The smart air purifier control strategy based on reinforcement learning for better IAQ and energy efficiency

PM2.5 has negative impact on human health. Although air purifiers can remove indoor PM2.5 effectively, occupants do not use them well to achieve best performance. It is important to develop automatic control strategy for air purifiers to achieve both indoor air quality and energy efficiency. As traditional air purifier control strategy cannot adapt to the stochastic behavior of residents and result in superfluous energy consumption, this study uses the deep reinforcement learning (DeepRL) approach to automatically control the air purifier, which provide better indoor air quality(IAQ) with lower energy consumption.