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Author: Ozbey, Mehmet FurkanWoS Q: Q2

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Now showing 1 - 4 of 4
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    Article
    Citation - WoS: 7
    Citation - Scopus: 6
    The 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, Cihan
    Thermal 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.
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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Latent Psychological Pathways in Thermal Comfort Perception: The Mediating Role of Cognitive Uncertainty on Depression and Vigour
    (MDPI, 2025) Ozbey, Mehmet Furkan; Turhan, Cihan; Alkan, Nese; Akkurt, Gulden Gokcen
    Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment, and it is assessed through subjective evaluation, according to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers. While research has traditionally emphasised physical factors, growing evidence highlights the role of the state of mind in shaping thermal perception. In a prior Monte Carlo sensitivity analysis, six mood subscales-Anger, Confusion, Vigour, Tension, Depression, and Fatigue-were examined for how they affect the absolute difference between actual and predicted thermal sensation. Depression and vigour were found to be the most influential, while confusion appeared least impactful. However, to accurately assess the role of confusion, it is necessary to consider its potential interactions with other mood subscales. To this end, a mediation analysis was conducted using Hayes' PROCESS tool. The mediation analyses revealed that confusion partially mediated depression's effect in males and vigour's effect in females. These results suggest that, despite a weak direct impact, confusion critically influences thermal perception by altering the effects of key mood states. Accounting for the indirect effects of mood states may lead to more accurate predictions of human sensory experiences and improve the design of occupant-centred environments.
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    Article
    Citation - WoS: 31
    Citation - Scopus: 31
    A Novel Data-Driven Model for the Effect of Mood State on Thermal Sensation
    (Mdpi, 2023) Turhan, Cihan; Ozbey, Mehmet Furkan; Ceter, Aydin Ege; Akkurt, Gulden Gokcen
    Thermal comfort has an important role in human life, considering that people spend most of their lives in indoor environments. However, the necessity of ensuring the thermal comfort of these people presents an important problem, calculating the thermal comfort accurately. The assessment of thermal comfort has always been problematic, from past to present, and the studies conducted in this field have indicated that there is a gap between thermal comfort and thermal sensation. Although recent studies have shown an effort to take human psychology into account more extensively, these studies just focused on the physiological responses of the human body under psychological disturbances. On the other hand, the mood state of people is one of the most significant parameters of human psychology. Thus, this paper investigated the effect of occupants' mood states on thermal sensation; furthermore, it introduced a novel "Mood State Correction Factor" (MSCF) to the existing thermal comfort model. To this aim, experiments were conducted at a mixed-mode building in a university between 15 August 2021 and 15 August 2022. Actual Mean Vote (AMV) and Profile of Mood States (POMS) were used to examine the effect of mood state on thermal sensation. The outcomes of this study showed that in the mood states of very pessimistic and very optimistic, the occupants felt warmer than the calculated one and the MSCFs are calculated as -0.125 and -0.114 for the very pessimistic and very optimistic mood states, respectively. It is worth our time to note that the experiments in this study were conducted during the COVID-19 Global Pandemic and the results of this study could differ in different cultural backgrounds.
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    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, Cihan
    Thermal 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.