Mingyang Sun
Mingyang Sun
Zhejiang University, Imperial College London
Verified email at zju.edu.cn - Homepage
Title
Cited by
Cited by
Year
Probabilistic Individual Load Forecasting Using Pinball Loss Guided LSTM
Y Wang, D Gan, M Sun, N Zhang, C Kang, Z Lu
Applied Energy, 2018
1222018
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
1112018
C-vine copula mixture model for clustering of residential electrical load pattern data
M Sun, I Konstantelos, G Strbac
IEEE Transactions on Power Systems, 2017
692017
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
522019
A Deep Learning-Based Feature Extraction Framework for System Security Assessment
M Sun, I Konstantelos, G Strbac
IEEE Transactions on Smart Grid, 2018
482018
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, 2019
472019
Deep Reinforcement Learning for Strategic Bidding in Electricity Markets
Y Ye, D Qiu, M Sun*, D Papadaskalopoulos, G Strbac
IEEE Transactions on Smart Grid, 2019
412019
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
412018
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
392018
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
302018
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
292019
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
26*2020
5G 通信与泛在电力物联网的融合: 应用分析与研究展望
王毅, 陈启鑫, 张宁, 冯成, 滕飞, 孙铭阳, 康重庆
电网技术 43 (5), 1575-1585, 2019
262019
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
262016
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
242019
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
212018
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
162018
Analysis of diversified residential demand in London using smart meter and demographic data
M Sun, I Konstantelos, G Strbac
2016 IEEE Power and Energy Society General Meeting (PESGM), 1-5, 2016
142016
Quantifying demand diversity of households
I Konstantelos, M Sun, G Strbac
Imperial College London, 2014
122014
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
92019
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