People

Dr Fanlin Meng

Lecturer
Department of Mathematical Sciences
Dr Fanlin Meng

Profile

Biography

I welcome enquiries from potential PhD students with good background in Computing or Mathematics (such as Data Science, Operational Research, Computer Science, Control Engineering/ Automatic Control/ Automation, Electrical/ Electronic Engineering, etc.). If you intend to pursue your PhD under my supervision, please send your academic CV to me via email. 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 ( “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 a member of EPSRC Associate Peer Review College, Senior Member of IEEE, and an 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).

Qualifications

  • 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)

Appointments

University of Essex

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

Research and professional activities

Research interests

Smart Energy and Mobility

energy data analytics, demand flexibility management, EVs integration, microgrids, energy systems modelling, smart buildings

Open to supervise

Machine Learning, Game Theory, Optimisation

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

Open to supervise

Intelligent Transportation Systems

game-theoretic modelling, autonomous driving, EVs modelling, energy and transport

Open to supervise

Energy Markets

dynamic pricing, energy retail market, local energy trading, demand response/ renewable energy integration, wholesale and local energy markets integration

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

Non-intrusive residential energy monitoring for dementia


More information about this project

Conferences and presentations

PC member

AAAI 2022, 22/2/2022

TPC

The 17th International Conference on Mobility, Sensing and Networking (MSN 2021), 13/12/2021

International Joint Conference on Neural Networks (IJCNN 2021) Special Session lead organiser

IJCNN 2021 Special Session on Computational Intelligence in Transactive Energy Management and Smart Energy Network (IJCNN-CITESEN 2021) https://sites.google.com/view/ijcnn-citesen-2021/, 18/7/2021

Organising Committee (Special Session Chair)

2021 International Conference on Life System Modeling and Simulation & 2021 International Conference on Intelligent Computing for Sustainable Energy and Environment (http://www.lsms-icsee.org.cn/), Hangzhou, China, 22/10/2020

Special Session Organiser

IEEE World Congress on Computational Intelligence Special Session on Computational Intelligence in Transactive Energy Management and Smart Energy Network (CITESEN 2020) https://sites.google.com/view/wcci-2020-ss-citesen/home, 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

  • Applied Statistics (MA321)

  • Databases and data processing with SQL (MA332)

  • Professional Placement (MA911)

Publications

Journal articles (12)

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

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

Reports and Papers (3)

Liu, J., Bo, R., Chen, J., Zhou, Y-P. and Meng, F., Dynamic Pricing Strategies Based on Customers’ Patience Times

Meng, F., Ma, Q., Liu, Z. and Zeng, X-J., (2021). Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids

Wang, Q., Meng, F. and Breckon, TP., (2020). Data Augmentation with norm-VAE for Unsupervised Domain Adaptation

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

2021

Visual Building Fire Protection System (VFPS)

Fisk Group

2020

Non-intrusive residential energy monitoring for dementia

University of Essex

Contact

fanlin.meng@essex.ac.uk
+44 (0) 1206 876510

Location:

STEM 5.35, Colchester Campus