Personalized kitchen air supply for reducing individual thermal discomfort and cooking pollution intake

Wu, Yuhang; Zhou, Jiaying; Zhou, Xiang; Gao, Jun; Cao, Changsheng; Zeng, Yingqi; Li, Jiajun

doi:10.1016/j.buildenv.2023.110921

Cited by 5 publications

(1 citation statement)

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“…In addition, Principal Component Analysis (PCA) was used to propose a new control structure in multizone buildings in the United States [9]. Factor analysis was also used to evaluate Personalized Kitchen Air Supply (PKAS) to reduce exposure to cooking pollution and thermal discomfort in residential kitchens in China [10]. In addition to these approaches, survival analysis was applied to study participants' reactions to dynamic thermal stimuli in offices in Norway [11], among other techniques.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Productivity Methods to Evaluate Thermal Comfort in Classrooms through Hierarchical Clustering

Mendes da Luz,

Lourenço Niza,

Broday

2024

Buildings

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This research examined the modeling of productivity with thermal comfort using various models in a case study conducted in classrooms at a university in Southern Brazil. A total of thirteen models were selected after performing a literature review to identify the main models. Through the application of hierarchical clustering to separate the models into groups with similarities, the results identified four groupings: the first focused on temperature, the second associated age groups with Thermal Sensation Vote (TSV), the third compared different age groups, and the fourth highlighted “Model 11”, derived from structural equations in air-conditioned classrooms in China, revealing poor performance due to its incompatibility with temperature variations in productivity. Meanwhile, “Model 5”, developed using ordinary regression in air-conditioned offices in Japan, showed the lowest Root Mean Square Error (RMSE), emerging as the most accurate in predicting productivity associated with thermal comfort. The use of objective methods to assess productivity and the application of regression analysis in modeling, as identified in the literature review, is noteworthy. The evaluation of the models’ performance also explored the impact of the independent variables on their scope. Through cluster analysis, reasons behind discrepancies in model performance were identified, providing insights into best practices for representing the relationship between thermal comfort and productivity. These results offer valuable perspectives for developing more effective models in this field and reveal a wide methodological diversity in the approach to the subject.

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Section: Introductionmentioning

confidence: 99%