Numerical analysis, especially the finite volume method (FVM), is one of the primary approaches employed when evaluating a building environment. A complicated geometry can degrade the mesh quality, leading to numerical diffusions and errors. Thus, this study develops and evaluates an automatic building geometry simplification method based on integrating similar surfaces for the geometry of an indoor space. A regression model showed that the complexity of the simplified geometry and its similarity to the original geometry decreased linearly with the threshold of the method. The mesh quality was significantly improved by the simplification. In particular, the maximum skewness decreased exponentially with the threshold of the method. It is expected that the simplification method and regression model presented in this study can be used to quantitatively control the mesh quality.

As part of ongoing research into saving lighting energy, studies on lighting control integrating location-awareness technologies have recently been increasing, but these have led to the indoor illuminance imbalance problem by controlling only the lighting adjacent to the occupant. Therefore, the purpose of this study is to develop a dimming lighting control system using general illumination and location-awareness technology by integrating general illumination and dimming technology with lighting control technology that is based on location-recognition, and to verify the effectiveness of this system. This study built a full-scale test bed to evaluate the performance of the developed technology, and derived the energy reduction rate and indoor light environment improvement rate to evaluate the performance of dimming lighting control (Case 1), dimming lighting control using location-awareness technology (Case 2), and dimming lighting control using general illumination and location-awareness technology (Case 3). The conclusions are as follows. (1) Case 3 reduces lighting energy by 47.9–64.2% as compared to Case 1, and Case 3 reduces lighting energy compared to Case 2 when there are three occupants. (2) Case 3 improves indoor light environment comfort by increasing the uniformity by 17.8–49% compared to Case 2. These results confirm the effectiveness of Case 3 proposed in this study.

In today’s society, where people spend over 90% of their time indoors, indoor air quality (IAQ) is crucial for sustaining human life. However, as various indoor activities such as cooking generate diverse types of pollutants in indoor spaces, IAQ has emerged as a serious issue. Previous studies have employed methods such as CO₂ sensors, smart floor systems, and video-based pattern recognition to distinguish occupants’ activities; however, each method has its limitations. This study delves into the classification of occupants’ cooking activities using sound recognition technology. Four deep learning-based sound recognition models capable of recognizing and classifying sounds generated during cooking were presented and analyzed. Experiments were carried out using sound data collected from real kitchen environments and online data-sharing websites. Additionally, changes in performance according to the amount of collected data were observed. Among the developed models, the most efficient is found to be the convolutional neural network, which is relatively unaffected by fluctuations in the amount of sound data and consistently delivers excellent performance. In contrast, other models exhibited a tendency for reduced performance as the amount of sound data decreased. Consequently, the results of this study offer insights into the classification of cooking activities based on sound data and underscore the research potential for sound-based occupant behavior recognition classification models.

Building envelope performance is an important factor used to calculate the energy loads required for heating and cooling buildings. Despite the large influence that building envelope thermal performance has over the energy consumption of buildings, thermal performance evaluations are often carried out using two-dimensional CAD drawings that require much time and effort. Recently, Building Information Modeling (BIM) has been used as a tool to enhance the efficiency of manually performed building information management practices. In addition, studies in the field of energy performance evaluations that have incorporated BIM have also been conducted. In this study, a method of automatically extracting information from BIM necessary for the derivation of building envelope performance was proposed. The first step of the proposed method was to classify parts that directly or indirectly made contact with the outdoor air. The second step was to classify the parts composing the envelope to appropriately reflect the standard used for the evaluation. Third, the thermal transmittance of each component composing the building envelope was calculated. Lastly, the area of each part was calculated. A case study was performed on a multi-residential unit to verify the accuracy of the proposed method. The verification results indicated that the proposed method was applicable in practice. Further research is planned to verify the performance of the proposed method with regard to its application to various standards and types of residential buildings.