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.

Exterior dynamic shading devices, installed on “kinetic façades”, generate shaded areas of various shapes on windows according to the shape of the shading elements and the direction of their movement. The calculation of the shaded area is vitally important because it is directly related to the solar heat gain calculation process in building energy assessments. This paper dis-cusses a dynamic calculation method for deriving shaded fractions in consideration of the irregular shapes and unique movements of the shading elements in kinetic façades. The planar-polygon method was adopted for calculating accurate shaded areas on a window generated by irregularly shaped shade elements. To account for movements of the shading elements, the range of movement directions (i.e., rotating, sliding, etc.) was divided into steps of equivalent intervals. Applying these two methods, a shaded area calculation tool for the kinetic façade was developed. Three movement directions of shading devices were chosen for calculating shaded area, and the values of shaded fractions for six kinetic façade types were derived for different façade orientations during the summer and winter solstices. Lastly, to simplify the detailed calculation method, estimation equations for two types of kinetic façade were derived from a trend analysis of the shaded fraction values. This study deals with both detailed and simplified methods (estimation equation) for deriving the shaded fraction. The detailed method can be a more accurate solution in deriving the shaded fractions generated by complex exterior movable shading devices. However, the simplified method can be adopted in the early design stages to review various shading devices within a brief duration of time.

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.