| 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 | 201 | 2023 |
| A scalable and distributed algorithm for managing residential demand response programs using alternating direction method of multipliers (ADMM) X Kou, F Li, J Dong, M Starke, J Munk, Y Xue, M Olama, H Zandi IEEE Transactions on Smart Grid 11 (6), 4871-4882, 2020 | 70 | 2020 |
| 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 | 60 | 2021 |
| 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 | 45 | 2021 |
| A comprehensive scheduling framework using SP-ADMM for residential demand response with weather and consumer uncertainties X Kou, F Li, J Dong, M Olama, M Starke, Y Chen, H Zandi IEEE Transactions on Power Systems 36 (4), 3004-3016, 2020 | 43 | 2020 |
| Ten questions concerning reinforcement learning for building energy management Z Nagy, G Henze, S Dey, J Arroyo, L Helsen, X Zhang, B Chen, ... Building and Environment 241, 110435, 2023 | 40 | 2023 |
| 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 | 34 | 2020 |
| Home energy management systems: an overview H Zandi, T Kuruganti, EA Vineyard, D Fugate Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2018 | 27 | 2018 |
| COVID-19 pandemic ramifications on residential Smart homes energy use load profiles S Chinthavali, V Tansakul, S Lee, M Whitehead, A Tabassum, M Bhandari, ... Energy and Buildings 259, 111847, 2022 | 20 | 2022 |
| Deep reinforcement learning for autonomous water heater control K Amasyali, J Munk, K Kurte, T Kuruganti, H Zandi Buildings 11 (11), 548, 2021 | 20 | 2021 |
| Agent-based system for transactive control of smart residential neighborhoods M Starke, J Munk, H Zandi, T Kuruganti, H Buckberry, J Hall, J Leverette 2019 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2019 | 18 | 2019 |
| 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 | 17 | 2020 |
| Real-time MPC for residential building water heater systems to support the electric grid M Starke, J Munk, H Zandi, T Kuruganti, H Buckberry, J Hall, J Leverette 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies …, 2020 | 17 | 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 | 16 | 2022 |
| A distributed energy management approach for residential demand response X Kou, F Li, J Dong, M Starke, J Munk, T Kuruganti, H Zandi 2019 3rd International Conference on Smart Grid and Smart Cities (ICSGSC …, 2019 | 16 | 2019 |
| Rl-hems: Reinforcement learning based home energy management system for hvac energy optimization O Kotevska, K Kurte, J Munk, T Johnston, E Mckee, K Perumalla, H Zandi Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2020 | 15 | 2020 |
| Comparative analysis of model-free and model-based HVAC control for residential demand response K Kurte, K Amasyali, J Munk, H Zandi Proceedings of the 8th ACM international conference on systems for energy …, 2021 | 10 | 2021 |
| Real-time automated hazard detection framework for health information technology systems OA Omitaomu, O Ozmen, MM Olama, LL Pullum, T Kuruganti, J Nutaro, ... Health Systems 8 (3), 190-202, 2019 | 10 | 2019 |
| PReach: reachability in probabilistic signaling networks H Gabr, A Todor, H Zandi, A Dobra, T Kahveci Proceedings of the International Conference on Bioinformatics, Computational …, 2013 | 10 | 2013 |
Jeffrey MunkNational Renewable Energy Laboratory在 nrel.gov 的电子邮件经过验证
