For evaluating the thermal comfort of occupants, human factors such as clothing thermal insulation (clo level) and metabolic rate (Met) are one of the important parameters as well as environmental factors such as air temperature (Ta) and humidity. In general, a fixed clo level is commonly used for controlling heating, ventilation, and air conditioning using the thermal comfort index. However, a fixed clo level can lead to errors for estimating the thermal comfort of occupants, because clo levels of occupants can vary with time and by season. The present study assesses a method for predicting the clo level of occupants using a thermoregulation model and an infrared (IR) camera. The Tanabe model and the Fanger model were used as the thermoregulation models, and the predicted performance for high clo level (winter clothing) was compared. The skin and clothing temperatures of eight subjects using a non-contact IR camera were measured in a climate chamber. In addition, the measured values were used for the thermoregulation models to predict the clo levels. As a result, the Tanabe model showed a better performance than the Fanger model for predicting clo levels. In addition, all models tended to predict a clo level higher than the traditional method.

With the development of metering technologies, data mining techniques such as machine learning have been increasingly used for the prediction of building energy consumption. Among various machine learning methods, the KNN algorithm was implemented to predict the hourly energy consumption of community buildings composed of several different types of buildings. Based on the input data set, 10 similar hourly energy patterns for each season in the historic data sets were chosen, and these 10 energy consumption patterns were averaged. The prediction results were analyzed quantitatively and qualitatively. The prediction results for the summer and fall were close to the energy consumption data, while the results for the spring and winter were higher than the energy consumption data. For accuracy, a similar trend was observed. The values of CVRMSE for the summer and fall were within the acceptable range of ASHRAE guidelines 14, while higher values of CVRMSE for the spring and winter were observed. In sum, the total values of CVRMSE were within the acceptable range.

Accounting for more than half of buildings in South Korea, the energy consumed by residential buildings has become a main concern and the cooing demand has rapidly increased. To reduce energy consumption, several passive and active design strategies have generally been applied. However, there has been an increasing demand for high window-to-wall ratios in residential buildings, it is imperative to block sunlight into a building effectively. Focusing on the reduction of cooling energy consumption in a residential building, the present study assessed the daylight and energy performance of shading devices. Among various types of shading devices, the Venetian blind, horizontal louver, light shelf, and egg-crate were selected. The illuminance levels in three different areas in a building were measured. In addition, the annual cooling energy consumption by these shading devices was investigated. As a result, both daylight and energy performance varied with different design options of these shading devices. Because of the slight performance difference among shading devices, the artificial loads of two best shading devices were compared. In sum, the egg-crate shading was the most proper shading device to block sunlight as well as reduce the cooling energy consumption effectively.