A Graph Based Framework to Support Data-driven Urban Building Energy Simulations

Usman Ali1, Sobia Bano1, Cathal Hoare1, Muhammad Haris Shamsi2, Divyanshu Sood1, James O'Donnell1
1 School of Mechanical and Materials Engineering and UCD Energy Institute, UCD, Dublin, Ireland
2 VITO, Hasselt, Belgium
DOI: 10.35490/EC3.2024.312
Abstract: Urban planners and energy policymakers increasingly focus on sustainable urban development and the challenges of analyzing complex urban energy systems. Current models often lack the integration of diverse urban datasets. This study proposes an ontology for urban building energy modeling and its implementation in a graph-based approach to integrating complex urban energy data. The proposed methodology is tested in residential buildings in Dublin City to examine and compare the modeling results. The study concludes that the proposed model offers a more comprehensive and adaptable approach to urban energy analysis compared to traditional methods.
Keywords: building energy performance, Machine Learning, Ontology, urban building energy modeling

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