Data driven framework to select best retrofitting strategies

Abstract

EU building sector consists mainly of outdated and inefficient properties with high energy consumption. Hence, building retrofit is being emphasized as a feasible alternative for addressing existing challenges, taking lots of time, effort, resources, and expertise in its traditional form. 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 and solve the shortcomings of conventional methods. It connects the dispersed Italian databases (CENED and TABULA) and determines desired building technology and retrofit strategy to obtain an optimum energy label.