Summary
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 novel Big Data Analytics and Artificial Intelligence methods for 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 about 80 scientific publications in Nature Communications and leading power system journals, and top AI and security conferences, including IEEE PES Transactions, Applied Energy, AAAI, IJCAI, USENIX Security, and NDSS with and 3 ESI highly cited papers. Four of the papers have been awarded the Best Papers for IEEE TSG 2023, IEEE PES GM 2016, PMAPS 2016, and IEEE TrustCom 2022, respectively. Also, he has been awarded as one of the World’s Top 2% Scientists released by Stanford University. Furthermore, 1 paper has been awarded the Highly Cited Paper Awards 2019 of Applied Energy.
He is currently the Associate Editor, Leading Guest Editor, and Guest Editor of a series of international journals, including IEEE Transactions on Industrial Informatics, Applied Energy, Advances in Applied Energy, and IET Smart Grid, etc. 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).
Selected Publications
Journal Articles
Sun M, Zhang T, Wang Y, 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
Sun M, Wang Y, Teng F, et al., 2019, Clustering-based residential baseline estimation: a probabilistic perspective, Ieee Transactions on Smart Grid, Vol:10, ISSN:1949-3061, Pages:6014-6028
Sun M, Strbac G, Djapic P, 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
Sun M, Wang Y, Strbac G, 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
Sun M, Teng F, Zhang X, 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