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The biggest challenge for smart buildings?

While smart building technology develops offers greater access to information, it also poses a growing challenge: how to organise the data.

Buildings contain a vast range of hardware and software, much of it proprietary. Ideally, smart building technology allows data from all of these systems to be captured and analysed to afford better management. Sometimes this may be across a portfolio of properties. There are even initiatives, such as the Data Clearing House project in i-Hub, which aggregate data from different property owners in one place, to enable whole-of-sector analysis and independent services that sit on top of this data.

Key to all this is having a standardised methodology for organising the information. This is where data ontologies come in.

Broadly speaking, ontology is a branch of philosophy that looks at how entities are grouped into basic categories, and which of these entities exist on the most fundamental level. Within buildings, it deals with defining the different pieces of equipment and elements, how they connect, and the data they produce.

“Put simply, it’s about relationships,” says Jon Clarke, M.AIRAH, Head of Smart Building Technology at Dexus.

“Just raw data is only part of the picture. If we understand what the data is related to, it provides some context to help us understand its meaning. If we add what the data is associated with, that provides another level and assists towards a conclusive meaning. However, now we have three pieces of data that are all just as important: a nano model of information relating to a raw value.

“This interrelationship is an ontology. And there is the need to standardise the language of the model so the analyst of the data – human or machine – can learn, understand, and make judgement.”

Clarke provides the example of fault-detection analytics in buildings, which use ontology to predict the probable cause.

Different ontologies such as Project Haystack and BrickSchema have emerged in recent years. And Clarke notes that this raises the question of which one to pick.

“They all have merit,” he says. “Some may offer a deeper level information than others. If the analyst has learnt the model and language it, they will be able to provide insights. But when buildings use different ontologies, it becomes a problem for scaling across a single platform.

“It’s a bit like going back to the LON versus BACnet days.”

These questions around data ontologies will be explored in a panel session as part of AIRAH’s Big Data and Analytics Forum on July 22. It will include Evren Korular, M.AIRAH, from Schneider Electric; Richard McElhinney from Project Haystack; and Carl Agar from A.G. Coombs. Clarke will chair the session.

“It’s a great opportunity to share some insights from subject matter experts who have a deep understanding of ontology standards,” he says.

“The industry is in the middle of huge transformation. Intelligent buildings have been talked about for decades but up until recent years, there has not been enough intelligence in the technologies.

“This panel session aims to dig deep and get technical. It may not provide all the answers, but it will hopefully leave the audience with a higher level of understanding.”

For more information about the Big Data and Analytics Forum, click here.

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