi, B., Zeng, X-J. and Dent, C., (2017). An optimal differential pricing in smart grid based on customer segmentation

Zhang, Y., Meng, F. and Wang, R., (2017). A comprehensive MPC based energy management framework for isolated microgrids

Meng, F., Su, J., Liu, C. and Chen, W-H., (2016). Dynamic decision making in lane change: Game theory with receding horizon

Meng, F-L. and Zeng, X-J., (2016). A bilevel optimization approach to demand response management for the smart grid

Meng, F-L. and Zeng, X-J., (2015). Appliance level demand modeling and pricing optimization for demand response management in smart grid

Meng, F-L. and Zeng, X-J., (2014). An optimal real-time pricing for demand-side management: A Stackelberg game and genetic algorithm approach

Meng, F-L., Zeng, X-J. and Ma, Q., (2013). Learning Customer Behaviour under Real-Time Pricing in the Smart Grid

Meng, F-L. and Zeng, X-J., (2012). A Stackelberg Game Approach to maximise electricity retailer's profit and minimise customers' bills for future smart grid

Grants and funding


Visual Building Fire Protection System (VFPS)

Fisk Group


Non-intrusive residential energy monitoring for dementia

University of Essex

+44 (0) 1206 876510


STEM 5.35, Colchester Campus

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