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|Title:||Predictive modelling of thermal comfort using physiological sensing||Authors:||Chaudhuri, Tanaya||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||31-Dec-2018||Source:||Chaudhuri, T. (2018). Predictive modelling of thermal comfort using physiological sensing. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||A thermally comfortable building indoor environment is imperative for human health and work productivity. Although the major energy consumption in a tropical building is attributed to its Air-Conditioning and Mechanical Ventilation (ACMV) cooling systems, yet complaints of thermal dissatisfaction is prevalent among occupants. Prediction of the occupant’s thermal comfort can be instrumental in bridging this gap by utilizing the predicted thermal state (Cool-Discomfort/Comfort/Warm-Discomfort) as a control criterion for the ACMV system. However, the existing thermal comfort prediction methods rely primarily on environmental information. We envision that a possibly simpler approach would be a direct investigation of the human physiological parameters, since the bodily responses are direct consequences of surrounding thermal ambiance. In this study, we investigate the potential of six different physiological parameters to predict thermal state namely, skin temperature, skin conductance, pulse rate, blood oxygen saturation, and systolic/diastolic blood pressure. Human subject experiments were conducted, during which these six physiological responses and four subjective responses (thermal comfort, thermal preference, humidity sensation, airflow sensation) were recorded in conjunction with a thermal sensation survey while environmental conditions were varied. An overall relational study of all the physiological, subjective, and environmental parameters is performed to gain a clearer understanding of human thermal comfort. An index termed as Thermal State Index (TSI) is introduced to quantitatively represent thermal comfort. A study of skin temperature of the selected hand skin location revealed that it strongly correlates with the overall thermal sensation. It is verified that the rate at which skin temperature changes carries significant information about the thermal state. However, considerable individual differences are revealed. We develop a novel normalization process to address both inter and intra individual differences by incorporating body surface area and clothing insulation, respectively. Based on this normalization process, we develop a TSI predictive model named Predicted Thermal State (PTS) model. While non-normalized skin temperature alone could predict only about (60-65)% of thermal states, the PTS model based on the normalized skin features could predict accurately 87% of thermal states. The combination of skin temperature and its gradient carried more predictive potential than the skin temperature alone. Therefore, the temporal profiles of skin temperature could portray adequate information without normalization. Based on these temporal profiles, we develop another TSI predictive model named the Temporal Profile based Predicted Thermal State (TPPTS) model. A 2-D sensor data to visual space domain-transformation process is developed. Based on a deep convolutional neural network, the TPPTS model achieves 93.33% accuracy. A study of pulse rate response also revealed considerable individual differences. A novel normalization process using logic gate operations is developed to address the differences with gender and BMI as individual factors. Additionally, a new potential feature of pulse rate is extracted using a spectral analysis method. Based on these features and novel processes, we develop another TSI predictive model named the enhanced Predicted Thermal State (ePTS) model; it achieves about 97.15% accuracy. A collective study of all six physiological and four subjective responses revealed significant gender differences in both responses. Hence, a separate comfort study for each gender-group is conducted, where potential predictive features are identified. Derivative features such as change rate and mean squared gradient are also studied. Based on the identified features and a Random Forest algorithm, we develop another TSI predictive model named the Gender based Predicted Thermal State (GPTS) model; it achieves 92.86% and 94.29% accuracies for males and females, respectively. A canonical correlation analysis reveals strong relations between physiological and subjective responses. The four proposed TSI predictive models significantly outperform the conventional PMV method. A comparative analysis of the proposed and existing methods is performed. Subsequently, we propose a novel framework for indoor climate control that can provide optimum comfort with minimum energy consumption of the ACMV system. It incorporates a TSI predictive model and a novel optimization algorithm named the OAT algorithm, that provides the optimal operating condition based on the current TSI. The framework exhibits 36.5% energy saving potential. Additionally, we study four less-explored non-physiological parameters namely, mean radiant temperature (MRT), age, gender and outdoor weather in context of their significance for thermal comfort. As for MRT, we investigate the effect of the common practice of assuming it to be equal to air temperature on comfort assessment, and subsequently develop an alternative model (R2 = 0.89) to estimate MRT. Age, gender, and outdoor effective temperature, which are not included in the existing comfort models, are found to be important factors for thermal comfort. We believe that the findings of this thesis will assist the built environment community with a better understanding of human thermal comfort and associated phenomena.||URI:||https://hdl.handle.net/10356/90161
|Appears in Collections:||IGS Theses|
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