Optimization of thermal comfort, indoor quality, and energy-saving in campus classroom through deep Q learning

Kuan Heng Yu, Yi An Chen, Emanuel Jaimes, Wu Chieh Wu, Kuo Kai Liao, Jen Chung Liao, Kuang Chin Lu, Wen Jenn Sheu, Chi-Chuan Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This study develops a control algorithm for optimization the energy consumptions of air-conditioning and exhaust fans through Deep Q-Learning in reinforcement learning. The proposed agent is able to balance indoor air quality (CO2), thermal comfort, and energy consumption. The algorithm was first trained in a similar environment simulation, and was then applied and tested in a classroom with maximum 72 occupants. Tests were conducted in one month during summer. The effects of outdoor environments and class conditions on the energy-saving and indoor air quality are examined in details. Via agent control, optimization of indoor air quality, thermal comfort, and energy consumption of air-conditioning units and exhaust fans can be achieved. With the same thermal comfort, the agent can offer energy-saving up to 43% when compared to air-conditioning with a fixed temperature of 25 °C, and on average the agent offers about 19% less of the energy consumption. Yet the corresponding CO2 level is reduced by about 24% with the agent control. Similarly, when compared with a fixed temperature of 26 °C, the agent can offer about 15% lower energy consumption on average and the concentration of carbon dioxide can be reduced by 13% in average.

Original languageEnglish
Article number100842
JournalCase Studies in Thermal Engineering
StatePublished - Apr 2021


  • Deep Q-Learning
  • Energy saving
  • Indoor air quality
  • Reinforcement learning
  • Thermal comfort

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