October 12, 2018 | Written by: Claire Penny
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Ask yourself these questions: when was the last time you experienced the latest and most innovative technology, at work? Is your workplace more technically equipped than your home?
It is widely accepted that buildings are getting smarter. But were they ever actually dumb? Or have they just been held back by closed proprietary systems and a lack of collaboration, which has stymied innovation and new ways of thinking?
Every building should be a smart building
Smart buildings are now an expectation for many of our employees. It’s an industry worth USD 7.42 billion in 2017, with projections of USD 31.74 billion by 2022, at a CAGR of 33.7 percent from 2017 to 2022. (Smart Building Market, Global Forecast to 2020, MarketsAndMarkets). The market is definitely changing, and with it is the emergence of a new wave of technologies and capabilities that will ensure that the workplace of the future enables us to be our most productive.
A combination of existing installed systems, such as BMS and metering systems, and the Internet of Things (IOT) is fundamentally changing the way buildings are operated. It’s also affecting how and when end-users engage with their buildings. If you combine these systems with cognitive computing and augmented intelligence (AI), this should mean that the older a building gets, the smarter it will become. But this notion is obviously at odds with our current thinking – only new buildings are considered “smart.”
Just how much can your building tell you? It might be smarter than you think.
The role of digital twins for buildings
Whether in an existing building or a new one, across its lifecycle, every building constantly generates valuable data. This data is the golden thread that links all phases of the lifecycle together. That’s where a digital twin comes in. Because this data should be the DNA of a building’s digital twin. A digital twin gives you an accurate digital representation of a physical asset. With them, you have better insights into what your actual building is doing. And if they’re maintained, you can get an accurate picture throughout the entire lifecycle of that building.
Why is this a good idea? In a CETCO Europe survey on Construction Trends for 2017, 25 percent of respondents said that “getting meaningful report data that helps me to make informed decisions” was their number one technology concern in 2017.
Ensuring that good quality data is passed from one lifecycle stage to another enables all stakeholders to be informed. It will also enable the ecosystem of constructors, designers, service providers etc to make future changes and advancements in their products and services, which in turn will drive innovation and change.
What is possible today?
Back to today’s older buildings: what can we actually do today? In the “operate” and “in-use” phases of a buildings lifecycle, things are beginning to change. The service industry, in its current state, is commoditized. Good service in terms of SLAs is expected, and not viewed as value-add by clients. Facility management providers are looking for new ways to show new value to their current clients and attract new ones, too.
To provide improved value-add, these companies have started to use data from assets to optimize the buildings they manage to deliver condition-based and predictive maintenance. This approach enables facility managers to complete time-based tasks when they are required instead of running them off a schedule when they may not be needed. That saves both time and money.
Beyond operations: using data in a more meaningful way
In 2016, IBM research and six other institutions, developed a uniform schema for representing metadata in buildings, called Brick. This schema defines a concrete ontology for sensors, subsystems and the relationships among them, which enables portable applications. (Brick: Towards a Unified Metadata Schema For Buildings, BuildSys, 2016).
What Brick actually provides is an information exchange platform that is focused on commercial buildings, where interactions among devices and people are core to sophisticated applications. Using this schema, we have built a knowledge graph, specifically for buildings, which allows us to deploy analytics, AI and conversational concepts. The resultant capabilities allow for continuous learning about the building and how occupants use it.
With this capability, we are continuously updating and ensuring that the digital twin of the building is accurate. End-users, not just maintenance technicians, can use the data in their everyday roles. We can understand and predict energy use for each individual building, including its nuances, across an entire building portfolio. We can plan, predict and adapt space requirements. And in the future, allow employees to adapt their surroundings to their individual preferences. It enables employees to identify free desks and meeting rooms, control the temperature of their space, find work colleagues and so on.
The utopian state for corporations is employee productivity throughout their day. This not only adds value to a business but can also be delivered in a cost-effective way with IOT.
Why bother? Because of the outcomes
So let’s circle back to the beginning of this article. If we start by developing a digital twin for a building, we can capture data across a buildings lifecycle, and ensure that we standardize the ontology for the subsystems and the sensors, and define the relationships between them. Then we can use AI to develop a true, cognitive building. The insight gathered from such a building will provide feedback to architects and designers, giving them accurate insight into how their designs really work in the operate in-use phase.
We can also provide data on what materials were used, where they were procured and who installed them if a major adaptation to the physical asset is required. We can provide in-use performance information of insulation materials, windows, HVAC systems and so on. We will constantly be learning about the building’s behavior and performance, to help quantify the true TCO (Total Cost of Ownership). But most importantly, we can provide information to the people who use the buildings on a daily basis and help to make them more productive, whatever their job.
A building with a digital twin and cognition, will help to deliver better designs, superior construction, higher quality service delivery and an ensure a superior building-user experience. And it will give us the WOW factor that we all desire!
Watch the video and explore how you can use AI to learn and predict building behavior.
Visit our website and discover more from your building data with IoT Building Insights.
We also invite you to join Claire at CoreNet Global, the largest gathering of corporate real estate professionals. At the conference, Oct. 14-17 in Boston, she’ll part speaking at these two sessions:
About the author:
Claire is the Global Leader for the IBM IOT for Buildings. Claire gets to combine all her passion, experience and learning from the past 18 years and focus it into IOT for Buildings and Retail, focusing on the user experience and operation of the building. In this role, Claire is also driving IBMs thinking and vision to strategically shape IBMs Building and Retail cognitive (IOT) solutions, which are helping clients to put AI to work! Find her on LinkedIn and Twitter.