Abstract

Buildings are complex systems that integrate mechanical, electrical, and plumbing (MEP) subsystems—such as HVAC, lighting, and fire safety—to optimise resource use while ensuring occupant comfort. These systems are managed by Building Management Systems (BMS), yet many
buildings remain energy inefficient, contributing significantly to carbon emissions. A key barrier to scalable energy solutions is the heterogeneous, proprietary nature of BMS, which severely hinders data interoperability and the implementation of advanced control strategies.
While researchers use traditional RDF/OWL-based ontologies to model building systems and address this heterogeneity, these ontologies typically lack the mechanisms necessary to enforce constraints, model dynamic behaviour, and integrate executable control logic.
This thesis addresses these gaps by introducing MetamEnTh, an object-oriented metamodel for modelling the operational phase of MEP subsystems in buildings. We adopt a grounded-theory approach,
informed by surveys and interviews with researchers and industry practitioners, to develop MetamEnTh. It employs structured classes with predefined relationships, methods, and validation rules to ensure accurate, dynamic models, complemented by interfaces that enable the integration of user-defined control logic.
We also evaluate the APIs of common BMS platforms, assessing their capabilities for exposing system data and integrating control logic. We validate MetamEnTh through real-world use cases and a comparative evaluation against existing ontologies, demonstrating improved accuracy,
constraint enforcement, and enhanced error prevention. MetamEnTh provides a practical, extensible metamodel that supports data-driven, energy-efficient building management by unifying data semantics and operational control.
Future work will broaden MetamEnTh by incorporating classes for occupancy modelling and utility methods for energy-efficiency tasks. We will extend our BMS API evaluation to cover predictive
control and digital twin integration. To support practitioners, we will develop user-friendly editors while aligning MetamEnTh with community initiatives such as ASHRAE, Brick, and Project Haystack. MetamEnTh opens new avenues for validated and executable representations of building systems, providing a foundation for future research on model-driven digital twins, self-adaptive control, and cross-domain interoperability in cyber-physical infrastructures. By unifying data and
control logic, it enables researchers to experiment with novel AI- and simulation-driven methods for optimising building energy use.