Prediction and Ranking of Moisture Severity of Climate Years Using Supervised Projection to Latent Structures Applied to Tall Wood Buildings

Abstract

Hygrothermal simulation tools are commonly used to assess the moisture performance of building envelope components. Owing to the computational costs required to complete simulations over the long-term, one approach to reduce simulation time when undertaking hygrothermal design analysis is to select representative year(s) amongst sets of long-term climate data. To properly select these moisture reference year(s), a method is required to rank or predict the moisture severity of climate years for sets of long-term climate data. Several methods are used in the literature for this purpose, but none seems to be sufficiently accurate. In this study, the supervised projection to latent structures, also known as partial least squares regression, was trained and validated on data obtained from hygrothermal simulations of tall wood building wall assemblies for several cities across Canada. Models developed at the city level, for a given greenhouse gas emission scenario or time period, or encompassing historical and future time periods, showed comparable scores for ranking. In respect to prediction of the moisture severity of climate year sets, models developed at the city level were shown to be more accurate.

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