Treffer: Studies from Chongqing University Have Provided New Data on Energy and Buildings (A Rl-based Human Behavior Oriented Optimal Ventilation Strategy for Better Energy Efficiency and Indoor Air Quality).

The article focuses on a study conducted at Chongqing University in the People's Republic of China, which developed a deep reinforcement learning (DRL) model to optimize ventilation in biopharmaceutical cleanrooms. The research addresses the high energy consumption associated with increased air change rates and stringent cleanliness standards in these environments. By implementing a multi-zone ventilation system model using the Modelica language and training the DRL model in Python, the study achieved a 14.7% reduction in energy consumption while maintaining regulatory pollutant concentration limits. The findings also indicated significant reductions in pressure fluctuations across controlled work zones, highlighting the potential for improved energy efficiency and indoor air quality in cleanroom settings. [Extracted from the article]