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| Round Up the Right Data
Not the Usual Suspects
all comfort complaints on HVAC can lead to wrong
actions, a broken automation framework and ever more complaining and
energy waste. A better approach is to open your inquiry to more of
what's happening across the whole building, using analytics to reveal
anomalies and correlations among a wider array of time-series data
“Round up the usual suspects,” is one of
the classic quotes from the
1942 film Casablanca. When this phrase is muttered as the plan of
action, you’re far from the truth. The 1995 crime thriller ‘The Usual
Suspects’ picked up on that thought and built it into one of the
twisted plots of all time. Like the collection of criminals who meet in
the movie’s police lineup, HVAC equipment has been an easy target of
blame. Hot and cold calls have been the most common type of occupant
comfort complaint in large commercial buildings for about as long as
people have been watching 1942’s Casablanca. “All complaints have
always rolled up to temperature as the common denominator and to the
HVAC system as culprit,” observes Matt Schwartz, Commissioning Engineer
Associates. This is finally changing due to data
“Thermal comfort depends on the individual as much as it does the HVAC. When building staff is coerced into adjusting temperature to the satisfaction of the squeakiest wheel, it often sets off a downward cycle for the calibration of the whole building. When you override HVAC tooling, you are often stealing capacity from one system to deliver to another or asking the system to operate outside of it’s basis of design. The root of the problem may be vents blocked by partitions, windows with no thermal barriers, the need to change seating–or some people should just consider a sweater,” says Schwartz.
Altura is one of the new breed of energy management consultancies that have expertise and passion for building operational analytics. Enterprises with big real estate portfolios and whose business value derives from interior comfort — hotels, hospitals, schools, offices — are looking to such firms to move their buildings staff beyond ‘usual suspects’ thinking. As John Davies, Greenbiz.com research analyst noted in a recent webcast, the hotel industry has been far ahead of other industries in using data analytics for purposes other than energy performance monitoring. That webcast brought together the sustainability and building operations chiefs from MGM Hotels along with peers from Intel and Siemens, the latter two working for companies now in the building energy management solution business.
In the conversation, Chris Magee, Executive Director of Sustainable Facilities, MGM Resorts International, reinforced the Altura engineer’s point about the dangers of HVAC overrides when he said “As we retrocommission, we realize that when HVAC tooling has been moved off by 2°, 3°, 4° — all that adds up. When tied to a big area of consumption, like a chiller, that means a big dent in accuracy. So calibration becomes a big issue.”
Operator mentality becomes a bigger issue! Operators are told that occupant comfort is number one priority so they are quick to move a setpoint in the absence of good analytics to indicate whether ambient temperature is truly the culprit. This accounts for major drift even in recently commissioned facilities. Also, until now, they have not had many options other than changing ambient temperature in their comfort-delivering arsenal. Office furniture that delivers heating and cooling to the complaining executive, such as that under development by Personal Comfort Systems, might be another way to deliver satisfaction — without causing the whole building to drift out of calibration.
Greenbiz’ Davies cued up this discussion by asking the panel whether they were satisfied with the quantity and quality of the data they were gathering from sensor networks in their large building portfolios. The panelists were quick to point out that quantity was certainly not an issue, but there were hurdles to getting to actionable information and to always being confident that limited dollars and staff resources are being spent on the most impactful projects. MGM’s Chris Magee and Intel’s Marty Sedler, Director of Global Utilities & Infrastructure, concurred that building automation systems offered a daunting number of potential collection points. The problem is to manage all the data.
“We start by ensuring that our automation framework is built upon an open architecture,” explained Magee. This enables MGM to pull in data in any format and to normalize it to do the customization that will allow apples-to-apples viewing in energy, financial or carbon equivalents terms. “If it’s about managing a cooling tower asset, for example, the operator should know what those values are in real-time to understand the implications of the unloading and loading of chillers.” The panel also concurred that their own building staff often doesn’t have the expertise or the passionate interest in data normalization and analyzing tasks to get to this point. “Someone helping remotely is another ingredient that we support. We’re looking for strong, long-term partners in this role,” said Magee.
Illustrating how widening the scope of analysis beyond BAS data can lead to breakthrough diagnoses and solutions to comfort complaints and energy waste, Altura’s Schwartz offers a hospital building example. He says, “Noise and vibration issues can be perplexing for a healthcare facility. One of our clients brought us in to apply our Connected Building Commissioning data analytics process to such a problem that had dragged on for years. The vibration caused by the operation of the roof-top HVAC units in the 7-story building were assumed the source of the problem because the complaints were coming from occupants in the top floor. So measures such as equipment vibration isolation had already been taken, but complaints persisted. The building owner, design project team, general contractor and acoustic engineers had all been brought in on the case. But until we normalized the data streams – HVAC data, seismic data, along with the complaint log – in time-series data correlations and output a visualization, no one really understood or could put a finger on what was happening. Actually it was a problem with heavy footfalls and a badly reinforced floor. Once all parties could see what was happening in terms of the correlated data the moment of insight came.”
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