Journal
ENERGY AND BUILDINGS
Volume 202, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2019.109440
Keywords
Deep Reinforcement Learning (DeepRL); Ventilation system; Autonomous control; Smart energy management; Indoor air quality; Human health risk
Funding
- Railway Technology Research Project of the Ministry of Land Infrastructure and Transport [19QPPW-B152307-01]
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Mechanical ventilation has been widely implemented to alleviate poor indoor air quality (IAQ) in confined underground public facilities. However, due to time-varying IAQ properties that are influenced by unpredictable factors, including outdoor air quality, subway schedules, and passenger volumes, real-time control that incorporates a trade-offbetween energy saving and IAQ is limited in conventional rule-based and model-based approaches. We propose a data-driven and intelligent approach for a smart ventilation control system based on a deep reinforcement learning (DeepRL) algorithm. This study utilized a deep Q-network (DQN) algorithm of DeepRL to design the ventilation system. The DQN agent was trained in a virtual environment defined by a gray-box model to simulate an IAQ system in a subway station. Performance of the proposed method over three weeks was evaluated by a comprehensive indoor air-quality index (CIAI) and energy consumption under different outdoor air quality scenarios. The results show that the proposed DeepRL-based ventilation control system reduced energy consumption by up to 14.4% for the validation dataset time interval and improved IAQ from unhealthy to acceptable. (c) 2019 Elsevier B.V. All rights reserved.
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