## 1 Introduction
In order to prevent indoor discomfort and energy waste due to fixed setpoints when controlling the outdoor airflow cooling of an AHU system, [PERSON] developed an algorithm for controlling the mixing temperature setpoint of an outdoor airflow cooling system using a prediction model and saved about 18% energy compared to the existing method [8]. As such, mixing temperature measurement is important in AHU systems and errors in mixing temperature measurement cause problems with system operation and energy consumption. Previous studies have proposed various approaches for efficient operation and energy saving of AHU systems, but further research is needed in terms of the accuracy of mixing temperature measurement and prediction. This study aims to develop a mixing temperature prediction model that can respond to various variables and improve the reliability of the system through sensor error compensation. Figure 1: Sensor error for existing mixing temperature in the BAS. This study was conducted in three steps, as shown in Figure 2, to develop a mixing temperature prediction model for the mixing chamber. 1. Step 1: Boundary condition data were collected and analyzed to model the mixing chamber using computational fluid dynamics (CFD). The data collection method utilized BAS data based on field experiments, experimental measurement data, and data from the South Korean meteorological administration to determine boundary conditions. Return airflow, return temperature, outdoor airflow, and outdoor temperature were set as the boundary conditions to check the temperature inside the mixing chamber. The real-time temperature values were obtained from the BAS data, and the temperature distribution and airflow values inside the mixing chamber were collected from the measurement data obtained through field experiments. In addition, to accurately verify the outdoor temperature values, they were compared with data from the South Korean meteorological administration, and seasonal outdoor temperature data were collected. Based on the collected data, the return airflow, return temperature, outdoor airflow, and outdoor temperature were set as the input boundary conditions for CFD modeling. 2. Step 2: In this step, simulations were carried out by substituting the boundary conditions, and the temperature distribution results obtained through the modeling process and those of the actual data were compared to evaluate the reliability of the model. This reliability evaluation approach was based on the criterion that a coefficient of variation (CV) of the root-mean-square error (RMSE) of less than 30% is considered reliable according to Guideline 14 of the American society of heating, refrigerating and air-conditioning engineers (ASHRAE) [9]. After the reliability evaluation, a total of 60 CFD simulation cases were tested according to return airflow, return temperature, outdoor airflow, and outdoor temperature to develop a mixing temperature prediction model. 3. Step 3: CFD simulations were carried out according to each case to derive the mixing temperature, and the mixing temperature prediction model was proposed. In addition, the mixing temperature prediction model was applied to the actual system and the mixing temperature prediction model was verified. Figure 2: Research flowchart for mixing temperature prediction model. A mixing temperature error prediction model that employs CFD simulation was developed and proposed in this study to reduce unnecessary energy wastage in the operation of AHUs.