Issue |
E3S Web Conf.
Volume 136, 2019
2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019)
|
|
---|---|---|
Article Number | 03029 | |
Number of page(s) | 4 | |
Section | Energy Conservation Renovation of Green Buildings and Existing Buildings | |
DOI | https://doi.org/10.1051/e3sconf/201913603029 | |
Published online | 10 December 2019 |
Development Adaptive Predicted Mean Vote (aPMV) Model for Naturally Ventilated Buildings in Zunyi, China
1 School of Engineering, Zunyi Normal University, Zunyi, Guizhou Province, China
2 School of Management, Zunyi Normal University, Zunyi, Guizhou Province, China
* Corresponding author: jimlau@vip.126.com
Fanger’s predicted mean vote (PMV) model which is as a result of climate-chamber-based experiments is a good tool to evaluate indoor thermal comfort for air-conditioned buildings in global wide. However, PMV model has defect of predicting people’s real thermal sensation under non-air-conditioned conditions. It is reflected by the significant discrepancies between PMV values and Actual Mean Vote (AMV) values. The aim of this study is to develop an Adaptive Predicted Mean Vote (aPMV) Model on the basis of ‘black box’ theory considering occupants’ adaptations to improve prediction performance. A field study was carried out in naturally ventilated educational buildings in Zunyi, China. The developed aPMV model produces more reliable results and shows better prediction performance, comparing with values predicted by PMV model. It indicates that aPMV model is of great benefit to connect traditional PMV model and adaptive comfort model and consequently to provide guidance on building design, operation and maintenance, which contribute to achieve building energy conservation and emission reduction target.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.