Thermal comfort is an important factor in evaluating indoor environmental quality. However, accurately evaluating thermal comfort conditions is challenging owing to the lack of suitable methods for measuring individual factors such as the metabolic rate (M value). In this study, a M value evaluation method was developed using deep learning. The metabolic equivalent of task was measured for eight typical indoor tasks based on the ASHRAE Standard 55 (lying down, sitting, cooking, walking, eating, house cleaning, folding clothes, and handling 50 kg books) in 31 subjects (males: 16; and females: 15); the measurements were analyzed in terms of gender and body mass index (BMI). The experimental results were assessed using the reliability of the measured data, the M value difference in terms of gender and BMI, and the measurement accuracy. We developed a M value self-evaluation model using artificial intelligence, which achieved an average coefficient of variation (CV) of 12%. A third-party evaluation model was used to evaluate the M value of one subject based on the learning data acquired from the other 30 subjects; this model yielded a low CV of 54%. For high-activity tasks, males generally had higher M values than females, and the higher the BMI was, the higher was the M value. Contrarily, for low-activity tasks, the lower the BMI was, the higher was the M value. The breakthrough M value evaluation method presented herein is expected to improve thermal comfort control.

Modern people spend most of their time indoors and so are chronicallyexposed to indoor air pollutants. To identify the health effects of pollutant exposure, it is necessary to understand the changes over time in indoor pollutant concentrations. There are two approaches for simulating pollutant concentration changes: mass balance model, computational fluid dynamics (CFD). Although the mass balance model is suitable for long-term simulation because it is simple, there is a limit to the detailed analysis considering concentration distribution. CFD can simulate the distribution of indoor air pollutants, but long-term analyses require too many computational resources. This study proposed a novel simulation method that couples the mass balance model with the contribution ratio of pollutant sources (CRPS) index, which indicates the individual impact of all pollutant sources and is extracted from CFD result. By introducing the CRPS index, long-term pollutant concentrations can be calculated as fast as the mass balance model while considering the pollutant distribution like CFD. The method was validated using previous experimental data. The case study was conducted and simulated changes in pollutant concentrations in a new residential unit for one week. The results showed that the CRPS-coupled method was different from conventional methods in that it more realistically calculates pollutant concentrations using relatively little computational resources.

The proper operation of venetian blinds in between-glass cavity airspaces is one of the most commonly used passive control techniques and can significantly reduce the cooling load and energy use in buildings. This study investigated the cooling load reduction effect of the blind integrated with the cavity operation. A full heat balance analysis was performed using EnergyPlus to provide a detailed understanding of the heat transfer mechanism that takes place around the blind and between-glass cavity. A sensitivity analysis was also carried out to evaluate the effects of different slat angles and blind operation hours. The results show that integration of the blind and between-glass cavity operations can significantly reduce the cooling load in buildings. The cooling load reduction effect of the cavity operation (by approximately 50%) was greater than that of the blind operation (by 5% to 40%, depending on slat angle and operating hours). It was found that the interzone heat transfers between the cavity and the room space and convection heat fluxes from each surface mainly contribute to the total cooling load reduction. In addition, the double-sided blind had a greater potential to reduce the cooling load compared with a conventional single-sided blind due to its greater capability of reflecting direct solar radiation and preventing diffuse solar radiation from penetrating the room space. The results of the study show that the largest reduction of cooling load can be achieved by the cavity operation, followed by the blind operation and the proper selection of operating hours for the blinds.