Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.; Stockdale, Michaela R.

doi:10.1177/1744259117701893

Title: Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

Abstract

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

Authors:

Buechler, Elizabeth D. ^[1]; Pallin, Simon B. ^[2]; Boudreaux, Philip R. ^[2]; Stockdale, Michaela R. ^[3]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center; Tufts Univ., Medford, MA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center; Tennessee Technological Univ., Cookeville, TN (United States)

Publication Date:: Tue Apr 25 00:00:00 EDT 2017

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center (BTRIC)

Sponsoring Org.:: USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS)

OSTI Identifier:: 1356931

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Building Physics

Additional Journal Information:: Journal Volume: 41; Journal Issue: 3; Journal ID: ISSN 1744-2591

Publisher:: SAGE

Country of Publication:: United States

Language:: English

Subject:: 29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 42 ENGINEERING; Indoor climate; probabilistic modeling; building simulation; moisture buffering; relative humidity

Citation Formats


                    Buechler, Elizabeth D., Pallin, Simon B., Boudreaux, Philip R., and Stockdale, Michaela R. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus.  United States: N. p., 2017. 
Web.  doi:10.1177/1744259117701893.

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                    Buechler, Elizabeth D., Pallin, Simon B., Boudreaux, Philip R., & Stockdale, Michaela R. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus.  United States.  https://doi.org/10.1177/1744259117701893

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                    Buechler, Elizabeth D., Pallin, Simon B., Boudreaux, Philip R., and Stockdale, Michaela R. Tue .  
"Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus".  United States.  https://doi.org/10.1177/1744259117701893.  https://www.osti.gov/servlets/purl/1356931.

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@article{osti_1356931,

  title        = {Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus},

  author       = {Buechler, Elizabeth D. and Pallin, Simon B. and Boudreaux, Philip R. and Stockdale, Michaela R.},

  abstractNote = {The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The},

  doi          = {10.1177/1744259117701893},

  journal      = {Journal of Building Physics},

  number       = 3,

  volume       = 41,

  place        = {United States},

  year         = {Tue Apr 25 00:00:00 EDT 2017},

  month        = {Tue Apr 25 00:00:00 EDT 2017}

}

Copy to clipboard

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https://doi.org/10.1177/1744259117701893

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Works referenced in this record:

Quantification of uncertainty in predicting building energy consumption: A stochastic approach
journal, December 2012

Brohus, H.; Frier, C.; Heiselberg, P.
Energy and Buildings, Vol. 55
DOI: 10.1016/j.enbuild.2012.07.013

Contrasting the capabilities of building energy performance simulation programs
journal, April 2008

Crawley, Drury B.; Hand, Jon W.; Kummert, Michaël
Building and Environment, Vol. 43, Issue 4, p. 661-673
DOI: 10.1016/j.buildenv.2006.10.027

Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study
journal, December 2011

Tian, Wei; de Wilde, Pieter
Automation in Construction, Vol. 20, Issue 8
DOI: 10.1016/j.autcon.2011.04.011

Comfort reliability evaluation of building designs by stochastic hygrothermal simulation
journal, December 2014

Sulaiman, Halimi; Olsina, Fernando
Renewable and Sustainable Energy Reviews, Vol. 40
DOI: 10.1016/j.rser.2014.07.162

Works referencing / citing this record:

Influence of Climate Conditions on Deficiencies of Building Roofs
journal, April 2019

Carretero-Ayuso, Manuel; Moreno-Cansado, Alberto; García-Sanz-Calcedo, Justo
Applied Sciences, Vol. 9, Issue 7
DOI: 10.3390/app9071389

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Title: Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

Abstract

Citation Formats

Quantification of uncertainty in predicting building energy consumption: A stochastic approach journal, December 2012

Contrasting the capabilities of building energy performance simulation programs journal, April 2008

Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study journal, December 2011

Comfort reliability evaluation of building designs by stochastic hygrothermal simulation journal, December 2014

Influence of Climate Conditions on Deficiencies of Building Roofs journal, April 2019

Quantification of uncertainty in predicting building energy consumption: A stochastic approach
journal, December 2012

Contrasting the capabilities of building energy performance simulation programs
journal, April 2008

Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study
journal, December 2011

Comfort reliability evaluation of building designs by stochastic hygrothermal simulation
journal, December 2014

Influence of Climate Conditions on Deficiencies of Building Roofs
journal, April 2019