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Autonomous building technology startup BrainBox AI today announced the closing of a CA$12 million (about $8.59 million) convertible note round, which it plans to use to expand in North America (particularly its home market of Canada) and international markets such as the UK, Ireland and Australia. The cash infusion comes as businesses not impacted by lockdowns seek to stave off the economic impact of the pandemic. BrainBox's AI platform enables autonomous operation of heating, ventilation and air conditioning (HVAC) in buildings, reportedly reducing overall energy costs by up to 25% and increasing employee comfort by 60%.

BrainBox, founded in 2017 by CEO Sean Neely and CTO Jean-Simon Venne, uses a combination of machine learning and cloud computing to optimize commercial HVAC control systems. The company first installs its system-agnostic, sensor-free solution to identify and catalog a building's operating behavior and energy flow by collecting data from internal and external sources, including hundreds of thousands of real-time data points such as temperature, sun/cloud position, fan speed, duct pressure, heater status, humidity, and occupant density. After six to eight weeks of data collection, BrainBox's AI engine can identify patterns to isolate each zone's habits and tendencies, as well as aggregate equations for each zone to create an overall profile.

After creating this profile, BrainBox's engine makes informed predictions about future power usage and sends feedback to the HVAC controller every five minutes to make adjustments. Neely claims that most customers see a 20-40% reduction in carbon footprint within the first few months of deployment.

AI for HVAC optimization isn't exactly new. Augury, a startup that develops sensors that plug into machines and record data that is then analyzed in the cloud, works with service companies to diagnose and optimize systems like industrial HVAC systems. GE Digital's Predix and startup Petasense offer similar Wi-Fi-enabled, cloud- and AI-driven monitoring sensors. Carbon Relay offers a product that uses sensor data to make predictions about data center cooling usage. And Google recently launched a feature on Nest-brand thermostats designed to detect HVAC problems.

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However, BrainBox says it is working with a number of notable partners and institutions to refine its platform, including the U.S. Department of Energy's National Renewable Energy Laboratory, the Institute for Data Valorization, Montreal's École de Technologie Supérieure and McGill University. In the short term, the company hopes to develop an algorithm that maps all of a building's HVAC control points while meeting government and industry standards.

Another plus point for BrainBox is its market momentum. In the 11 months since the company launched in May 2019, its platform has been installed on over 4.5 million square meters of commercial space in 15 cities on three continents. Locations include airports, hotels, residential buildings, nursing homes and grocery stores. Installations are active in over 40 buildings, some of which are over one million square meters in size.

“The autonomous nature of our technology makes it truly scalable, and because we reduce energy consumption in buildings by over 20%, we are positioned to have a really positive impact on greenhouse gas emissions from the real estate sector, one of the largest emitters globally,” Sam Ramadori, BrainBox's chief business development officer, told VentureBeat via email. “It's very exciting for the entire BrainBox AI team to be building a global company that can make a real difference on climate change.”

Montreal-based BrainBox currently employs 50 people and expects to hire over 100 people by year-end. This latest round of funding, led by Esplanade Ventures with participation from Desjardins Capital, brings the company a total of over 19 million Canadian dollars (approximately $13.6 million).

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The post BrainBox raises $8.59 million to optimize HVAC techniques with AI appeared first on Los Gatos News And Events.

Last month, BrainBox AI announced the first installation of its HVAC monitoring software in New York City.

In information it sent to GlobeSt.com, the company claims that a building operator can gain up to 25% in total energy cost savings within three months, a 20% to 40% reduction in carbon footprint, and a 50% improvement in HVAC- related operations and maintenance costs.

Not that this is BrainBox’s first attempt. The company says that over three years it has gained installations in 100 million square feet of commercial real estate across nearly 20 countries.

“We do something very simple,” Frank Sullivan, BrainBox AI’s chief commercial officer, tells GlobeSt.com. “Inside buildings large and small, largely commercial malls and multi retail, resides HVAC equipment. We optimize the way that equipment behaves.”

The approach uses Internet-connected control equipment, machine learning (where software uses data feedback to improve its operations), and third-party data like weather and occupancy.

The software doesn’t replace maintenance and HVAC staff in buildings. Instead, it uses data to make multiple changes in buildings per hour and anticipates what conditions need to be in the immediate future.

Buildings have thermal reaction times—or, as Sullivan says, they “glide.” Turning heat or air conditioning on doesn’t immediately warm or cool a space and turning the HVAC system off doesn’t right away cease affecting the temperature. The amount and rate of change depends on current and arriving or departing occupancy, external temperatures, and patterns of use throughout a building. For example, why spend energy on changing the air temperature of an area in the morning that isn’t generally used until later in the day? Or assume that the HVAC needs in April are the same as those in October?

“We’re not taking next week’s weather prediction, but three to six hours in advance, using weather sources closest to the building,” Sullivan says. It takes about three months for the company to create the initial model and then for the machine learning algorithms to make adjustments in response to building-specific data, with the system ultimately providing “a high degree of certainty around the space temperature two to three hours in advance.” While building classes and locations differ, the aggregate information from all of them is useful. “The more data allows us to be more confident with our predictions and provide in many cases a much better outcome.”

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