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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14411/18
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Article Next Generation Mood Adaptive Behavioral Modeling for Decarbonizing Office Buildings and Optimizing Thermal Comfort(MDPI, 2026-04-08) Alkan, Nese; Turhan, Cihan; Doruk, Ozgur Resat; Ozbey, Mehmet Furkan; Thapa, Samar; Chen Austin, Miguel; Austin, Miguel Chen; Pekcan, PoyrazConventional Heating, Ventilation, and Air Conditioning (HVAC) control systems primarily rely on environmental and physiological parameters, largely ignoring the critical influence of psychological states on thermal comfort. Overlooking this factor often leads to suboptimal occupant satisfaction, energy inefficiency and thus carbon dioxide (CO2) emissions. To this aim, this study introduces a novel mood-adaptive HVAC control system integrating psychological feedback to decrease CO2 emissions in office buildings by reducing energy consumption and optimizing comfort. A total of 7000 thermal facial measurement records and high-resolution camera images were collected across seven mood state conditions using video stimuli and the Profile of Mood States (POMS) questionnaire to evaluate mood variations. A dual artificial intelligence system was developed: a Convolutional Neural Network (CNN) for analyzing facial expressions and an Artificial Neural Network (ANN) for processing facial temperatures via thermal imaging. These models collectively predict occupant mood in real-time, and a custom-designed wearable necklace interface transmits this data to dynamically adjust HVAC setpoints. To evaluate system performance, energy consumption was directly measured in real-life operations using an energy analyzer, without relying on simulations. Results indicate that this prototype personalized mood-driven system has the potential to enhance perceived thermal comfort while achieving up to a 20% reduction in carbon emissions compared to conventional systems. This human-centered approach significantly advances intelligent building management and climate change mitigation.Article Citation - WoS: 3Citation - Scopus: 3Modelling the Positive and Negative Interaction Between Mood and Thermal Sensation in the Built Environment Using a Combined Markov Chain Monte Carlo Algorithm and Morris Method(Sage Publications Ltd, 2025-05-24) Ozbey, Mehmet Furkan; Turhan, CihanMood states, categorized into subscales such as Tension (TEN), Anger (ANG), Fatigue (FAT), Vigour (VIG), Confusion (CON), and Depression (DEP), affect occupants' perceptions of thermal environments. This study investigates the influence of these subscales on thermal sensation, exploring both positive and negative effects. Experiments were conducted in a temperate climate zone over an extended period, including both heating and cooling seasons, with 1159 volunteers. The Morris Method was used to assess the impact of psychological parameters (TEN, ANG, FAT, VIG, CON, DEP) on thermal sensation. Markov Chain Monte Carlo (MCMC) simulations, performed via Python code developed by the authors, evaluated the positive and negative impacts of these subscales across 30,000 simulations. Results showed that VIG was the most influential parameter, while CON and FAT had negative effects (feeling cooler) on thermal sensation. These findings emphasize the complex relationship between psychological factors and thermal perception, underlining the importance of mood states in designing environments that enhance thermal comfort. The study offers valuable insights into the interplay of emotional well-being and physiological responses, contributing to environmental psychology and climate-responsive design.Article Citation - WoS: 2Citation - Scopus: 2Recycling Decommissioned Wind Turbine Blades for Post-Disaster Housing Applications(Mdpi, 2025-03-12) Turhan, Cihan; Durak, Murat; Saleh, Yousif Abed Saleh; Kalayci, AlperThe growing adoption of wind energy has resulted in an increasing number of decommissioned wind turbine blades, which pose significant disposal challenges due to their size, material composition, and environmental impact. Recycling these blades has thus become essential. To this aim, this study explores the potential of using recycled wind turbine blades in post-disaster housing applications and examines the feasibility of re-purposing these durable composite materials to create robust, cost-effective, and sustainable building solutions for emergency housing. A case study of a post-earthquake relief camp in Hatay, T & uuml;rkiye, affected by the 2023 earthquake, is used for analysis. First, the energy consumption of thirty traditional modular container-based post-disaster housing units is simulated with a dynamic building simulation tool. Then, the study introduces novel wind turbine blade-based housing (WTB-bH) designs developed using the same simulation tool. The energy consumption of these (WTB-bH) units is compared to that of traditional containers. The results indicate that using recycled wind turbine blades for housing not only contributes to waste reduction but also achieves 27.3% energy savings compared to conventional methods. The novelty of this study is in demonstrating the potential of recycled wind turbine blades to offer durable and resilient housing solutions in post-disaster situations and to advocate for integrating this recycling method into disaster recovery frameworks, highlighting its ability to enhance sustainability and resource efficiency in construction. Overall, the output of this study may help to present a compelling case for the innovative reuse of decommissioned wind turbine blades, providing an eco-friendly alternative to traditional waste disposal methods while addressing critical needs in post-disaster scenarios.Article Citation - WoS: 6Citation - Scopus: 10Impact of Green Wall and Roof Applications on Energy Consumption and Thermal Comfort for Climate Resilient Buildings(Mdpi, 2025-04-01) Turhan, Cihan; Carpino, Cristina; Austin, Miguel Chen; Ozbey, Mehmet Furkan; Akkurt, Gulden Gokcen; Chen Austin, MiguelNowadays, reducing energy consumption and obtaining thermal comfort are significant for making educational buildings more climate resilient, more sustainable, and more comfortable. To