In order to evaluate the thermal and lighting energy performance of a kinetic façade using external movable shading devices, it is important to consider the operation of the shading devices since it can influence the performance significantly. This study proposes a calculation methodology which assesses the performance of the movable shading devices with the consideration of the movements of the shading devices. Calculation methods were derived by which solar heat gain, lighting energy requirement, and the primary energy equivalent to heating and cooling energy requirement can be obtained. Using the calculation methods, the optimal operation scenario for the movable shading devices was presented which can minimize the solar heat gain and lighting energy requirement. A comparison case study was carried out to demonstrate the use of the control strategies for a building where a drop awning was installed. The results showed that the optimal operation scenario suggested in this study can potentially deliver effective energy performance. The limitation and applicability of the suggested method were also discussed.

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 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.

Since the thermal environment of large space buildings such as stadiums can vary depending on the location of the stands, it is important to divide them into different zones and evaluate their thermal environment separately. The thermal environment can be evaluated using physical values measured with the sensors, but the occupant density of the stadium stands is high, which limits the locations available to install the sensors. As a method to resolve the limitations of installing the sensors, we propose a method to predict the thermal environment of each zone in a large space. We set six key thermal factors affecting the thermal environment in a large space to be predicted factors (indoor air temperature, mean radiant temperature, and clothing) and the fixed factors (air velocity, metabolic rate, and relative humidity). Using artificial neural network (ANN) models and the outdoor air temperature and the surface temperature of the interior walls around the stands as input data, we developed a method to predict the three thermal factors. Learning and verification datasets were established using STAR CCM+ (2016.10, Siemens PLM software, Plano, TX, USA). An analysis of each model’s prediction results showed that the prediction accuracy increased with the number of learning data points. The thermal environment evaluation process developed in this study can be used to control heating, ventilation, and air conditioning (HVAC) facilities in each zone in a large space building with sufficient learning by ANN models at the building testing or the evaluation stage.