Novel Integrated and Optimal Control of Indoor Environmental Devices for Thermal Comfort Using Double Deep Q-Network

Abstract

Maintaining a pleasant indoor environment with low energy consumption is important for healthy and comfortable living in buildings. In previous studies, we proposed the integrated comfort control (ICC) algorithm, which integrates several indoor environmental control devices, including an air conditioner, a ventilation system, and a humidifier. The ICC algorithm is operated by simple on/off control to maintain indoor temperature and relative humidity within a defined comfort range. This simple control method can cause inefficient building operation because it does not reflect the changes in indoor–outdoor environmental conditions and the status of the control devices. To overcome this limitation, we suggest the artificial intelligence integrated comfort control (AI2CC) algorithm using a double deep Q-network(DDQN), which uses a data-driven approach to find the optimal control of several environmental control devices to maintain thermal comfort with low energy consumption. The suggested AI2CC showed a good ability to learn how to operate devices optimally to improve indoor thermal comfort while reducing energy consumption. Compared to the previous approach (ICC), the AI2CC reduced energy consumption by 14.8%, increased the comfort ratio by 6.4%, and decreased the time to reach the comfort zone by 54.1 min.