An interoperable ontology-based information model for better integration of building physics and IoT data analytics models
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2025Subject/s
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Abstract
Developing ontologies and information models is crucial for structuring knowledge and enhancing interoperability across various fields, particularly in the building and energy data sectors. This article examines the evolution of ontology development methodologies, emphasizing their significance in managing complex data and overcoming interoperability challenges. An interoperable ontology-based information model has been developed to better integrate IoT with building physics analysis model data. This multi-component model is created by defining an overall goal, reusing existing ontologies, and establishing the necessary classes and properties. A specific use case has been implemented in Montreal, Canada, where static building data (related to building physics) from the TOOLS4Cities hub hub [1], [2] and dynamic time-series energy consumption data have been harmonized. This application demonstrates how building energy models can automatically incorporate static data and utilize time-series measured consumption datasets to calibrate simulated energy demands. This approach highlights the potential of ontology-based data models to enhance energy efficiency and sustainability in urban environments, facilitating more informed decision-making and optimizing energy consumption management in buildings. © 2025 IEEE.
Developing ontologies and information models is crucial for structuring knowledge and enhancing interoperability across various fields, particularly in the building and energy data sectors. This article examines the evolution of ontology development methodologies, emphasizing their significance in managing complex data and overcoming interoperability challenges. An interoperable ontology-based information model has been developed to better integrate IoT with building physics analysis model data. This multi-component model is created by defining an overall goal, reusing existing ontologies, and establishing the necessary classes and properties. A specific use case has been implemented in Montreal, Canada, where static building data (related to building physics) from the TOOLS4Cities hub hub [1], [2] and dynamic time-series energy consumption data have been harmonized. This application demonstrates how building energy models can automatically incorporate static data and utilize time-series measured consumption datasets to calibrate simulated energy demands. This approach highlights the potential of ontology-based data models to enhance energy efficiency and sustainability in urban environments, facilitating more informed decision-making and optimizing energy consumption management in buildings. © 2025 IEEE.
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