Developing thermal perceptions of occupants in UK mixed-mode office building
Creators: name: Luo, Xiaoyan
Publisher: name: Loughborough University
Year: 2024
Subjects: classificationCode: 339999
subject: Other built environment and design not elsewhere classified

description: Purpose: Thermal comfort has long been a subject of significant research, as it not only impacts energy consumption but also affects individuals' physical and mental well-being. In contemporary society, people spend approximately 90% of their time indoors, making the state of indoor environments a crucial factor in determining life quality. According to the BEES report, buildings have become one of the most energy-intensive sectors globally, consuming 33% of the world's energy, with this proportion continuing to rise. Mixed-mode buildings are hybrid structures that integrate natural ventilation and air conditioning systems, capitalizing on both indoor and outdoor environments to reduce energy consumption. However, limited research has been conducted on mixed-mode buildings, and no criteria currently exist for assessing thermal comfort in such structures. Therefore, this research aims to predict and develop thermal perceptions of occupants in UK mixed-mode office buildings.Method: Investigating occupant thermal comfort can be based on two types of parameters: participation-oriented assessments, which provide information about individuals' thermal comfort based on environmental parameters, and bio-signal-oriented assessments, which offer insights into personal thermal comfort based on signal patterns generated by the human autonomic nervous system. This study employs both methods to examine the performance of occupant thermal perception in mixed-mode buildings. Three sets of experiments - Experiment 1, Experiment 2, and Experiment 3 - were designed and conducted to collect environmental and physiological parameters from participants. Statistical and machine learning methods were utilized to achieve the research aims and objectives.Findings: The experimental results indicate that the PMV model and the adaptive model are unsuitable for predicting and evaluating occupants' thermal comfort in mixed-mode buildings. Infrared thermography proves to be a viable method for interpreting occupants' thermal comfort in mixed-mode office buildings. The machine learning results demonstrate that employing Random Forest and Support Vector Machines classifiers enables the development of more accurate and efficient predictions of occupant thermal comfort in mixed-mode buildings. The accuracy of the proposed model is 90%, which is more than twice the 46.7% accuracy of the PMV model.Research Implications: The proposed models enhance the accuracy of predicting thermal comfort in mixed-mode buildings, thus serving as a valuable indicator in designing mixed-mode buildings, indoor thermal environments, and heating, ventilation, and air conditioning systems, ultimately leading to reduced energy consumption.Originality: The primary contribution of this thesis to the existing body of knowledge is the generation of a new dataset for thermal comfort research. Additionally, it provides further support and discussion for existing models and technology for predicting thermal comfort in mixed-mode buildings. Lastly, it offers a novel machine learning-based model for predicting and assessing occupants' thermal perceptions in UK mixed-mode office buildings.