In a heat pump system, performance is an important indicator that should be monitored for system optimization, fault diagnosis, and operational efficiency improvement. Real-time performance measurement and monitoring during heat pump operation is difficult because expensive performance measurement devices or additional installation of various monitoring sensors required for performance calculation are required. When using a data-based machine-learning model, it is possible to predict and monitor performance by finding the relationship between input and output values through an existing sensor. In this study, the performance prediction model of the air-cooled heat pump system was developed and verified using artificial neural network, support vector machine, random forest, and K-nearest neighbor model. The operation data of the heat pump system installed in the university laboratory was measured and a prediction model for each machine-learning stage was developed. The mean bias error analysis is −3.6 for artificial neural network, −5 for artificial neural network, −7.7 for random forest, and −8.3 for K-nearest neighbor. The artificial neural network model with the highest accuracy and the shortest calculation time among the developed prediction models was applied to the Building Automation System to enable real-time performance monitoring and to confirm the field applicability of the developed model.

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.

This study assessed a variable flow rate control method for a circulation pump based on the geothermal water temperature difference in a geothermal heat pump system. As interest in energy conservation and efficient use is increasing around the world, the development and use of renewable energy is increasing and various related studies are currently underway. Among the renewable energy systems, the interest in geothermal energy system is high because of its efficient year-round operation. Geothermal heat pump system installations have increased in number, but the systems operate inefficiently. Generally in Korea, geothermal heat pump system operates under partial load conditions, but the circulation pump operates at constant speed and supplies a constant flow rate. Therefore, this study examined the operation of the current problems of a geothermal heat pump system. A variable flow rate control method of the circulation pump is proposed to improve the efficiency of the geothermal heat pump system and save energy during the cooling operation. As a result, the total energy consumption was reduced by 57,017 kW compared to the existing flow rate control method and the energy consumption of the circulation pump system was reduced by 35,209 kW.

As the global energy trend continues, the importance of energy savings and efficient use is being emphasized, and the installation and operation of geothermal heat pump systems is increasing. In many buildings, where an actual geothermal heat pump system has been installed, problems of efficiency deterioration occur frequently because of the inefficient operation after installation of the heat pump system. The purpose of this study was to develop and verify the operating method for energy saving and performance improvement of multiple geothermal systems. A coefficient of performance (COP) prediction model using an artificial neural network for real-time COP predictions was developed. The operating method of a multi-geothermal heat pump system using a variable water flow rate control method and COP prediction model was developed. The geothermal heat pump system operates sequentially depending on the water flow rate of the circulation pump. The COP prediction model enabled real-time performance prediction during system operation. The circulation water flow rate was reduced by up to 29% compared to the existing operating method. Approximately 23% of the energy was saved. The COP system, including the consumption power of the circulation pump, was improved.

The installation and operation of geothermal systems increased due to the expectation of good cooling and heating performance due to stable heat source temperatures. In actual geothermal system operations, heat source temperature rises or falls due to an imbalance of heating and cooling energy usage. Problems of source side temperature result in reduced geothermal system performance. The purpose of this study is to develop hybrid geothermal system operation technology to stabilize temperature and improve system performance by utilizing auxiliary heat source system. The auxiliary heat source system is operated by comparing the performance when operating the geothermal heat pump system alone and the performance when operating the hybrid geothermal heat pump system. The performance of a hybrid geothermal system is determined by the circulating water temperature of the geothermal system and the circulating water temperature of the auxiliary heat source system. Hybrid geothermal heat pump system performance is predicted through numerical analysis and collection of hybrid geothermal system performance data at various temperature ranges through field test. An operating method was developed using the predicted performance as the changeover operating point of the hybrid geothermal heat pump system. When applying the development and operation technology, it handled about 11% more load than the existing geothermal system operation. The addition of an auxiliary heat source increases the initial investment cost compared to the existing geothermal system, but decreases energy consumption, confirming that the initial investment cost of 15.3 years is recovered.

Accurate measurement of air flow rate is essential in automatic building control using the variable air volume (VAV) system. In order to solve the problems of the existing air flow measurement method and improve the accuracy of air flow control, this study developed a data-based multiple regression air flow prediction model. The independent variables used in the development of the predictive model were selected as the factors used for control and monitoring when operating with variable air flow rate in the existing air conditioning system. Data collection and correlation between independent variables and air flow rate of the terminal unit were analyzed. Using the IBM SPSS statistics version 25, an air flow rate prediction model was developed using multiple regression analysis. Reliability of model was evaluated by comparing the measured airflow. The relative error of −9.3% to 10.4% is shown when comparing the estimated air flow rate by the developed model with the measured air flow rate.