“You're never going to fix the decarbonization challenges we have today with just data and adjusting active systems. You also have to fix the envelope. And vice versa: envelope only approaches don't solve it either.
We need data science AND building science."
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Episode 86 is a conversation with with Beth Eckenrode and Craig Stevenson, founders of Auros Group, a consultancy dedicated to designing, constructing and building the highest performing buildings at the lowest possible cost.
We talked about how building science, or the practice of creating high performing building envelopes, combines synergistically with data science.
Then we turned our attention to how that actually happens in practice and what it costs. These insights are key to our decarbonization journey.
Without further ado, please enjoy the Nexus podcast with the Auros Group.
Mentions and Links
- Auros Group (0:36)
- The Power of Existing Buildings (3:15)
- Nexus Podcast #079: Unpacking Stanford University's Smart Buildings Program and Road to Net Zero (15:18)
- Nexus Newsletter #109: 2021 Reflections: Carbon Tech (38:42)
- Building science + Data science = Zero Carbon future (11:37)
- Building science deeper dive (16:14)
- Data science deeper dive (25:04)
- How to deliver this in real buildings (36:21)
Note: transcript was created using an imperfect machine learning tool and lightly edited by a human (so you can get the gist). Please forgive errors!
James Dice: hello friends, welcome to the nexus podcast. I'm your host James dice each week. I fire questions that the leaders of the smart buildings industry to try to figure out where we're headed and how we can get there faster without all the marketing fluff. I'm pushing my learning to the limit. And I'm so glad to have you here following along.
James Dice: This episode is a conversation with Beck Ekin road and Craig Stevenson, founders of Auros Group. It consultancy dedicated to constructing, designing, and building the highest performing buildings at the lowest possible cost. We talked about how building the science or the practice of creating high-performing building envelopes. Combined synergistically with data science. The topic that we're more used to talking about here on the podcast. Then we turned our attention to how that actually happens in practice and [00:01:00] what it costs in the real world. These insights are key to our de-carbonization journey and I hope you enjoy them. So without further ado, please enjoy the next podcast with the Auros Group.
Hello, Craig and Beth, welcome to the show. Can we start with you, Beth? Can you introduce.
Beth Eckenrode: Sure, thanks for having us James really excited to be here. We're rather new to nexus labs and we've really enjoyed learning so much from you and, and the whole ecosystem. So the background on me is I've experienced, uh, with big companies running billion dollar businesses.
And in about 2014, I got bit by the. To take my skills and, and, uh, and go do something that matters. And I connected with Craig on the future of the built environment. And what was interesting was how much change was necessary in an industry that is fundamentally resistant to change. And as a, as a strategist and as a practitioner, I found that to be really interesting.
I thought between the two of us, we have. And [00:02:00] approach and some thoughts around how to get any building owner, whether it's an owner of an existing building, lots of buildings that developer, uh, university, uh, of existing buildings or somebody attempting to build a new building in today's times where goals are aspirational, zero carbon, zero energy, healthy, indoor air quality, you know, We had the pieces of the puzzle to put that together in a way that you know, owners could get excited and feel like they could deliver on those goals.
And as we processed it through what we realized is that we had to be ready to explain and justify and support with. To building owners that they got what they paid for. So the combination of, you know, the, the, the aspirations and the need to prove, uh, performance to us was an interesting bringing together of spaces that weren't together.
There are a lot of gaps that exist in the construction cycle, for sure. And we felt like if you closed a couple of the right [00:03:00] ones and you did it in a way. That didn't cause as much disruption. So how do you, how do you go after something that's a disruptive innovation in the least disruptive way possible?
That's what we set out to do that what was kind of our mission and so much. So we wrote a book, it was published last year, called the power of existing buildings. Does exactly that it kind of takes an owner developer or somebody who has a lot of buildings that says, okay, how do you think about taking the steps all the way from, you know, uh, either renovation or an existing or a new building?
How do you go all the way from design through to operations and start to close some of those gaps.
James Dice: Cool. And those of you that are watching on YouTube can see
Craig Stevenson: this book, uh,
James Dice: across the best left shoulder here. But for those of you that are listening on audio, we'll put the link to the. And the show notes.
So Craig, could you introduce yourself for us?
Craig Stevenson: Absolutely. Hi James I'm Craig Stevenson, co-founder of the ORAS group and Beth and I are business partners here. We really appreciate the opportunity to speak with you, uh, at this podcast I've [00:04:00] had, uh, I've learned a lot going back through your podcast libraries and, and watching them.
So thank you for doing what you do. My background is I grew up in the construction and. After I graduated college, I went into the construction, uh, as a general construct, general contractor is working for mid-sized general contractors. And I did that pretty much for 25, 30 years. I grew up estimating, I grew up understanding fundamentally how buildings get built.
And one of the things that I learned very quickly as We can do better with that spending a premium. I was early adopter in the sustainability and I've watched greenwashing in my career and I've watched people checking boxes and calling it sustainable. And it frustrated me because like I said, climate change is upon us and I know we can do better.
And I know we can do better without spending a premium to get there. So when I dove in. Everything. I've researched and everything. I looked into pushed me to envelope first approaches, efficiency, first solutions to buildings. It's the cheapest way to do it efficiencies the cheapest form of energy. And when you [00:05:00] see a detail for an envelope first approach, you really can't unsee it.
