Mehmet Furkan Özbey

Educational buildings account for approximately 17% of the building sector’s energy consumption, making them critical for zero-emission building (ZEB) strategies. With students spending over 50% of their time indoors, life-cycle carbon assessments and targeted retrofitting are essential for realizing UN sustainability goals on campuses. This study develops a comprehensive life-cycle carbon assessment framework using a Calibrated Building Energy Simulation to simultaneously evaluate embodied and operational carbon emissions in campus facilities. This research evaluates two distinct university buildings for analysis: a historical 1970 educational building in Cosenza, Italy (Mediterranean climate) and a modern 2009 building in Ankara, Türkiye (semi-arid/continental climate). To minimize the total carbon footprint, seven distinct retrofitting scenarios are systematically simulated and compared: adding photovoltaic (PV) panels, integrating solar films on windows, applying internal and external insulations, implementing green wall applications, applying psychological-adaptive HVAC control and decreasing the heating set-point temperature. Results indicate that psychological-adaptive HVAC control is the most effective, achieving approximately 19.3% operational carbon savings across both cases with a low embodied carbon penalty. Conversely, the green wall application was the least effective, with a carbon payback period of 53.55 years. Ultimately, this study provides actionable engineering pathways for transforming campus buildings into net-zero emission educational facilities.

Conventional 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.

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.

Mood 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.

Nowadays, 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.

The state of mind is a crucial factor in thermal sensation and should be thoroughly understood in studies of thermal comfort while integrating human psychology into the literature. Fanger's predicted mean vote (PMV) and predicted percentage of dissatisfied percentage of dissatisfied (PPD) method is a cornerstone in thermal comfort research. On the other hand, the adaptive thermal comfort (ATC) model provides a broader perspective by including behavioral and psychological adjustments, along with the personal and environmental parameters outlined in Fanger's PMV/PPD method. However, literature investigates the ATC model predominantly focuses on behavioral adaptations, neglecting psychological adjustments emphasized by ASHRAE as integral to "the state of mind. Moreover, qualitative approaches dominate the literature, with limited quantitative investigations. Therefore, this paper aims to address the importance of human psychology by systematically reviewing previous field studies to elucidate the magnitude and significance of psychological adjustments to the thermal comfort. Additionally, it introduces the Turhan and Özbey coefficients, derived from a quantitative study, to provide a more comprehensive understanding of the impact of psychological factors on thermal comfort. This work is highlighted the importance of the human psychology to achieve better indoor environmental quality in aspects of thermal comfort.