When the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal heat pump system with an added auxiliary heat source is used. For the efficient operation of the system, it is necessary to check the performance coefficient of the hybrid geothermal system. The coefficient of performance can be monitored based on a mathematical model using a measuring instrument. However, in the case of mathematical models, there are a lot of input data required, and many measurement sensors are required for this. If there is an input factor that is omitted among the necessary input factors, the accuracy of the predicted performance coefficient is lowered or a problem occurs that it is impossible to predict. In this study, we intend to create a model that predicts the coefficient of performance (COP) by using ANNs and SVMs that can accurately predict at low cost using small input factors. Hyper-parameter optimization is performed to increase prediction accuracy in machine learning models. We compared the accuracy of ANN and SVM-based prediction models. In this study, the ANN model showed higher CvRMSE by 5.4% and SVM by 8%. It is expected that the predictive model will be able to be used in the operation of the hybrid geothermal system in the future.

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