Buildings account for around 40% of carbon emissions in the U.S., even accounting for the vast improvements to HVAC systems in the last three decades.
Multi-family and commercial buildings are prone to wasted energy through heating, cooling, and lighting because they try to serve a multitude of needs with a singular system. Unused shared spaces – lobbies, conference rooms, shared gyms – unnecessarily pull energy for temperature control during off-times because they never know when they might be occupied.
In 1992, the Energy Department set out to change the heating and air industry and incentivize it to focus on conservation. The push was successful, and today, HVAC systems require around 50% less energy to cool a space.
But the reduction in carbon emissions has plateaued since 2010. So while leveling off is better than increasing, we can do better.
Evaluating Your Ability to Reduce Greenhouse Gas Emissions with Computer Vision and AI
Businesses have been trying to come up with better ways to manage buildings for decades. Buildings have been using carbon dioxide sensors, people counters, and timers. But these systems are riddled with flaws.
Legacy devices used to count people may be relatively accurate for some spaces that aren’t crowded but struggle to keep up if an area becomes too congested and just aren’t precise enough for HVAC control. Plus, they have a single function, so additional systems are required to make the information useful.
Similarly, CO2 detectors have significant limitations. These detectors are supposed to improve air quality, but by the time the CO2 reading takes place and is relayed back to the HVAC system the room is already uncomfortable. Not to mention, if a room becomes unoccupied, the CO2 sensor will take 20-30mins to register that nobody is in the room. That equals 20-30 mins of wasted energy.
Motion detectors are often utilized for lighting purposes, and while they serve their purpose to a point, they don’t collect any useable information or integrate with larger systems and are not accurate enough for HVAC.
Additionally, fixed schedules can be used for ventilation and lighting systems alike but are generally set based on estimated busy times without verification of actual occupancy. Too often, timers are set to run more hours than necessary and generate substantially wasted energy.
These techniques were a start but didn’t eliminate enough wasted energy to continue to be a viable option.
Now – through computer vision and artificial intelligence – building systems don’t need to be run based on loose estimates. Instead, they can be operated based on concrete data, including occupancy and safety compliance measures.
With precision data, building systems don’t have to waste energy based on general estimates or assumed busy times. Computer vision accurately reflects the occupancy of the space in real-time, triggering systems like HVAC only when needed.
Also, historical data is stored and presented so that building owners and managers understand how frequently an area is in use and the duration. The information helps eliminate wasted space and potentially repurpose them or limit the resources allotted to keeping them up and running.
Nomad Go AI for buildings isn’t a one-function setup but a comprehensive network that provides invaluable data and communicates directly with vital building systems.
With a visual intelligence system like Nomad Go, you don’t have to invest in expensive, single-purpose equipment. Nomad Go runs on standard smart devices and uses their processing system to extract diverse pieces of valuable data at once, making the setup more efficient than other options on the market.
Moreover, since Nomad Go computer vision with AI isn’t just hardware, the solution can be adapted and updated as new needs emerge. This distinction ensures you aren’t buying into a solution that could quickly become obsolete as circumstances change. As well, Nomad Go can integrate with your existing camera infrastructure, so a zero-cost hardware solution with the added power of AI and computer vision.
Cutting Carbon Emissions: An Inside Look
Specializing in multi-unit property management, Greystar is always on the lookout for technology that will help its properties function more sustainably.
Greystar piloted Nomad Go’s computer vision technology in the shared spaces of one of their high-rise residential buildings. They were hoping to reduce the building’s variable energy usage and reduce carbon emissions.
Impressively, Greystar cut their HVAC usage to 34% (down from 92%) in their building’s main common area, reducing 280 tons of carbon emissions annually.
McKinstry is another large organization that has used Nomad Go, but in this case, it was to reduce the run time of their HVAC system. Before using our computer vision, its HVAC system ran 11 hours a day, but after utilizing our AI-advanced insights, the time was cut to 4 hours.
The drastic drop cuts down on maintenance and wear and tear of equipment, and of course, decreases their emissions.
What’s more, the air quality and comfort of the room improved because the system is immediately responsive to increased occupancy.
Buildings operating based on accurate, precise data are the best way to reduce carbon emissions as we move into the future. But the reduction in emissions is only one benefit. Businesses will also save resources by eliminating unnecessary equipment deterioration and lowering the costs associated with energy waste.
Nomad Go Joins with Microsoft and Sony
The demand for modern practices that improve sustainability and reduce emissions will continue to rise. Consequently, businesses must seek versatile solutions that can adjust to the times without requiring another investment.
Nomad Go has recently partnered with Microsoft and Sony to enhance the insights provided to building management, enabling them to make educated choices on how to best control their spaces. In addition, the Sony/MSFT partnership enables the deployment of Nomad Go without any CAPEX cost (low-cost sensors that cost zero dollars upfront is a great bonus).
Using AI-driven technologies, your building’s systems can be transformed from inefficient to on-demand — at a fraction of the price it cost not long ago.