A Survey on Semantic Modeling for Building Energy Management

A comprehensive survey examines semantic modeling approaches for Building Energy Management (BEM), analyzing 60 semantic models and 20+ ontology-based use cases to address data interoperability challenges. The research identifies significant gaps in how current ontologies represent abstract operational concepts like performance indicators and control logic, highlighting the need for more integrated semantic frameworks to enable autonomous, context-aware building systems.

This academic survey addresses a critical infrastructure challenge in the built environment: the fragmentation of building data systems. As IoT sensors proliferate across commercial and residential properties, the lack of standardized semantic representations prevents different building management systems from communicating effectively. This interoperability gap directly undermines efforts to optimize energy consumption and reduce carbon emissions at scale, making the research timely given global net-zero commitments.

The study's introduction of Ontology Evidence Completeness (OEC) as a measurement framework provides a quantitative lens for assessing whether semantic models actually capture operational reality. By analyzing where existing ontologies fall short—particularly in representing key performance indicators, optimization tasks, and computational workflows—the authors expose why many BEM implementations require extensive custom extensions and workarounds. This fragmentation increases deployment costs and limits the transferability of solutions across different building portfolios.

For the building automation industry, these findings suggest that semantic standardization remains a frontier opportunity. Current ontologies excel at representing static physical structures but struggle with dynamic operational knowledge. Organizations developing BEM software face a choice: invest in proprietary solutions tailored to specific use cases, or advocate for standardized semantic frameworks that could unlock broader interoperability. Industry leaders in IoT, building controls, and energy management software stand to benefit from frameworks that reduce integration friction.

The survey points toward a future where semantic models become foundational infrastructure for building operations. Success requires coordinated efforts to extend existing ontologies with better coverage of control logic, performance assessment, and optimization workflows—areas where current solutions leave developers dependent on ad-hoc extensions.

• →Current semantic models consistently represent physical building structures but lack comprehensive coverage of abstract operational concepts like KPIs and optimization tasks.
• →The Ontology Evidence Completeness metric reveals that most BEM studies fail to explicitly map operational concepts to their ontological representations.
• →Fragmented semantic models force building automation developers to create custom extensions, reducing interoperability and increasing deployment costs.
• →Critical gaps exist in representing control logic, assessment frameworks, and computational workflows within existing building energy ontologies.
• →Standardized semantic frameworks could unlock significant efficiency gains across the building sector by enabling autonomous, context-aware energy management systems.