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

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