So it's just a logical way to build. And when we started talking to clients about this they were interested they're, they're definitely interested in efficiency. They're interested because it's the right thing to do. And they're interested because regulations are forcing them to figure it out, figure it out, but they were skeptical and their skepticism was around, you know, those prescriptive programs do this and you should get that.
And they did that forever. And the buildings never really changed fundamentally in how they operated. So what we figured out very quickly as we needed to have a feedback, Right. We needed to have some level of connectivity from the design and construction to operations. So our owners would be in trust and in how we're approaching this building, uh, efficiency solution.
That's what started this whole concept. That's what drove us. That's where Beth and I spent a lot of long days and weekends thinking about and hatching and eventually. Developed to what is now or group, uh, where we merge building science and data science [00:06:00] to get at zero energy, zero carbon, and world-class indoor air quality.
James Dice: Awesome. And we met, I think at real calm this past, uh, this fall, this past fall. And you had like this mic drop moment where you were talking about like, you're speaking to a room of MSIs and you're basically just, I don't remember what the exact quote was, but it was about building science and data science.
And I was like, I have to get this guy. On the podcast. So I'm so glad you guys are right here. I think you also came to me through Joe Gasper donate. So a shout out to Joe for making the introduction. Uh, well, let's dive in.
So, tell me a little bit about your, your clients and the types of projects that you guys, uh, work on.
It sounds like construction projects all the way through to operations. What point in the construction process to you guys, uh, get involved in a project?
Craig Stevenson: Well, I'll pick it up on clientele and I'll let Beth come in and talk about how we engage and how we should start when we should start thinking about this.
But in terms of our markets we, we [00:07:00] focus, uh, at Auris group on larger commercial projects projects in the mush market, municipal university schools, hospital. Um, Lot of multifamily these days, cause multifamily is using the past five strategy to get to zero. It's just natural for those types of buildings to do it.
Envelope first approach for other buildings that have high process loads is natural as well, but you don't see that one-on-one relationship like you would at a multifamily, which is really kind of. It's it's super easy to do. So those are the types of clients that we, we tend, we typically will engage with.
And when we come on those project teams, we can come on project teams for new buildings. So there's a concept that we want to do something new. We can come into buildings for existing buildings that have a massive trigger, like, major retrofit. Or they have lifecycle triggers for systems or windows.
That would be a natural point to bring us on. And we can also come into projects that have existing buildings with no triggers. And what that entails is really creating a master plan so that when that trigger hits, we know what to do because the [00:08:00] concept of replacing in kind, you know, our mechanical systems go down and put the same sized system in and replace it and not try to.
Influence those loads and look at our envelope at that same time, which is the right time to do it. You miss the opportunity to spend your money more wisely. So we do some master planning for existing buildings, and we also talk about existing and new. And in terms of when, when Beth I'll defer to you, if you want to come in on.
Beth Eckenrode: Sure. Um, So Craig laid out all the places, the how, and when we come in differs based on project goals. So depending on what an owner's interest is, it affects the way we might approach a new project, whether it's an existing building or a new building, for example a university system that wants to figure out w which is the next building that they could do.
In the most cost effective way, drive that building to zero energy or zero carbon. W w how do they rack and stack their investment options? That's one way we might [00:09:00] come in with an existing building, somebody who has a specific building, like Craig says with triggers and already knows what they're trying to accomplish.
Now, we go more into execution mode as opposed to planning mode. But what, what lines, what kind of weaves together? And. All of our clients is the, is the set of goals that they go after. So our clients, whether they're a university or they're a, uh, K through 12 or a multifamily or all different sizes and types of municipalities, they, they will have a set of goals or objectives that they're targeting.
Some that are required. Of them and others that are just elective and, and that's what really weaves together. Our clients, they're all going after zero carbon, zero energy, they want, you know, especially post COVID. They want to know that they're putting their people in, in the highest quality buildings that they can be in.
And there's a certain equity of that, right? The equity is everybody should be able to live and learn and play and. In environments that contribute to their overall health and wellbeing. Those are the kinds of clients that, you know, most [00:10:00] definer our base.
James Dice: Cool. And in my perception, I've been writing a lot about this recently, but this trigger or this goal setting or whatever then, but it says for action.
I find that a lot of building owners are, are sort of, it depends on the industry, right? Some are more advanced than others, uh,
different industries, but
the actual concrete plan with concrete steps and a deadline isn't necessarily there for a lot of the organizations that are setting these big, you know, big targets and doing press releases and that kind of thing.
Is that what you guys, you guys would come in right there and say, here, let us help you put together an exact plan. For you to get to that target and have it be less of a greenwashing situation
Beth Eckenrode: for sure. We would. We, we would come at that from the standpoint of how do you make the most amount of progress towards your goals with the least amount of cost?
So we look for the biggest bang for the buck. Like, you know, you, you have a, a building that is facing [00:11:00] you know, failure in something, a system of some sort, that's the opportunity now to rethink that entire building. That system. And so, you know, as Craig was saying, we use tools like, you know, modeling and simulation of what building performance modeling and simulation.
We do that in order to predict performance by predicting performance, we're able now to take cost estimates along with. Predicted performance and be able to, you know, in a much more definitive way line out options for clients on, Hey, here's where you want to spend your next dollar. You don't want to spend it over here.
You want to spend it over here. And this is why this is the return on investment. You'll get on that spend. Cool.
James Dice: So let's, let's circle back to this concept of building science and data science. So, Beth, can you just kind of explain this concept at a high level?