achieve these goals, a sustainable passive method is that of applying green walls and roofs that provide extra thermal insulation, evaporative cooling, a shadowing effect, and the blockage of wind on buildings. Therefore, the objective of this study is to evaluate the impact of green wall and roof applications on energy consumption and thermal comfort in an educational building. For this purpose, a university building in the Csb climate zone is selected and monitored during one year, as a case study. Then, the case building is modelled in a well-calibrated dynamic building energy simulation tool and twenty-one different plant species, which are mostly used for green walls and roofs, are applied to the envelope of the building in order to determine a reduction in energy consumption and an increase in thermal comfort. The Hedera canariensis gomera (an ivy species) plant is used for green walls due to its aesthetic appeal, versatility, and functional benefits while twenty-one different plants including Ophiopogon japonicus (Mando-Grass), Phyllanthus bourgeoisii (Waterfall Plant), and Phoenix roebelenii (Phoenix Palm) are simulated for the green roof applications. The results show that deploying Hedera canariensis gomera to the walls and Phyllanthus bourgeoisii to the roof could simultaneously reduce the energy consumption by 9.31% and increase thermal comfort by 23.55% in the case building. The authors acknowledge that this study is solely based on simulations due to the high cost of all scenarios, and there are inherent differences between simulated and real-world conditions. Therefore, the future work will be analysing scenarios in real life. Considering the limited studies on the effect of different plant species on energy performance and comfort, this study also contributes to sustainable building design strategies.Article Citation - WoS: 8Citation - Scopus: 12Enhancing Urban Sustainability With Novel Vertical-Axis Wind Turbines: a Study on Residential Buildings in Çeşme(Mdpi, 2025-04-24) Saleh, Yousif Abed Saleh; Durak, Murat; Turhan, CihanThis study investigates the integration of three types of vertical-axis wind turbines (VAWTs)-helical, IceWind, and a combined design-on residential buildings in & Ccedil;e & scedil;me, T & uuml;rkiye, a region with an average wind speed of 7 m/s. The research explores the potential of small-scale wind turbines in urban areas, providing sustainable solutions for renewable energy generation and reducing reliance on conventional energy sources. The turbines were designed and analyzed using SolidWorks and ANSYS Fluent, achieving power outputs of 350 W for the helical turbine, 430 W for the IceWind turbine, and 590 W for the combined turbine. A total of 42 turbines were mounted on a five-storey residential building model, and DesignBuilder software was utilized to simulate and evaluate the energy consumption. The baseline energy consumption of 172 kWh/m2 annually was reduced by 18.45%, 22.93%, and 30.88% for the helical, IceWind, and combined turbines, respectively. Furthermore, the economic analysis showed payback periods of 12.89 years for the helical turbine, 10.60 years for the IceWind turbine, and 10.49 years for the combined turbine. These findings emphasize the viability of integrating VAWTs into urban buildings as an effective strategy for reducing energy consumption, lowering costs, and enhancing energy efficiency.Article Estimation of the Mean Radiant Temperature in Office Buildings Using an Artificial Neural Network Developed in a Phyton Environment(Taylor & Francis Ltd, 2025-01-30) 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: 15Citation - Scopus: 11The Relation Between Thermal Comfort and Human-Body Exergy Consumption in a Temperate Climate Zone(Elsevier Science Sa, 2019-12) Turhan, Cihan; Akkurt, Gulden Gokcen; Gokcen Akkurt, GuldenHuman body exergy balance calculation method gives minimum human body exergy consumption rates at thermal neutrality (TSV = 0) providing more information on human thermal responses than other methods. The literature is lacking the verification of this method in various climatic zones. The aim of this study is to investigate the relationship between thermal comfort and human body exergy consumption in a temperate climate zone. A small office building in Izmir Institute of Technology campus, Izmir/Turkey, was chosen as a case building and equipped with measurement devices. The occupant was subjected to a survey via a mobile application to obtain his Thermal Sensation Votes. Objective data were collected via sensors and used for predicting occupant thermal comfort and for exergy balance calculations. Under given conditions, the results show that Thermal Sensation Votes are generally zero at a T-i range of 21-23 degrees C and, are mostly lower than Predicted Mean Votes in summer while the opposite is observed in winter. Predicted Mean Votes at minimum Human Body Exergy Consumption rates were on slightly warm side while Thermal Sensation Votes are zero. It means that for given case, the HBexC rate calculation gave a better prediction of the environmental parameters for the best thermal comfort. (C) 2019 Elsevier B.V. All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 2Hardware-In Assessment of a Fault Tolerant Fuzzy Control Scheme for an Offshore Wind Farm Simulator(Elsevier, 2022) Simani, Silvio; Farsoni, Saverio; Turhan, CihanTo enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real-time solutions have to implement the proposed fault tolerant control strategy. Finally, a high-fidelity simulator relying on a hardware-in-the-loop tool is exploited to verify and validate the reliability and robustness characteristics of the developed methodology also for on-line and more realistic implementations. Copyright (C) 2022 The Authors.Article Citation - WoS: 21Citation - Scopus: 22Sensitivity Analysis of the Effect of Current Mood States on the Thermal Sensation in Educational Buildings(Wiley-hindawi, 2022-08) 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: 21Citation - Scopus: 24A Novel Comfort Temperature Determination Model Based on Psychology of the Participants for Educational Buildings in a Temperate Climate Zone(Elsevier, 2023-10) 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.