| Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning Y Du, F Li IEEE Transactions on Smart Grid 11 (2), 1066-1076, 2019 | 324 | 2019 |
| Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning F Li, Y Du Deep Learning for Power System Applications: Case Studies Linking Artificial …, 2023 | 205 | 2023 |
| A cooperative game approach for coordinating multi-microgrid operation within distribution systems Y Du, Z Wang, G Liu, X Chen, H Yuan, Y Wei, F Li Applied energy 222, 383-395, 2018 | 185 | 2018 |
| Optimal bidding strategy and intramarket mechanism of microgrid aggregator in real-time balancing market W Pei, Y Du, W Deng, K Sheng, H Xiao, H Qu IEEE Transactions on Industrial Informatics 12 (2), 587-596, 2016 | 148 | 2016 |
| Achieving 100x acceleration for N-1 contingency screening with uncertain scenarios using deep convolutional neural network Y Du, F Li, J Li, T Zheng IEEE Transactions on Power Systems 34 (4), 3303-3305, 2019 | 92 | 2019 |
| Temporal-spatial analysis and improvement measures of Chinese power system for wind power curtailment problem W Pei, Y Chen, K Sheng, W Deng, Y Du, Z Qi, L Kong Renewable and Sustainable Energy Reviews 49, 148-168, 2015 | 91 | 2015 |
| A hierarchical real-time balancing market considering multi-microgrids with distributed sustainable resources Y Du, F Li IEEE Transactions on Sustainable Energy 11 (1), 72-83, 2018 | 74 | 2018 |
| Approximating Nash equilibrium in day-ahead electricity market bidding with multi-agent deep reinforcement learning Y Du, F Li, H Zandi, Y Xue Journal of modern power systems and clean energy 9 (3), 534-544, 2021 | 66 | 2021 |
| Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control Y Du, F Li, J Munk, K Kurte, O Kotevska, K Amasyali, H Zandi Electric Power Systems Research 192, 106959, 2021 | 61 | 2021 |
| Deep reinforcement learning from demonstrations to assist service restoration in islanded microgrids Y Du, D Wu IEEE Transactions on Sustainable Energy 13 (2), 1062-1072, 2022 | 58 | 2022 |
| Real-time microgrid economic dispatch based on model predictive control strategy Y Du, W Pei, N Chen, X Ge, H Xiao Journal of modern power systems and clean energy 5 (5), 787-796, 2017 | 58 | 2017 |
| Model-based and data-driven HVAC control strategies for residential demand response X Kou, Y Du, F Li, H Pulgar-Painemal, H Zandi, J Dong, MM Olama IEEE Open Access Journal of Power and Energy 8, 186-197, 2021 | 46 | 2021 |
| Emerging smart grid technology for mitigating global warming X Zhang, W Pei, W Deng, Y Du, Z Qi, Z Dong International journal of energy research 39 (13), 1742-1756, 2015 | 46 | 2015 |
| Fast cascading outage screening based on deep convolutional neural network and depth-first search Y Du, F Li, T Zheng, J Li IEEE Transactions on Power Systems 35 (4), 2704-2715, 2020 | 39 | 2020 |
| Evaluating the adaptability of reinforcement learning based HVAC control for residential houses K Kurte, J Munk, O Kotevska, K Amasyali, R Smith, E McKee, Y Du, B Cui, ... Sustainability 12 (18), 7727, 2020 | 36 | 2020 |
| Learning and fast adaptation for grid emergency control via deep meta reinforcement learning R Huang, Y Chen, T Yin, Q Huang, J Tan, W Yu, X Li, A Li, Y Du IEEE Transactions on Power Systems 37 (6), 4168-4178, 2022 | 33 | 2022 |
| Coordinating multi-microgrid operation within distribution system: A cooperative game approach Y Du, F Li, X Kou, W Pei 2017 IEEE Power & Energy Society General Meeting, 1-5, 2017 | 22 | 2017 |
| Methodology for interpretable reinforcement learning model for HVAC energy control O Kotevska, J Munk, K Kurte, Y Du, K Amasyali, RW Smith, H Zandi 2020 IEEE International Conference on Big Data (Big Data), 1555-1564, 2020 | 18 | 2020 |
| Demonstration of intelligent HVAC load management with deep reinforcement learning: real-world experience of machine learning in demand control Y Du, F Li, K Kurte, J Munk, H Zandi IEEE Power and Energy Magazine 20 (3), 42-53, 2022 | 17 | 2022 |
| Applying deep convolutional neural network for fast security assessment with N-1 contingency Y Du, F Li, C Huang 2019 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2019 | 16 | 2019 |
Fangxing (Fran) LiJohn W. Fisher Professor at Univ. of Tennessee, IEEE Fellow, Director of CURENT, EIC of IEEE OAJPE在 utk.edu 的电子邮件经过验证
