The evaluation of building energy consumption is heavily based on building characteristics and thus often deviates from the true consumption. Consequently, user-based estimation of building energy consumption is necessary because the actual consumption is greatly affected by user characteristics and activities. This work aims to examine the variation in energy consumption as a function of user activities within the same building, and to employ an artificial neural network (ANN) to predict user-based energy consumption. The study exploited the actual 24-h schedules of 5240 single-person households and computed the respective energy consumption using EnergyPlus V 8.8.0 software. The calculated values were clustered according to gender, age, occupation, income, educational level, and occupancy period and the difference among them was analyzed. The simulation results showed that for single-person households in Korea, females used more energy than males did, and the difference increased with age. Furthermore, unemployed and low-income individuals consumed more energy whereas consumption was inversely proportional to the educational level. Energy consumption increased with the occupancy period. Based on the simulation results and six user characteristics, the ANN model indicated a correlation between user characteristics and energy usage. This study analyzed the differences in energy usage depending on user activity and characteristics that affect building energy consumption.

As-built building information modeling (BIM) currently is regarded as a tool with the potential to manage buildings efficiently in the operation and maintenance phases. However, as-built BIM modeling is a labor-intensive process that requires considerable cost and time in modeling existing buildings. Although active research on scan-to-BIM automation has addressed this issue, previous studies modeled only major objects such as walls, floors, and ceilings, consequently requiring modeling other objects in indoor spaces. In addition, there was a limitation in modeling objects located in the occluded areas of scanned point clouds. Therefore, this study extracted various indoor objects from a point cloud based on deep-learning, and compensated for incomplete object information from occluded point clouds for automating the process of scan-to-BIM. The number of object classes extracted from the semantic segmentation of a deep learning network was increased to 13, and spatial relationships between objects were defined to improve the accuracy of bounding boxes extracted from point clouds. Furthermore, a parametric algorithm was developed to match the bounding boxes and objects in a BIM library to generate BIM models automatically. In a case study involving an office room, the accuracy of the bounding boxes of some object classes improved by as much as 53.33%. The study verified the feasibility of the proposed method of scan-to-BIM automation for the three-dimensional (3D) reality capture of existing buildings.