Article by Steve Nguyen, VP of product & marketing at BuildingIQ
To most individuals, commercial buildings are viewed as brick and mortar, static structures.
There is, however, a complex technological side to commercial buildings —from the software platforms that control elevators to smart lighting — that is often overlooked.
It is these features that underscore how commercial buildings can benefit from disruptive technologies like Artificial Intelligence (AI).
Falling costs, increased accessibility, and greater sophistication of IoT devices have made it easier to generate data on the performance of buildings, and the systems within them, on a more granular level.
At its core, IoT enables different components to communicate with each other, without any intelligence. The lack of intelligence means that a building may generate a deluge of data that needs to be manually sifted through to glean operational insights.
This has created a prime opportunity to apply AI to turn data into actionable information.
Without AI, the combing of data from a building is either time-consuming or deemed useless information.
As AI continues to infiltrate the market, below are three ways in which it can be used to make buildings smarter:
When it comes to reducing energy consumption, buildings are reliant on after-the-fact reporting.
Essentially, analyzing what energy was used and then implementing a change in the hope that less energy will be used next time. AI and predictive analytics are disrupting this in favor of a proactive approach.
Let’s use the optimization of heating and cooling within a building as an example.
Controlling room temperature within a building is like controlling speed when riding a bicycle. Many forces change the speed of a bicycle when it is in motion.
Pedaling creates a force that pushes the bicycle forward. There is also friction, gravity, and other forces working to slow the rider down. The bicycle travels at a constant speed when forces used to propel the bicycle forward are in equilibrium with the forces acting to slow it down.
In the case of a heating and cooling (HVAC) system, there are numerous thermal loads that influence the temperature of a space. To cool a room, the system blows cold air into the space to decrease the temperature.
However, other thermal loads such as human activity, solar radiation, heat from electronics and more, increase room temperature. When these loads add up to zero, the room temperature is fixed.
Imagine that you are riding a bicycle on the road with uphill and downhill grades. Will you ride at a constant speed? Probably not. You’ll build up kinetic energy (pedal faster) to go up a hill and perhaps coast going downhill.
AI-based energy management platforms can identify the “uphills” and “downhills” for building operations by applying AI in the form of machine learning to advanced models of a building’s thermal characteristics.
It will identify when it makes sense to precool the building to avoid energy use during hours when energy is at the highest price (the uphill), or when to decrease cooling due to periods of inactivity within a building based on historical usage patterns (the downhill).
This is all achieved while keeping temperatures within a range that is comfortable for building tenants.
In addition to optimizing day-to-day operations, AI and machine learning can be relied upon for fault detection. AI techniques are well-suited in learning the relationship between input and output variables using only data, without mathematical models.
This technology can excel at analyzing data from various systems and IoT devices within a building to identify anomalies and inconsistencies. After identifying these symptoms, AI can be used to target a diagnosis.
It’s also important to note the limits of AI. While at its core fault detection is a technical problem - that AI can help expedite - human intuition and expertise is still needed.
In an ideal world, data anomalies would be automatically detected by AI-algorithms, and then immediately triaged and root cause identified.
However, within a building there is a deeper issue of resource constraint. There are often a lot more subtle, qualitative aspects to detection issues that requires a person to filter.
Cost, ROI and available funds must be considered from a budget perspective. There could be 10 -20 items on a list that have good ROI and comfort impact, but AI is not going to know that a room needs to be operating for an upcoming event or that a department is out of town so prioritizing that section of a building won’t cause a disruption.
For these reasons, the combination of AI within a building, paired with a national operations center (NOC) to filter the qualitative needs of clients is the best strategy for resource-constrained facilities.
Using AI to optimize building operations and prevent faults will inherently create a more comfortable environment for tenants.
Exploring the relationship between comfort, direct tenant feedback, and AI is perhaps one of the more recent developments in smart buildings.
Companies are actively racing to find the best ways to personalize comfort for individuals within a shared workplace. While there is no clear-cut path to how this will develop in the future, it is certain that humans act as the ultimate sensor within a building.
Thus, integration of mobile apps - and perhaps wearables - will likely have a large role in the way tenants interact with buildings.
As previously mentioned, AI can be used to refine advanced models of how a building performs based on a variety of variables. Using an app or other feedback mechanism for tenant input could potentially be another data stream to improve that model.
This is an early concept, and it is still unknown what this might uncover or in what way it will impact how smart buildings are operated. The goal of any smart building is to create a better experience for those within it - making tenant feedback vital.
The future of AI in buildings is bright but human expertise will always be needed to properly utilize and direct the technology.
The building space has been traditionally slow to adopt new technologies but embracing AI-based solutions is inevitable as it capitalizes on the boom in the adoption of IoT-driven devices within facilities.