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.

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This study aims to propose a pose classification model using indoor occupant images. For developing the intelligent and automated model, a deep learning neural network was employed. Indoor posture images and joint coordinate data were collected and used to conduct the training and optimization of the model. The output of the trained model is the occupant pose of the sedentary activities in the indoor space. The performance of the developed model was evaluated for two different indoor environments: home and office. Using the metabolic rates corresponding to the classified poses, the model accuracy was compared with that of the conventional method, which considered the fixed activity. The result showed that the accuracy was improved by as much as 73.96% and 55.26% in home and office, respectively. Thus, the potential of the pose classification model was verified for providing a more comfortable and personalized thermal environment to the occupant.

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