Data driven framework to select best retrofitting strategies

Abstract: EU building sector consists mainly of outdated and inefficient properties with high energy consumption and seismic vulnerability. Hence, building retrofit is being emphasized as a feasible alternative for addressing existing challenges, taking a lot of time, effort, resources, and expertise in its traditional form. Moreover, conventional case-based retrofit scenarios fail to deliver quick and objective solutions for massive datasets. This research benefits from Artificial Intelligence, particularly clustering techniques, to enhance strategic decision-making for building retrofit. It connects the dispersed Italian databases (CENED and TABULA) and determines current and desired building technology and retrofit strategy to obtain an optimum energy label.