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Application of typical and extreme weather data sets in the hygrothermal simulation of building components for future climate – A case study for a wooden frame wall

Nik, Vahid M. LU (2017) In Energy and Buildings 154. p.30-45
Abstract

A method for synthesizing representative weather data for future climate out of regional climate models (RCMs) was introduced previously for the energy simulation of buildings (Nik, 2016). The method suggests creating one typical and two extreme data sets based on the distribution of the outdoor dry bulb temperature (Tdrybulb). This article extends the application of such weather data in the hygrothermal simulation of buildings by simulating a pre-fabricated wooden frame wall. To investigate the importance of considering moisture and rain conditions in creating the representative weather files, two more groups of weather data are synthesized based on the distribution of the equivalent temperature (Tequivalent) and... (More)

A method for synthesizing representative weather data for future climate out of regional climate models (RCMs) was introduced previously for the energy simulation of buildings (Nik, 2016). The method suggests creating one typical and two extreme data sets based on the distribution of the outdoor dry bulb temperature (Tdrybulb). This article extends the application of such weather data in the hygrothermal simulation of buildings by simulating a pre-fabricated wooden frame wall. To investigate the importance of considering moisture and rain conditions in creating the representative weather files, two more groups of weather data are synthesized based on the distribution of the equivalent temperature (Tequivalent) and rain. Moisture content, relative humidity, temperature and mould growth rate are calculated in the façade and insulation layers of the wall for several weather data sets. Results show that the synthesized weather data based on Tdry bulb predict the hygrothermal conditions inside the wall very similar to the original RCM weather data and there is no considerable advantage in using the other two weather data groups. This study confirms the applicability of the synthesized weather data based on Tdry bulb and emphasizes the importance of considering extreme scenarios in the calculations. This enables having distributions more similar to the original RCM data while the simulation load is decreased enormously.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Climate change, Hygrothermal simulation, Regional climate models, Representative weather data, Typical and extreme climate, Wooden frame wall
in
Energy and Buildings
volume
154
pages
16 pages
publisher
Elsevier
external identifiers
  • scopus:85028466916
ISSN
0378-7788
DOI
10.1016/j.enbuild.2017.08.042
language
English
LU publication?
yes
id
c2792f67-a06e-4a42-baf7-4005421a0c8b
date added to LUP
2017-09-05 14:49:15
date last changed
2017-09-05 14:49:15
@article{c2792f67-a06e-4a42-baf7-4005421a0c8b,
  abstract     = {<p>A method for synthesizing representative weather data for future climate out of regional climate models (RCMs) was introduced previously for the energy simulation of buildings (Nik, 2016). The method suggests creating one typical and two extreme data sets based on the distribution of the outdoor dry bulb temperature (T<sub>drybulb</sub>). This article extends the application of such weather data in the hygrothermal simulation of buildings by simulating a pre-fabricated wooden frame wall. To investigate the importance of considering moisture and rain conditions in creating the representative weather files, two more groups of weather data are synthesized based on the distribution of the equivalent temperature (T<sub>equivalent</sub>) and rain. Moisture content, relative humidity, temperature and mould growth rate are calculated in the façade and insulation layers of the wall for several weather data sets. Results show that the synthesized weather data based on T<sub>dry bulb</sub> predict the hygrothermal conditions inside the wall very similar to the original RCM weather data and there is no considerable advantage in using the other two weather data groups. This study confirms the applicability of the synthesized weather data based on T<sub>dry bulb</sub> and emphasizes the importance of considering extreme scenarios in the calculations. This enables having distributions more similar to the original RCM data while the simulation load is decreased enormously.</p>},
  author       = {Nik, Vahid M.},
  issn         = {0378-7788},
  keyword      = {Climate change,Hygrothermal simulation,Regional climate models,Representative weather data,Typical and extreme climate,Wooden frame wall},
  language     = {eng},
  month        = {11},
  pages        = {30--45},
  publisher    = {Elsevier},
  series       = {Energy and Buildings},
  title        = {Application of typical and extreme weather data sets in the hygrothermal simulation of building components for future climate – A case study for a wooden frame wall},
  url          = {http://dx.doi.org/10.1016/j.enbuild.2017.08.042},
  volume       = {154},
  year         = {2017},
}