Zhengkai Ding1,2, Qiming Fu1,2,*, Jianping Chen2,3,4,*, You Lu1,2, Hongjie Wu1, Nengwei Fang4, Bin Xing4
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2759-2785, 2023, DOI:10.32604/cmes.2023.026091
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… More >
Login
Register
