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A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

Author

Listed:
  • Parastoo Delgoshaei

    (Mechanical Systems and Controls Group, Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA)

  • Mohammad Heidarinejad

    (Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA)

  • Mark A. Austin

    (Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA)

Abstract

Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.

Suggested Citation

  • Parastoo Delgoshaei & Mohammad Heidarinejad & Mark A. Austin, 2022. "A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5810-:d:813141
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    References listed on IDEAS

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    1. Nicola Guarino & Daniel Oberle & Steffen Staab, 2009. "What Is an Ontology?," International Handbooks on Information Systems, in: Steffen Staab & Rudi Studer (ed.), Handbook on Ontologies, pages 1-17, Springer.
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