Physics-informed machine learning framework to model buildings from incomplete information

Abstract This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of information, enabling the automated generation of building energy models for specific target buildings. The proposed framework comprises five mod...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 2600; no. 7; pp. 72013 - 72018
Main Authors Kuo, T, Manikkan, S, Bilionis, I, Liu, X, Karava, P
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.11.2023
Subjects
Buildings
Machine learning
Physics
Statistical methods
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Summary:Abstract This paper introduces a physics-informed machine learning framework that leverages statistical methods to seamlessly integrate diverse sources of information, enabling the automated generation of building energy models for specific target buildings. The proposed framework comprises five modules: building survey, building asset database, building information schema, multi-class classification, and physics-based energy model. To illustrate the framework’s effectiveness, we present a case study involving a building with two possible baselines. The results demonstrate that our developed framework successfully generates comprehensive building energy models even when faced with incomplete, effectively capturing baseline scenarios.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2600/7/072013