## 4 Conclusions  
In this study, a mechanical ventilation system of the same brand was used in 85 houses in different cities in the cold region of China, and indoor air parameters were monitored for a year. We statistically analyzed the operation duration, behavior patterns, ventilation volume and IAQ of households with different types of mechanical ventilation systems. Clustering analysis was utilized to classify households, which revealed the similarity and diversity in behaviors of different occupants. Logistic regression and random forest were used to develop models for predicting mechanical ventilation behavior with all features inputted, and their prediction results were compared. It fills the gap in the mechanical ventilation model of residential building and contributes to future studies on improving the accuracy of building simulations. The main conclusions are as follows:  
1. About 24% households operated mechanical ventilation system nearly all day. The average daily operating system duration of the centralized system was 7.3 h/day, which was longer than that of the split system. The split system was operated more frequently. The IAQ of the households using the system almost all-day is better than that of intermittent operation.
2. Using K-means clustering, five patterns were discovered for the behavior of mechanical ventilation operation, including outdoor air PM\({}_{2.5}\) concentration-driven, outdoor temperature-driven, temperature and time-driven, mixed factors-driven and random behavior pattern.
3. Based on the five clusters of households, the models established by the random forest algorithm showed a better performance than that of logistic regression, and guaranteed high accuracy even when the imbalance rate of dataset was high. Therefore, the random forest algorithm can well predict the behavior in mechanical ventilation operation in residential buildings, and it can be applied in building simulation to improve the performance in future studies.  
H.S.: Conceptualization, project administration and writing--review and editing. C.Z.: Data curation, formal analysis, investigation, methodology, software and writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tianjin Key Research Program of Application Foundation and Advanced Technology grant number 19 JCYBJC22500 and The APC was funded by Tianjin University.  
Ethical review and approval were waived for this study due to the study used physical data of the ventilation equipment to infer occupant behaviors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** & **Cluster 5** \\ \hline ACC & 0.999 & 0.985 & 0.989 & 0.992 & 0.994 \\ AUC & 0.999 & 0.985 & 0.989 & 0.992 & 0.990 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The predictive results of random forest using the test datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** & **Cluster 5** & **Non-Classified** \\ \hline ACC & 0.785 & 0.664 & 0.718 & 0.784 & 0.875 & 0.698 \\ AUC & 0.774 & 0.650 & 0.700 & 0.790 & 0.807 & 0.647 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The predictive results of logistic regression using the test datasets.  
**Acknowledgments:** The research was supported by Tianjin Key Research Program of Application Foundation and Advanced Technology through no. 19 JCYBJC22500. Thanks to the selected households for their assistance with the work.  
**Conflicts of Interest:** The authors declare no conflict of interest.