| Title |
Performance Analysis of an Outdoor Air-Introduced Ventilation Cleaner Applied with an AI based Indoor PM2.5 and CO2 Integrated Control Algorithm |
| Authors |
Jea Yoon Byun ; Tae Won Kim ; Eun Ji Choi ; Jin Woo Moon |
| DOI |
https://doi.org/10.6110/KJACR.2025.37.4.175 |
| Keywords |
제어 알고리즘; 실내공기질; 예측모델; 환기시스템 Control algorithm; Indoor air quality; Prediction model; Ventilation systems |
| Abstract |
Amid growing concerns about health risks, the demand for safer indoor air quality has significantly increased, necessitating advanced control solutions. Previous studies focused on ventilation, air purification control, and the proactive management of indoor air pollutants. However, most of these studies relied on computer simulations, with few applications in real-world environments. In response, this study developed an AI-based integrated control algorithm for indoor PM2.5 and CO2 using empirical data. The algorithm incorporates prediction models for indoor PM2.5 and CO2 based on artificial neural networks, enabling proactive control. It was applied in a living lab to validate its effectiveness in improving indoor air quality and reducing energy consumption. The integrated control algorithm demonstrated an average reduction of 19.69% in indoor PM2.5 concentrations and 6.59% in CO2 concentrations compared to the existing rule-based algorithm, while also decreasing energy consumption by 9.42%. Furthermore, the algorithm maintained indoor PM2.5 and CO2 concentrations below established criteria for a longer duration, confirming its superior performance. The integrated control algorithm developed in this study effectively achieved both indoor air quality management and energy consumption reduction, contributing to the sustainable development of building environment management and control. |