Proceedings of the 2022 European Conference on Computing in Construction


Data driven framework to select best retrofitting strategies

Ania Khodabakhshian, Luca Rampini, Fulvio Re Cecconi
Politecnico di Milano, Italy

DOI: 10.35490/EC3.2022.190
Abstract: EU building sector consists mainly of outdated and inefficient properties with high energy consumption and seismic vulnerability.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.
Keywords: Building Retrofit, Artificial Intelligence, Clustering, Energy Saving, Decision Support Systems

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