Smart buildings need standardized semantic metadata so that the data from the building and all its subsystems is organized in a way that enables people to easier understand and engage with it. Brick Schema is one such method of standardizing metadata in buildings.
Brick Schema and RealEstateCore joining forces
One standard is RealEstateCore, which recently announced that Brick Schema and RealEstateCore will join forces and publish a common standard. This initiative cherrypicks the best of the two worlds, where Brick Schema delivers powerfully for BMS and IoT and RealEstateCore for business processes and spatial descriptions (blueprints).
The two standards will work together. Rather than modeling a building using only Brick Schema or RealEstateCore, buildings will be modeled using the best parts of Brick Schema and RealEstateCore. For example, in the semantic model for a building, a piece of HVAC equipment and sensors would be modeled using Brick. In contrast, the equipment’s location, the building’s lease, and critical real estate concerns would be modeled using RealEstateCore.
The Brick Schema and RealEstateCore harmonization enable real estate owners and stakeholders to unlock the value of big data in their buildings, which in turn will support new applications for reducing energy consumption and interacting with energy sources and grids. These harmonized standards allow the real estate industry to drive the digital transformation forward in line with tenants, building owners, and society. They are achieving the objectives of lowering carbon emissions and contributing to a future sustainable smart city.
What is Brick Schema?
These days everyone agrees that smart and automated buildings need standardized semantic metadata about the building to make better use of the data available from a building and the subsystems of the building. There are different names for this — some call it a “standard tagging library” or a “sound and grounded ontology” or an “extensible schema” or something along those lines. But basically, they all mean the same thing: by using common metadata, the meaning of the building’s data can be understood by people and other software applications that weren’t involved in the original creation and collection of the building’s data.
Brick Schema is an open-source development effort to standardize metadata about buildings and the systems within the buildings. Using this metadata and information generated from IoT sensors around the building, Brick Schema is essential for healthy building concepts, which include digital twins, personalized environments, touchless operations, automation, improved efficiency, and maintenance.
Brick Schema ontology explained
Ontology seeks the classification and explanation of entities. Brick is an ontology-based metadata schema, hence Brick Schema, that captures the entities and relationships necessary for effective representations of buildings and their subsystems. Brick describes buildings in a standardized format to enable programmatic exploration of a building’s different operational, structural, and functional facets.
A brick model is a digital representation of a building described according to the standard of the Brick Schema. Entities in a brick model are classified according to the classes defined by Brick and are connected using the relationships defined by Brick.
On the Brick Schema GitHub page, you can find example code. The examples directory contains executable code samples with extensive documentation introducing Brick concepts and idioms. Another great tip is that the Brick Schema official website has some helpful reference models that are representative examples of how Brick Schema can be used to model actual buildings.
Brick Schema vs Project Haystack
Another common standard in the world of building management and metadata standardization is Project Haystack. Haystack uses a tagging system with no rules for how tags can be used, resulting in highly customized and inconsistent modeling practices. Brick Schema includes a tagging system similar to Haystack, but more formal semantic rules promote consistency and interpretability.