Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings-Reference-Cited by-同舟云学术

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Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings

Published:2018-08 Issue: Volume:173 Page:117-127
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Wu Zhibin,Li Nianping,Peng Jinqing,Cui Haijiao,Liu Penglong,Li Hongqiang,Li Xiwang

Funder

China National Key R&D Program

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference57 articles.

1. Conditioning strategies of indoor thermal environment in warm climates;Zhao;Energy Build.,2004

2. A review on buildings energy consumption information;Pérez-Lombard;Energy Build.,2008

3. Using footwarmers in offices for thermal comfort and energy savings;Zhang;Energy Build.,2015

4. Review of energy efficient features in vernacular architecture for improving indoor thermal comfort conditions;Chandel;Renewable Sustainable Energy Rev.,2016

5. The impacts of the thermal radiation field on thermal comfort, energy consumption and control—A critical overview;Halawa;Renewable Sustainable Energy Rev.,2014

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