Mingyang Sun
Mingyang Sun
Professor (Research), Peking University, Imperial College London
Verified email at - Homepage
Cited by
Cited by
Probabilistic Individual Load Forecasting Using Pinball Loss Guided LSTM
Y Wang, D Gan, M Sun, N Zhang, C Kang, Z Lu
Applied Energy, 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
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
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
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
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
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
Electricity Consumer Characteristics Identification: A Federated Learning Approach
Y Wang, IL Bennani, X Liu, M Sun, Y Zhou
IEEE Transactions on Smart Grid, 2021
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
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
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
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
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
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
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
Deep Reinforcement Learning-based Demand Response for Smart Facilities Energy Management
R Lu, R Bai, Z Luo, J Jiang, M Sun, HT Zhang
IEEE Transactions on Industrial Electronics 69 (8), 8554-8565, 2022
Federated Clustering for Electricity Consumption Pattern Extraction
Y Wang, M Jia, N Gao, LV Krannichfeldt, M Sun*, H Gabriela
IEEE Transactions on Smart Grid, 2022
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
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
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
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