How Data Can Help Your School Brace for Impact with Jack Bullock of Cenergistic


With 2018’s hurricane season coming to a close, it’s safe to say the last two years of extreme weather events have been more than catastrophic. The effect that these storms can have on schools and universities is particularly potent. Running on tight budgets, severe weather can damage aging infrastructure and put teachers and students at risk of drowning, contamination, disease, and stress. Not only that, but schools often act as evacuation centers during hurricanes, introducing new issues of comfort and safety for large crowds of people. These extreme weather events create reactive and proactive challenges for facility managers in education; how can they better prepare, and where should they look first?

To give us insight on how schools are looking to get ahead and stay ahead of severe weather, we sat down with Jack Bullock, Chief Engineer for Cenergistic, a company which provides cost saving benefits on utilities for K-12 districts, universities and municipalities. Bullock himself holds patents in interval data analytics, whole building simulations analytics, and even has a patent pending for active fault diagnostics for commercial buildings. With varied experience in the field, Bullock said the answer is in the data. “We feel like interval data is the key for really optimizing buildings and understanding how they’re operating during a major storm event or hurricane,” Bullock said.

Recent challenges like varying dew points and hurricanes accelerating at a faster rate have put a strain on facility managers, but with a varied approach to data analysis, facility manager can find some reprieve. Monthly data for reducing utility bill costs, interval data for prescriptive daily measures, and EMS information all bundled together can better prepare facility managers for severe weather. Bullock gives examples of this data at play, and how it’s saved schools thousands in the wake of devastating hurricanes. He also gives insight on which is the most powerful kind of data for school facility managers to harness.

Highlights From the Episode

Daniel Litwin: What would you say is the most damaging weather event in the last few years, or kind of weather event that facility managers are having to readapt their safety structure or how they prepare or react to said weather event.

Jack Bullock: The biggest event we’ve seen has not been a single hurricane, it’s been the higher dew points that we’re seeing across the south. The extended rain periods that’s causing moisture management issues in our schools. We’ve had a number of articles in the paper where schools have had mold and complaints and they’ve had to shut schools down. So, higher dew points and the rise of moisture management issues has really been the issue that we’ve seen broadly across our schools.

DL: Let’s focus on the facility management professionals first. What are some steps that you encourage your clients to take when dealing with dew points, specifically? Since this is a particularly new issues, how can they get ahead of the curve on this?

JB: Yeah, in monitoring relative humidity’s in your schools and dew points in your schools and keeping track of what the dew points are outside is critical for facility managers. The relative humidity is the measure of the distance the actual temperature in a room is from dew point. So, if you can keep your temperature 20 degrees off dew point, you’re at 50% relative humidity. So, knowing what the dew points are, internal to your buildings and external to your buildings and managing your temperatures is really critical.

The first strategy that a lot of people use is to lower the temperature in the space to make it cooler because they think the air conditioning runs more. But what you’re actually doing is you’re moving closer to dew point, so you’re increasing the relative humidity. You want to separate that internal temperature from the dew point. Knowing what your dew points are and trying to manage the internal temperatures to stay away from dew point is critical.

DL: I’d like to analyze the data that you can actually observe and maybe breakdown–I know you have a data pyramid that you adhere to. I’d like to break down each chunk to really look at some examples of how facility managers can use each type of data set to see proactive and reactive results. Let’s start with the monthly data. Now, obviously this isn’t something that you can really get to a head on because by nature it’s monthly. So, you’re only getting it once a month, it’s old already. What are some of the reactive changes or decisions you can make based around this data as a facility manager?

JB: Yeah, there’s a lot of gold in this data. All of the different layers of our pyramid give us real cost savings results for our clients. Monthly date we focus on was the bill right? Did the bill look reasonable from the previous year? Is the billing period for the correct period? Is the rate the right rate they’re on? Is there an anomaly, like a leak or something that’s popped up on the bill? There’s a spike this month. All those things our analytics run through and notify our energy specialists when there’s any issues with monthly data. Then they would take the action item to complete the resolution of those and get the refund for the client.

DL: Okay. The monthly data really is more on the cost savings side of things. It’s analyzing the bills and then making informed decisions on how to save more for the following month.

JB: That’s right.

DL: Right. So, then we get to interval data which you describe as prescriptive data. Breakdown that set and how facility managers can use that to their advantage.

JB: Great. Interval data is data that’s every 15 minutes. So, it takes a read of how much energy the building is consuming every 15 minutes. We’ve written analytics and gotten patents on analytics to look at that data stream. We look at what is the base load of the building? What is its minimal energy consumption when it’s unoccupied? We want to make sure every day we get to the minimal base load. And, if we don’t, then we want to dispatch our energy specialists to figure out what is running that’s not allowing it to get to that base load. We want to know when the building is starting up in the morning. If people are going to arrive at 8:00, we don’t want it starting at 3:00 in the morning.

So, we’re calculating exactly when that startup period is and then we’re monitoring it. If a building is starting early, that’s again, a dispatch of our energy specialist to out and figure out why we’re starting that building early. We want to analyze where it’s setting its peak? Is it setting the peak at start up in the morning, or is it setting the peak in the middle of the heat of the day? So, we have analytics that look at that. We make decisions on how we schedule the building based on where the peak is getting set.

Then we look at that shut down curve of the building. How is it shutting down at night? Is it running almost at full load until 10:00 when we can get it down to half a full load at 6:00 p.m. and then cost the rest of the day? Then we go back and see what the base load is at midnight and make sure that we’re shutting the building down all the way to that base load that we’re targeting. We use these analytics to do this every day with every meter from every client and it works very well.

DL: Then, the last data set is predictive data or energy management system data. Which, I guess, is in even more real time than the prescriptive data. So, tell me with that rapid level of data, you’re getting it so quickly, what can you do with that on a minute to minute basis?

JB: Energy management data is data from the system that maintains comfort in the building. So, it knows the temperatures of every room. As we look around this room in here, we have temperature sensors that are monitoring, so we’ll know the temperatures of every room in a school. We can identify when rooms are uncomfortable and we can dispatch our energy specialist to go and work that problem. We can identify when valves and dampers aren’t working properly. We can identify when a system is not delivering the capacity it should to a building and it’s struggling to recover. So, with that data we can really get it down to predicting failure before they happen.

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