Advanced Optimal Control of Indoor Environmental Devices for Indoor Air Quality Using Reinforcement Learning

This study aims to develop and evaluate an advanced control method for acceptable indoor air quality (e.g., particulate matter and CO2) with low energy consumption in a residential space. A ventilation system, an air purifier, and a kitchen hood system are installed in the testbed to maintain a healthy IAQ. To accomplish the objective, we use a double deep Q-network (DDQN) which is one of the reinforcement learning. This study utilizes a co-simulation platform with EnergyPlus and Python.