## 5 Conclusions  
In this study, 15 temperature sensors were installed inside an AHU's mixing chamber to determine the degree of error in the AHU mixing temperature measurements through experiments and to develop a mixing temperature prediction model for a single-duct VAV system. The mixing temperature was also measured using the existing AHU mixing temperature sensor. The mixing chamber was modeled using CFD, and a CV (RMSE) value of 7.927% was obtained, indicating that the model was reliable. The CFD simulation cases were then formulated, and the temperature distribution of the mixing chamber was analyzed. From the temperature distribution acquired through the CFD simulations, it was found that the amount of outdoor air input was directly proportional to the change in the temperature distribution of the mixing chamber. Further, it was found that the temperature measured in the mixing chamber was not accurate. The mixing temperature prediction model was developed through multiple regression analysis, and field application and validation were carried out. The mixing temperature prediction model was developed through multiple regression analysis, and field application and validation were conducted. Compared to the default mixing temperature sensor, the mixing temperature prediction model yielded a low absolute error of 0.008-0.422 \({}^{\circ}\)C and validated the performance of the mixing temperature prediction model.  
This study utilizes CFD to develop a mixed temperature prediction model by simulating various boundary conditions that are difficult to apply in real experiments. By applying the mixed temperature prediction model, it can be utilized to improve the efficiency of HVAC systems. It can also be utilized to develop a predictive control algorithm for HVAC systems by simulating various boundary conditions. This can contribute to energy savings and improved indoor air quality by monitoring the system's mixing temperature prediction in real time and optimizing the indoor environment. But, this study evaluated the results of the mixing temperature prediction model over a short period of time. For effective future field applications of the prediction model, long-term validation is required, and it is necessary to verify the prediction model in real time in relation to the automatic control system of the AHU.  
M.K.: conceptualization, methodology, software, visualization, validation, formal analysis, and writing of original draft; H.K. and J.L.: investigation, resources, review and edit  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Return** & **Outdoor** & **Outdoor** & **HVAC** & **Prediction** & **Absolute** \\  & **Airflow** & **Airflow** & **Temp.** & **Mixing** & **Model** & **Error** \\  & **(CMH)** & **(CMH)** & **(\({}^{\circ}\)C)** & **Temp.** & **Temp.** & **(\({}^{\circ}\)C)** \\ \hline A & 574.56 & 246.24 & 29.3 & 26.000 & 26.030 & 0.0  
ing of manuscript; Y.C.: supervision and project administration; H.K. and Y.C.: funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2022 Yeungnam University Research Grant (222A380129). This research was supported by a National Research Foundation of Korea (NRF) grant provided by the Korea government (MSIT) (No. 2022R1C1C2010251).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.