Improvements in the indoor environment at schools are required due to the long periods of time spent in school facilities by children, who are particularly vulnerable to poor indoor air quality (IAQ). This study thus developed an AI‐based model predictive control algorithm (MPCA) for an outdoor air‐based energy recovery ventilation (ERV) system and evaluated its performance. The MPCA was based on artificial neural networks that predicted the particulate matter (PM) concentration, carbon dioxide (CO 2 ) levels, and temperature to achieve real‐time IAQ control. This algorithm was employed in an elementary school facility, and its performance was empirically compared with a rule‐based algorithm and occupant discretionary control (ODC). All three methods were able to improve the IAQ, with the proposed MPCA outperforming the rule‐based algorithm by maintaining a 21% lower PM 2.5 concentration and 7% lower CO 2 concentration. In addition, ODC required 2.4–2.5 times higher energy consumption than the other control methods, whereas the MPCA achieved a 5.7% reduction in energy consumption compared with the rule‐based algorithm. Therefore, the MPCA developed in this study is capable of predictive IAQ control for ERV systems and requires less energy than conventional methods, demonstrating its potential to effectively improve the IAQ of school facilities.
Byun et al. (Thu,) studied this question.