Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.

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.

Thermal bridges in building envelopes can cause significant heat loss and heat gain. In this study, the developed thermal bridge breaker was applied to an interior insulation finishing system in residential buildings to minimize the thermal bridges in building envelopes. To investigate the thermal and energy performance of the developed thermal bridge breaker, the surface temperatures and heat flow at the wall and floor junctions were predicted using Physibel. In addition, the heating and cooling energy consumption in a residential building was analyzed by EnergyPlus. As a result, the use of the thermal bridge breaker can minimize the effective thermal transmittance in the building envelope system. Moreover, when the building envelopes were equipped with the thermal bridge breaker, the heating and cooling load through the exterior walls was decreased by 15–27%. Thus, the thermal bridge breaker can play an important role in minimizing the heat loss and occurrence of condensation in building envelopes.

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30% of total energy has been consumed by buildings, and much attention has been paid to reducing building energy consumption. There are many ways to reduce building energy consumption. One of the most relevant methods is machine learning. While machine learning methods provide accurate energy consumption predictions, they require huge datasets. The present study developed an artificial neural network (ANN) model for building energy consumption predictions with small datasets. As mechanical systems are the most energy-consuming components in the building, the present study used the energy consumption data of a direct-fired absorption chiller for the short term. For the optimization, the prediction results were investigated by varying the number of inputs, neurons, and training data sizes. After optimizing the ANN model, it was validated with the actual data collected through a building automation system. In sum, the outcome of the present study can be used to predict the energy consumption of the chiller as well as improve the efficiency of energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets, which can improve the efficiency of building energy management.

In commercial buildings, HVAC systems are becoming a primary driver of energy consumption, which already account for 45% of the total building energy consumption. In the previous literature, researchers have studied several energy conservation measures to reduce HVAC system energy consumption. One of the effective ways is an economizer in air-handling units. Therefore, this study quantified the impact of the outdoor air fraction by economizer control type in cooling system loads based on actual air-handling unit operation data in a hospital. The optimal outdoor air fraction and energy performance for economizer control types were calculated and analyzed. The result showed that economizer controls using optimal outdoor air fraction were up to 45% more efficient in cooling loads than existing HVAC operations in the hospital. The energy savings potential was 6–14% of the differential dry-bulb temperature control, 17–27% of the differential enthalpy control, 8–17% of the differential dry-bulb temperature and high-limit differential enthalpy control, and 16–27% of the differential enthalpy and high-limit differential dry-bulb temperature control compared to the no economizer control. The result of this study will contribute to providing a better understanding of economizer controls in the hospital when the building operates in hot-humid climate regions.

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30 % of total energy has been consumed by buildings and much attention has been paid to reducing building energy consumption. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.