Why, why is this combination
Beth Eckenrode: important? Sure. So we naturally. This, our history and our background and kind of everything based on where we started is from the building science perspective. And what we [00:12:00] realized is we were processing through the building science, is that the building science, without connecting in to the world of data science was, was not, we don't have perfect context if we don't do that.
So we look at kind of the, the juncture of building science of data. As, as a tool in terms of physics-based whole building, physics-based modeling and simulation. And the reason why we think that's so important is that both worlds of scientists speak that language. So building scientists speak the language of whole building performance and modeling and simulation and data scientists speak that same way.
And so it, our, our objective in every project is to try and pull more of that together, do more collaboration. So simulation and modeling in the hands of building scientists results in the highest performing building at the lowest possible cost. And the achievement of the aspirational goals that owners want in terms of the zeros, you know, the zero carbon, the zero energy and indoor air quality and [00:13:00] the rest of that.
And, but simulation in the hands of data, scientists provides context, provides context of either what's possible or what should be. And that should be important to building owners and developers because, you know, just comparing a building against itself, it's not the right paradigm for the type of progress we're looking to make to achieve these.
And especially for clients who are ESG focused, you know, there's a certain amount of ESG you get like the, the low-hanging fruit, you pick a couple pieces of the low-hanging fruit and you've got something, but you get to a point where you're like, how do I get the rest of this? How do I get the rest of them?
The rest of the meal comes by bringing together these worlds, these worlds of building science and data science and using tools that everybody shares. Everybody has the access to the same data. Everybody then is informed by the same choices by the same options. And then building owners and developers have more information to make higher quality decisions.
So we view it as kind of a way to close the gap and bring together the [00:14:00] type of collaboration that just hasn't been there in the past.
Craig Stevenson: want to add something. So I think that the way in which Beth described how we merge building science and data science is spot on, but I wanted to mention an analogy for the data science community that should resonate.
Right? What's the common thread that goes through everything we do. When we talk about setting goals and targets, and we do that through establishing an owner's project requirements we're using. We're not talking about, you know, hanging a plaque or doing prescriptive criteria. We're saying this building wants to be at a 14 DUI.
The air quality and air parameters in the building want to be at these set levels. Those are all based on metrics. The OPR is based on metrics. When we go into the data, the design support using building energy modeling, the building energy modeling is giving us Metro. And we're making sure that those metrics align with our OPR.
What are we doing in the data science community? Right. We talk about time series data, and we're looking at metrics. We're looking at numbers, that's the common thread across all this. [00:15:00] That's why it's so logical that when we talk about merging building science and data science, it makes so much sense because everybody is now talking about the same levels of goals and targets.
And they're all based on that.
Beth Eckenrode: And I'll, I'll add to Craig's comment about real-world challenges. There was, you had a podcast not too long ago, uh, with a couple of folks from Stanford. And I think it was, uh, Jerry Hamilton, who was talking about a real-world problem that they're trying to solve regarding.
In their rooms that there's the temperature that's on the thermostat, but then there's the actual temperature in the middle of the room or the temperature on the end of the room or the temp temperature in front of the window when the sun's out or when the sun's not up. And the kinds of things that we're talking about in terms of, you know, building science and bringing together the data science and the building science, that's where you start to fix those problems.
So in, in a building science environment, those problems go away when. Put the systems together properly. [00:16:00] And you'll cover that in. We'll cover that in the discussion on building site.
James Dice: I make sense. I love when we have cross podcast, episode conversations, going people reacting and commenting on past episodes.
I love that. So. Craig, why don't you do a deeper dive to let's because I want to circle back and make sure I understand what building science and data science, what you guys mean by that. But maybe we do a deep dive on each of them, and then we'll kind of reflect at the end on what they mean together.
Uh, and maybe have some follow-up questions there. So, Craig, what do you mean by building science?
Craig Stevenson: Yeah, I'll give you an example, cause it's, sometimes it's easy to imagine a story, right? In terms of what we do. So let's assume I want to build a new building and I go to my code and I'm doing it in a Northeast party.
United States code is going to dictate that Miami. My wall envelope, my assembly from cladding all the way dry walls gotta be around like an RA team to an R 20 to meet code based standards. They're going to require me to put an air barrier in, but they're not going to require me [00:17:00] to test it. And they really don't care what kind of windows I put in.
Right. So they're going to give me certain code criteria that I have to meet for how I build that building. And what we say is, well, wait a second. You know, what is the, what is the inflection. We're installation stopped paying because installation's always cheaper than systems. Right? I want to reduce my loads.
That's how I reduce my energy. That's how I go to zero. So then the question becomes at what point, then that line, when I go to an RET and in our 20, nor 30 in our 40, at what point does it stop paying? And I can go to an R 500 and it's not going to make a difference. That's what energy modeling gives us.
That's what we understand. So now when we're working on these project teams, we can come in there and do targeted our values for all of the fabric of the day. And all the fenestration. And then once we identify our loads, then we can look to decouple those systems. Because when you talk about low flow equipment and technology that you use in high-performance buildings, it's not sticking that big box on the roof that does everything and turning it on and off all day and telling ourselves it's the [00:18:00] most sustainable box in the world.
I want to get rid of that box. Right. I want to decouple. So we look for ventilation strategies that are separated from heating and cooling strategies and things like the BRF and air source heat pumps become affordable because you're in such a low load environment that they become, you know, they're not as expensive as they would be in a code base environment.
