Buildings have been among the most enthusiastic adopters of IoT devices. Smart buildings, in example, use linked devices to monitor everything from temperature to lighting to air quality, noise, vibration, occupancy levels, and energy consumption, to name a few.
Building automation is enormous and rising bigger, with over 6 million commercial buildings and an estimated 2.2 billion linked devices in the United States alone. In 2022, the global market for building automation systems will be worth approximately $80 billion.
Fleets of IoT devices are used in this form of automation. Many condition-action responses are automated; for example, if a fire is detected, alarms are triggered automatically, frequently with voice instructions, and fire agencies are notified. That was true before the Internet of Things; now, fire alarms are connected first and foremost via cellular connectivity.
The value of IoT, in building automation specifically, is realized in two main areas:
- The data generated by in-building devices and how it is analyzed and leveraged.
- The actions and management performed by building automation systems
Rich, ongoing data streams provide valuable insights into building operations, but there’s an issue: large device fleets create large volumes of data that humans alone cannot properly parse and understand. To realize the potential payoff from deploying these sensors (and cameras), artificial intelligence (AI) and machine learning (ML) is needed to continuously monitor and assess the data streams.
Automation can’t do the job alone
Until 2020, the emphasis of smart buildings systems, including building automation, was the responsibility of facilities’ management. Then, the focus shifted to employee health and ESG initiatives, in addition to facilities management. This opened demand for capabilities that ML enables.
An AI system can observe air quality and find correlations with occupancy limits, for instance. It can also learn how to reassign conference rooms and cubicles, relating to occupancy and ventilation, with the goals of maximizing the physical distance between employees and improving air quality, to reduce the chance of employee illness.
AI can also help analyze the usage of water supply pipes and water temperature to warn when there is an elevated risk of legionella and other harmful pathogens. Legionella thrives in specific temperature ranges of warm water.
The relevance of new AI-enabled capabilities does not rule out the traditional functions such as tracking and managing energy consumption. With an AI-driven platform, a building can power down areas that are not in use and try different window shade settings at different times, to minimize energy usage. Experiment and learn as it goes. This is a bottom-line issue and will become more important in 2022 due to energy prices.
AI can even play a role in cleaning efficiency, identifying which desks have been used and which toilets have seen increased usage. In the age of COVID-19, facilities managers are focused on cleanliness.
AI can greatly enhance systems that support physical security, too. Once a system learns what constitutes normal access and movement behavior, it can identify anomalous behavior and alert security. Other AI-driven applications can detect duress situations, abandoned objects, recognize weapons, pinpoint shots fired and carry out emergency lockdowns.
An intelligent infectious disease control system can learn to leverage data on local infection rates. AI systems can do things people cannot, like staring at a wall for 20 years and looking for signs of change in the concrete that could herald a pending structural collapse.
Applying AI for smart buildings
The standard starting point for a new AI-driven system is, of course, teaching it. That process begins with a foundation of data that represents the realities that the system will confront. Many will find, however, that good base training data for smart-building systems does not exist. The answer can be to create the training data by running ‘experiments’ in the physical building.
In energy consumption, for example, you can train a system by experimentally adjusting window shades and AC based on the time of day and office occupancy, to lower AC bills without triggering a manual override. Such a system could rely on temperature sensors and occupancy readings, as well as sunlight detection.
There are basic best practices to follow. Be scientific and rigorous when collecting ground truth datasets and collect data from multiple sources to increase confidence that your samples are representative.
AI-driven systems can learn from the occupancy patterns of specific office areas and help reduce human error in space planning. Upgrading space is costly and preserving flexibility is vital. Space utilization and occupancy obviously became a health issue during the pandemic. Employees may now prefer to gather for conversation and coffee on an open-air balcony or patio, not in a small break room.
Where AI-driven building management is heading
AI-powered systems can recommend changes to facilities management and allow building management to be more predictive. When it comes to reactivity, they enable a more effective response to surprise challenges as well. A recent example: before 2020, identifying employees who are running hot (fever) and reducing the probability of infection probability was not a thing, but it is within current capabilities to address this problem.
It takes careful thought and putting in the time, to get the ground truth right. Many commercial buildings have a digital twin; a virtual replica delivered by the architect to the building owner or manager. The digital twin, as a starting point, may well be a testing ground for AI-driven facilities management and smart building management.
We expect that IT, facilities management, HR, and security will become more integrated and make increased use of AI. There is a range of likely benefits from joining their information silos to create data streams for AI applications.
The importance of healthy workplaces, physical security and energy conservation makes it urgent to go beyond simple automation and develop reliable AI-based building operating systems that are founded on robust, up-to-date data. Any of these applications support a strong business case; taken together, they make a persuasive argument that facilities’ management should look at AI-driven applications for operating smart buildings and making