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RDF description of Data-driven research into the inaccuracy of traditional models of thermal comfort in offices - http://orbis.uaslp.mx/vivo/individual/scopus11911
rights: termsOfUse
date: 2026-08-26T23:50:32
publisher: vivo
type: Document

Article
type: Class

Information Content Entity
type: Class

Building and Environment
type: Generically Dependent Continuant
type: Document
type: Continuant
type: Journal
type: Entity
type: Information Content Entity
type: Thing
type: Periodical
type: Collection
publication venue for: Data-driven research into the inaccuracy of traditional models of thermal comfort in offices

Data-driven research into the inaccuracy of traditional models of thermal comfort in offices
type: Continuant
doi: 10.1016/j.buildenv.2023.111104
artnum: 111104
type: Document
crisid: 11912
abstract: The accurate prediction of thermal sensation among office workers, at design and post-occupancy stages, is crucial for controlling indoor temperature efficiently and correcting deficiencies in workspaces, ensuring healthy and productive working conditions. Traditional analytical comfort models are still the best tool for this purpose given their potential for interpretation. However, their reliability is undermined by their poor accuracy. Based on 304 data series of point-in-time measurements of quantitative and qualitative comfort-related parameters collected in an experimental campaign in three office buildings, one air-conditioned and two in free evolution, in San Luis Potosí (Mexico), this work aims to identify the major error-causing factors of steady and adaptive comfort models. The divergences between predicted and reported thermal sensation were set as a dependant variable of two multiple regressions, one for each model. Eighteen independent demographic, environmental, contextual and subjective variables were considered. No multicollinearity problems were identified. Our findings show that contextual factors and humidity perception were relevant in the adaptive model error. Clothing insulation highly impacted the accuracy of both models while age and body mass were not statistically significant for either of them. Metabolic rate was the factor with the greatest influence in the error of the steady model. Although not covered, other influential factors played a key role in models’ accuracy and further research is needed to integrate these in a new generation of more accurate and flexible analytical models.
date time value: article date 11912
has publication venue: Building and Environment
accesstype: Open Access
most specific type: Document
type: Thing
type: Information Content Entity
related by: authorship_74147
most specific type: Article
type: Article
estado: published
related by: authorship_74149
freetext keyword: Analytical comfort models; Comfort in offices; Comfort monitoring and assessment; Comfort prediction in workspaces; Multiple regression in comfort analysis; Thermal comfort Mexico [North America]; San Luis Potosi; Air conditioning; Analytical models; Errors; Office buildings; Regression analysis; Analytical comfort model; Comfort analysis; Comfort in office; Comfort monitoring and assessment; Comfort prediction in workspace; Data driven; Monitoring and assessment; Multiple regression in comfort analyse; Multiple regressions; Thermal sensations; accuracy assessment; analytical framework; data set; perception; regression analysis; temperature anomaly; Thermal comfort
e iscopus: 2-s2.0-85179758175
volume: 248
type: Generically Dependent Continuant
type: Entity
related by: authorship_74148