AI: The brains.

Internal building facilities that can be controlled and optimized by an automated, centralized system include heating, ventilation and air conditioning (HVAC) and lighting, and can account for as much as 50% of energy use in an average commercial building2. Reducing day-to-day ‘operational’ emissions from HVAC systems is one way that building operators can satisfy increasingly stringent environmental legislation, as well as keep energy costs down in light of spiralling electricity prices.

AI platforms that connect directly to a building’s HVAC system, and utilize data to optimize control, can help reduce a building’s carbon footprint, supporting net zero and cost reduction goals.

AI also is being used to power chatbot tech support for smart home and commercial building technology which has a doubleadvantage of also gathering customer feedback data for future product innovation and development.

Intelligent Hvac Systems

Existing HVAC systems, even in larger buildings, will likely use a basic, non-programmable thermostat that automatically triggers the heat or air conditioning through an electrical connection when the temperature deviates from the desired setting. Using AI transforms the building’s HVAC system from reactive to pre-emptive, monitoring it 24/7 and making decisions based on advanced deep learning models. Autonomously driving HVAC systems in this way eliminates the need for human interaction.

Let’s take the scenario of an office lobby to demonstrate how an HVAC system can be upgraded to learn, reason, and even solve problems. Newly installed revolving doors in the lobby let outside air in. A standard HVAC system would have wasted energy as it struggled to regulate the fluctuating temperatures, whereas the AI upgrade quickly learns and adapts to its changing surroundings.

Similarly, using machine learning (ML), AI reasons that the office’s communal area will grow warmer at lunchtime as more workers congregate in that particular space. Not only that, predicting that the room will be warmer at this time, AI can automatically adapt and adjust the air conditioning prior to lunchtime, simultaneously ensuring maximum comfort for the occupants while conserving energy.

The system can even optimize the energy mix by turning on or off earlier or later in a given zone, based on energy costs (in peak times), runtime hours and energy mix – further improving the ROI and reduction in carbon footprint.

Connecting software and control hardware to existing HVAC systems so they become selfadaptive and more intelligent can delivers an ROI of more than 150 percent4, with building owners and operators report decreased energy costs of 30-50 percent through investing in smart automation.

Building A Cleaner Future

Our recent collaboration with pioneering Montreal-based scale-up BrainBox AI, is a realworld example of how AI is being used to great effect to optimize HVAC systems in the urban built environment across a wide range of asset classes, including commercial office towers, retail shopping centers and hotels.

The resulting technology solution is built on a digital platform and powered by cloud-based artificial intelligence, in order to provide the owners of smart buildings with a simple way of optimizing their energy consumption.

It does this by employing predictive algorithms that eliminate the need for human intervention, as well as an array monitors, analyses and reports that help building owners and managers to track energy efficiency, all of which can be seamlessly integrated into a building’s existing HVAC system.

The solution is currently being used in around 100,000,000 ft2 of commercial real estate and BrainBox AI report companies achieving up to 40% decreases in carbon footprint, up to 25 percent in energy costs, and up to 60 percent improvements in occupant comfort. This adds up to 50 percent to the lifecycle of HVAC equipment.

As a result the solution is already providing cost and energy savings in buildings as diverse as shopping centers in Australia, office and retail buildings in Canada, and an 82,500ft2 medical center in New York, US.

How Ai Is Optimizing Customer Support

Another collaborative innovation involves an AI-driven hardware support platform called Mavenoid, which combines AI and ML to reduce the complexity and cost of customer support by solving enquiries with high success rates and limiting the number of escalations to human service teams.

The technology utilizes data from sources such as manuals, product documentation and online communities to train its ML and AI engines to resolve repetitive support requests. Providing automated support via the existing App, enabling self-resolution and significantly reducing the number of support cases referred to help centers.

Despite $1.3trn8 being spent globally every year on 256 billion customer support calls, 50 percent of them still go unresolved. In addition to responding to this challenge and improving customer satisfaction, the Mavenoid’s platform allows installers to add new services and leverage end-user feedback to drive future product developments, and is already being used in selected markets with smart home solutions.

These latest innovations not only demonstrate the flexibility and scale of industry 4.0 technologies such as AI, ML and the internet of things across multiple industries and use cases, but also their almost limitless potential to reduce both complexity and cost, improve efficiencies and contribute to a cleaner, more sustainable world.