While the global architecture development trends in 2023 will all be closely related to the employment of AI, the top priority applications will focus heavily on three major areas: energy management, healthy living, and security surveillance.
Growing Health Awareness Post COVID-19 Prompts Smart Tuning of Indoor Air Quality
COVID-19 has boosted a general increase in global health awareness. As the pandemic slows down, people begin to return to their workplace, where better and healthier environmental conditions are gaining attention. This has driven the health applications of AI in buildings in areas such as: 1) air quality monitoring and water resources management—monitoring water pipes and water temperature, warning of germs; 2) environmental cleaning management—detecting which desks and toilets have just been used to facilitate precise cleaning; and, 3) comfort improvement—automatically adjusting curtains according to sunlight and occupancy.
Air quality monitoring employing AI smart control enables a comparison of the air data collected by sensors against the density of the space’s occupants through an AI model, which can continuously learn about the correlation between air conditioning, air quality, and the number of occupants before making optimized decisions. To welcome employees returning to work in the post-pandemic era, the British life insurance company RSA installed many IAQ air quality sensors in its building to monitor various office spaces of different sizes. The collected real-time data are imported into an AI cloud platform. Once the air quality in a certain working space is found not meeting the standard, the platform will automatically propose countermeasures, such as increasing the supply of fresh air or guiding people to a space with better air quality.
Applying AI in Energy Efficiency Strategies Involving Renewable Energy to Ensure Comprehensive, Timely Results
In addition to the rise in health awareness following the COVID-19 outbreaks, another important driver to smartening up buildings through AI is the sustainability norms such as net-zero carbon reduction and ESG goals. Many buildings have installed solar solutions on their roofs to reduce their carbon footprints, but the nature of solar energy being dependent on the weather and intermittent in generating power makes power generation unpredictable. If external data such as real-time weather data can be connected and fed to predict the power generated from the rooftop solar panels, it would provide maximal energy use efficiency by using AI analytics results to automate the power sources deployment or to regulate high energy-consuming equipment.
Believed to be the greenest commercial building in the world, the Bullitt Center in Seattle, USA, has installed solar panels and energy storage equipment on its roof to feed electricity to the city grid when the sunlight is strong in summer and retrieve it from the grid when there is not enough sunlight in winter. These moves can be highly automated through AI, by which the system automatically interprets the energy consumption data collected by smart meters and decides in real time whether to store or sell electricity accordingly. A smart building in Chicago also uses an AI energy management system to pre-cool or pre-heat the space according to various factors that affect the indoor temperature, such as personnel activities, weather, and heat generated by electrical equipment.
With Security Surveillance Highly Valued, Big Data Integration Reduces Intrusion Risks
Enterprises are paying more and more attention to the physical security of buildings. In the past, AI applications in security were relatively mature but most of them were about face or image recognition of surveillance images. The latest AI security applications, on the other hand, integrate multiple data, including surveillance cameras, access control, entry and exit motion sensing, and others. For a long time, these security devices have been generating data every second as time passes, but most of them have not been utilized or looked at unless called for by an incident or anomalies. Now with AI, there is a better use for this security big data.
Tailgating is one of the greatest threats to the physical security of buildings, but it is difficult to prevent tailgating with surveillance cameras or access control alone. Existing smart buildings have already used AI to analyze image data and, coupled with access control systems, solve the problem of tailgating. For example, every time someone swipes their card to enter a building, the AI-powered solution can analyze the corresponding images of the surveillance cameras in real time, and if the number of people (or vehicles) does not match, the system will set off an alarm. AI can also comprehensively analyze the data of all incidents of tailgating and identify the most likely locations for preventive measures.
Internally, it constitutes a risk to corporate security when employees access unauthorized areas of the building. However, in light of the massive flow of people entering and exiting the building every day and false alarms due to inadvertent moves, it is impossible to accurately determine whether the intrusion is intentional or just a mistake. AI is able to examine and analyze all invalid entry messages, and if it detects multiple attempts by the same employee to enter a sensitive server room, it can issue a notice for corrective actions. Other AI security applications include monitoring elderly people’s movements in the space for safety in response to the super-aged society. In the event anyone falls or an accident takes place, immediate and timely assistance can be provided.