So now I decoupled my ventilation. I put on some energy recovery and I'm getting that. I'm getting that almost for free because of the, you know, the variable frequency drives. And I can write. Dan all day long and increase my ventilation. I've excluded all my infiltration exfiltration on the building.
Cause I have an air barrier that actually works at a certain level. And then I look for my heating and cooling and most buildings in the United States and the Northeast part of the United States. Most buildings don't require heat. You're going to put a heater in, I mean, a heating system in because you have to buy.
But in a passive house, for example, in the Northeast party, United States, that is not being used very often at all. So it becomes cooling, [00:19:00] right? Cooling becomes our challenge. And how do we solve for that? But by then, I've already gone from this concept of a hundred, 150 energy use intensity all the way down to a 14, which is a passive house energy use intensity KBT you per square foot per year.
And even if I miss that by 20%, I'm still, I'm still hands in the air winning. And then when I want to look to my renewable strategy, that renewable strategy is there for that much smaller because my loads are that much smaller. So now we can actually fit on my roof. So when we come at the problem, we want to talk to our clients about using envelope, first strategies, vet them so that they meet the.
Aesthetic goals and their square foot on the inside of the building goals and whether or not it's existing or new, and then work candidly with the entire team about respecting the natural order sustainability and the processes of how we get to zero energy. You're never going to fix the carbon challenges we have today with digital science is great as it is, and you're never going to fix it by [00:20:00] adjusting active systems.
You have to touch the envelope.
James Dice: So Craig you've mentioned passive house a few times. I don't think I'm thinking back on 80 plus episodes at this point. I don't think there's been a lot of passive house. Uh, an introduction or a deep dive on what that is. Can you just explain kind of what that is real quick before we kind of move on?
Craig Stevenson: Yes, I can. Um, Passive house in its simplest form as a performance-based standard. It's really what it is. So, you know, if you look at the history of sustainability, we start with you know, aspirations back in the sixties, early seventies. And then we went to prescriptive criteria eighties, nineties, and then it went into.
Performance-based uh, sustainability programs like, well, building a living building challenge and even pass a pass started coming out at that time. And now it's going to accountability. So when we talk about passive Haas and the reason why we use that as an example is because it is the most rigorous performance-based standard for efficiency, for [00:21:00] solutions, for buildings, it represents the spectrum of what you can do.
I would offer respectful. Code is the other end of that spectrum code is the worst building we're legally allowed to build passive Haas is the most efficient building you can build. So when we look at the new emerging standards that are out there for performance-based criteria, like passive house and like reset air for air quality we use those because it ties back to our common thread of using metrics to set goals, to maintain goals during our planning processes, and then to measure those goals, to make sure we.
That we met them once we're . An operations.
James Dice: And, and you, you mentioned in your description building science earlier, but passive has, it's not only an envelope, but they view ventilation differently as well. Can you talk a little bit more about that?
Beth Eckenrode: Sure. So there's a concept called hygienic ventilation and hygienic ventilation. Essentially what that means is never breathing the same air twice. [00:22:00] And, and we're not quite there yet, although what's important for people to understand about passive house. It sets the stage for that. It creates, it enables the environment for you know, that type of ventilation where essentially, you know, you limit exfiltration and infiltration.
You start to control your envelope. You keep the outdoor air out in the indoor air in, and you own. In air that you want in, and only under prescribed circumstances and conditions. So it's, it's kind of hand in glove with passive house and there's an equivalent standard to passive house on the indoor air quality side.
And that's reset air. And I don't know if that's something that you know, folks affiliated with nexus labs have been talking much about, but we see that coming to very aggressively. It's the same type of performance standard, both passive house and reset air. Set the expectations of performance, but they don't require a certain set of strategies to deliver.
So it leaves the project teams up to figure out how to [00:23:00] deliver those performance results without having to impose on them in terms of what they have to do. Step-by-step check, check box by checkbox in getting to that.
Craig Stevenson: It's worth mentioning as well. When you think about simulation and that really is our super power is simulation and, and building energy modeling. You know, when Beth is talking about passive house for hygienic ventilation and reset air to basically help measure that. The energy modeling that we do can all simulate exactly that we can simulate all forms of energy by zone by use, by whole billing, we can simulate temperature, humidity, and CO2 again by zone by whole building by floor.
And we can then understand before we ever spend a nickel on our construction project, whether it's new or existing retro. Whether or not we're we're magnitudes. Are we going to reach our goals? Now, simulation is a tool at the end of the day, we still have to build the project. Right. We have to commission the project, right.
We've got to put the systems in and make sure that they're set up in the right way. [00:24:00] But without the modeling, we're really poking and hoping, and then trying to fix everything. Post-construction modeling gives us the chance to really start on the right foot so that we can at least magnitude, really get.
And then measure it once we get there. So again, the difference here, James and you and I talked about this earlier, the difference is modeling historically has been used for transactional purposes, right? And the MEP creates a model. They give out a paper report and then they size their equipment. You never see the model again.
We feel that repurposing that technology because the technology is sophisticated enough today to do it. Reese reusing that to then do predictive analysis on the envelope, on energy, on IQ, and then continuing to inform it all the way through construction processes with submittals and change orders and value engineering, uh, or change the systems all the way through field testing, QA QC reinform that model all the way.
And by the time you get to the end of your record, You've got a very highly calibrated model that then you can use on operations. That's [00:25:00] where the market's going. That's what we do on pretty much all of our projects.
