I am currently a Research Associate in the Department of Electrical and Electronic Engineering and a DSI Affiliate Fellow at Imperial College London, U.K.
My research focuses on the investigation of Big Data analytics, Artificial Intelligence methodologies and their applications in energy systems, aiming to establish novel data-driven energy system modelling approaches at national and international scale to radically facilitate the transition to future intelligent, low-carbon energy system paradigm.
In particular, my research interests include probabilistic flexibility quantification, probabilistic energy forecasting, statistical modelling, data-driven scenario generation and selection for system investment planning, system security assessment, and deep reinforcement learning-based retail broker design in electricity markets. My research has been funded by EPSRC, Horizon 2020, Innovate UK and National Grid.
I received the Best Paper Awards in 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) and 2016 IEEE PES General Meeting.
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., Data-driven representative day selection for investment decisions: a cost-oriented approach, IEEE Transactions on Power Systems, ISSN:0885-8950
et al., 2019, Preheating Quantification for Smart Hybrid Heat Pumps Considering Uncertainty, IEEE Transactions on Industrial Informatics, ISSN:1551-3203, Pages:1-1
et al., 2019, Clustering-Based Residential Baseline Estimation: A Probabilistic Perspective, IEEE Transactions on Smart Grid, ISSN:1949-3053, Pages:1-1
Sun M, Konstantelos I, Strbac G, 2018, A Deep Learning-Based Feature Extraction Framework for System Security Assessment, IEEE Transactions on Smart Grid, ISSN:1949-3053
Sun M, Konstantelos I, Strbac G, 2017, 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