Office thermal environment that maximizes workers’ thermal comfort and productivity

Kawakubo, Shun; Sugiuchi, Masaki; Arata, Shiro

doi:10.1016/j.buildenv.2023.110092

Cited by 20 publications

(15 citation statements)

References 39 publications

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“…For example, for score 7 (no change), the productivity value is 100%, while for score 13, it is 160%. In this way, this scale is similar to the scales used in studies by Liu et al [1], Kawakubo, Sugiuchi, and Arata [20], Geng et al [21], Jiang et al [22] and Wang et al [23], which also take each individual's average productivity value as a reference.…”

Section: Model Performance Comparisonmentioning

confidence: 98%

“…To carry out the comparison, the original equations, which calculated productivity on a scale from 0 to approximately 100, were adjusted to vary from 0 to about 1, facilitating the comparison. In addition, specific adaptations were made to the equations of the models developed by Kawakubo, Sugiuchi, and Arata [20], which were subsequently renamed models 5 and 6, as shown in Table 2. The models were adjusted to provide a score on a scale ranging from 1 to 13, reflecting changes in productivity ranging from a reduction of 60% about the standard (score 1) to an increase of 60% about the standard (score 13) at 10% intervals.…”

Section: Model Performance Comparisonmentioning

confidence: 99%

“…The cognitive tests encompassed a variety of abilities, such as attention, memory, perception, and thinking, evaluating response time, accuracy in specific tasks, and neurobehavioral tests. The Relative Performance (RP) metric has been used to compare individual performance in a typical thermal condition against the average performance in all conditions evaluated in different studies [1,2,[20][21][22][23]26,27].…”

Section: Selected Modelsmentioning

confidence: 99%

“…In contrast, subjective methods are related to individual perception of productivity, that is, how a person feels at a given moment. This type of personal assessment can occur directly by questioning the individual about their productivity on a percentage scale or by comparing current productivity with the usual average, as observed in the study by Kawakubo, Sugiuchi, and Arata [20], the only one that adopted a subjective method. Another personal approach includes using tools that consider not only self-perception of productivity but also other factors such as fatigue, mood state, physical symptoms, and satisfaction with performance, as seen in the case study conducted by Bueno et al [13].…”

Section: Specific Aspects Of Models and Their Contextsmentioning

confidence: 99%

“…The studies present significant variations in their methodologies for calculating productivity. Among them, three studies use individual maximum productivity as a reference to calculate the percentage [2,26,27], while the other five studies employ everyone's average productivity [1,[20][21][22][23]. This discrepancy results in models with scales ranging from up to 100% to values that surpass this mark.…”

Section: Specific Aspects Of Models and Their Contextsmentioning

confidence: 99%

See 4 more Smart Citations

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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