A key to this transition is creating an adaptive technology framework for the future of artificial intelligence in the building environment.
By Michael Nark
With the ushering in of the Internet age, many argue that every aspect of daily life will soon be fully integrated with the web. Internet pioneer Steve Case of AOL fame coined this era as the Internet of Everything (IoE), spanning beyond today’s Internet of Things (IoT).
In the buildings industry, an Internet of Everything building might look like artificial intelligence (AI) working in perfect harmony with increasingly powerful, embedded processors within devices installed in the building. These intelligent buildings would have self-diagnostics capabilities and self-adjust to fluctuations in external or internal conditions.
According to Navigant Research, the rapidly growing intelligent building market is projected to grow 18% over the next decade, indicating a developing trend. Yet the day-to-day reality of most building operations illustrates just how large the gap is from today to these IoE buildings. The majority of facilities teams today are constantly in firefighting mode, lacking the actionable information needed to take an active operational stance let alone go all-in on an IoE Building Management Systems (BMS). Operations executives are flying blind — struggling to find the insights needed to make data-driven decisions that meet financial objectives and promote future growth.
How, then, does the industry bridge the gap between the buildings of today and the IoE buildings of tomorrow? The answer lies in the cloud.
Building The Way To Internet Of Everything
New construction presents a clear opportunity to build-in the latest futuristic technologies, but even greenfield projects must overcome issues of IoE adoption by designing systems meant to work in close harmony with cloud computing and AI from inception.
The advantage with new construction is the ability to embed capabilities of cloud-based optimization into the DNA of the building and integrate secure cloud-based control, testing, validation, and commissioning as a new build paradigm. However, these sophisticated buildings will still require fine-tuning in the commissioning process.
For example, in a pre-commissioning situation, the cloud can make sure all the “nuts and bolts” are in place and that sensors and devices work digitally and can be accessed remotely. Then during commissioning, the cloud can send commands and measure the efficacy of those commands. It can override units into a specific mode for all zones on a floor, see that the units responded, and look to see if the data correlates with the action.
In so doing, the cloud is not only eliminating the human error inherent in commissioning, it is also creating a digital history of the building’s performance. This knowledge platform can be measured against to create benchmarks and used as the unbiased source of truth when things go wrong.
Bridging The Gap With Existing Buildings
In the best-case scenario, a new building is fine-tuned with intelligence from the cloud and reaps the benefits of cloud optimization in its daily operations from day one. But how does the industry address existing buildings?
The U.S. Energy Information Administration reports about half of all commercial buildings were constructed before 1980, and the annual building replacement rate (accounting for both renovation and new construction) is around 2%, according to the U.S. Green Building Council. At this rate, it will take a very long time to rebuild the way to IoE buildings, not to mention the billions of dollars required in CapEx and disruption costs.
Cloud services enable the existing building stock to cost-effectively progress from rudimentary systems to creating a digital connection in the cloud regardless of a building’s state. As buildings are retrofitted toward IoE, the current, standard infrastructure that supports a cloud service is an important transition step. Most cloud connections rely on a highly secure, managed device that adheres to a building’s security protocols — a less daunting scenario than hundreds or thousands of IoE devices speaking directly to the cloud servers. This is an important step both technically and culturally as companies turn the corner to accept the IoE. Bespoke as well as IoT-as-a-Service secure architectures are cost effective and secure, ensuring a smooth transition.
Once transitioned, the logic that governs the behavior of a system is safely locked away in the cloud — able to utilize the latest-generation of server-less standalone devices, to optimize energy, tenant comfort, and operations.
The cloud and artificial intelligence fill in the missing communications and sensing that is lacking in older systems and expands the set of sensing, communicating and remotely controllable devices. Think of it as not only adding new awareness, but also the connective tissue and the brains that make it all work. As it falls into place, cloud-AI has the computing power to begin filling in the gaps by using, for instance, downstream data as a proxy for upstream performance. This ‘image’ of the building is called a digital twin.
How is the digital twin used? Consider for example morning warm up strategies — a simple, rules-based strategy to preheat the building at a certain, pre-set time. Using the cloud enables a facility manager to observe historical system response to this sequence and troubleshoot or tune where needed. Instead of focusing on the usual sequence logic of ensuring a single air discharge unit meets setpoint, they can observe system thermal response. In buildings without critical sensors required for control strategies like this, they can tune their start-up time based on downstream system response. With a cloud-based, AI-enabled system, the facilities manager then has tools to go a step further to allow system models to learn the building’s thermal response on a zone by zone basis (e.g., north versus south facing exterior zones, zones with work cubicles versus closed offices, etc.) to automatically control morning warm up sequences and optimize for energy consumption.
Bridge Building With A Digital Twin
Creating a digital twin is the first step required to embrace old BMS and new IoT systems. This digital replica of a building breaks down walls between data silos to understand how systems fit together. It allows for the creation of a model for how the building should perform under new conditions based on historical performance and can be adjusted for real-time performance.
With a digital twin, AI is used to understand how devices, equipment, and data actually interact as opposed to how they are wired. In the example above, zones heat and cool very differently due to factors like the physical layout, use, existing equipment, density of people, and more. The digital twin leverages the history of the various zones and can factor in people’s perception of comfort in the model. Doing so reveals the discrepancy between design and function at the soul of the operations within a building. This is a learning model that adjusts and evolves with changes in the building, systems within the building, and tenants.
Easing The Transition To IoE Buildings
Whether retrofitting older building stock or new construction projects, the cloud is making buildings more intelligent and more economical to operate, while also bridging the gap between the systems of today and the IoE buildings of tomorrow. The key is creating an adaptive technology framework for the future of artificial intelligence in the building environment that helps ease the transition.