| Probabilistic Individual Load Forecasting Using Pinball Loss Guided LSTM Y Wang, D Gan, M Sun, N Zhang, C Kang, Z Lu Applied Energy, 2018 | 322 | 2018 |
| An Ensemble Forecasting Method for the Aggregated Load with Sub Profiles Y Wang, Q Chen, M Sun, C Kang, Q Xia IEEE Transactions on Smart Grid, 2018 | 191 | 2018 |
| Deep Reinforcement Learning for Strategic Bidding in Electricity Markets Y Ye, D Qiu, M Sun*, D Papadaskalopoulos, G Strbac IEEE Transactions on Smart Grid 11 (2), 1343-1355, 2020 | 187 | 2020 |
| Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting M Sun, T Zhang, Y Wang, G Strbac, C Kang IEEE Transactions on Power Systems 35 (1), 188 - 201, 2020 | 174 | 2020 |
| Fusion of the 5G communication and the ubiquitous electric internet of things: application analysis and research prospects Y Wang, QX Chen, N Zhang, C Feng, F Teng, M Sun, CQ Kang Power System Technology 43 (5), 1575-1585, 2019 | 151* | 2019 |
| A deep learning-based remaining useful life prediction approach for bearings C Cheng, G Ma, Y Zhang, M Sun, F Teng, H Ding, Y Yuan IEEE/ASME transactions on mechatronics 25 (3), 1243-1254, 2020 | 134* | 2020 |
| A Deep Learning-Based Feature Extraction Framework for System Security Assessment M Sun, I Konstantelos, G Strbac IEEE Transactions on Smart Grid 10 (5), 5007-5020, 2019 | 124 | 2019 |
| C-vine copula mixture model for clustering of residential electrical load pattern data M Sun, I Konstantelos, G Strbac IEEE Transactions on Power Systems 32 (3), 2382-2393, 2017 | 119 | 2017 |
| Electricity Consumer Characteristics Identification: A Federated Learning Approach Y Wang, IL Bennani, X Liu, M Sun, Y Zhou IEEE Transactions on Smart Grid, 2021 | 104 | 2021 |
| Clustering-Based Residential Baseline Estimation: A Probabilistic Perspective M Sun, Y Wang, F Teng, Y Ye, G Strbac, C Kang IEEE Transactions on Smart Grid, 2019 | 78 | 2019 |
| A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration M Sun, J Cremer, G Strbac Applied energy 228, 546-555, 2018 | 70 | 2018 |
| An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources M Sun, F Teng, I Konstantelos, G Strbac Energy 145, 871-885, 2018 | 63 | 2018 |
| Probabilistic peak load estimation in smart cities using smart meter data M Sun, Y Wang, G Strbac, C Kang IEEE Transactions on Industrial electronics 66 (2), 1608-1618, 2018 | 62 | 2018 |
| Data-Driven Representative Day Selection for Investment Decisions: A Cost-Oriented Approach M Sun, F Teng, X Zhang, G Strbac, D Pudjianto IEEE Transactions on Power Systems, 2019 | 60 | 2019 |
| Using Vine Copulas to Generate Representative System States for Machine Learning I Konstantelos, M Sun*, S Tindemans, S Issad, P PANCIATICI, G Strbac IEEE Transactions on Power Systems, 2018 | 42 | 2018 |
| Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid Y Yang, J Hao, M Sun, Z Wang, G Strbac, C Fan IJCAI-ECAI-18, 2018 | 42 | 2018 |
| A Confidence-Aware Machine Learning Framework for Dynamic Security Assessment T Zhang, M Sun*, J Cremer, N Zhang, G Strbac, C Kang IEEE Transactions on Power Systems, 2021 | 32 | 2021 |
| Robust and automatic data cleansing method for short-term load forecasting of distribution feeders N Huyghues-Beaufond, S Tindemans, P Falugi, M Sun, G Strbac Applied energy 261, 114405, 2020 | 32 | 2020 |
| Benefits of smart control of hybrid heat pumps: an analysis of field trial data M Sun, P Djapic, M Aunedi, D Pudjianto, G Strbac Applied Energy, 2019 | 31 | 2019 |
| Evaluating composite approaches to modelling high-dimensional stochastic variables in power systems M Sun, I Konstantelos, S Tindemans, G Strbac 2016 Power Systems Computation Conference (PSCC), 1-8, 2016 | 29 | 2016 |
Goran StrbacProfessor of Energy Systems, Imperial College LondonVerified email at imperial.ac.uk
