Hyperaware Smart Buildings: The Evolution of Building Automation

The development of the Internet of Things (IoT) has paved the way for smart buildings that can detect, analyse, and react to their surroundings. These structures can reduce energy consumption, improve occupant comfort, and streamline building operations. Demand for such a solution is booming, with the global commercial Building Automation Systems (BAS) market expected to reach 273 billion dollars worldwide in 2023. But what if we could take it a step further and design a smart building empowered to be “hyperaware” which allows a building to anticipate its occupants’ needs before they do?

A hyperaware smart building is a building that uses powerful artificial intelligence (AI) and machine learning (ML) algorithms to create a highly-efficient building that provides a comfortable and personalised environment for its occupants.

The implementation of AI and ML is still a fairly new subject in building management, but it has a huge potential in making building hyperaware. For example, Miller, C., Nagy, Z., & Schlueter, A. (2018) highlighted the usage of motif detection (a machine learning method of identifying patterns that occurred frequently in a data stream) in chiller optimisation as a means to identify the best mode of operation for optimal performance. Such implementation not only reduces the frequency of physical inspection but also allows analysis of lifetime data stream that shows lifetime performance and health of building systems.

Utilizing the IoT

In hyperaware smart buildings, data collected from IoT sensors are processed by IT systems to provide critical insights into a building. While IoT sensors are used in regular smart buildings as well, in a hyperaware building, the number of sensors, the level of detail they provide, and how AI uses the information allows us to take smart buildings to the next level. 

Using IoT sensors in HVAC systems of hyperaware buildings reveals trends in air temperatures, air volumes and other factors that affect comfort occupancy. Such insights and trends are turned into AI and machine learning-powered decisions that are adjusted without the need for human supervision to create a comfortable environment for tenants.

While in a regular smart building room temperatures can be adjusted using automation software, if the setpoints are not optimized the first time they are implemented, it will require human intervention to change them. In a hyperaware smart building, AI and ML will learn from the constant data stream and adjust HVAC systems automatically to create the best possible environment.

Predictive Maintenance or 97% Accuracy

Hyperaware smart buildings can improve facility management by enabling predictive maintenance. Any area within a building that is difficult to access or requires medium to long-term intervention can be monitored using IoT sensors and managed using AI and ML. One example of such application is the utilization of artificial neural networks (ANNs) and support vector machines (SVM) to predict which mechanical, electrical and plumbing (MEP) components require maintenance. Research done by Cheng et. al. (2020) shows that both ANN and SVM can be used to predict the health of MEP components with up to 97% accuracy.

Reducing Energy Use

Hyperaware smart buildings can also optimise energy consumption by forecasting and reacting to occupancy trends. The building utilizes machine learning to analyse data from numerous systems to make real-time modifications and reduce energy consumption.

A methodology used by Ruiz et.al. utilizes ANN to process historical data of energy consumption and then produces forecasts of future energy use. The results of such a forecast are used to predict and then prevent energy waste to maximize energy efficiency. Moreover, this framework is able to process data from multiple sites, allowing energy monitoring, forecast, and management of buildings worldwide.

Efficient Daily Maintenance

A hyperaware smart building can also improve operations by automating duties like cleaning and maintenance. Data from sensors can be used to schedule cleaning and maintenance chores based on usage patterns, reducing downtime and increasing efficiency.

For example, utilizing wireless ammonia sensors in a building’s public toilet notifies janitors for cleanup when presence of ammonia in the air has reached a certain level. This reduces janitorial workload and allows janitors to focus on toilets that requires attention. Water leak detectors installed in water lines notify building owners of early-stage pipe failures and triggers a water shutdown to prevent water damage.

The Future of Buildings

Hyperaware smart buildings represent the next step in the evolution of smart buildings. Not only can they create a comfortable and personalised environment for their occupants, they also improve security, optimise energy usage, and streamline operations by leveraging advanced AI and ML algorithms. The benefits of hyperaware smart buildings show the way to an exciting future in building development.