[0:00] Jotham Kildea
Good morning, everyone. This is Jotham Kildea here with ICONICS. I am on the Solution Sales team. I'm also one of the people that has been involved in working between ICONICS and ITCN putting together this SUSTIE project. So, I was asked to speak a little bit on details of what we've put together and happy to do so. This is the dashboard that we've been using for the application so far, We use the navigation on the left side for controlling what you see in the main display. Of course, behind the scenes, we use our standard asset model that organize all the information that's seen on the screen. That's still behind the scenes, uses that based upon all the different subsystems that we're pulling into the system to organize the data, organize the data, logging, the alarming all of that, all that fun stuff. And then we just use the visualization that we see to extract data from that asset model. So let me show you in a little bit more detail what we've been doing on this project. I'm going to start off with the floorplan. Okay, so we're looking at the first floor of the building. How we build these displays is pretty standard. Actually, we take the BIM model, the building model for the SUSTIE building, strip it down into individual floors, take the ceiling off each level, so you can kind of see within the rooms and render it out as an isometric that we can use for the visualization here. Of course, if you're not familiar with this building, you might want something that gives a little context to where you are. So, it's different labeling and organization of this system. But this is by and large what we use to contextualize the data for the floorplan based information. So, one of the things I want to show you is this comfort widget. This is something that we use to represent some of the many data sources coming from the comfort sensors in the building. They’ve got a lot of great centralization going on. If I hover over this conference room, you can see the breakdown of how those different sensors are being monitored, of course, a lot of different types of data in one gauge. So usually, we use a color coding to indicate normal operations versus abnormal operations. Don't worry; the air quality is perfectly fine there. We deliberately set a very sensitive sensor on this because we really want to try to fine tune this in a lot of different ways. But looking at different zones very easily. How do you see where the problem areas in your facility? A lot of fun stuff I could say about this type of visualization and control. We also do more traditional things with just kind of a heat map organization. So, looking at different types of data, cross sectionally. So here we've got temperature; we can look at humidity. We could look at carbon dioxide. So how fresh is the air in different spaces, the lighting in the area, all those different good stuff, as far as how do you make use of a floor plan to convey information about the spaces.
[2:57]
One of the other things that we also use this for, and I wanted to use as the bridge to talk about it in particular, is occupancy. So, some of the larger workspaces, they have detailed occupancy sensors within them. And you can see here, there's a few different rooms that have variety of number of people in them. If I click into details on one of them, we'll see a lot more analysis on that room over time. So let me pull up the occupancy specific dashboard for that. And you can see that here. An interesting quirk of starting this project over time is that originally the project was designed around, you want to use this utilize this space, as much as you can make efficient use of your assets, which is the building, you want to make sure it's at or near occupancy during working hours the day. Obviously COVID changed that a bit. So now we're looking at defining a target occupancy limit, in this case about 1/3 capacity. And trying to aim to stay within that category. We don't want to be go full occupancy because of course there's space concerns about people saying distance. So it's a little bit of interesting adjustment to the dashboard over time. You can see that being played out in some of the things that we're monitoring such as if I switch over to looking at the average occupancy by hour of the day, you can see that they're saying well below the threshold was that blue line, but they can check how to track over time, did they have certain hotspots that exceeded the occupancy limits that might be a cause for triggering a notification to go out and say, “Hey, maybe we should adjust schedules, maybe we should adjust how we're using the space because we're exceeding the target occupancy at different times.” So interesting dashboards, it's pulling from real information over time. So, it's a great use of that data set. Just as an aside, this is coming from the AnalytiX-BI model.
[4:44]
If you're not familiar with AnalytiX-BI, it's really great at taking raw data streams and subscriptions which this is just data directly out of the sensors within the room and converting it into a data set and a data model that we can query in interesting ways. That's how we get data sets like the average occupancy by hour of the day or by day of the week looking at these peaks and aberrations. All that's because we can make intelligent queries to that data model. So, you'll see that come up in a lot of visualizations that we use in this project. So, I want to show you now a little bit more detail on the energy dashboards that we have. Okay, so I just pulled up one of the energy dashboards which is for our overall energy consumption, just give you the tour. On the top, we've got the heatmap or data diagram that shows the energy usage by hour of the day by past month effectively. This is a great at a glance view to see when was my best and worst performers over time, am I fitting to the model I'm expecting typically, for an office space, you'd expect a five day work week, you'd have hotspots during that time, you should have pretty low energy usage on the nights and on the weekends. You can see how that plays out. Anytime you have equipment that's running overnight, when it shouldn't be or things not shutting off during holidays, it becomes very, very obvious very quickly with a visualization like this which is why we like using it quite a bit. On the bottom, we also see the average energy usage by hour of the day. This is a good breakdown, again, to see when particularly if you have high energy load systems like furnaces, or HVAC systems that kick out in the morning, you can pretty clearly see: when is my scheduling of the energy spend and utilization to make sure it's, it's being well managed throughout the day.
[6:40]
Let me show you one of the other things in the energy analysis. Alright, let's just take a quick look at this visualization which is again breaking the system down into different subsystems. So, apologies for the Japanese, but we've got HVAC systems. We've got the plug load. We've got lighting. We've got elevator systems, the ventilation, all sorts of different systems within the building. And we're monitoring how is that comprising the overall energy spent. You can see that on the bottom, where out of the 100%, you can see elevators is less than 1%. That's that purple here, not a big worry to prioritize and optimize that. But things like the ventilation here, absolutely something you want to try to optimize where you can because that's about a quarter of your overall building energy load. So, a great way to add a glance see where's that being used by hour of the day? And what is the aggregate spend for different systems. Last thing I wanted to draw your attention to on this is the green line there. That's the photovoltaic generation. Because entire buildings lined with solar panels on the top, they are trying to be achieving a net zero energy building. So, you can see, hour by hour of the day when is the best energy production and how well does that correlate to meeting the demands of the building, of course, there's never going to be a truly one for one comparison. So, it's going to be positive and negative over time. Of course, being net zero, the goal is to net that out to zero or a positive game. At a glance view to see, at least as the building performance married up well with what it's generating. So, it's been a very minimal burden on the grid as a whole.
[8:22]
Last thing I wanted to show you is some of the things that are more traditional building control related in the equipment. So, if I look at this, they use a lot of packaged air conditioner units within the facility. So, these are units that sit up in the ceiling provide heating and cooling directly to that zone. You can see at a glance; we call this a list view. It's a quick, easy way to see the performance of a lot of similar types of equipment. We can use this as I said, at a glance to see where the problems are. But I can also click into that if I wanted to get a really drill down nuanced view at any particular equipment. So here I've pulled up a PAC unit, I can see the high level overview, I can also see the details of the equipment, all the nitty gritty point details, as well as a historical trend. So, nothing too crazy here. But this is a standard visualization that we use in a lot of applications, gives a lot of consistency because we can use this for really any type of equipment where we have this overview point details, trends, alarms, faults, all of that information can be organized in a consistent fashion. So as an operator, you don't have to jump around to a lot of different visualization modalities, you just kind of stick with: This is the proper way to look at and analyze the data, regardless of the subsystem you're looking at. So that's the quick overview of what I want to show you today. Thanks for your time, and I'm sure hopefully we'll hear from more from you later in my session. So, thanks all.