skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

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:
 [1];  [2];  [2];  [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center; Tufts Univ., Medford, MA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center
  3. 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:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Lab. (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) (SC-27)
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.
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. doi:10.1177/1744259117701893.
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. doi:10.1177/1744259117701893. https://www.osti.gov/servlets/purl/1356931.
@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 = {2017},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

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