Building energy performance assessment using linked data and cross-domain semantic reasoning

Shushan Hu (Corresponding author), Jiale Wang, Cathal Hoare, Yehong Li, Pieter Pauwels, James O'Donnell (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Cross-domain information is essential for building energy performance assessment. The heterogeneous nature of this information is a major source for inefficient assessments. The semantic web provides a flexible pathway for addressing recognised interoperability issues. However, further implicit knowledge in cross-domain information could provide meaningful solutions for such assessments. This paper aims to develop a conceptual framework that links cross-domain information, infers implicit knowledge, and empowers building managers with insightful assessments. The framework integrates Web Ontology Language (OWL) ontologies, Resource Description Framework (RDF) instances, and a set of predefined rules to infer implicit knowledge, which can satisfy data requirements of performance metrics and enable meaningful performance assessments. Then building managers can identify inefficient building operations and improve energy efficiency while maintaining desired building functions. This approach reduces burdensome intervention from the managers when compared with traditional solutions. A demonstration highlights the engineering value by evaluating energy performance of a university building.
Original languageEnglish
Article number103580
Number of pages13
JournalAutomation in Construction
Volume124
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Building energy performance
  • Cross-domain semantic reasoning
  • Data interoperability
  • Semantic web

Fingerprint Dive into the research topics of 'Building energy performance assessment using linked data and cross-domain semantic reasoning'. Together they form a unique fingerprint.

Cite this