The impact of updated climate data on dynamic energy simulations: evidence from a social housing case study in Northern Italy

The building sector is a major contributor to global energy consumption and greenhouse gas emissions, playing a key role in achieving energy transition and climate neutrality targets. The revised EPBD emphasises the need to improve energy performance through building renovation and nearly zero-energy buildings (NZEBs). Accurate energy assessment is essential, yet conventional quasi-steady-state methods based on monthly average climate data may not reflect current climatic conditions. Climate change is increasing extreme events, challenging climate stationarity assumptions in building simulations. This study investigates the reliability of CMCC DDS climate datasets in dynamic energy simulations by comparing them with observation-based datasets. A social housing building in Trieste (Italy) was modelled in DesignBuilder under both past and future climate scenarios. For past conditions, Typical Meteorological Year (TMY) files were generated for 2000–2010 and 2011–2024 using three datasets: in-situ observations from ARPA Friuli Venezia Giulia, CMCC DDS dataset, and a CMCC DDS bias-corrected dataset based on the Quantile Delta Mapping (QDM). The standard DesignBuilder weather file for Trieste was also considered for comparison. Future scenarios were defined using Future Meteorological Year (FMY) files for 2040–2060 and 2075–2095 based on CORDEX GCM–RCM projections. Results show that different climate datasets affect building energy performance, particularly for cooling demand, highlighting the need for updated, location-specific weather files for accurate energy assessments.