Simulated building energy demand biases resulting from the use of representative weather stations-Reference-Cited by-同舟云学术

Simulated building energy demand biases resulting from the use of representative weather stations

Published:2018-01 Issue: Volume:209 Page:516-528
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Burleyson Casey D.,Voisin Nathalie,Taylor Z. Todd,Xie Yulong,Kraucunas Ian

Funder

Office of Science of the U.S. Department of Energy, Office of Biological and Environmental Research as part of the Integrated Assessment Research Program

Laboratory Directed Research and Development Program

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference36 articles.

1. Climate change and electricity demand in California;Franco;Clim Change,2008

2. Climate change impacts on residential and commercial loads in the Western U.S. grid;Lu;IEEE Transact Power Syst,2010

3. Impact of climate change on energy use in the built environment in different climate zones – a review;Li;Energy,2012

4. Understanding the social cost of carbon: a technical assessment;Rose,2014

5. Impact of climate change heating and cooling energy use in buildings in the United States;Wang;Energy Build,2014

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