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Developing smart air purifier control strategies for better IAQ and energy efficiency using reinforcement learning by Wenzhe Shang; Junjie Liu; Congcong Wang; Jiayu Li; Xilei Dai is a Environmental Science article available to read on EtoBox.

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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 such as PM2.5 emissions and occupants’ window behavior 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 with lower energy consumption. To make the DeepRL applicable in real daily life, we first develop a stochastic model based on measured indoor air quality data, which is able to simulate the indoor PM2.5 process in real time. To improve the energy efficiency of air purifier under this condition, we further trained DeepRL approach to control the air purifier under the simulated PM2.5 process. By virtue of adaption to the stochastic environmental parameters, RL strategy can make the best fit decision in advance to achieve more stable control effect. Compar

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Author

Wenzhe Shang; Junjie Liu; Congcong Wang; Jiayu Li; Xilei Dai

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)