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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]
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  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 Laboratory (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 Volume: 12; Journal Issue: 4; 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.
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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. https://doi.org/10.1080/19401493.2018.1477833
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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. https://doi.org/10.1080/19401493.2018.1477833. https://www.osti.gov/servlets/purl/1457762.
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@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 = 4,
volume = 12,
place = {United States},
year = {Wed May 30 00:00:00 EDT 2018},
month = {Wed May 30 00:00:00 EDT 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1080/19401493.2018.1477833
Copyright Statement
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Citation Metrics:
Cited by: 8 works
Citation information provided by
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Figures / Tables:

Table 1 Table 1: Key code items identified in the sensitivity analysis.
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Works referenced in this record:

Urban building energy modeling – A review of a nascent field
journal, February 2016

Urban building energy modeling – A review of a nascent field
journal, February 2016

Energy savings potential from improved building controls for the US commercial building sector
journal, September 2017

An international survey of building energy codes and their implementation
journal, August 2017

EnergyPlus: creating a new-generation building energy simulation program
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Improving building energy efficiency in India: State-level analysis of building energy efficiency policies
journal, November 2017

Longer-Run Evidence on Whether Building Energy Codes Reduce Residential Energy Consumption
journal, March 2017

  • Kotchen, Matthew J.
  • Journal of the Association of Environmental and Resource Economists, Vol. 4, Issue 1
  • DOI: 10.1086/689703

Are Building Codes Effective at Saving Energy? Evidence from Residential Billing Data in Florida
journal, March 2013

  • Jacobsen, Grant D.; Kotchen, Matthew J.
  • Review of Economics and Statistics, Vol. 95, Issue 1
  • DOI: 10.1162/REST_a_00243

Why the Monte Carlo method is so important today: Why the MCM is so important today
journal, June 2014

  • Kroese, Dirk P.; Brereton, Tim; Taimre, Thomas
  • Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 6, Issue 6
  • DOI: 10.1002/wics.1314

A long-term, integrated impact assessment of alternative building energy code scenarios in China
journal, April 2014

Monitoring-based HVAC commissioning of an existing office building for energy efficiency
journal, February 2013

What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum
journal, October 2015

Methodology for Evaluating Cost-Effectiveness of Residential Energy Code Changes
report, August 2015

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    Figures / Tables found in this record:

    Table 1 (p. 4)table
    Table 2 (p. 5)table
    Table 3 (p. 5)table
    Figure 1 (p. 6)figure
    Table 4 (p. 6)table
    Table 5 (p. 7)table
    Table 6 (p. 8)table
    Table 7 (p. 9)table
    Figure 2 (p. 10)figure
    Table 8 (p. 11)table
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