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Smart Buildings and Their Digital Twins

We are entering a time when everything is getting connected, computers are ubiquitous and the amount of data that can be collected, aggregated and analyzed is practically limitless due to cloud architectures. It is now within reach to create a full proxy of a building in the cloud.

Every piece of infrastructure, sensor, personal mobile device, and business process in a building today is a potential source of valuable data for improving operations and user experience. Insightful facilities project teams are beginning to direct it towards the creation and maintenance of digital twins. A digital twin is a dynamic software model of a physical thing or system.

The digital modeling world has been working toward this moment since the first computer-aided design (CAD) tools for drawing symbols and geometries were introduced in the 1960s. Early CAD led to the very sophisticated BIM (building information models) that performance design engineers working in architecture & engineering firms use today to analyze and optimize systems. The big advancement that distinguishes Digital Twin modeling is that it encompasses not just predictive design-phase data, but also time-series data captured from an occupied and operating building.

Digital twins can serve as repositories of data from BIM, building automation systems (BAS) and sensor networks associated with lighting, physical security or other infrastructure. The replicas will come alive as they are fed time-series data from actual operations. A range of analytics packages will be run against the real-time data to glean insight about operations on a continuous basis or on demand by users. The information contained will become more granular as more data is accumulated, organized and interpreted. We’re likely to interface with digital twins by simply viewing a piece of equipment or space via augmented-reality(AR) apps and glasses. Ultimately, anytime anyone has a query about the building, they’ll start by consulting its digital twin.

Consider, for example, representing a building’s chiller in software. The model might start as a simple block diagram showing component parts like condenser, motor, pipes, etc. As you add chiller performance data, the virtual twin becomes more information-rich, like a 3D wireframe view. By adding IoT sensor data, you can get more granular information about aspects of chiller operation. Metaphorically, you’re adding detail, shape and color to the digital twin. As you pull in more data, you can make it more and more like the physical chiller. The digital twin can also include equipment documentation, with links to online resources.

Fault detection and diagnostics (FDD) for specific equipment, like chillers, can be run against a relatively sparse ‘young’ digital twin. When there is need for more granular data on specific aspects of operations, wireless sensors can be placed to gather the information of interest. For example, hot/cold calls from occupants may trigger interest in air supply temperatures at a handful of points. There is no necessity to bring every point captured by a sensor system into a BAS. Likewise, there’s no reason not to keep populating a digital twin with the information. With today’s cloud architectures, the added cost to store and manage the additional data is minimal, and you don’t know what new use for the data will arise in the future.

FDD analytics are an important tool in the arsenal, but they are not the only tool. To optimize chiller operations, for example, you want to be able to query the chiller’s observed heat curve, then adjust the Sequence of Operations (SOO) programming accordingly. Today there are many commercial off-the-shelf statistical programs that do curve fitting. Another category of operational analytics is model-based predictive and prescriptive control algorithms. Fed historical and real-time trend data, these tools look for patterns to predict what will happen next. If predicted performance would result in energy waste or other undesired outcome, they can prescribe actions to course correct, and sometimes affect the necessary adjustments—like changing variable-speed motor settings, for example. These analytics packages are leading the buildings industry closer to machine-learning and AI. Project teams will want to plan for the eventuality of running this type of analytics against the data stored in their digital twin.

How much time-series data would a building project team need to feed its digital twin? If trend data were collected for 50,000 points over five years, about 4.2 terabytes of time-series data would be created. To navigate this enormous data store, a well-defined reference architecture and standard meta tagging system like Project Haystack is needed. As an estimate, about 200MB of Haystack meta data would be sufficient to navigate the 4.2TB of time-series data collected from a 50,000-point building space.

Inherent to the digital twin concept is the idea that its value increases over time. As the information contained gets more granular, you will get more meaningful and reliable results to the analyses run against it, and the what-if scenarios you run through can start to get more complex.

In short, a digital twin platform should accommodate tools we know today, and those to come. It should not be tied to any specific analytics type or brand. The architecture should feature security as well as open, low-friction data interoperability at each level. Software stacks supported by open-source communities provide the safest future growth path today. A digital twin should be designed to scale, evolve and reincarnate for the lifespan of the building it represents.

We also must expect that the digital twin trend is going to accelerate technology disruption and the remaking of many business and industrial processes. Nevertheless, the most forward-thinking facilities project teams are going to embrace the concept. Users of the Connexxion® Platform are already on their way toward creating digital twins. They rely on this scalable, secure data management and data visualization platform to transform and unify disparate data sources, to bridge heterogeneous networks, and to quickly deploy the analytics and other applications that various stakeholders are demanding. With Connexxion as their partner, these users are leading the way into the ‘digital twin’ era of smart building operations.

Anno Scholten, President, Connexxion

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CRE Tech Innovation Showcase - Uncovering the Next Generation of New Ideas - 7/12/2018

Never before in the history of the Commercial Real Estate market has technology been so prevalent. Since 2009, hundreds—if not thousands—of companies selling tech to the Commercial Real Estate Industry have emerged. Estimates now suggest there are more than 2,000 new CRE Tech companies in the space. The sheer number of new companies makes it almost impossible to keep up and organizations are struggling to establish an effective process to discover, organize, vet, test and implement new technologies. This webinar will evaluate the categories of technologies, best practice criteria, and clarify different solutions that are making their way to the top of the list.