The outdoor pedestrian thermal comfort and behavior in a traditional residential settlement – A case study of the cave dwellings in cold winter of China-Reference-Cited by-同舟云学术

The outdoor pedestrian thermal comfort and behavior in a traditional residential settlement – A case study of the cave dwellings in cold winter of China

Published:2021-05 Issue: Volume:220 Page:130-143
ISSN:0038-092X
Container-title:Solar Energy
language:en
Short-container-title:Solar Energy

Author:

Ma Xuan^ORCID,Zhang Lei,Zhao Jingyuan,Wang Mengying,Cheng Zhi

Publisher

Elsevier BV

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Reference43 articles.

1. Thermal comfort and energy consumption in a UK educational building;Barbhuiya;Build. Environ.,2013

2. A cross-country comparison of the building energy consumptions and their trends;Berardi;Resour. Conserv. Recycl.,2017

3. Urban greening to cool towns and cities: a systematic review of the empirical evidence;Bowler;Landsc. Urban Plan.,2010

4. Assessment of energy consumption in existing buildings;Brady;Energy Build.,2017

5. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI);Bröde;Int. J. Biometeorol.,2012

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