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Artificial intelligence, or simply AI, is the new business buzzword. It’s hard to go even a few hours without hearing the term — from commercials on TV, to billboards, and of course throughout the workday. But what does AI really mean? And what impact does this have on operating buildings today and in the future?

The Brookings Institution calls AI one of the most misunderstood terms amongst business leaders. Quoting researchers who have studied AI, the Brookings article says that “AI generally is thought to refer to ‘machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.’”

Many of the AI products in facility management are software-based, and, as the Brookings article puts it: “‘make decisions which normally require [a] human level of expertise’ and help people anticipate problems or deal with issues as they come up.” This idea reflects the goal of commercially available products that make buildings more efficient and operationally sound.

Adding to the confusion around AI, many of these solutions for commercial buildings could instead be termed “machine learning” (ML) offerings, as they take substantial amounts of data and use it to predict or calculate scenarios. The building operator can then act on the insights.

Despite confusion around this nascent technology, it’s important for facility leaders to understand AI and how it can impact their operations. The McKinsey Global Institute estimates that by 2030, AI will deliver $13 trillion in additional economic output worldwide. To put this in perspective, the value of US commercial real estate is estimated at $15 trillion.

Moreover, adoption of AI is on the rise. The Wall Street Journal recently cited a report from Deloitte, which found that 25 percent of firms surveyed already have implemented AI or ML solutions. And, within two years, Deloitte expects 75 percent of firms to have implemented (or developed plans to implement) these solutions.

AI in FM today

There have been some highprofile examples of AI in building management. Google announced in 2018 that it was using AI to manage cooling at some of its data centers. The software had been operating for a few years, adjusting cooling in real time, without human intervention. Google reported that it had saved 40 percent on energy use in these cooling systems. Data centers are a particularly good candidate for AI because the cooling demands are high and the risks of not providing enough conditioned air have an extremely detrimental impact on computing performance. And these performance impacts are quantifiable and immediately clear.

The Edge in Amsterdam is another example. Currently considered one of the most advanced and greenest buildings in the world, the Edge has deployed 30,000 sensors, collecting data about the building’s operations and how occupants interact with it.

A common use case of AI is to take significant amounts of data and distill the key inputs that a user can act on. At the Edge, commuters are directed to open parking spaces. Additionally, if the occupancy is less than expected, certain areas of the building are closed to reduce resource consumption. With all the data being collected and the use of AI, it’s possible to develop estimates of occupancy, and use that to change operations in real time.

At this point, only some buildings are investing heavily in AI. But more will follow.

Advanced analytics like artificial intelligence and machine learning present significant opportunities to reduce operating costs and improve outcomes for occupants. Most AI applications first collect substantial amounts of data and normalize it.

In a building, energy management is a common use case.

Actual energy consumption data can be compared to weather, occupancy, and other factors. AI solutions can then determine the dependencies between weather and energy. When temperatures rise by X degrees, it’s likely that energy demand will rise by Y kilowatts, for example. A model is built to represent these dependencies. Then, moving forward, actual or future weather data or predictions can be used to forecast energy use. More data means that a more accurate model is built, which improves the accuracy of the predictions.

7 Ways AI Is the Future of FM

A range of realistic uses make artificial intelligence the future of facilities management. Here are  seven ways AI will be provide major benefits for FMs.

The future looks bright for data-driven building operations. These solutions can save time when operating a building and deliver better outcomes for occupants. One difficulty with understanding AI in buildings is the fact that it can be applied in a variety of ways. There is a range of realistic use cases. The breadth of the technology occasionally creates confusion about what AI actually can do. Here are a variety of use cases that facility managers may be interested in applying in their own buildings:

1. Energy monitoring and measurement and verification (M&V). M&V is a great example of AI, because it takes what can be a very complex set of calculations (creating a building’s performance model) and automates them. Then, new variables, like weather and occupancy, can be used to provide energy consumption estimates using the same model. With enough data to observe the correlation between energy, weather, and occupancy, an accurate model can be used to calculate one of these variables if the others are available. In the case of M&V, actual weather and occupancy can be used to estimate energy use under a pre-retrofit scenario, which can be compared to the actual energy consumption after the retrofit. The difference between actual and predicted energy is a more accurate way to track energy savings (compared to looking at energy bills before and after the retrofit).

2. Demand management — in front of or behind the meter. Understanding, current energy demand is very important for grid reliability. Utilities have to know how much power they’ll need to supply and want to avoid generating too much. Facility managers want to avoid high demand charges for using too much energy at the wrong times. More data on energy consumption and the characteristics that drive that use (such as weather and occupancy) can translate into better predictions about how the grid will behave. With this knowledge, it’s possible to reduce energy demand, save money, and increase grid reliability.

3. HVAC optimization. In terms of demand management, understanding the performance of subsystems, like HVAC, is important. In the summer, cooling demands in an office can be the difference between setting a new, costly demand peak, or avoiding a hefty charge. AI can provide cost savings by pre-cooling a building in the early mornings based on calendar/meeting and historic occupancy data. The building’s HVAC system would start early in the morning, when energy is less expensive, and begin cooling space for the day ahead, all without human intervention. Moreover, if a building has used a pre-cooling strategy in the past, AI may help improve future precooling efforts. As the Google data center example suggests, there are significant opportunities for savings by employing AI.

4. Equipment predictive analysis. Data pulled from complex machines found in buildings, like chillers and boilers, can be overwhelming to facility managers. But, when these data streams are analyzed by a software solution, trends may appear. This analysis may indicate a high likelihood of failure in the near term, based on the condition of the equipment and reasonable estimates about how it is used (such as expected operating times). The additional insight, which may help a facility team plan upcoming maintenance, can reduce unexpected equipment outages, add predictability to the budget, and keep occupants comfortable.

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