James Dice: All right. That's a great segue to data science. So let's go deeper into that side of the equation.
Beth Eckenrode: So when we, when we talk about data science, what, what we think about in terms of bringing together design, construction and operations is how do we take simulation, provide context of data, but more importantly, how do you set up the data to receive it properly? So the, the interesting thing about where we're at in the industry today in terms of data, is that.
It's a little bit like a cobblestone street. It's uneven, it's disconnected sometimes it's, you know, small pieces, large pieces, and it there's, there's not one version, one view of how to bring it all together in, in a way that. Owners, which is event essentially what we want is the eventual ownership, control and transparency over data to owners.[00:26:00]
And when you set out and you figure, try to figure out how to do that, it really starts with some basic and simple architecture that, uh, I know Craig wants to go a little bit deeper into.
Craig Stevenson: So, yes, I do. Only because I'm the one that's usually trying to get the data from those systems. And I'm learning the easiest ways and James, your, your blogs are going along way to talking about that. And I appreciate that very much, but when we talk about, you know, how does. Or as group um, why do we care about data science and how do we get to data science?
We want to be involved in the conversations when the data science folks are setting out their meters and sensors and their operational technology deployment, why we want to make sure we connect it to our goals and targets right. Going all the way back to the beginning. When we established an OPR and we talked about all those metrics before we did any intervention into the buildings, we want to make sure that our meters and sensors are set up properly.
So we have the right feedback loops. And I can't tell you how many times we get out of. And one of two things happen either. We're not measuring [00:27:00] a goal that we've established that it's easily measurable or we're over majoring. And we're spending a lot of money in our buildings that we don't necessarily have to spend because our engineers got very excited when we talked about meters and now I've got a meter on every outlet in my, in my entire building.
We don't necessarily need that from our perspective, right? If there's a compelling reason on why the facility management team or the owner would like to have that thing great. But from our perspective, it is as simple as creating a use case to the opiod. To make sure that we have a feedback loop on that goal and that target.
And once we have that, then the question becomes, do I have the data from that? Do I have the time series data? Because if I had the time series data, now I can give that data context. And when I say that historically, what have we done in data science, right? To get contexts where you use our historics on the building, I'm better than I was last year or the average of the last five years.
Again, that doesn't give us a lot of context because it doesn't answer the question. At that building when it was performing under its baseline, was it performing as it was designed or was it [00:28:00] underperforming it over perform? We don't know the answer to that question. So historic doesn't necessarily answer the question for us.
That's number one, the context and number two, we're going to see a lot of people using contexts like national meeting and saying, well, you're, you're better than your peer group. Okay, great. That's interesting for about 30 seconds. And then I'm done looking at that step. The only context that really matters, and it's almost a perfect context is merging simulation with.
Data science, trended data. Now I can answer the question is the building performing as it's supposed to perform number one, and number two, we can also work with those layers of advanced data analytics when they want to come into the building and create these really, really cool analytics and put them in a building.
Well, I can tell you. That analytic is going to work different in a passive house building and a really bad code base building, right. You're going to have to put it in place and you're going to have to refine it and play with it until you get it to do what you want to do. That causes frustration from the client.
It causes thermal comfort complaints. It [00:29:00] causes building occupants that distrust, right. What's going on in the building. And what we're doing is we're working with our teams and our digital twins that we have, we have collaborated with. And we're using the building energy model. The test. Those are the analytics before they ever go in the building, we could test out the BAS sequence of operations and set points.
We could test that all the operational characteristics on a building before we ever deploy it. We're not poking and hoping we're putting in place. What we believe will be close to. It gets us very, very close. And then there's still that refinement. You always got to refine the buildings.
James Dice: Hey guys, just another quick note from our sponsor Nexus labs. And then we'll get back to the show. This episode is brought to you by nexus foundations, our introductory course on the smart buildings industry. If you're new to the industry, this course is for you. If you're an industry vet, but want to understand how technology is changing things.
This course is also for you. The alumni are raving about the content, which they say pulls it all together, and they also love getting to meet the other students on the weekly zoom calls and in the private [00:30:00] chat room, you can find out more about the firstname.lastname@example.org lab. Start online. All right, back to the interview
So you mentioned benchmarking traditional process for benchmarking.
It was to get 12 months of utility bills to compare them against national media. And like you said, or the rest of the buildings and the, and the portfolio. And what you're saying is why not? Why aren't we comparing it against what the building is supposed to be doing? If it was performing. Makes total
Craig Stevenson: sense.
They can know that immediately. We can know that in the first year warranty period, we can know that before billing occupants are fully deployed. I mean, with those, those are no answers. And the technology exists today to answer those.
Beth Eckenrode: flip that question over to an existing building or a portfolio of buildings, right.
And you want to know what's the building capable of achieving, you know, there's this, there's this, there's an aspirational goal of, well, I want to get to zero energy. Well, without the context of is the building capable of, of that. And how far can the building go based on. You know, constraints and then what's that going to [00:31:00] cost?
You know, we, we kind of flip the idea of national media to something better and incremental move down. What we say is no simulate and figure out what the building's capable of and then start trading off from there based on either what you can afford or what's important to you. Right. So, The look the prism, uh, to look at it a little bit differently and use simulation in order to, and you mentioned
James Dice: analytics as well, right?
