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.

这项研究开发了一种控制算法，用于通过强化学习中的“深度Q学习”来优化空调和排风扇的能耗。建议的代理能够平衡室内空气质量（CO ₂），热舒适性和能耗。该算法首先在类似的环境模拟中接受训练，然后在最多可容纳72人的教室中进行应用和测试。在夏季的一个月内进行了测试。详细研究了室外环境和课堂条件对节能和室内空气质量的影响。通过代理控制，可以实现室内空气质量，热舒适性以及空调单元和排气扇的能耗优化。在相同的热舒适度下，与固定温度为25°C的空调相比，该代理可节省多达43％的能源，平均而言，该代理可减少约19％的能耗。然而相应的CO ₂代理商控制可将药物水平降低约24％。类似地，当与26°C的固定温度相比时，该试剂可平均降低约15％的能耗，二氧化碳浓度可平均降低13％。