###### Abstract
The Random Forests model was used to predict the COP, which showed good results with an error level of 16-24% at high output temperatures and more accurate predictions of 3-11% at low output temperatures. [PERSON] [23] proposed a method for predicting operation information of air-source heat pump systems using sensor data initially provided by the manufacturer. A deep learning-based power consumption and COP prediction model were proposed. Fully Connected Deep Neural Network (FCDNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models were used to compare the performance differences between the models. The FCDNN-based model showed an RMS error of 2.4% in power consumption and 3.4% in COP. [PERSON] [24] developed an ANN-based energy consumption prediction model. [PERSON] et al. [25] used a CNN-based regression model to predict the critical driving data, and [PERSON] [26] developed a back propagation neural network (BPNN) algorithm deception-performance prediction model. [PERSON] [27] used simulation to analyze the performance of air-source heat pump systems under different climatic conditions and developed a COP prediction model that considers the influence of operating conditions. The error range of the developed model was within \(\pm\)5%, showing excellent performance. [PERSON] [28] developed a prediction model based on a gray-box model. The model was evaluated by comparing the results provided by Energy Plus, a dynamic building energy simulation program, with actual collected data. Many studies were conducted to verify operational data, such as cooling capacity and COP of air-source heat pump systems in operation. Studies have been conducted on developing data-driven prediction models to replace data that are difficult to measure in situ. On the other hand, the proposal to use the inlet and outlet refrigerant temperature and pressure data of the evaporator and condenser, which are the main factors of the proposed prediction model, and the data of the embedded sensors of the system may have difficultiesin situ applications. In addition, a large amount of useful data is required to develop a data-driven prediction model [25]. Applying this to air-source heat pump systems that do not store operational data is challenging. Therefore, studies on measuring the performance are needed to provide key operating data of air-source heat pump systems by minimizing the use of data that are difficult to measure, such as refrigerant enthalpy, pressure, flow rate, and power consumption, and using data that are easy to measure. If the user or manager can know more about the operation information of the air source heat pump system, the efficiency of the system can be improved and energy consumption can be saved. In this study, a method for measuring operation information and characteristics of an air source heat pump system was proposed using air-side data, which is easier to measure than refrigerant-side data. A method of inferring information on the refrigerant side using air side data without direct measurement on the refrigerant side is proposed. The pressure-enthalpy diagram, a Mollier diagram that shows the relationship between pressure and enthalpy as the refrigerant changes, was used in this study. The refrigeration cycle of the Mollier diagram represents the refrigerant enthalpy and pressure during evaporation, compression, condensation, and expansion of the air source heat pump system (hereafter, the refrigeration cycle refers to the refrigeration cycle of the Mollier diagram). To measure the refrigeration cycle, a method was developed to minimize the refrigerant-side data measurement and use the air-side measurement data. To minimize the number of measurement points, the heat exchange between the refrigerant and air was assumed to be equal, in accordance with the first law of thermodynamics, the law of conservation of energy. In addition, the refrigeration cycle was assumed to be an ideal cycle in order to use the Mollier diagram.