Dr Mingyang Sun is a Professor under the Hundred Talents Program at Zhejiang University. Also, he is an Honorary Lecturer at Imperial College London, UK. He received his Ph.D. degree in Electrical and Electronic Engineering in the Control and Power (CAP) group at Imperial College London in 2017. From 2017 to 2019, he was a Research Associate and a DSI Affiliate Fellow at Imperial College London.
His research mainly focuses on the investigation of the novel Big Data Analytics and Artificial Intelligence methods to energy systems, with a special emphasis on dealing with uncertainties arising from RES and energy consumers for system investment planning and operation. In particular, his research interests include data-driven cyber-physical energy system security assessment, smart meter data analysis, energy forecasting, flexibility quantification, and large-scale energy system investment planning. He has authored more than 60 scientific publications in leading power system journals and AI conferences, including IEEE Transactions, Applied Energy, AAAI, IJCAI. Three of his conference papers have been awarded the Best Paper for IEEE PES GM 2016, PMAPS 2016, and IEEE TrustCom 2022.
Furthermore, he is the PI/CO-PI for a series of key projects funded by the NSFC, National Key R&D Program of China, The Royal Society （UK）, and China Association for Science and Technology. Furthermore, he was involved in many multi-partner collaborative projects and led the work regarding machine learning for trial data modelling and analysis. The projects concerned were “EU-SysFlex” (Horizon 2020, EUR20 million), “Innovative Tools for Electrical System Security within Large Areas (iTesla)”, (FP7, EUR19.4 million), and “Low Carbon London” (UK Power Networks, GBP28 million).
et al., 2020, Using Bayesian deep learning to capture uncertainty for residential net load forecasting, IEEE Transactions on Power Systems, Vol:35, ISSN:0885-8950, Pages:188-201
et al., 2019, Clustering-based residential baseline estimation: a probabilistic perspective, Ieee Transactions on Smart Grid, Vol:10, ISSN:1949-3061, Pages:6014-6028
et al., 2019, Preheating quantification for smart hybrid heat pumps considering uncertainty, IEEE Transactions on Industrial Informatics, Vol:15, ISSN:1551-3203, Pages:4753-4763
et al., 2019, Probabilistic peak load estimation in smart cities using smart meter data, IEEE Transactions on Industrial Electronics, Vol:66, ISSN:0278-0046, Pages:1608-1618
et al., 2019, Data-driven representative day selection for investment decisions: a cost-oriented approach, IEEE Transactions on Power Systems, Vol:34, ISSN:0885-8950, Pages:2925-2936
Sun M, Konstantelos I, Strbac G, 2018, A Deep Learning-Based Feature Extraction Framework for System Security Assessment, IEEE Transactions on Smart Grid, Vol:10, ISSN:1949-3053, Pages:5007-5020
Sun M, Konstantelos I, Strbac G, 2016, C-Vine copula mixture model for clustering of residential electrical load pattern data, IEEE Transactions on Power Systems, Vol:32, ISSN:0885-8950, Pages:2382-2393