A critical analysis on the use of different weather datasets to assess moisture-related risks in building components for a Mediterranean location

In the field of numerical hygrothermal simulations, the literature suggests that multiyear weather datasets should be used to predict long-term moisture-related risks in building components. However, to improve the computational time-efficiency of hygrothermal analyses, single year simulations are preferable, although their reliability might be questionable. This study investigates different commonly used methodologies to define single year datasets for hygrothermal simulations, in order to identify advantages and limitations in terms of time-efficiency, data availability, and reliability of the results. To this aim, an initial literature analysis has reviewed many previous studies to identify the most used types of weather dataset, namely Multiyear (MY), Moisture Reference Years (MRY), and Typical Years (TY). Then, numerical simulations of different wall assemblies located in a Mediterranean city show the effect of the above datasets in the evaluation of mold growth risk and increased heat losses due to moisture accumulation, without disregarding the importance of computational time. The paper demonstrates that, in a warm European climate, an MRY does not necessarily ensure more conservative results than a TY, compared with MY-based simulations. Non-negligible discrepancies in the assessment of the mold growth risk emerge in case of interior wall insulation, while walls with exterior insulation are far less affected by the choice of the weather dataset. Moreover, a TY allows estimating the increased heat losses with an error less than 0.5%. One further result is that an MRY does not ensure low time effort compared to MY datasets, due to the pre-processing required to collect and elaborate the weather data for a long-term period