The present study sets out to review the thermal and optical properties of electrochromic windows (ECWs) through an analysis of the improvement in the energy performance of a building resulting from their application. The performance analysis was based on the change in the room temperature according to the solar transmittance and the orientation of the ECWs, the energy consumptions of the building’s heating/cooling systems, and that of the building’s lighting according to the visible light transmittance (VLT). To achieve this, the Quick Energy Simulation Tool (eQUEST), a building energy interpretation program, was used. The solar heat gain coefficient (SHGC) of the ECWs was found to be significantly reduced. This had the effect of lowering the room temperature in summer, such that the effect on the summer cooling energy consumption was also remarkable. However, with a reduction in the VLT, the lighting energy consumption increased. The net result of the changes in the heating/cooling and lighting energy consumptions was a reduction of about 11,207 kWh/yr (8.89%). The ECWs were found to realize a greater reduction in a building’s energy consumption than was possible with windows glazed with low-E coated glass.
The objective of this study is to develop (1) a pose-categorization model that classifies the poses of an occupant based on their image in an indoor space and (2) an activity-decision algorithm that identifies the activity being performed by the occupant. For developing an automated intelligent model, a deep neural network is adopted. The model considers the coordinates of the joints of the occupant in the image as input data and returns the pose of the occupant. Datasets composed of indoor images of home and office environments are used for training and testing the model. The training and testing accuracies of the optimized model were 100% for both the home and office environments. A representative activity of an occupant for a certain period has to be decided to control an indoor environment for comfort. The activity-decision algorithm employs a frequency-based method to determine the representative activity type for real-time occupant poses using the pose-categorization model. This study highlights the potential of the developed model and algorithm to determine the activity of occupants to provide an optimal thermal environment corresponding to the individual’s metabolic rate.
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