So the ability to have a model on for this given hour of the day on today's Wednesday, when we're recording this Wednesday at noon, this building is supposed to be doing this right now, right? And then now we can compare it to what it's actually doing. It's doing this, and that's a great basis for analytical results to come from.
Craig Stevenson: Absolutely. I mean, most of that work is done and I seen your last blog where you're talking about psychometric charts, you know, and that's the merging of temperature and humidity. They determined thermal comfort in a building. We're doing the [00:32:00] analysis in psychometric charts for simulation before we ever operationalized the building.
I mean, to me, that's answering the question that you want to answer, right. The fee for entry in this discussion is energy and air quality. It really is because if you do an envelope first approach to your buildings, and you're looking for an efficiency based solution, you're going to get that energy and you're gonna get.
Temperature and humidity and CO2 simultaneously with the same solutions. So that's the feed entry, right? And then once you're there, then you can start looking at other things that we established in our OPR. We don't ignore light and sound and water quality and water, quantity and materials, toxic and embodied.
I mean, we cover the full spectrum of building performance. And today we're focusing on operational carbon and we're focusing on. Measurement verification and supporting that from building science and data science, but you can get at, at every aspect of building sustainability when you solve the minimum viable product and the minimum viable product is energy and air quality, always.
Plus it solves the two [00:33:00] macro, the macro trends in the industry climate change. And COVID
James Dice: I think I'm, I think I'm grasping it. Let me try to try to summarize the equation here. Building science plus data science. So building science could come in at a retrofit situation or a new building.
It's essentially basically saying I'm going to create a model and I'm going to minimize the, or I'm going to try to meet whatever outcome the building owner has with this model before we turn any wrenches or anything like that. Data science comes in when the buildings operation. Oh, or you're in the commissioning phase, like you said, something like that before the building's operational, when you're actually monitoring and collecting data from the building merging, the two has a bunch of benefits that has never really in the industry had been captured, have never been captured before.
It makes total sense to me. Anything else to add to my rudimentary
Craig Stevenson: explanation there? A couple of things. Number one. If you have an existing bill. Having the right smart building infrastructure in place gives us [00:34:00] the ability to calibrate that model easier. Right? So you had mentioned, how do we historically do this?
We get 12 months of bills and we're looking at that. Well, that's 12 points a data, right? For every utility. If I can get hourly data on that, then all of a sudden, now I can run that calibration at a much, much higher level. So we use the smart building infrastructure to calibrate the model, and then we use the model to calibrate the building.
You'd see. That's kinda, that's kinda how it works from our perspective. And they work hand in hand. That's why having a conversation about smart building infrastructure deployment planning is so important early in projects and conceptual design because without the right infrastructure, we're never going to know the answer to the question.
Beth Eckenrode: And, and I think when you pull those together, so who wins, I guess, is maybe the most important question. How, and what does the win look like? Its owners, developers, operators, and the win is about risk, right? So reducing risk. How do we reduce risk? We reduce financial risk by you know, being able to [00:35:00] provide evidence.
You know, here are the paths and here are the steps you take and here's how much it's going to cost. And so now you have a better idea of before you even put a shovel in the ground, or you retro commission, a building, you don't have a better idea of what your risk profile is, financially performance risk, right?
You reduce performance risk because now you have something that predicts and, and kind of gives you an idea of what you're able to achieve. So you don't overstate. There's a lot of overspending going on and trying to get to some of these goals. And so you don't overspend and now then there's the operational risk, which really is all about the context that once you have different contexts where you see what the building is supposed to be doing versus what it's done in the past, or what some other building down the street.
Now all of a sudden you reduce operating risks. Cause you have something more tangible, you've got context that more is more tailored, more customized to what you're trying to do and achieve in your building. So there's that, there's that whole risk issue. And then at the end, you know, it's just more affordable.
[00:36:00] So if you want to tackle the idea of zero carbon or you're going after, you know, higher fruit and the tree of the ESG goals, right. And you're trying to go higher and you're trying to get more. The most cost-effective way to do that is to bring together the building science and the data science and have kind of one approach as you think through your choices in your options.
James Dice: So, cool.
All right. Let's talk about how to deliver this. The first thing that's coming to my mind is that Delta between code worst building, legally, that you can legally build and pass. And I was just writing a newsletter this morning. And so that's on my mind. And the newsletter was just summarizing.
Like, what does society have to do to get to net zero? Right? Well essentially we have to get every new building to zero carbon ready by the time the grid is ready for it. And that has to happen by 2030, which means. Codes have to happen by 2030 to close that gap between worst building [00:37:00] possible and passive house.
What do you guys think? Is that going to be possible at all? To close that gap between. You know, worst building possible and, and the buildings we actually need.
Beth Eckenrode: Well, I would say if we're, if we're depending on code to drive us there, I'd say probably not. The, as we've talked about, the tools exist in the, in the building science strategies and thought processes and principles exist to get there today.
But, you know, it, where are we on this innovation curve is really the question, you know, how do we hit that inflection point where everybody kind of says, oh, okay, got it. You know, Craig said early on, once you see some of these details, you can't unsee it. So how do we get more people to see, uh, what this is?
And then, and then that's when we hit that inflection point, but you know, code, certainly not going to drive us there. There are some cities who are implementing some objectives. Pittsburgh's in one, it's got a zero energy. Zero energy ready goal and all of it's, uh, city buildings, new York's going after it hard.
