Aydın Ege ÇETER

Adaptive thermal comfort is a model which considers behavioral and psychological adjustments apart from Fanger's Predicted Mean Vote (PMV)/Percentage of Dissatisfied (PPD) method. In the literature, the differences between the PMV/PPD method and adaptive thermal comfort were mainly considered in aspects of behavioral adjustments in an environment. Conversely, limited studies related to psychological adjustments were considered in detail for thermal comfort. This study purposes to investigate the effects of current mood state subscales on thermal sensation of the occupants for the first time in the literature. To this aim, the Profile of Mood States (POMS) questionnaire is used to determine the mood state of the occupants with six different subscales: Anger, Confusion, Vigor, Tension, Depression, and Fatigue. The experiments were conducted in a university study hall in Ankara, Turkey, which is in warm-summer Mediterranean climate (Csb) according to Köppen-Geiger Climate Classification. The distributions of each subscale were examined via Anderson Darling and Shapiro-Wilk tests accordingly given responses from the occupants. The sensitivity analysis was applied to the six subscales of the POMS with Monte Carlo simulation method by considering the distributions of each subscale. The results revealed that the current mood state has a crucial effect on the thermal sensation of the occupants. The subscales of the Depression and Vigor were found as the most vital ones among the six subscales. Only the pure effects of the Vigor and Depression would change the thermal sensation of the occupants 0.31 and 0.30, respectively. The Confusion was determined as the least effective subscale to the thermal sensation of the occupants. Moreover, with the combination of all the six subscales, the thermal sensation might change up to 1.32. Findings in this study would help researchers to develop the personalized thermal comfort systems.

Thermal comfort depends on four environmental parameters such as air temperature, mean radiant temperature, air velocity and relative humidity and two personal parameters, including clothing insulation and metabolic rate. Environmental parameters can be measured via objective sensors. However, personal parameters can be merely estimated in most of the studies. Metabolic rate is one of the problematic personal parameters that affect the accuracy of thermal comfort models. International thermal comfort standards still use a conventional metabolic rate table which is tabulated according to different activity tasks. On the other hand, ISO 8996 underestimates metabolic rates, especially when the time of activity level is short and rest time is long. To this aim, this paper aims to determine metabolic rates from physical measurements of heart rate, mean skin temperature and carbon dioxide variation by means of nineteen sample activities. 21 male and 17 female subjects with different body mass indices, sex and age are used in the study. The occupants are subjected to different activity tasks while heart rate, skin temperature and carbon dioxide variation are measured via objective sensors. The results show that the metabolic rate can be estimated with a multivariable non-linear regression equation with high accuracy of 0.97.

Traditional ventilation systems supply fresh air to the building on a consistent basis, however, do not ensure adequate comfortable areas in indoor environments. On the other hand, smart ventilation systems adjust indoor environment parameters optionally by occupancy schedules to provide desired indoor air quality while minimizing energy consumption. ASHRAE Standard 62, which shows ventilation strategies for acceptable indoor air quality, uses steady-state occupancy detection algorithms, however, dynamic methods are required behind simulation studies. To this aim, this study presents a dynamic novel ventilation control strategy by detecting occupants in an office building in Ankara-Turkey. The novel control strategy includes deployment of several sensors which measure carbon dioxide concentration, indoor air temperature and position of the door. A prototype of the controller is manufactured and tested in a real environment with the help of regulation of air flow in HVAC system. The proposed control strategy is tested between January 1st, 2020 and August 15th, 2020 and compared with traditional controller in terms of thermal comfort and energy consumption. The results showed that proposed controller decreased energy consumption by 16% while satisfying thermal comfort for the 94% of total number of occupants.