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Compared to methodology provided by standards concerning thermal comfort, by using models based on various approximation methods or artificial intelligence, it may be possible to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of this study is to compare Predicted Mean Vote (PMV) computation model established by using multiple linear regression and trained artificial neural network, from the standpoint of accuracy. Both models are established on the basis of the same dataset which consists of 400 combinations of 4 thermal comfort parameters. These parameters are the air temperature, mean radiant temperature, relative humidity and clothing resistance, while activity level and air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are considered constant values for selected type of indoor environment. Clothing resistance is adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the air temperature, mean radiant temperature and relative humidity are values which are randomly generated within appropriately selected ranges. Taking into account that coefficients of determination which correspond to it are over 95%, resulting first degree polynomial relation obtained by using multiple linear regression can be considered a satisfactory approximation of PMV model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value combinations for which PMV values obtained by using this model coincide with the ones calculated by using algorithm which is provided by standard. However, results obtained by using trained neural network with one hidden layer coincide with PMV values calculated on the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from the standpoint of accuracy, it is concluded that neural network provides significantly better approximation of PMV model.

Compared to methodology provided by standards concerning thermal comfort, by using models based on various approximation methods or artificial intelligence, it may be possible to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of this study is to compare Predicted Mean Vote (PMV) computation model established by using multiple linear regression and trained artificial neural network, from the standpoint of accuracy. Both models are established on the basis of the same dataset which consists of 400 combinations of 4 thermal comfort parameters. These parameters are the air temperature, mean radiant temperature, relative humidity and clothing resistance, while activity level and air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are considered constant values for selected type of indoor environment. Clothing resistance is adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the air temperature, mean radiant temperature and relative humidity are values which are randomly generated within appropriately selected ranges. Taking into account that coefficients of determination which correspond to it are over 95%, resulting first degree polynomial relation obtained by using multiple linear regression can be considered a satisfactory approximation of PMV model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value combinations for which PMV values obtained by using this model coincide with the ones calculated by using algorithm which is provided by standard. However, results obtained by using trained neural network with one hidden layer coincide with PMV values calculated on the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from the standpoint of accuracy, it is concluded that neural network provides significantly better approximation of PMV model.

The majority of studies dealing with the correlation between human body exergy consumption and thermal comfort use adopted, constant values of metabolic rate for appropriate activity level. Taking into account correlations between metabolic rate and the occupant’s exhaled CO2 volumetric flow rate, this study proposes a novel method for the calculation of metabolic rate using measured CO2 concentration in the indoor air. Using measured values of the air and radiant temperature, relative humidity and air velocity, and calculated values of metabolic rate, the human body exergy consumption rate and corresponding Predicted Mean Vote (PMV) are calculated. Moreover, a novel polynomial relation between them is proposed. Results show that metabolic rate values vary in the range of 0.97 to 1.54 met which leads to significantly wider range of PMV and human body exergy consumption rate comparing to the assumed value of 1.2 met for sedentary school activity. According to the obtained relation, the minimal value of the human body exergy consumption rate occurs for PMV = − 0.02. Results of this study imply that treating CO2 concentration as variable does have an impact on the thermal comfort assessment, providing enhanced correlations between thermal comfort and human body exergy consumption rate.

This paper deals with the thermal comfort assessment in two business offices in the same building, located in Belgrade, Serbia. They are approximately the same size, on the same floor, but on the opposite sides of the building. Office 1 is South-West orientated and Office 2 is North-East oriented, with one occupant in each office. One occupant is male and the other one is female, so the gender difference in thermal sensation votes (TSV) was taken into the consideration. During the researched period, the following thermal comfort parameters were measured: air temperature, relative humidity, radiant temperature, and air velocity. The input value for the occupants’ activity was 1.2 met, which corresponds to the sedentary office activity. The clothing level was adopted for the summer period, which corresponds to the value of 0.6 clo. The measurements were performed two times per day, during one working week. The assessment was performed for the spring period, when heating and cooling devices were turned off. The results of measurements showed that South West orientated office had higher air temperatures, approximately 0.5 to 1.5°C, in comparison with Office 2 which was North East orientated. The female occupant was satisfied with higher air temperatures in the office, voting as neutral, while the male occupant had the impression of a neutral environment for a set of parameters with a lower air temperature.

The paper deals with an analysis of existing thermal comfort models based on exergy approach and the impact of models’ input parameters to the calculation results. The aim of this paper is to present the results of the application of five different thermal comfort models based on the exergy analysis, to compare them, and to determine if they coincide with the results obtained by using Fanger’s model. While models that are the most commonly used to evaluate and predict thermal comfort conditions are based on the first law of thermodynamics, a handful of authors used both the first and the second law of thermodynamics in order to develop new thermal comfort models. Even though the optimal comfort conditions according to these models may not differ by large margin from the optimal thermal comfort conditions according to Fanger’s model, it is concluded that justification of using models based on the exergy analysis to evaluate thermal comfort is very dependent on the input parameters, which should all be taken into consideration separately.