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Comparative Evaluation of Different Multi-Agent Reinforcement Learning Mechanisms in Condenser Water System Control

Author(s): ORCID



Medium: journal article
Language(s): English
Published in: Buildings, , n. 8, v. 12
Page(s): 1092
DOI: 10.3390/buildings12081092
Abstract:

Model-free reinforcement learning (RL) techniques are currently drawing attention in the control of heating, ventilation, and air-conditioning (HVAC) systems due to their minor pre-conditions and fast online optimization. The simultaneous optimal control of multiple HVAC appliances is a high-dimensional optimization problem, which single-agent RL schemes can barely handle. Hence, it is necessary to investigate how to address high-dimensional control problems with multiple agents. To realize this, different multi-agent reinforcement learning (MARL) mechanisms are available. This study intends to compare and evaluate three MARL mechanisms: Division, Multiplication, and Interaction. For comparison, quantitative simulations are conducted based on a virtual environment established using measured data of a real condenser water system. The system operation simulation results indicate that (1) Multiplication is not effective for high-dimensional RL-based control problems in HVAC systems due to its low learning speed and high training cost; (2) the performance of Division is close to that of the Interaction mechanism during the initial stage, while Division’s neglect of agent mutual inference limits its performance upper bound; (3) compared to the other two, Interaction is more suitable for multi-equipment HVAC control problems given its performance in both short_term (10% annual energy conservation compared to baseline) and long-term scenarios (over 11% energy conservation).

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10688454
  • Published on:
    13/08/2022
  • Last updated on:
    10/11/2022
 
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