WoS
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 From Facial Expressions to Thermal Sensation: POMS-Validated AI-Based Mood Estimation Driving Psychology-Adaptive HVAC Control(Elsevier Science SA, 2026-06) Saleh Saleh, Yousif Abed; Sümer, Mustafa Erdi; Saleh, Yousif Abed Saleh; Lotfi, Bahram; Turhan, Cihan; Özbey, Mehmet FurkanThe psychological state of building occupants plays a critical role in how thermal environments are perceived and experienced, yet conventional Heating, Ventilation, and Air Conditioning (HVAC) control strategies largely ignore this factor. Current systems typically focus on environmental and personal parameters, overlooking the influence of mood state on thermal comfort. This omission can lead to suboptimal comfort levels, decreased occupant satisfaction, and inefficient energy use. Integrating psychological feedback into the HVAC control has the potential to transform indoor climate management into a truly occupant-centric process. To this aim, this study presents a novel framework that employs artificial intelligence (AI) and image processing together to estimate occupants' mood states in real time. Facial expressions are analysed using a deep learning-based computer vision model, and the resulting mood predictions are validated with the Profile of Mood States (POMS) questionnaire to ensure accuracy and reliability. Validated mood data directly informs the HVAC setpoint adjustments, enabling psychology-adaptive control that responds dynamically to occupants' current mood states. Moreover, the system operates in real time, combining low-latency image analysis with adaptive control algorithms to continuously align thermal conditions with validated mood estimations. Additionally, implementing mood-driven HVAC control shows potential for enhancing perceived comfort while improving energy efficiency. By bridging the gap between psychological state assessment and environmental control, this research contributes to the advancement of intelligent building systems, paving the way for more responsive, energy-conscious, and human-centered indoor environments.Article Simulation-Based Optimization of HVAC Systems in Aging Educational Facilities: Addressing IAQ Challenges Through Retrofitting(MDPI, 2026-03-20) Saleh, Yousif Abed Saleh; Turhan, Cihan; Turhan, BurcuIndoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO2 levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO2 levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation.Article Citation - WoS: 4Citation - Scopus: 5Latent Psychological Pathways in Thermal Comfort Perception: The Mediating Role of Cognitive Uncertainty on Depression and Vigour(MDPI, 2025-07-18) Ozbey, Mehmet Furkan; Turhan, Cihan; Alkan, Nese; Akkurt, Gulden GokcenThermal 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.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 Citation - WoS: 4Citation - Scopus: 4Integrating Personalized Thermal Comfort Devices for Energy-Efficient and Occupant-Centric Buildings(Mdpi, 2025-04-26) Turhan, Cihan; Carpino, CristinaPersonalized thermal comfort (PTC) systems aim to satisfy the individual thermal preferences of occupants rather than relying on average comfort indices. With the growing emphasis on sustainability and reducing energy consumption in buildings, energy efficiency has become a critical factor in the design and selection of PTC systems. While the development of PTC tools has accelerated in the last decade, selecting the most appropriate system remains a challenge due to the dynamic, uncertain, and multi-dimensional nature of the decision-making process. This study introduces a novel application of the KEMIRA-M multi-criteria decision-making (MCDM) method to identify the optimal PTC system for university office buildings-an area with limited prior investigation. A case study is conducted in a naturally ventilated office space located in a temperate climate zone. Eight distinct PTC alternatives are evaluated, including data-driven HVAC systems, wearable devices, and localized conditioning units. Six key criteria are considered: estimated energy consumption, capital cost, indoor and outdoor space requirements, system complexity, mobility, and energy efficiency. The results indicate that wearable wristbands, which condition the occupant's carpus area, offer the most balanced performance across criteria, while radiant ceiling/floor systems perform the poorest. Energy efficiency plays a crucial role in this evaluation, as it directly impacts both the operational cost and the environmental footprint of the system. The study's findings provide a structured and adaptable framework for HVAC engineers and designers to integrate PTC systems into occupant-centric and energy-efficient building designs.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.