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.

The construction industry has made considerable energy-saving efforts in buildings, and studies of energy-savings are ongoing. Shading is used to control the solar radiation transferred through windows. Many studies have examined the position and type of shading in different countries, but few have investigated the effects of shading installation in Korea. In this study, the case of the shading installation according to the standard of Korea, and variations of the heating and cooling load in the unit area on the performance of the windows were examined. This study compared the variations of the heating and cooling load in the case of horizontal shading and the changing position of venetian blinds. This study confirmed that horizontal shading longer than the standard length in Korea saved a maximum of 13% energy consumption. This study confirmed the point of change of energy consumption by the Solar Heat Gain Coefficient (SHGC) variations. The exterior venetian blinds and those between glazing were unaffected by the SHGC. On the other hand, in the case of a south façade, the interior venetian blinds resulted in 24% higher energy consumption than the installation of horizontal shading in case of Window to Wall Ratio (WWR): 80%, U-value: 2.1 and SHGC: 0.4.

The objective of this study was to propose a control method with a ventilation requirement of variable air volume (VAV) system in multi-zone. In order to control the VAV system inmulti-zone, it is essential to control the terminal unit installed in each zone. A VAV terminal unit with conventional control method using a fixed minimum air flow can cause indoor air quality (IAQ) issues depending on the variation in the number of occupants. This research proposes a control method with a ventilation requirement of the VAV terminal unit and AHU inmulti-zone. The integrated control method with an air flow increase model in the VAV terminal unit, AHU, and outdoor air intake rate increase model in the AHU was based on the indoor CO₂ concentration. The conventional and proposed control algorithms were compared through a TRNSYS simulation program. The proposed VAV terminal unit control method satisfies all the conditions of indoor temperature, IAQ, and stratification. An energy comparison with the conventional control method showed that the method satisfies not only the indoor thermal comfort, IAQ, and stratification issue, but also reduces the energy consumption.

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.

The Korean government is fostering a renewable energy industry as a means of handling the energy crisis. Among the renewable energy systems available, geothermal energy has been highlighted as highly efficient, safely operable and relatively unaffected by outdoors air conditions. Despite the increasing use of renewable energy, the devices using renewables may not be operating appropriately. This study examined current problems in the operation of a geothermal heat pump (GHP) system. The efficiency of a geothermal heat pump system to studied to maximize the operation plan. Our study modelled the target building and analyzed the energy using TRNSYS, which is a dynamic energy simulation tool, to apply the coefficient of performance (COP) and evaluate the operation method. As a result, the GHP total energy consumption from the COP control method was reduced by 46% compared to the current operation. The proposed control method was evaluated after applying the system to a building. The results showed that efficient operation of a geothermal heat pump system is possible.