How important are smart buildings in creating a smart city?
They’re essential. By definition, a city is an area with a lot of buildings; likewise, a smart city must be an area with a lot of smart buildings. I’d only add that public spaces are also part of a smart city. There are smart parks, smart roads and so on.
So maybe we should imagine “smart spaces” instead of just smart buildings?
You could, but it’s fine to talk about smart buildings – they are a huge component. Offices, factories, schools and hospitals, stadiums: any building can become a smart building.
How would you explain the relationship of a smart building to the smart city?
In a way, you can compare smart buildings with Legos. Imagine a smart building as one Lego piece. Now, Lego pieces have different shapes and sizes, but the basic characteristic is that any one piece can connect to any other piece. And, when you start connecting the pieces, you build something bigger and more interesting, with endless configurations. That’s how it works for smart buildings, with the end result being a smart city.
In a smart city, the pieces might be shopping centers, offices, apartments, factories – the more the better. The key is that each smart building is connectable with all the other buildings and spaces.
And the thing connecting smart buildings is data, right? This huge, two-way flow of information between buildings.
Exactly. Data collected throughout each building and its surroundings, and by the people who use them. And that’s linked over the Internet by data platform and hubs. This is the Internet of Things, of people, of everything.
What are the tools used to create a smart building?
You definitely work with sensors a lot. And in fact, any commercial or industrial building will have some sensors in place, even in buildings that aren’t smart – meters for power; we have temperature sensors for HVAC cooling and heat; and many buildings have some form of basic people-counting.
You can already get an initial picture by combining these basic data sources, but, generally, the insights you get about what happens in the building will be very basic. The optimization options will be limited. Ideally, you want to get a much more detailed, granular understanding.
So you’re saying there are different levels of “smartness”?
What I’m saying is, with support from someone like Adenergy, most commercial buildings already have the equipment they need to reach a basic level of “smartness”. But what’s exciting is that there’s no top limit for smart buildings; it all depends on the objectives you want to achieve. There is no maximum on how much data you can collect.
Really, no limit at all?
Almost none. But there’s an important point to make here: data is not magic. It’s a strategic tool needing skillful use. Collecting massive data is valuable only if you know what you are looking for, what your targets are and how you’ll create real change when the data speaks. That’s why strategic partnership with clients is as important to Adenergy’s business as our IoT technology and platform.
It is true that the more data you collect, the more precise your models can be. But, practically, it’s also crucial that people in the organization see changes being implemented and experience the improvement for themselves. Otherwise, you risk doing a lot of work and spending a lot of money for nothing. The huge data pool companies dream of can turn into more of a data swamp if you haven’t planned carefully.
Let’s say I own commercial property, but it’s not smart yet. Where do we begin? How much time do I need to make it smart?
It depends. For Adenergy, we always start by getting an understanding of your energy use and maintenance operations, and how they dynamically change over a typical day, month or season. Actually, organizations rarely have a super-clear view of their operational dynamics – they often can’t give detailed answers to questions like: “When do I have spikes in my consumption?"; "What pieces of equipment are breaking down more often?"; "What is my 24-hour load curve?”
We have to work with our partners to get the clearest possible picture of this. It’s all about measuring value and defining a baseline that lets you very precisely measure efficiency gains over time. That used to be really hard; audits required lots of time and very specialized skills. But with experience, we’ve created digital models which allow us to work much more efficiently and reach the implementation phase faster.
Why do you think companies don’t have a clear view of their consumption dynamics?
There is more than one answer to that. On one hand, building OpEx is not always the number one value-driver for asset managers.
The second challenge is that the information needed to make sense of the situation is kept in different silos and people don’t see the full picture. There are a number of costs which are normally hidden in different parts of an organization – energy cost isn’t just how much you spend on energy per se. It’s also how many people must be employed to do certain work, or how much maintenance and replacement costs. It’s money you lose because a certain system is not working. That’s why it is so important to start by clarifying the baseline and make the data available in a common platform, where it can be analyzed and correlated.
So, let’s say this stage is complete and we want to take next steps. What do you need?
Generally, after you have clarified the starting situation, and analyzed the data, some areas will pop out as having more room for improvement. Then you look at the building owner’s other priorities. Maybe they want to improve productivity of the space, for example. In any case, at this point you set specific objectives for the project – let’s say for example a 10% improvement in HVAC efficiency, or decreasing maintenance costs by 15%.
At this point we would map the equipment and data we need to reach the target. We’d find out what’s available and what is realistic to achieve with a simple upgrade. We’d also want to include some “human sensor” data, preferences and experience information.
That’s all taken from sensors inside the building. But you have to remember that smart technology is about information exchange, so you want to correlate with external data like weather and traffic information.
The key is that you view internal and external information together – data in an interconnected relationship.
When you partner with clients, what’s your process for deciding how much data to collect?
It’s like I’ve already said: you start by defining the end game. You must understand the reason for setting up the platform. What targets does the client have? What do they want to manage and to what degree? Then you start to understand if additional sensors and data collection are required beyond what the client already has.
In some cases, clients might just want to make better use, more integrated use, of their existing data. Because, building owners can already get lots of data through existing building management systems and energy management systems. The problem is that that this information usually disappears. It just becomes numbers on an occasional report from a technical guy… if you’re lucky.
