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

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.

An End-to-End Implementation of a Service-Oriented Architecture for Data-Driven Smart Buildings

“…These ontologies not only standardize the semantics but also enable linking above heterogeneous domains. The use of ontologies in this context has been demonstrated in a variety of use cases, including BIM and IoT integration [22], context-aware control of mechanical systems [71], [72], automating KPI calculation for building performance [73], automatic setup of FDD for BMS [74], etc.…”

Objective-Driven Modular and Hybrid Approach Combining Machine Learning and Ontology

“…In artificial intelligence, inductive reasoning is possible through using machine learning models, while deductive reasoning can be provided by using ontologies. By combining these two models, it becomes possible to build a hybrid reasoning model closer to the human cognitive process [1][2][3].…”