Event‐triggered distributed model predictive control scheme for temperature regulation in multi‐zone air conditioning systems with improved indoor thermal preference indicator

Gao, Jingjing; Xu, Yang; Zhang, Shilong; Tu, Rang; Ma, Hongjun

doi:10.1002/acs.3578

Cited by 3 publications

(2 citation statements)

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“…traditional algorithm [7] The proposed DMPC scheme can reduce the energy consumption of multizone buildings and improve the thermal comfort of occupants.…”

Section: Approach Reference Contributionsmentioning

confidence: 99%

“…The core of central air conditioning energy-saving optimal control is to find the best air conditioning control parameters while maintaining indoor comfort requirements to achieve the goal of minimum energy consumption. Gao et al [7] proposed an event-triggered distributed model predictive control (DMPC) scheme for improving indoor temperature regulation in multizone buildings. By comprehensively considering energy consumption and thermal comfort, the scheme determines the optimal temperature set point and verifies its effectiveness in practical cases.…”

Section: Introductionmentioning

confidence: 99%

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Optimization Control Strategy for a Central Air Conditioning System Based on AFUCB-DQN

et al. 2023

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The central air conditioning system accounts for 50% of the building energy consumption, and the cold source system accounts for more than 60% of the total energy consumption of the central air conditioning system. Therefore, it is crucial to solve the optimal control strategy of the cold source system according to the cooling load demand, and adjust the operating parameters in time to achieve low energy consumption and high efficiency. Due to the complex and changeable characteristics of the central air conditioning system, it is often difficult to achieve ideal results using traditional control methods. In order to solve this problem, this study first coupled the building cooling load simulation environment and the cold source system simulation environment to build a central air conditioning system simulation environment. Secondly, noise interference was introduced to reduce the gap between the simulated environment and the actual environment, and improve the robustness of the environment. Finally, combined with deep reinforcement learning, an optimal control strategy for the central air conditioning system is proposed. Aiming at the simulation environment of the central air conditioning system, a new model-free algorithm is proposed, called the dominant function upper confidence bound deep Q-network (AFUCB-DQN). The algorithm combines the advantages of an advantage function and an upper confidence bound algorithm to balance the relationship between exploration and exploitation, so as to achieve a better control strategy search. Compared with the traditional deep Q-network (DQN) algorithm, double deep Q-network (DDQN) algorithm, and the distributed double deep Q-network (D3QN) algorithm, the AFUCB-DQN algorithm has more stable convergence, faster convergence speed, and higher reward. In this study, significant energy savings of 21.5%, 21.4%, and 22.3% were obtained by conducting experiments at indoor thermal comfort levels of 24 °C, 25 °C, and 26 °C in the summer.

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“…traditional algorithm [7] The proposed DMPC scheme can reduce the energy consumption of multizone buildings and improve the thermal comfort of occupants.…”

Section: Approach Reference Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%