Dr Fanlin Meng

Department of Mathematical Sciences
Dr Fanlin Meng



I am a Lecturer in Data Science in the Department of Mathematical Sciences at the University of Essex. Prior to my current appointment, I held several research positions in Cardiff (Engineering), Edinburgh (Mathematics), Durham (Engineering and Computing Sciences) and Loughborough (AAE). I received my PhD in Computer Science (Machine Learning and Optimisation) from the University of Manchester in 2015. My main research interests are Machine Learning, Game theory, and Optimisation including their applications to real-world data science problems, intelligent systems learning and modelling and digital/ sustainable infrastructure.


  • PhD University of Manchester, (2015)

  • MSc Xiamen University, (2011)

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


University of Essex

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

Research and professional activities

Research interests

Machine Learning, Game Theory, Optimisation

distributed learning, privacy-preserving learning, game-theoretic modelling in real-world applications, data-driven optimisation, distributed and large-scale optimisation

Open to supervise

Smart Energy and Smart Grids

demand flexibility management, microgrids, energy systems modelling

Open to supervise

Energy Markets

local energy trading, demand response/ renewable energy integration, wholesale and local energy markets integration

Open to supervise

Intelligent Transportation Systems

game-theoretic modelling, autonomous driving, EVs modelling

Open to supervise


Smart buildings, thermal comfort, digital infrastructure

Open to supervise

Cyber Physical Systems

Open to supervise

Current research

A distributed and real-time learning framework for Smart Meter big data

More information about this project

Towards an intelligent hybrid energy and transportation system in the presence of large-scale EVs and renewable energy

More information about this project

Conferences and presentations

Organising Committee (Special Session Chair)

2020 International Conference on Life System Modeling and Simulation & 2020 International Conference on Intelligent Computing for Sustainable Energy and Environment, 9/10/2020

Special Session Organiser

IEEE World Congress on Computational Intelligence Special Session on Computational Intelligence in Transactive Energy Management and Smart Energy Network (CITESEN 2020), 19/7/2020

PC Member

Invited presentation, 29th International Joint Conference on Artificial Intelligence (IJCAI), 11/7/2020

PC Member

Invited presentation, 24th European Conference on Artificial Intelligence, 8/6/2020

TPC Member

International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health (CyberLife 2019), 16/12/2019

Special Session Chair

2017 International Conference on Life System Modeling and Simulation & 2017 International Conference on Intelligent Computing for Sustainable Energy and Environment, 22/9/2017

Teaching and supervision

Current teaching responsibilities

  • Mathematics Careers and Employability (MA199)

  • Exploratory Data Analysis and Data Visualisation (MA304)

  • Applied Statistics (MA321)


Journal articles (9)

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 (6)

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

Other (3)

Chen, X., Weng, K., Meng, F. and Mourshed, M., Smart Energy Management for Unlocking Demand Response in the Residential Sector. Proceedings. 2(15),MDPI AG

Meng, F., Weng, K., Shallal, B., Chen, X. and Mourshed, M., Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response. Proceedings. 2(15),MDPI AG

Weng, K., Meng, F. and Mourshed, M., Model-based optimal control of window openings for thermal comfort. Proceedings. 2(15),MDPI AG

Grants and funding


Non-intrusive residential energy monitoring for dementia

University of Essex

+44 (0) 1206 876510


STEM 5.35, Colchester Campus