Survey Identifies Gaps in Semantic Models for Building Energy Management Systems
A comprehensive survey of 60 semantic models and 20+ ontology-based building energy management (BEM) use cases reveals that current systems effectively represent physical building structures and sensors but struggle with abstract operational concepts like performance metrics and control logic. Semantic modeling using ontologies is critical for enabling interoperable, autonomous energy management across heterogeneous IoT devices and data sources. The findings suggest that addressing these gaps could improve the development of generalized, context-aware building energy systems needed to reduce sector-wide energy consumption and emissions.
Researchers conducted a systematic survey examining how semantic models and ontologies are used in building energy management during the operational phase of buildings. The study analyzed 60 semantic models and reviewed more than 20 real-world ontology-based BEM use cases, introducing new metrics called Ontology Instantiation Rates (OIR) and Ontology Evidence Completeness (OEC) to assess coverage and documentation quality. The analysis found that existing semantic models consistently represent tangible elements—building structures, technical systems, sensors, and operational data—but inadequately cover abstract and dynamic concepts such as key performance indicators, service definitions, control logic, optimization tasks, and computational workflows. As a result, applied BEM studies frequently require custom extensions, integration of multiple ontologies, or specialization to fill interoperability gaps. The survey synthesizes common patterns in how practitioners address these limitations and identifies directions for developing more interoperable and generalisable semantic models that could enable truly autonomous, context-aware building energy management systems.
What's missing
The study does not discuss the computational overhead or scalability challenges of implementing ontology-based systems in real buildings, nor does it address adoption barriers or cost-benefit analyses that might explain why some BEM applications do not use semantic models despite their potential benefits.
What different sources said
- arXiv cs.AICenter
A Survey on Semantic Modeling for Building Energy Management
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