jaue2023036: Proposal and Analysis of Air Conditioning Schedules for Air Conditioning Operation in the Outpatient Department According to the Number of Visiting Patients

Abstract

The outbreak of the CORVID-19 has heightened interest in infectious diseases, and many cases of infections occurred indoors. Specifically, general hospitals have a significant likelihood of nosocomial secondary infections resulting from visiting patients with undefined illnesses, hence transmitting infections to both medical personnel and patients. In general hospital settings, the implementation of natural ventilation is impractical, necessitating the use of air conditioning systems to regulate infectious airflow. However, the outpatient department is considered a transient space where visiting patients stay for short durations. Consequently, an investigation found that recirculating air conditioning should be maintained even during periods of increased number of visiting patients. Thus, it was expected that the regulation of indoor infection airflow would be vital, necessitating adjustments to the air conditioning operation in accordance with the number of visiting patients. A machine learning system was employed to make predictions regarding the number of visiting patients and infectious bacteria. Furthermore, a mockup process was developed to suggest the operation of air conditioning based on the number of visiting patients, and a mockup room test was carried out to validate this approach. In this study, the algorithm results were used to protect visiting patients and medical staff by reducing the rate of nosocomial secondary infections and preventing indoor spread in spaces such as general hospitals where natural ventilation is not possible. Therefore, the results of this study were expected to be highly applicable to actual air conditioners.