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

摘要

This study develops a control algorithm for optimization the energy consumptions of airconditioning 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.