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Title: Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs

Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to the interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity.
Authors:
 [1] ; ORCiD logo [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Report Number(s):
NREL/JA-5500-68907
Journal ID: ISSN 0378-7788
Grant/Contract Number:
AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 165; Journal Issue: C; Journal ID: ISSN 0378-7788
Publisher:
Elsevier
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; moisture buffering; effective moisture penetration depth; humidity; building energy modeling; moisture
OSTI Identifier:
1422029

Woods, Jason, and Winkler, Jon. Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs. United States: N. p., Web. doi:10.1016/j.enbuild.2018.01.040.
Woods, Jason, & Winkler, Jon. Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs. United States. doi:10.1016/j.enbuild.2018.01.040.
Woods, Jason, and Winkler, Jon. 2018. "Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs". United States. doi:10.1016/j.enbuild.2018.01.040.
@article{osti_1422029,
title = {Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs},
author = {Woods, Jason and Winkler, Jon},
abstractNote = {Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to the interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity.},
doi = {10.1016/j.enbuild.2018.01.040},
journal = {Energy and Buildings},
number = C,
volume = 165,
place = {United States},
year = {2018},
month = {1}
}