Researchers tell us that, in the United States, 20 to 30 per cent of a building's annual energy bill is wasted.
With all the advances we've seen in the last couple of decades in building automation, despite all the sensors used in modern buildings, despite all the data they generate, 20 to 30 per cent of energy is simply gone.
Americans, on average, spend 90 per cent of their days indoors. In Canada the figure might be even higher, given the longer winters we have. So indoor comfort is important.
Building operators walk a thin line. Operating a building requires that a perfect balance be struck between heating, cooling and ventilation. It also requires repair and maintenance of all the equipment and systems that allow that delicate equilibrium to exist.
Now the U.S. Department of Energy has given two-year grants to researchers in three schools to support their work on building energy efficiency, and to encourage partnerships with industry.
The University of California — Davis, and the Georgia Institute of Technology are two of the schools involved. The third is Drexel University in Philadelphia, where Jin Wen is developing a powerful tool that can analyze the big data generated by various components in building control systems — thermostats, air- and water-flow sensors and energy meters, for example — and alert building operators when a problem arises, and suggest options for fixing it. The goal is not only to have happier building occupants, but also to cut down on inefficient energy use.
"Operators deal with a lot of raw data from sensors and control systems," Wen says. "They're constantly getting updates and fault-detection warnings, but there is so much to process that it's hard to know where to start and what the proper solution is for these problems."
Wen says it's "as if a doctor just hands you a list of test results and expects you to interpret them yourself..."
"We want to process the raw data and give operators a diagnosis and some possible solutions, instead of a mountain of readings and warnings."
By way of an example, she says that a building might be using an inordinate amount of cooling, but always seems to be too warm.
This is often handled through a somewhat arduous manual process of checking and adjusting various parts of the HVAC system, then waiting for a temperature change to occur — the signal that the problem has been solved. It's often trial and error, in other words, and she wants to eliminate that.
Wen and her team are developing algorithms that will automatically check and analyze the volumes of building data, so operators can be made aware of problems even before they are noticed by building occupants. They are using a building on the Drexel campus as a test-bed.
"Faults can arise from a number of small problems that lead to a big one," she says. "A lack of cooling could be due to a refrigerant leak, a leaky duct, hot water valve or malfunctioning fan, but most times it's actually a combination of several of these problems."
"Our automated fault-detection and diagnosis tool will point operators toward the most efficient solution."
The grant money will not only fund development of Wen's automated fault-detection and diagnosis tool, it will also help in developing a plan to make the tool a commercial product. To this end, Wen is working with KGS Buildings, a software company that works in the field of building diagnostics and performance management.
"The challenge in bringing a product like this to the marketplace is showing that it's easy to use and effective in saving energy," Wen says. "Building owners need to see that something like this will quickly pay for itself and more in the energy savings they'll see when they use it."
Korky Koroluk is a regular freelance contributor to the Journal of Commerce. Send comments or questions to email@example.com.