Join leaders in San Francisco on January 10 for an exclusive night of networking, insights, and conversation. Request an invite here.

Autonomous building tech startup BrainBox AI today announced the closure of a $12 million CAD (~$8.59 million) convertible debt round, which it plans to put toward expansion in North America (particularly its home market of Canada) and international markets like the U.K., Ireland, and Australia. The cash infusion comes as companies unaffected by shelter-in-place orders aim to head off the economic impacts of the pandemic. BrainBox’s AI platform enables the heating, ventilation, and air conditioning (HVAC) in buildings to operate autonomously, netting up to a claimed 25% reduction in total energy costs and a 60% boost in employee comfort.

BrainBox, which was founded in 2017 by CEO Sean Neely and CTO Jean-Simon Venne, taps 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 gathering data from both internal and external resources, including hundreds of thousands of real-time data points, like temperature, sun/cloud positioning, fan speed, duct pressure, heater status, humidity levels, 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 aggregating equations for each zone to create an overall profile.

After creating this profile, BrainBox’s engine makes educated predictions about future power consumption, writing back 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.

HVAC-optimizing AI isn’t exactly novel. Augury, a startup developing sensors that attach to machines and record data that’s then analyzed in the cloud, works with service companies to diagnose and optimize systems like industrial HVAC. GE Digital’s Predix and startup Petasense offer similar Wi-Fi-enabled, cloud- and AI-driven monitoring sensors. Carbon Relay boasts a product that leverages sensor data to make predictions about datacenters’ cooling usage. And Google recently rolled out a feature to Nest-branded thermostats that’s designed to detect HVAC problems.

VB Event

The AI Impact Tour

Getting to an AI Governance Blueprint – Request an invite for the Jan 10 event.

Learn More

But BrainBox says it’s collaborating with a number of prominent partners and institutions to refine its platform. These include 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 near term, it hopes to develop an algorithm that will map all building HVAC control points while abiding by government and industry standards.

Another point in BrainBox’s favor is its market momentum. In the 11 months since the company’s May 2019 launch, its platform has been installed in over 15 million feet of commercial real estate across 15 cities on three continents. Locations include airports, hotels, residences, long-term care facilities, and grocery stores. Over 40 buildings have active installations, some of which are larger than a million square feet.

“The autonomous nature of our technology makes it truly scalable, and as we are reducing energy consumption in buildings by over 20%, we are in a position to make a real positive impact on GHG emissions from the real estate sector, one of the largest emitters globally,” BrainBox chief business development officer Sam Ramadori told VentureBeat via email. “For the entire team at BrainBox AI, it is very exciting to be growing a global business that can truly make a difference on climate change.”

Headquartered in Montreal, BrainBox has 50 employees today and expects to employ over 100 by year-end. This latest funding round, which was led by Esplanade Ventures, with participation from Desjardins Capital, brings the company’s total raised to over $19 million CAD (~$13.6 million).

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

The post BrainBox raises $8.59 million to optimize HVAC techniques with AI appeared first on DAILY SAN FRANCISCO BAY NEWS.

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.”

The post BrainBox AI Appears to Software program to Reduce HVAC Working Prices appeared first on DAILY SAN FRANCISCO BAY NEWS.