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

Research output: Contribution to journalArticle


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.


External organisations
  • Chalmers University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Building Technologies


  • Climate change, Hygrothermal simulation, Regional climate models, Representative weather data, Typical and extreme climate, Wooden frame wall
Original languageEnglish
Pages (from-to)30-45
Number of pages16
JournalEnergy and Buildings
Publication statusPublished - 2017 Nov 1
Publication categoryResearch