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Title: Toward intelligent multizone thermal control with multiagent deep reinforcement learning
Authors: Li, Jie
Zhang, Wei
Gao, Guanyu
Wen, Yonggang
Jin, Guangyu
Christopoulos, Georgios
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Li, J., Zhang, W., Gao, G., Wen, Y., Jin, G. & Christopoulos, G. (2021). Toward intelligent multizone thermal control with multiagent deep reinforcement learning. IEEE Internet of Things Journal, 8(14), 11150-11162.
Project: NRF2015ENC_GBICRD001-012
Journal: IEEE Internet of Things Journal
Abstract: Energy usage and thermal comfort are the pillars of smart buildings. Many research works have been proposed to save energy while maintaining a comfortable thermal condition. However, most of them either make the over-simplified assumption on thermal comfort with unsatisfied comfort performance or deal with the single-zone thermal control only with limited practical impact. A few preliminary pieces of research on multi-zone control are available, but they fail to keep pace with the latest advancements in the deep learning-based control techniques. In this paper, we investigate the multi-zone thermal control with optimized energy usage and canonical thermal comfort modeling. We adopt the emerging multi-agent deep reinforcement learning techniques and propose to model each zone as an agent. A multi-agent framework is established to support the information exchange among the agents and enable intelligent thermal control in the heterogeneous zones. Accordingly, we mathematically formulate a problem to optimize both energy and comfort. A multi- zone thermal control algorithm (MOCA) is proposed to solve the problem by deriving optimal control policies. We validate the performance of MOCA through simulation in professional TRNSYS, configured based on our real-world laboratory. The results are promising with up to 15.4% energy-saving as well as satisfied thermal comfort in different zones.
ISSN: 2327-4662
DOI: 10.1109/JIOT.2021.3051400
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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