## 5 Conclusions  
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. The performance was predicted using artificial neural networks, support vector machines, random forests, and K-nearest neighbor models, and the accuracy and computation time of the predicted performance were compared. The field applicability was confirmed by applying the developed predictive model to BAS. The detailed conclusion of this paper is as follows.  
1. To develop a predictive model, a university laboratory equipped with an air-cooled heat pump system was selected as a target building and operational data were measured. A statistical analysis was performed between the data and performance collected through the mathematical model and BAS, and through this, the inlet/outlet temperature of the heat source side and the inlet/outlet temperature of the load side were selected as input values.
2. The training data constructed while developing a predictive model using artificial neural network, support vector machine, random forest, and K-nearest neighbor was subjected to data preprocessing to improve the accuracy of the predictive model. A total of 5124 pieces of data corresponding to 70% of the collected data were used as training data for predictive model development, and 30% of the data (2196 pieces) not used as training data are testing data to verify the performance of the developed model. A predictive model was developed using R studio.
3. As a result of evaluating the accuracy of the developed performance prediction model, MBE of ANN was \(-\)3.6, MBE of SVM was \(-\)5, MBE of RF was \(-\)7.7, and MBE of KNN was \(-\)8.3. This satisfies the verification criteria of ASHRAE Guideline 14 and confirmed that the developed predictive model has excellent performance. ANN 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.  
Real-time performance can be checked through the developed performance prediction model, and it can be used for system failure diagnosis and optimal control. When applied to a heat pump system, it can be used as a control point for a heat pump system through prediction of performance that is difficult to predict, and it is expected that energy-saving and performance improvement will be possible.  
Figure 11: Comparison of predicted and measured performance.  
**Author Contributions:** All authors contributed to this work. [PERSON] investigated and wrote the original draft. [PERSON] did project administration and supervised. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government, grant number NRF-2019R1A2C1009445.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.