## 1 Introduction  
The performance of the heat pump system must be monitored for efficient operation and system optimization, and is an important indicator that can be used to determine fault during system operation. After the heat pump system is manufactured or installed, the performance is measured only under specific operating conditions to check the standard performance, and continuous performance monitoring is not performed during system operation. Real-time measurement and monitoring are difficult due to the lack of devices or diagnostic sensors that can measure the performance of the heat pump system. To solve this problem, it is possible to predict performance using a large amount of data measured with measurement equipment such as BAS (Building Automation System) based on a machine-learning model that finds the relationship between input data and output data using various theoretical techniques [1].  
Various studies related to prediction and optimal control of a building system using a predictive model using a machine-learning model are being conducted according to the need for massive information processing according to the automation of buildings [2, 3, 4]. A study was conducted to develop a model for predicting energy consumption in buildings using machine learning [5, 6, 7] and to predict the heating and cooling load [8, 9]. In relation to the heat pump system, a study to predict performance using a mathematical model [10], a study to develop a model to predict the performance of a geothermal heat pump system based on artificial neural networks [11, 12] and random forest [13] models has proceeded A study was conducted to predict the performance of air-cooled heat pump systems [14; 15; 16] and to develop a performance prediction model for heating tower heat pumps [17]. Most of the studies related to the performance prediction of the aforementioned heat pump system develop a predictive model based on one machine learning. It can be confirmed that machine-learning models such as artificial neural networks and random forests can be used to predict heat pump performance. According to the characteristics of each machine-learning model, models with high usability are different depending on the system, such as data characteristics. Studies have been conducted on improving prediction performance by comparing performance according to changes in model configuration parameters, but studies on the comparative analysis of prediction performance for various types of machine-learning models are lacking. Performance verification of the development model using simulation is mainly performed [18; 19], and the application of the developed model to the actual operating system and performance evaluation are insufficient [20; 21; 22].  
In this paper, the performance prediction model was developed and tested using a machine-learning model based on the actual operation data of the air-cooled heat pump system. Learning data were collected using the heat pump system installed in the university lab, and input variables were selected through statistical analysis, prediction model selection, prediction model development and performance verification, and the prediction model developed in the BAS system were applied in the following order. R studio (ver. 1.2.1335), which is used as a research and industrial application for statistics, machine learning, and data mining, was used for model development. ANN (Artificial Neural Network) with the best accuracy and short computation time among the developed prediction models was applied to the BAS of the laboratory. Real-time performance monitoring is possible through BAS, and the field applicability of the development model was confirmed.