@Article{cmes.2023.026091,
AUTHOR = {Zhengkai Ding, Qiming Fu, Jianping Chen, You Lu, Hongjie Wu, Nengwei Fang, Bin Xing},
TITLE = {MAQMC: Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {136},
YEAR = {2023},
NUMBER = {3},
PAGES = {2759--2785},
URL = {http://www.techscience.com/CMES/v136n3/51801},
ISSN = {1526-1506},
ABSTRACT = {The optimization of multi-zone residential heating, ventilation, and air conditioning (HVAC) control is not an
easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads. Deep
reinforcement learning (DRL) methods have recently been proposed to address the HVAC control problem. However, the application of single-agent DRL for multi-zone residential HVAC control may lead to non-convergence or
slow convergence. In this paper, we propose MAQMC (Multi-Agent deep Q-network for multi-zone residential
HVAC Control) to address this challenge with the goal of minimizing energy consumption while maintaining
occupants’ thermal comfort. MAQMC is divided into MAQMC2 (MAQMC with two agents:one agent controls
the temperature of each zone, and the other agent controls the humidity of each zone) and MAQMC3 (MAQMC
with three agents:three agents control the temperature and humidity of three zones, respectively). The experimental
results show that MAQMC3 can reduce energy consumption by 6.27% and MAQMC2 by 3.73% compared with the fixed point; compared with the rule-based, MAQMC3 and MAQMC2 respectively can reduce 61.89% and 59.07%
comfort violation. In addition, experiments with different regional weather data demonstrate that the well-trained
MAQMC RL agents have the robustness and adaptability to unknown environments.},
DOI = {10.32604/cmes.2023.026091}
}