With an increase in the aging population in many countries worldwide, much attention is being paid to the study of thermal comfort for the elderly. Because the elderly spend most of their time indoors, the demand for air conditioning is expected to increase, and it is important to study the thermal comfort of the elderly and appropriate operation plans for air conditioning. In this study, we conducted a field survey of thermal comfort and building energy simulation for an air-conditioned nursing home in Nagano, Japan. The field survey was conducted between June 2020 and June 2021. Over 80% of the subjects were satisfied with the indoor thermal environment. The thermal neutral temperature of the elderly was 25.9 °C in summer and 23.8 °C in winter. Future weather data was used to predict the future heating and cooling loads of the nursing home. The results showed that the total heat load may not change significantly, as the decrease in heating load compensates for the increase in cooling load. This study will serve as a useful reference for a wide range of stakeholders, including managers and designers of nursing homes.

Comfort temperature is important to investigate because the chosen office indoor temperatures affect the energy used in a building, and a thermally comfortable environment makes the occupants be more productive. The effects of temperature on comfort are broadly recognized for thermal comfort. Japanese office buildings are well equipped with air-conditioning systems to improve the thermal comfort of the occupants. The main objectives of this research were to compare the winter comfort temperature in mixed mode (MM) and heating, ventilation and air-conditioning (HVAC) office buildings and to investigate the relationship between the comfort temperature and the indoor air temperature. This study measured the thermal environmental conditions of the office buildings and surveyed the thermal comfort of the occupants. The field survey was conducted during winter in seven office buildings located in the Aichi prefecture of Japan. In total, 4466 subjective votes were collected from 46 occupants. The result suggested that the occupants were found to be more satisfied with the thermal environment of MM buildings than that of HVAC office buildings. Overall, 95% of comfort temperatures were in the range 22~28 °C in MM and HVAC buildings, which were higher than the indoor temperature of 20 °C recommended by the Japanese government. The comfort temperature was highly correlated to the indoor air temperature of the MM buildings than to that of HVAC buildings. This indicated that the occupants were more adapted towards the given thermal environment of MM buildings.

Occupant behavior based on natural ventilation has a significant impact on building energy consumption. It is important for the quantification of occupant-behavior models to select observed variables, i.e., features that affect the state of window opening and closing, and to consider machine learning models that are effective in predicting this state. In this study, thermal comfort was investigated, and machine learning data were analyzed for 30 houses in Gifu, Japan. Among the selected machine learning models, the logistic regression and deep neural network models produced consistently excellent results. The accuracy of the prediction of open and closed windows differed among the models, and the factors influencing the window-opening behaviors of the occupants differed from those influencing their window-closing behavior. In the selection of features, the analysis using thermal indices representative of the room and cooling features showed excellent results, indicating that cooling features, which have conflicting relationships with natural ventilation, are useful for improving the accuracy of occupant-behavior prediction. The present study indicates that building designers should incorporate occupant behavior based on natural ventilation into their designs.

This report presents the 丘ndings of a field study of thermal com 丘)rt and thermal environment in 30 detached houses in Hyogo ， 30 apartment houses in Osaka ， Japan． A total of 70 subjects provided 459 sets of data f ()r summer ， each accompanied by indoor climatic measurements ． The sample of subjects consisted mostly of housewifb ． The average clothing insulation levels were O28clo ． The average metabolic rate was 1 ． 3met ． Thermal neutrality ， according to responses on the ASHRAE seven ・ point sensation scale ， occurred at 27 ． 6℃ ． The Operative temperature corresponding to the accepted thermal environment by 80 ％ ofthe occupants is under 28 ． 4℃ ．

A field survey of indoor environmental measurements and questionnaires on thermal sensation, overall comfort, and behaviors was conducted in four office buildings in Japan by visiting each office every month over a duration of more than a year during the coronavirus disease 2019 (COVID-19) pandemic. The indoor environment was measured concurrently. We obtained 1047 votes from office workers in their 20s to 60s. The regression and Griffiths’ methods were used to calculate the indoor comfort temperature. A logistic regression analysis was used to develop the occupant behavior model. Over 70% of the occupants found the indoor environment comfortable at a mean comfort temperature of 23.2 to 25.9 °C. Gender differences were observed in thermal sensation and overall comfort, but a gender difference was observed only in the cooling mode for the indoor comfort temperature. An adaptive model was developed for the office buildings in Nagasaki city to predict the indoor comfort temperature from the outdoor air temperature. The proportions of heating, cooling, and fan usage can be predicted from the outdoor air temperature using a logistic regression analysis. The adaptive model and occupant behavior model are useful for the indoor temperature control of the existing buildings and thermal simulation of the new building design.