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2021 | OriginalPaper | Buchkapitel

11. Development of an Adaptation Table to Enhance the Accuracy of the Predicted Mean Vote Model

verfasst von : Yu Li, Yacine Rezgui, Annie Guerriero, Xingxing Zhang, Mengjie Han, Sylvain Kubicki, Yan Da

Erschienen in: Data-driven Analytics for Sustainable Buildings and Cities

Verlag: Springer Singapore

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Abstract

The Predicted Mean Vote (PMV) model is extensively used by current thermal comfort standards, such as ASHRAE 55 and ISO 7730, despite its discrepancy in predicting Thermal Sensation (TS). The implicit assumption is that PMV can be applied for predicting TS of a large population. Our statistical analysis of a subset of ASHRAE global database of thermal comfort field study shows that occupants’ expectations towards TS are affected by factors that are not accounted for in the classic PMV model, such as climate, building type, age group, season and gender. The influences of the climate and building type are more determinant. An adapted PMV (PMVa) model and an adaptation table were developed based on the selected samples to reduce this discrepancy. After adaptation, the medians of each category corresponding to the discrepancy are zero or near zero. The results also show that the adapted PMV outperforms the classic PMV in predicting TS, while increasing the overall accuracy from 36 to 39%.

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Titel: Development of an Adaptation Table to Enhance the Accuracy of the Predicted Mean Vote Model
verfasst von: Yu Li
Yacine Rezgui
Annie Guerriero
Xingxing Zhang
Mengjie Han
Sylvain Kubicki
Yan Da
Verlag: Springer Singapore
Buch: Data-driven Analytics for Sustainable Buildings and Cities
Print ISBN: 978-981-16-2777-4

Electronic ISBN: 978-981-16-2778-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-16-2778-1_11

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