What we do is put all this information to use by periodically optimizing and improving the way the building operates.
And your IoT platform is one of the main optimization tools, right?
Yes our AEMO platform is a big part of this, but just as important is being able to take the needed next steps after the platform delivers its insights.
The second part, a crucial part, is modifying your facility’s operations based on what an advanced platform like AEMO can reveal. It’s a balance – you really benefit from these amazing big-data analytics but only when the teams managing the facility use the insights to streamline operations.
This is why Adenergy doesn’t believe in providing software-only solutions. Adenergy is here to set up the system according to client priorities and then operate the facility, delivering a superior performance thanks to the smart data available, from energy to e-maintenance.
When property managers convert their buildings to be smart, is energy always the best place to start?
It’s definitely a very safe starting point. The advantage of focusing on energy first is that it quickly generates savings for the property owner, no matter how developed or under-developed the smart city infrastructure is. Regardless, the business will create some savings from energy optimization. And at the same time, this energy platform is setting up the infrastructure you need to further develop your smart building.
The longer-term payback is that once that connection is up, and the smart city continues its development, you can plug in so much more information to optimize other services and improve the value of your building. But even before then, optimizing energy operations has measurable payback. Everything that follows is able to piggyback on that first energy infrastructure.
What are the differences between converting an existing property into a smart building, versus starting from scratch? Is one easier than the other?
I wouldn’t say one is necessarily easier than the other. They both have their own challenges and benefits.
For an existing property, your ‘’hard challenge’’ is upgrading and integrating new parts with old ones. And you have the ‘’soft challenge’’ of getting engagement from the people in the building – they’ve been working in the same building for years, but now you want them to operate as a smart building. They may need to break long-time habits and ways of doing things.
On positive side, when you convert an existing building, it’s very easy to quantify the value of becoming smart. Number-by-number, you can clearly see the before-and-after wins in savings and efficiency.
Building an all-new smart building is different. The positive is that it’s a fresh start, so you can do things right from the beginning. There is no infrastructure bias. The challenge is that there’s no mutual baseline to compare with, no before-and-after, so demonstrating the value isn’t as simple. One way we can show new smart buildings’ value is through very granular data we collect from our IoT energy platform, AEMO.
Who is the target user for the AEMO platform?
When we developed the platform, we wanted a tool that has two faces. One side is geared towards technical people, the energy specialists. Obviously at Adenergy we’re industry specialists, so we made sure it provides us with very detailed analytics that we can use to optimize maintenance and management of energy systems. Mainly, this side of AEMO is Adenergy’s tool to squeeze out every last bit of efficiency when we manage a facility for our clients, although it is also available to clients.
On the other side, we designed a client-facing version of the platform that can be customized and accessed in real time. We wanted it to look and function like a web or app interface, and to create a dynamic picture of current and past energy consumption. The idea is that each client will have a tool in their own hands to see the evolution of their energy use patterns and be able to do some of their own metrics analysis.
But unlike the specialist side of the app, we didn’t want to overload it with data. The idea is that we help clients tailor the platform to their own metrics and then see the data points they’ve identified as important.
How does AEMO factor into energy management?
There are many other platforms on the market which can give very specific analytics. But for Adenergy, we want to go beyond just showing the analytics and lots of metrics. We want AEMO to take the data and feed back into the automatic control system of the building, so that the loop is closed. And with more time and more data, buildings can become more automated, more efficient.
You’re talking about machine-learning, right?
Yes. It works because the AEMO platform will have access to all the external and internal data that can be collected – historical data of previous days, months and years. And with any change in conditions, AEMO will use this to analyze different scenarios and responses, create a model of your building and find the most optimal path. As the smart-building infrastructures develop, this capability will only improve and eventually can interface with smart city infrastructure.
For energy management of a smart building, who’s making the main decisions? People or the AI?
Both. It’s important to understand that in our platform, AI is a tool to improve life for people, not a dictator. It’s really about the client defining the experience they want from their smart building and letting AI achieve this in the most efficient way.
To give a very small example, you could set a range of acceptable light settings for different parts of your building, which would be managed by the AI. Or temperature - you might decide that during working hours, no room should be colder than 18 degrees or warmer than 23.
Maybe, after work hours, you want a different range of acceptable temperatures. But then, maybe someone is working late; sensors in the room pick up on this and can make temperature adjustments based on the settings for the work day and current grid prices.
Round-the-clock, the building’s sensors are continually monitoring this and much more. Meanwhile, the AI in the AEMO platform is actively finding the most cost-efficient and energy-efficient solution; running ongoing risk analyses and making energy decisions based on this.
So, the smart building is always at work, then.
Always. It’s like the brain of the building - always awake, always learning, always improving.
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Founded in China and dedicated to delivering the next wave of smart green energy in Asia, ADENERGY specializes in energy optimization and smart buildings for commercial properties, as well as distributed energy generation and storage solutions for the industrial sector.
All ADENERGY solutions are managed through AEMO, our cutting-edge IoT platform. AEMO is the 2018 winner of Microsoft and Envision Digital’s award for “Best Machine Learning Energy Optimization Platform”.