The Importance of Data Tagging Standards for Smart Buildings
Smart buildings require communication. Open-source data tagging standards make that communication...
In smart buildings, devices, software, and building equipment must work together seamlessly. This requires unifying several naming and tagging standards currently used for building automation. While a seemingly simple issue, many data modeling approaches create roadblocks to real-world application of building data. But there is a better way. The Ontology Alignment Project lets you harness the full potential of building data, allows smart solutions to evolve and grow, and supports better performance and efficiency.
A uniform approach to data modeling is critical to ensure that innovative new technologies don’t face unnecessary roadblocks once installed. It helps you integrate new software applications or equipment into buildings and allows cutting-edge solutions to be added seamlessly, regardless of their manufacturer. But with numerous data modeling approaches available, how do you know which is best?
Before considering the answer to this question, let’s look at how data modeling works.
When applied to buildings, data modeling refers primarily to the tagging or naming standards applied to a source system. A systemic data modeling approach helps different products work together and allows for better tracking of the various physical and digital aspects of smart buildings. It also offers a better view of relationships between software applications, equipment, and systems.
Common data modeling approaches used for building automation include:
Of these, Brick Schema and Project Haystack are most widely used.
The Ontology Alignment Project (OAP) is setting a new standard for data modeling. Developed as an open-source taxonomy, it uses distinctive coding based on Haystack tagging to model elements within the built environment, including HVAC equipment, lighting infrastructure, IoT devices, and underlying points.
The OAP is excellent for:
The OAP automatically translates other ontologies, allowing building owners to take advantage of technologies that work under different protocols. Users then needn’t conform to specific variants when leveraging different data modeling approaches. The OAP also allows data models to be created to define relationships within building systems. This means you can designate data models for such functions as airflow, water usage, heating water, Internet connectivity, electricity usage, and utility metering.
Advantages of the OAP
Adds documentation instantly to the ontology
Allows efficient use of system-based and root cause analytics
Available via API
Offers better transparency
Provides real-world tagging documentation
Shows how to apply models
Simplifies classification ontology
Ultimately, the OAP seeks to sustain and expand data modeling approaches like Brick and Haystack to encourage the compatibility of building data when integrating new equipment or software into existing systems. Using OAP standards, these tags should be applied to equipment, electrical points, and other elements being integrated. And because the OAP is essentially an API, users can also build interconnected applications employing OAP’s data model.
Buildings IOT knows smart buildings. From master systems integration to custom software to digital services, our end-to-end solutions are redefining what smart buildings can do. We developed the OAP to support and extend existing standards to make data models compatible with real-world integration projects. With our leading-edge approach, building data becomes a powerful force for change and your building becomes more agile, responsive, and efficient than ever before.
Patrick Gilhooly is a Customer Onboarding Engineer at Buildings IOT and a member of the OAP working group and advisory council. Patrick is based out of Ontario, Canada, and a graduate of the University of Waterloo. Before joining Buildings IOT, Patrick held project engineering positions at Bombardier Inc, SAP, and HTS Engineering.