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Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs

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Building energy simulations can be used to predict a building?s interior conditions, along with
the energy use associated with keeping these conditions comfortable. These models simulate the
loads on the building (e.g., internal gains, envelope heat transfer), determine the operation of the
space conditioning equipment, and then calculate the building?s temperature and relative
humidity (RH) throughout the year. The indoor temperature and RH are affected not only by the
loads and the space conditioning equipment, but also by the capacitance of the building
materials, which buffer changes in temperature and humidity.
The thermal capacitance is typically included in these models, because it can strongly affect energy
use. The moisture capacitance has a smaller effect on energy use, and the modeling of moisture
capacitance is either simple (and inaccurate) or nonexistent in most building energy simulation
programs. But this moisture capacitance has become increasingly important for modeling
residential buildings because higher efficiency building codes have led to reduced sensible loads
without a corresponding decrease in the moisture (latent) load. Researchers and builders are
actively studying humidity control in homes, either through energy recovery ventilators, standalone
dehumidifiers, or packaged air-conditioning systems with enhanced dehumidification.
This research developed an empirical method to extract whole-house model inputs for use with a
more accurate moisture capacitance model (the effective moisture penetration depth, or EMPD,
model). The experimental approach was to subject the materials in the house to a square-wave RH
profile, measure all the moisture transfer terms (e.g., infiltration, air conditioner condensate), and
calculate the only unmeasured term: the moisture sorption into the materials. After validating the
method with laboratory measurements, we performed the tests in a slab-on-grade house with
concrete block walls at the Florida Solar Energy Center in Cocoa, Florida. We used a least-squares fit
of an analytical solution to the measured moisture sorption curves to determine the three independent
model parameters representing the moisture buffering potential of this house and its furnishings.
After deriving these parameters, we measured the RH of the same house during tests with
realistic latent and sensible loads, and then compared that to the RH predicted by the EMPD
model using these inputs. This showed good agreement (Figure ES-1(a)), especially compared to
the commonly used effective capacitance approach (Figure ES-1(b)). Even if we adjust the single
parameter used in the effective capacitance model to try and match the data, we can only
improve the R2 from 0.40 (for a commonly used effective capacitance of 10) to 0.52 (effective
capacitance ~5). Both are considerably worse than the EMPD model (R2 = 0.81).
These results show that the EMPD model, once the inputs are known, is an accurate moisture
buffering model. A sensitivity analysis showed that the model is fairly insensitive to changes in
the model inputs up to 20%.
This experimental method can be used in houses of other constructions (e.g., wood frame), and
with other levels of furnishings, to develop a more comprehensive dataset. This can provide
guidance on moisture buffering model inputs for use in building simulations, such that the indoor
RH can be predicted with greater accuracy. This can help answer questions about the effects of
insulation levels, cooling equipment selection, and ventilation practices on the indoor RH, and
help anticipate potential problems.

Citation Formats

National Renewable Energy Laboratory. (2016). Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs [data set]. Retrieved from https://data.openei.org/submissions/5554.
Export Citation to RIS
Woods, Jason, Norton, Paul. Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs. United States: N.p., 21 Sep, 2016. Web. https://data.openei.org/submissions/5554.
Woods, Jason, Norton, Paul. Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs. United States. https://data.openei.org/submissions/5554
Woods, Jason, Norton, Paul. 2016. "Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs". United States. https://data.openei.org/submissions/5554.
@div{oedi_5554, title = {Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs}, author = {Woods, Jason, Norton, Paul.}, abstractNote = {Building energy simulations can be used to predict a building?s interior conditions, along with
the energy use associated with keeping these conditions comfortable. These models simulate the
loads on the building (e.g., internal gains, envelope heat transfer), determine the operation of the
space conditioning equipment, and then calculate the building?s temperature and relative
humidity (RH) throughout the year. The indoor temperature and RH are affected not only by the
loads and the space conditioning equipment, but also by the capacitance of the building
materials, which buffer changes in temperature and humidity.
The thermal capacitance is typically included in these models, because it can strongly affect energy
use. The moisture capacitance has a smaller effect on energy use, and the modeling of moisture
capacitance is either simple (and inaccurate) or nonexistent in most building energy simulation
programs. But this moisture capacitance has become increasingly important for modeling
residential buildings because higher efficiency building codes have led to reduced sensible loads
without a corresponding decrease in the moisture (latent) load. Researchers and builders are
actively studying humidity control in homes, either through energy recovery ventilators, standalone
dehumidifiers, or packaged air-conditioning systems with enhanced dehumidification.
This research developed an empirical method to extract whole-house model inputs for use with a
more accurate moisture capacitance model (the effective moisture penetration depth, or EMPD,
model). The experimental approach was to subject the materials in the house to a square-wave RH
profile, measure all the moisture transfer terms (e.g., infiltration, air conditioner condensate), and
calculate the only unmeasured term: the moisture sorption into the materials. After validating the
method with laboratory measurements, we performed the tests in a slab-on-grade house with
concrete block walls at the Florida Solar Energy Center in Cocoa, Florida. We used a least-squares fit
of an analytical solution to the measured moisture sorption curves to determine the three independent
model parameters representing the moisture buffering potential of this house and its furnishings.
After deriving these parameters, we measured the RH of the same house during tests with
realistic latent and sensible loads, and then compared that to the RH predicted by the EMPD
model using these inputs. This showed good agreement (Figure ES-1(a)), especially compared to
the commonly used effective capacitance approach (Figure ES-1(b)). Even if we adjust the single
parameter used in the effective capacitance model to try and match the data, we can only
improve the R2 from 0.40 (for a commonly used effective capacitance of 10) to 0.52 (effective
capacitance ~5). Both are considerably worse than the EMPD model (R2 = 0.81).
These results show that the EMPD model, once the inputs are known, is an accurate moisture
buffering model. A sensitivity analysis showed that the model is fairly insensitive to changes in
the model inputs up to 20%.
This experimental method can be used in houses of other constructions (e.g., wood frame), and
with other levels of furnishings, to develop a more comprehensive dataset. This can provide
guidance on moisture buffering model inputs for use in building simulations, such that the indoor
RH can be predicted with greater accuracy. This can help answer questions about the effects of
insulation levels, cooling equipment selection, and ventilation practices on the indoor RH, and
help anticipate potential problems.}, doi = {}, url = {https://data.openei.org/submissions/5554}, journal = {}, number = , volume = , place = {United States}, year = {2016}, month = {09}}

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Data from Sep 21, 2016

Last updated Sep 21, 2016

Submitted Sep 21, 2016

Organization

National Renewable Energy Laboratory

Contact

Jason Woods

Authors

Jason Woods

National Renewable Energy Laboratory

Paul Norton

paulnorton.net

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