Identification of Italian user archetypes for building energy simulations based on a national survey

• Four user archetypes identified via clustering, based on demographic variables. • Building features correlate with the probability of specific user archetypes. • User archetypes and dwelling traits influence energy consumption patterns. • Standards underestimate electricity consumption by 35–60% for one archetype. • Heating system working hours can be overestimated by up to 114% Traditional building energy simulations often rely on generalized assumptions about user-related variables, leading to discrepancies between predicted and actual energy consumption. This study addresses this challenge by proposing a data-driven approach to identify and incorporate user archetypes into building energy simulations based on responses to a national survey. Focusing on the Italian residential sector, the study analyzes a comprehensive dataset to uncover distinct patterns in occupant demographics, building characteristics, and energy use patterns. Applying unsupervised clustering techniques, four distinct user archetypes emerged, characterized by demographic features like age, household size, and education level. The analysis shows a correlation between building characteristics and the probability of specific user archetypes inhabiting those buildings. Notably, building age and size emerged as strong predictors of archetype occupancy. Further analysis revealed that user archetype, in conjunction with specific dwelling characteristics, significantly influences energy consumption patterns. Daily heating system usage and annual electricity consumption varied considerably across user archetypes and building types. Integrating these findings into Standards or technical literature would enhance the accuracy of simulation results where there is a lack of information about residents, enabling targeted energy efficiency strategies and contributing to a more sustainable built environment.