BUILDINGS editor Chris Olson talked recently with Jack McGowan, principal of The McGowan Group, about the benefits of building analytics. McGowan’s sixth book, Energy and Analytics: Big Data and Building Technology Integration for the 21st Century, will be published this year by Fairmount Press. He will be a speaker at the Haystack Connect Conference and at Realcomm.
What is building analytics and what are the benefits to building owners?
In a nutshell, it’s the interconnection of identifiable devices in order to extract data from them for analysis. In the case of facilities, it is the interconnection of identifiable devices in heating, air conditioning, lighting and other energy-intensive building systems. Sophisticated analytics software is used to evaluate the data from the systems and provide new insights that can improve operations and reduce energy costs.
A recent example of the benefit is a 1.3 million-square-foot cancer center that is monitoring some 36,000 BAS data points in labs, offices and operating rooms. The solution has resulted in energy savings of $1.7 million annually. At that rate, the owner’s payback on the analytics investment is less than seven months.
How can a building owner evaluate analytics solutions from different suppliers?
Facility analytics requires know-how about buildings as well as data, software and connectivity. That’s why I am not a fan of the broader term “big data” applied to these analytics solutions because the data alone without significant knowledge of building systems will be inadequate. A supplier needs to understand what the data means for the building and make appropriate recommendations. Of course, the intelligence and cost of the solutions also vary widely.
What specifically should an owner look for in analytics solutions and providers?
Data sufficiency and resiliency are important. Sufficiency means a solution must provide and/or access enough data to do the job. Analysts rely first on data from existing sources, including BAS, CMMS and meters, plus external sources like utility databases. New sensors and data points can be added to a building, but there is a cost for such installations.
Building a sufficient and effective data architecture requires a wide range of expertise with technology, data communications, IT and computer systems. Analysts must also apply business process experience and conduct ongoing communication and coordination with client teams.
Once enough data is available for analysis, then it is critical for providers to ensure that data resiliency is good, in other words, that the flow of data is not interrupted. The analytics software is relying on information supplied through other systems rather than its own independent network of sensors.
Building changes – such as tenant improvements, new equipment or communication devices, and IT updates – may disrupt the flow of data, a situation that building operators may not recognize. The analytics system should be capable of detecting interruptions and raising an alert for operators.
What should the analytics solution do with the data?
The solution should deliver a robust set of rules or algorithms that constantly analyze data to find opportunities for efficiency, comfort, safety and predictive maintenance. Ideally, the algorithms are tailored to specific buildings as well as customers, who can add more rules as necessary.
Robust solutions also incorporate effective software planning tools that both list recommendations and prioritize them. Not all solutions are able to prioritize and reprioritize recommendations as a building changes.
What is involved in tweaking these solutions after the initial installation?
Fine-tuning is critical to analytics. The solution provider should be steadily learning more about your building and applying that knowledge by monitoring new data points and creating new rules. It’s a process that evolves over time. Owners should evaluate the track record of prospective suppliers in terms of their ability and commitment to such fine-tuning.