## 2 Theoretical Analysis
### COP Prediction Model Based on Machine Learning  
For optimal control and energy saving of HVAC systems, it is necessary to accurately predict the coefficient of performance of a geothermal heat pump system [7]. For the coefficient of performance of the heat pump, there are theoretical calculations through mathematical models, short-term monitoring through simulations, long-term monitoring through measuring instruments [8; 9], or predictive model development through machine learning [10; 11]. Theoretical calculations using a mathematical model require a large number of input values, as shown in Figure 2. Hence, the model is expensive due to the need for various measuring equipment to measure it. Long-term on-site monitoring is the most accurate and reliable way of providing operational management guidance to maintain proper operation. On the other hand, the long-term monitoring method is expensive and time-consuming. A simulation is performed using commercial software, such as TRNSYS and theoretical models. The simulation results often appear more accurate than experimental field results because the simulation conditions are theoretical and simplified, ignoring real uncertain factors. In the case of a simulation, it requires professional knowledge and information collection on various conditions for the simulation, and it takes considerable time. A performance coefficient prediction through machine learning is a method of expressing the dynamic behavior of a system through measurement data. It does not require a high degree of expertise compared to other methods and does not require the selection of various variables [7; 12]. Figure 1: Conceptual diagram of hyper-parameter optimization method. Figure 2: Input data for mathematical model. Regarding the prediction of the coefficient of performance of geothermal systems, prediction techniques using mathematical models, such as multiple linear regression (MLR) [13; 14], calculation of the coefficient of performance through simulation, and various machine learning models [15; 16], such as artificial neural network (ANN)s and support vector machine (SVM)s, have been conducted in previous studies. Table 2 lists the contents of the existing literature research on the coefficient of a performance prediction model using various machine-learning models. [PERSON] applied MLR to model the COP of a geothermal heat pump but used simulated data. [PERSON] used a multiple polynomial regression approach to evaluate the effects of the intake air temperature, relative humidity, flow rate, and working air ratio on the COP of a dew point air cooler using simulation data. [PERSON] et al. developed MLR and ML neural network models to evaluate the essential parameters for predicting the COP of geothermal heat pump systems in hospital buildings [14]. [PERSON] predicted the coefficient of performance of the geothermal system through a mathematical model, and [PERSON] developed a prediction model using an ANN model with heat pump energy consumption as input [17]. [PERSON] et al. developed a coefficient of the performance prediction model [18]. In the existing coefficient of performance prediction study, the coefficient of performance was predicted using various methods, but the coefficient of the performance prediction was mainly performed by machine learning using the directly related input values used for mathematical calculation of the coefficient of performance, such as power consumption and flow. In addition, things that are difficult to measure during operation or measured through multiple monitoring sensors, such as heat capacity, were used as input values. In addition, the coefficient of the performance prediction model was developed mainly using the data in the limited temperature range provided by the manufacturer or the data limited to the simulation, and studies related to the optimization of hyper-parameters to improve the accuracy of the prediction model are lacking.