Modern smart buildings seek to enhance the energy efficiency, occupant comfort, and indoor air quality. Building owners and facility managers can reduce costs through energy savings across unoccupied spaces by automatically turning off lights and air conditioning services. To enable these opportunities through data-driven insights, it is necessary to collect and manage all data from devices, sensors, actuators in a standardized manner.
The industry challenge is making sense of this data: vendors, buildings, and equipment all name data differently. One widely implemented solution to this is semantic data models. The model adds facts or attributes to the data to give it context. The most implemented models are Project Haystack and Brick Schema and a function-based model, Google Digital Building.
What is a Semantic Data Model?
A semantic data model explains building data by defining relationships and quantities for data analytics. It is a conceptual model that tags data with essential information and relationships between other data sets. The models are all fully open-sourced.
Semantic data models enforce a common language between sensors and actuators by defining unique names for every concept in the building domain and representing all critical aspects.
These models ease system data flow and translate its native representation of any system. Open-source models also enable interoperability between subsystems and external data sources while facilitating the renovation of any building or system within that building.
Semantic Data Models express data through binary relationships between data elements. These are:
- Tagging data with descriptive words, for example, fan, unit, air, etc.
- Organizing words into groups, for example, descriptive, equipment, location, etc.
- Explaining how it all relates, for example, air handle unit feeds a VAV.
How can it benefit the Built Environment?
Flexibility + Future Proofing
Semantic Data Models help to translate the different languages that plants and equipment from various suppliers speak. They can benefit buildings by creating flexibility and not needing to be locked into a particular vendor.
Semantic Data Models’ human-readable data allows contractors and maintenance teams to locate infrastructure in a building quickly. Tagged data assists contractors in looking at classifications and relationships of the system vocabulary, seeing the location of all relevant plants, and understanding what the issue is and where it is coming from before it even becomes a problem.
Integrating AV, HVAC, lighting, and personnel
Semantic Data Models can add value to smart buildings by using one language to communicate across disciplines, compiling data into a single, easy-to-manage system. Systems that are interconnected and communicating can identify when spaces aren’t in use and automatically make changes to the area. It can provide precise and concise data, which helps building owners or facility managers to understand where the building uses energy and maximize optimization and minimize wasted energy.
What are the sustainability advantages of Semantic Data Models?
When a building’s energy and use cases are fully understood and modeled, it is easy to see where energy is used and understand the primary energy consumption. Possibilities for minimizing energy can be easily found and altered, which helps to achieve net-zero energy and maintain the status.
Systems reaching the end of life can be caught earlier and fixed before they fully break down, increasing their lives and reducing unnecessary replacement. This advantage, in turn, reduces waste and increases reuse within the entire building.
When we achieve system interactivity and interoperability, they can work together and understand when energy is not required, such as when lights are off in a meeting room or when to turn off the air-conditioning.
Where are the future opportunities?
Globally, net-zero building guidelines and legislation are rapidly developing. Our future buildings will be highly automated with low-touch improving COVID-19/health protection. This only results in more sensors, actuators, and data. This data will always have to be interpreted and understood by someone or something. A semantic data model would make this task significantly easier while also reducing energy and labor.
Semantic data models offer the opportunity to standardize results in the sustainability and robustness of any building. The implementation of this concept could be global, reducing energy waste or increasing the possibility of automation within buildings worldwide.
Tess Evardsen is an electrical engineer in our Queensland office who has recently completed her thesis on the role and benefits of Semantic Data Models in Smart Buildings.