[0:00]
So now I'm going to do a bit of a live demo. Let's see how this goes. So, what we have here is an Energy AnalytiX solution. That's collecting data from IoT sensors, using IoTWorX. And all this is brought in and aggregated in the cloud. So this is a dashboard that is able to allow me to easily know the energy consumption patterns and history of a portfolio of buildings. What we're looking at right now is a building called Science Center. And what we can do here is look at not just the total kBTU of that building, but I can switch to various meter types that I have set up to be collected from that building as well. I can switch over to say if I'm really interested in the electric consumption, I can change that to look at electric consumption. And what we have here in the view is it totalizes the consumption for that chosen utility source, it calculates the average consumption and tells you which day is the minimum and maximum. And you can see on the right as well, a heatmap that shows you for each hour of each day for that selected time period, which hour was the one that consumed the most electricity. And this gives you an idea and a pattern that you can easily recognize that says, okay, you know, I don't think anybody should be there at midnight. If that has a higher consumption, we should take a look into that. Maybe somebody turned things on and forgot to shut them off before they left.
And then some other statistics on the far right there is it calculates the minimum and maximum hours, so you can know which points are the minimum and maximum, and the average for the hour for that given time period. At the bottom, we have a consumption trend per day. But I can also switch to a different kind of chart, which is the consumption per time and day. What this gives me, as you can see from the legend in the top is not only the total consumption for each day, but also broken up by the time of the day. So early morning, morning, afternoon, and evening. So again, this allows you to key in on times of the day when energy consumption should have happened, versus times of day where energy consumption shouldn't have happened. You want the evening sections to be small, but the morning and afternoon ones to be bigger, perhaps.
[2:49]
So switching back to the total consumption, in addition to looking at consumption, as a day, I can look at it as hour by hour as well, by changing it into this line chart. So that gives me hour-by-hour consumption for this given building for this given energy type. Now I'm looking at a small timeframe here, the 16th to the 29th of April. But at the bottom, you see this timeline picker. What that allows me to do is I can scroll that blue section to change to a different timeline, but I can also expand that. Let's say I want to look at all the way from January, or, you know, the last day of December. And what that lets the system do is it changes the query that is sent to the back end, which is AnalytiX-BI, to get the data back for that given timeframe, do all the aggregation that the front end is requesting, and present that as you see here, almost instantly. Now because we're looking at now multiple months, the dashboard is programmed to sort of understand that context and change the calculation, the time bucket periods from daily to monthly. So now I'm looking at monthly consumption trends, both from the heatmap standpoint, and also from the aggregated standpoint.
[4:20]
Now this is a good overview. But what if I want to compare energy consumption between a couple of buildings that I have? So here we have a different view, which is the comparison view. When you go to that view, it gives me a listing of all the buildings that I have in the portfolio and to the right to start with is a blank chart, so I have to start adding buildings that I want to compare here. So let's say I want to compare the Administration building, the Science Center that we looked at, and also this dorm room, Taylor Hall, for example. By clicking on those plus icons in the navigation tree, it adds those buildings to the chart on the right and shows me two contexts of charts. One is the totalized consumption for each of those buildings, so you can easily tell, hey, Science Center is using the most energy among those three, and Taylor Hall is using the least. And at the bottom, there's a stacked chart that tells me for every single day, for each of those buildings, how much do they contribute to the totalized consumption for that given day. So, the orange one here is Taylor Hall, the blue one is Science Center, and the green one is Administration. You see a bit of the pattern of consumption and how each of those halls contribute to the totalized amount for that given day.
[5:44]
The other interesting type of chart in addition to the stacked one is if I just want to look at consumption of each of those buildings, clustered next to each other, I can do that. And while that's a little difficult to see, let's look at it from the perspective of the month; I can change it to the month. So now I can know for each type of building, how was their consumption trending, month to month, among those three buildings. This gives me a way to analyze if I want to compare like buildings, for example. And on the very right side, you have some metrics that get calculated dynamically as you select buildings, like what is the total consumption of all three buildings added together for that given timeframe? What is the average consumption per day for all these given buildings, and which building had the minimum amount of consumption at what month, in this case, and which building had the maximum amount of consumption, in what month as well? So, this gives me a good way of comparing buildings.
[6:49]
Now let's take a look at a report. I mentioned in the presentation earlier, dashboards are great and more often than not, that may be the preferred way to view data. But sometimes when you want to share information, reports are what people kind of expect, right? You know, “Send me a report or get it to me in an email,” for example. So reports are an important part of any kind of sustainability solution. So here I have a report that looks at the carbon impact of the energy consumed by the building. The carbon impact for the most part is a conversion factor from the type of utility. However, as I mentioned before, depending on the types of sources that are used to generate that energy, some energy sources have a higher carbon impact, and some energy sources have a lower one. So, it's not necessarily just a straight conversion, if you will, from energy consumption to carbon. So here I have a carbon impact report that I’ve executed before. Let's take a look. Going to open a PDF version of this.
[8:08]
Alright, so here we have some aggregated charts that show us the carbon impact of the chilled water, year by year for given building, comparing every month for the three years, 2019, 2020, and 2021, in a chart form and also in a table form. We also have the carbon impact of the electric consumption, same thing, for the three years, in chart and table form. And we also have a pie chart that breaks down the CO2 impact for the three different kinds of utility sources and energy sources that we have for heating, hot water, electricity, and chilled water. So you can see that, you know, chilled water has the highest carbon impact, in this case, in 2019. But in 2021, that pie looks quite different now. Heating became the bigger contributor to carbon impact. Maybe they switched over from the chilling chiller to more electrical-based or cleaner ways of doing that chiller. This gives you a way ofgetting accountability, keeping track of how you're performing, and using tools like ReportWorX mining data from Hyper Historian or Energy AnalytiX, these reports can be generated ad hoc, automatically, or scheduled so that, you know, once a month it automatically generates it, emails it out to the people that need to have a look at this. And so, it reduces a lot of administrative tasks that traditionally may be something that has to be manually done, somebody has to scramble at the end of the month to go get the data. So, if you have a system like Energy AnalytiX, that continuously collects the data, continuously contextualizes it, and with a system like ReportWorX, that automates the whole process of getting these reports out, it simplifies the whole thing and makes the process of getting to a more sustainable footprint easier. And you know, more enjoyable.