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Article Citation - WoS: 7Citation - Scopus: 6The Importance of the Calculation of Angle Factors To Determine the Mean Radiant Temperature in Temperate Climate Zone: a University Office Building Case(Sage Publications Ltd, 2022) Ozbey, Mehmet Furkan; Turhan, CihanThermal comfort depends on four environmental (air velocity, relative humidity, air temperature, mean radiant temperature) and two personal (clothing insulation and metabolic rate) parameters. Among all parameters, the mean radiant temperature (t(r)) is the most problematic variable in thermal comfort studies due to its complexity. Measurement methods, calculation methods and assumptions are mostly used to obtain the t(r). Researchers mainly prefer to obtain the t(r) via measurement methods or assumptions due to their easiness compared to the calculation methods. Besides, some researchers use constant values of angle factors in calculation methods. However, using constant values is not proper for every indoor environment, and it causes wrong estimations in the t(r) and thus the thermal comfort. This paper gives the importance of calculation of angle factors, with an example of a university office building in temperate climate zone, according to the ISO 7726. The angle factors of the room were calculated for a seated occupant from the centre of gravity in three different locations and compared with the constant angle factors. The results indicate that a significant difference (MAPE of 1.02) was found in the t(r) values, which were obtained by calculation of constant values of angle factors.Article Citation - WoS: 20Citation - Scopus: 21Sensitivity Analysis of the Effect of Current Mood States on the Thermal Sensation in Educational Buildings(Wiley-hindawi, 2022) Ozbey, Mehmet Furkan; Ceter, Aydin Ege; Orfioglu, Sevval; Alkan, Nese; Turhan, CihanAdaptive 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 Koppen-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.Article Citation - WoS: 20Citation - Scopus: 23A Novel Comfort Temperature Determination Model Based on Psychology of the Participants for Educational Buildings in a Temperate Climate Zone(Elsevier, 2023) Ozbey, Mehmet Furkan; Turhan, CihanMaintaining thermal comfort in the educational buildings is vital due to the impacts on learning effectiveness of students. Therefore, development of a proper comfort temperature in educational buildings is a must. In naturally ventilated and mixed-mode buildings, the adaptive thermal comfort model, which considers additively psychological, and behavioural factors to the Fanger's PMV/PPD model, is commonly applied based on regression analyses. However, the psychological adjustments based on current mood state are very limited in these adaptive thermal comfort models. Therefore, this study focuses on the psychological adjustments in terms of Profile of Mood States in order to predict comfort temperature of students in a case building. The experiments are conducted in a university on a temperate climate zone for a long period-data including both heating and cooling seasons. In this study, the comfort temperatures for each student are determined via Griffith method for the case building. Moreover, the current mood states of students are assessed utilizing the Profile of Mood States survey, which are collected via a developed mobile application. As a conclusion, the relation between the current mood state of the students and comfort temperature are statistically investigated. The results show that a Griffith constant are found as 0.332/K and mean annual comfort temperature is found as 21.32 degrees C in the case building. Additionally, a significant difference is found in the comfort temperatures among the students who have more, or fewer concerns than typically reported. The novelty of the study is to present a comfort temperature determination model which considers human psychology as a starter study in the literature.Article Citation - WoS: 16Citation - Scopus: 18A Comprehensive Comparison and Accuracy of Different Methods To Obtain Mean Radiant Temperature in Indoor Environment(Elsevier, 2022) Ozbey, Mehmet Furkan; Turhan, CihanThermal comfort is defined as "the state of mind which expresses satisfaction with the thermal environment" by the American Society of Heating, Refrigerating and Air Conditioning Engineers in the standard of the ASHRAE55. Thermal comfort is affected by six main parameters which are split into two categories; personal (basic clothing insulation value and metabolic rate) and environmental (air temperature, relative humidity, air velocity, and mean radiant temperature) parameters. The mean radiant temperature is a problematic parameter in thermal comfort studies due to its complexity. The mean radiant temperature approaches are based on different techniques such as calculation methods, measurement methods, and assumptions. Although the assumptions are utilized by researchers to abstain complexity, their accuracies are uncertain. To this aim, this study purposes to find the accuracies of calculation and assumption methods by comparing with reference measurement method. An office building in a temperate climate zone is selected as a case study. Two calculation methods and eight assumptions on obtaining mean radiant temperature are compared via in-situ measurements. The results revealed that using assumptions or calculation methods to obtain the mean radiant temperature caused a significant error compared to the reference method and researchers should consider accuracies of these methods before utilizing them in their applications.Article Estimation of the Mean Radiant Temperature in Office Buildings Using an Artificial Neural Network Developed in a Phyton Environment(Taylor & Francis Ltd, 2025) Ozbey, Mehmet Furkan; Lotfi, Bahram; Turhan, CihanThermal comfort describes an occupant's state of mind in a thermal environment, influenced by six parameters: air velocity, relative humidity, air temperature, mean radiant temperature (MRT), clothing value, and metabolic rate. MRT is the most problematic parameter since the obtaining process is difficult and time-consuming. MRT can be acquired by several methods such as calculations, measurements, assumptions, and software programmes. However, the methods have complexities and uncertainties. Comprehensive models are needed to obtain MRT. To this aim, this study presents an alternative method using one of the artificial intelligence methods, Artificial Neural Network (ANN), to predict MRT for indoor environments to abstain from the difficulties and complexities. A case building is selected in a university office building in Ankara, T & uuml;rkiye. The proposed model is developed and coded in a Python programming environment to predict the MRT using ANN. The results indicate that the ANN model, using only four inputs, predicts MRT with an R-2 value of 0.94 compared to the globe thermometer measurement method. The model's advantages over methods include simplicity, time efficiency and learning from the limited datasets such as difficulty in calculating terms like MRT.Article Citation - WoS: 19Citation - Scopus: 21Gender Inequity in Thermal Sensation Based on Emotional Intensity for Participants in a Warm Mediterranean Climate Zone(Elsevier France-editions Scientifiques Medicales Elsevier, 2023) Ceter, Aydin Ege; Ozbey, Mehmet Furkan; Turhan, CihanThe deficiencies of the one of the most preferred conventional thermal comfort models, the Predicted Mean Vote/ Percentage of Predicted Dissatisfied (PMV/PPD) method have emerged over time since the model does not take psychological parameters such as personal traits, mood states and adaptation into account. Therefore, re-searchers have focused on Adaptive Thermal Comfort models that integrate human behaviours into the model for better prediction of thermal comfort. In addition to the influence of the behaviours of occupants, thermal comfort may be evaluated as a subjective term, thus, the effect of one of the psychological parameters, current mood state, on thermal sensation cannot be ignored for predictions. Although, the effect of current mood state on thermal sensation is a vital concept, the findings of the studies are not effective and comprehensive in the literature. For this reason, the aim of this study is to examine the relationship between current mood state and thermal sensation in gender difference aspect. Therefore, a series of experiments were conducted in a university study hall between August 16th, 2021 and August 1st, 2022. The current mood states of the participants were evaluated with the Profile of Mood States (POMS) questionnaire and the results were represented by a novel approach called Emotional Intensity Score (EIS). One tailed t-test was applied for investigating the relationship between the EIS and the thermal sensation. Findings of the research showed that a significant association exists between the EIS and thermal sensation for male participants while no relationship was found for female.Article Citation - WoS: 8Citation - Scopus: 8Modeling the Mood State on Thermal Sensation With a Data Mining Algorithm and Testing the Accuracy of Mood State Correction Factor(Pergamon-elsevier Science Ltd, 2025) Yerlikaya-Ozkurt, Fatma; Ozbey, Mehmet Furkan; Turhan, CihanPsychology is proven as an influencing factor on thermal sensation. On the other hand, mood state is one of the significant parameters in psychology field. To this aim, in the literature, mood state correction factor on thermal sensation (Turhan and Ozbey coefficients) is derived utilizing with data-driven black-box model. However, novel models which present analytical form of the mood state correction factor should be derived based on the several descriptive variables on thermal sensation. Moreover, the result of this factor should also be checked with analytical model results. Therefore, this study investigates the modelling of mood state correction factor with a data mining algorithm, called Multivariate Adaptive Regression Splines (MARS). Additionally, the mood state is also taken as a thermal sensation parameter besides environmental parameters in this algorithm. The same data, which are collected from a university study hall in a temperate climate zone, are used and the model results are compared with the thermal sensation results based on mood state correction factor which is driven via black-box model. The results show that coefficient of correlation "r" between the MARS and black-box model is found as 0.9426 and 0.9420 for training and testing. Hence, the mood state is also modelled via a data mining algorithm with a high accuracy, besides the black-box model.
