This week on Monday Live, we kicked off our April theme—Ontology for Building Month—with an eye-opening discussion featuring Steve Bushby on how semantic data unlocks smarter buildings.
“The big gain isn’t in buildings already using these tools—it’s in the millions that aren’t doing anything yet. Ontologies lower the entry cost so more owners can deploy AI and automation without reinventing the wheel.”
— Steve Bushby
What Problem Does Ontology Solve?
Today, buildings drown in disconnected data. A sensor reading of “72” means nothing without context—is it °F or °GPM? Which system does it belong to? Ontology provides the missing semantic layer that:
- Adds meaning to raw data (units, locations, system relationships)
- Enables AI/analytics to “understand” building operations
- Slashes deployment costs for fault detection and automation
What is Ontology?
In the context of smarter buildings, ontology is a structured framework that defines the relationships between different data points in a building. As Steve explained:
“An ontology helps us interpret data by adding semantic meaning. If a sensor reads ’72,’ we need more than just the number—we need to know if it’s a temperature, pressure, or flow rate, where it’s measured, and how it fits into the building’s systems.”
Without ontology, data remains siloed and difficult to use for analytics, AI, or automation.
Why Does It Matter for Buildings?
- Interoperability – Ontologies allow different systems (HVAC, lighting, security) to share data in a standardized way.
- AI & Analytics – Structured semantic data enables AI tools to detect faults, optimize energy use, and automate maintenance.
- Cost Efficiency – Instead of manually mapping data for every new application, ontologies let software automatically find and interpret the right data points.
Steve also discussed ASHRAE’s 223P standard, an emerging ontology for buildings that aims to provide a ground-truth model of a building’s systems without replacing existing standards like Brick, Haystack, or RealEstateCore but rather complementing them.
What’s Next?
- How ontologies enable digital twins and AI-driven building management.
- The role of cross-industry collaboration (like the C4SB Semantic Tiger Team) in advancing semantic modeling.
- Why building owners should care—even if they don’t need to understand the technical details.
Watch the full discussion here and join us next Monday as we explore AI’s role in leveraging ontologies for smarter buildings!
The C4SB Semantic Tiger Team: Unifying the Ontology Landscape
A major hurdle? Competing standards (Brick, Haystack, ASHRAE 223P). The new C4SB Semantic Tiger Team (under Linux Foundation) is tackling this by:
- Mapping overlaps between standards
- Developing conversion tools (e.g., auto-translate ASHRAE 223P to Brick)
- Creating implementation guides for owners
Why it matters: This collaboration could finally deliver on the promise of plug-and-play smart buildings, where systems share data as easily as websites link to each other.
Next week: How ontologies supercharge AI in buildings.
Watch the full discussion:
#SmartBuildings #SemanticInterop #ASHRAE223P
This summary was written with the assistance of Gemini Ai and Deepseek