There are a lot of [00:38:00] cities in California, but for the most part, you know, code's going to follow probably not lead. So now we need leaders to kind of step in and take over and, and how we identify those and, and or how people self identify as leaders in this space. That's, that's the question, especially for, you know, people like you, James, who are writing on these subjects, how do you.
How do you invigorate and compel audiences to at least want to, you know, learn more at least want to dive in and, and try that. That's where we're at today. Once people jump in, they're not jumping out. So you know, that, that bodes very well. I think for the future and trying to get to, to where you just suggested we go in 2030, I think it's possible.
James Dice: Yeah. I was the newsletter that I was writing this morning. We'll have come out by the time this gets published, but I was basically saying. For 2022 for your career, what problem would you rather work on then? This one, you know, it was a challenge and I will put that in the show notes. Anyone that wants to read my [00:39:00] challenge to Craig, you mentioned OPR a few times.
Can you talk about what the importance of the, the OPR to actually making this.
Craig Stevenson: What is the OPR is the OPR in simplest terms is a plan. That's really all it is. And when you think about what you had mentioned before, I mean, I want to connect a couple dots here. So buildings use 47% of the energy in the United States.
They're by far the biggest users of energy, they by far are the biggest contributors to climate change. Number one, number two by year 20, 50, 80% of the buildings that will exist in 2050 exists right now. So the problems we face are not going to be fixed with new construction. I think the codes are getting better and are stretch codes and they're trying, but it's, you know, we're not going to build our way out of this problem.
Number one, number two, we're not going to renewable our way out of the problem either because we don't have enough rooftop space in urban environments. We simply. Fit enough PV in those areas to renewable our way out of the problem, we have to honestly [00:40:00] address existing buildings and we have to figure out how to do them.
So when you asked me about an OPR and OPR in its simplest terms is a plan. We are sitting down with the clients and we're saying, what are you, what, what's the culture of your business and your building occupants, and how do you want this building to operate? And we define that by metrics and we apply to building, we can apply it at scale.
And then we bring up a model, especially in existing. Cause this is where we need to focus our efforts to, to get at these goals, we can bring up a model and we can match that model against the OPR is analysis. Then we can create a plan and investment plan if you will, for owners. So any owners that own group of buildings and come here and say, okay, building one is an underperforming building twos and over performer, we can see where all of our low-hanging fruit at us.
We can start to make investment decisions. There is an ROI on transforming a building from a hundred, a UI to a 14, a UI. There's an ROI on it, and we've done it frequently. A lot of building owners are starting to understand that. So they're making those decisions for business decisions are making those decisions [00:41:00] for regulatory decision-making and they're making those decisions because they have natural triggers.
That's imagine. You're in a building and that building mechanical systems need to be replaced and you have to invest $4 million or $10 million in new investment, or to replace that system. Are you going to want to look at efficiency first strategies at that point in time? I mean, it's a natural trigger.
If I have my plan, my OPR in my model up then I know with the low efficiency solution of that building is I know what its technical capability is. Then I can make those business decisions like best said, you know, rather than working from Kodak. Which is insane. That's work from what the building can do, what its technical performance ability is at the highest levels and work our way up.
And the only thing that stops us is financial decision making. That's it. So to us, when we look at that, the OPR is nothing more than a sophisticated plan, just like the building energy model is, and it gives us the pathway to make better decisions on how we want to address these bills.
James Dice: It sounds like this, this doesn't need to cost more. And Craig, you [00:42:00] mentioned that a few times, the cost for getting to these targets doesn't necessarily need to be more than we were planning on spending to accomplish business goals.
Beth Eckenrode: Right. So, when you think about the OPR and the benefits of the LPR, again, who wins with the OPR? The owner wins because in the OPR is embedded metrics and the metrics are to reflect what's important to the owner and what they want to measure in operations. What, what happens generically organically?
What happens is it aligns all project team members to those metrics. The best way to get architects, engineers, construction management firms, commissioning agents, building management companies, operating teams. The best way to align them is to metrics. All of a sudden it kind of unleashes people because paragraphs of narrative.
Do not align people, paragraphs of narratives, create gaps in accountability. Numbers creates aligned accountability. And so when, [00:43:00] when we talk about the OPR and, and you know why it's so important and what does it happen? It's all numbers. It's one page, a very long page sometimes depending on our client, but it's not a book that sits on a shelf with a bunch of names.
In terms of what does that do for costs? Well, I mean, think back to this entire conversation, there's nothing we talked about. That's new. There's nothing we talked about that doesn't exist already. It's not about any extra costs. It shouldn't cost any extra building, a really good building versus building a code base.
Shouldn't cost any more. It's all in the order of operations, it's all in how you bring the right people into a project at the right time. So we, we fundamentally believe that the, the, the, the cost argument is it's legitimate and it exists today because we haven't done some of these things in the past because owners have invested in things and they've walked away a little bit, you know, frustrated.
Like, I don't know if I got what I paid for. I have a plaque on that building over there. It's not performing, you know, [00:44:00] smart does not necessarily mean high performing. And you know, that was the subject of one of the eBooks we put together because we had to kind of unravel this idea that just because you have data doesn't mean you're actually going to be able to get to a really high performing building.
There's there are other things you have to do. Other things you have to consider in order to really get to the optimal performance of a building. What's it theoretically capable of achieving that takes a little bit different. Look through the prison.
Craig Stevenson: Hey, James, I love this question on cost because it's a loaded question, right?
