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Title: Assessing overall building energy performance of a large population of residential single-family homes using limited field data

Abstract

Building energy simulation plays a significant role in building design and retrofit. Most applications deal with individual buildings which allow for the specification of detailed model inputs. However, building energy simulation can be a powerful tool for assessing energy performance even when comprehensive building characteristics are unavailable. Here, in this study, limited field data were collected on randomly selected new homes in eight US states with a goal of evaluating energy code compliance and energy savings potential. The limited data do not allow the derivation of comprehensive model inputs for each individual home sampled, let alone for the entire unknown residential construction stock. Therefore, we used prototype buildings to construct a large number of models and utilized bootstrap sampling to draw inputs from the limited data. In conclusion, this research demonstrates that overall energy performance of a large population of new homes can be assessed by the novel framework, given limited data.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [2];  [1];  [1];  [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Brigham Young University, Rexburg, ID (United States). Mathematics Dept.
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1457762
Report Number(s):
PNNL-SA-131346
Journal ID: ISSN 1940-1493
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Building Performance Simulation
Additional Journal Information:
Journal Name: Journal of Building Performance Simulation; Journal ID: ISSN 1940-1493
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 29 ENERGY PLANNING, POLICY, AND ECONOMY; Building energy simulation; building energy code compliance; energy savings potential; Monte Carlo; bootstrap sampling

Citation Formats

Xie, Yulong, Mendon, Vrushali, Halverson, Mark, Bartlett, Rosemarie, Hathaway, John, Chen, Yan, Rosenberg, Michael, Taylor, Todd, and Liu, Bing. Assessing overall building energy performance of a large population of residential single-family homes using limited field data. United States: N. p., 2018. Web. doi:10.1080/19401493.2018.1477833.
Xie, Yulong, Mendon, Vrushali, Halverson, Mark, Bartlett, Rosemarie, Hathaway, John, Chen, Yan, Rosenberg, Michael, Taylor, Todd, & Liu, Bing. Assessing overall building energy performance of a large population of residential single-family homes using limited field data. United States. doi:10.1080/19401493.2018.1477833.
Xie, Yulong, Mendon, Vrushali, Halverson, Mark, Bartlett, Rosemarie, Hathaway, John, Chen, Yan, Rosenberg, Michael, Taylor, Todd, and Liu, Bing. Wed . "Assessing overall building energy performance of a large population of residential single-family homes using limited field data". United States. doi:10.1080/19401493.2018.1477833. https://www.osti.gov/servlets/purl/1457762.
@article{osti_1457762,
title = {Assessing overall building energy performance of a large population of residential single-family homes using limited field data},
author = {Xie, Yulong and Mendon, Vrushali and Halverson, Mark and Bartlett, Rosemarie and Hathaway, John and Chen, Yan and Rosenberg, Michael and Taylor, Todd and Liu, Bing},
abstractNote = {Building energy simulation plays a significant role in building design and retrofit. Most applications deal with individual buildings which allow for the specification of detailed model inputs. However, building energy simulation can be a powerful tool for assessing energy performance even when comprehensive building characteristics are unavailable. Here, in this study, limited field data were collected on randomly selected new homes in eight US states with a goal of evaluating energy code compliance and energy savings potential. The limited data do not allow the derivation of comprehensive model inputs for each individual home sampled, let alone for the entire unknown residential construction stock. Therefore, we used prototype buildings to construct a large number of models and utilized bootstrap sampling to draw inputs from the limited data. In conclusion, this research demonstrates that overall energy performance of a large population of new homes can be assessed by the novel framework, given limited data.},
doi = {10.1080/19401493.2018.1477833},
journal = {Journal of Building Performance Simulation},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {5}
}

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