And I'll explain why it's a loaded question for your audience in terms that they can understand. So let's assume we want to get a, you know, a data analytics platform, a layer on our building, right? Digital twin doing analytical data. How do they get through. Right. They come in and the owner wants them.
How do they get their data? They got to connect to the individual teams or distributed sensors. They've got to connect to the BAS, whether it's proprietary open and they get to pull their data out of that, building themselves. It's a. Labor-intensive product process to get that system up. [00:45:00] And then from an O and M and operations and maintenance perspective, it becomes an expensive system to maintain because when you have a failure and a point you're chasing 800 different connectors and, you know, not just one.
So it becomes a challenge. So when we look at that and we say, well, wait a second, can we do things better? What if I put a, a data aggregation appliance in that building, and then I created a central point for the data to come out. I can control security. I can control single pane of glass. I can start now all of a sudden we can make better decisions, but what happens on projects?
Traditionally, we let our MEP. Who don't understand division 25 as well as maybe they should come in and design a traditional system with the traditional BAS. And then they try to plow OTs into the BAS. That's not cost-effective that doesn't work or we're doing things post-construction. After the building is already built by coming in and swinging 25 more networks in the building instead of using a converged network, you know, even if it's just a simple OT converged network.
So the point I'm trying to make here is that the question you've asked us about building [00:46:00] science and, and costs relates to the data science, the same exact way, if done. And if done early, you can do it apart. You can actually might even be able to save a little bit more money if you're not spending money on all the proprietary systems that are out there, you can manage your own system, any robust, durable way, flexible way and do that.
Right. The same concept applies to building sites. And I'll give you an example. One of the developers that we work with a large developer was doing a large. Well, the 250 acre development project and they have one of their key buildings and they wanted to have this building be the aspirational, right. Be a high performance building.
So they went to their contractor and said, okay, here's the estimate on that building that you have for us? We want this to be a high-performance building. Can you rerun your own? The contractor went on and it's called the construction standard index. That's how they organize estimates. Right? Perspective.
Look, they went through the CSI and he applied a 5% back there on every single element of work. And we asked them, said, why, wait a second. You know, if you ask them how much more it's going to cost you, it's going to tell you it's [00:47:00] cost more because you just loaded the question. When does masonry cladding know it's in a past.
When does it care? It's in a passive house. It doesn't the past has to do with insulation, thermal barrier fenestration systems. That's it. That's the only elements I want to talk to you about. But when we D we don't educate ourselves on how to get to that process, we do, we tend to pet, spend a premium on the learning curve, just like in data science.
So when we look at this, our argument is simple, done, right? Done early done with the right factors. We can do a passive house project of any size at par because installations always cheaper than systems. Always
James Dice: fascinating. Thanks for that. It's a good, I like that passion. Uh, that's a good place to sort of wrap up.
So it's pretty early in the year still. Uh, what are you guys looking forward to? And in 2022.
Beth Eckenrode: I would say I'm looking forward to seeing people jump on the bandwagon of some of the leaders. And we talked earlier about, you know, what [00:48:00] New York is doing and what California is doing. And I think now 2022 is going to be where the fast follower step in. There are a lot of municipalities and building owners, large hospital systems, universities that, you know, didn't necessarily want to be on the fuzzy, fuzzy front end.
Of innovation and of disruption, but they absolutely want to be fast followers. I think 20, 22 is going to be the year for fast followers and I'm super excited to watch and see, you know, kind of who decides to step up and, and what they do when they, when they do step awesome.
Craig Stevenson: So for me, I think we just spent the last two years and probably most of this year, trying to figure out how do we deal with climate change and COVID within our buildings.
And I think that we're starting to see a lot of really interesting solutions emerge from that. I think what I'm looking forward to seeing as owners wake up and understand that this is coming. We see so many owners at different events that put their head in the sand and figure out that, you know, New York city [00:49:00] passed legislation on carbon reduction.
They didn't tell you how they didn't care. They said by 2030, you got to reduce by 40% in 2050 by 80%. That's massive and you don't get there without touching the envelope. And the building owners that we talk to are simply, you know, some of them are saying, wow, that'll never happen. They're never going to do that.
And then you have, some of them are starting to figure out how to get there and what we look and see is that, that that is not isolated to New York city. We're seeing all cities and states starting to take on that same challenge and they're adopting it. If you look at. Cities that adopted the Paris climate action climate action plan.
You're seeing those cities that are going to step forward and say, listen, we're going to require buildings to be carbonate and deal with this, this problem. So for us, we're excited because we're speaking to a lot of building owners about being proactive in that discussion and the way you be proactive as you take advantage of your triggers right now, and start to transform your buildings.
Otherwise, you're going to be happy. You're going to have to do this all at once. [00:50:00] And that's where it's probably going to cost the premium to get there, but it's not going to change the decision-makers from getting there. They're gonna, they're gonna make us get there. Fascinating. Thanks for that
James Dice: update. I have hope, uh, talking to you guys, so I appreciate you coming
Craig Stevenson: on the show.
Thank you. It was very exciting. We really appreciate the opportunity.
Beth Eckenrode: Thank you. Looking forward to a reading and seeing everything you do in 2022, also James,
James Dice: who knows what's going to be.
James Dice: All right friends, thanks for listening to this episode of the Nexus Podcast. For more episodes like this and to get the weekly Nexus Newsletter, which by the way, readers have said is the best way to stay up to date on the future of the smart building industry, please subscribe at nexuslabs.online. You can find the show notes for this conversation there as well. Have a great day.