HVAC systems represent one of the largest components of building energy usage and carbon emissions. An average industrial facility is wasting over 20% of electricity leading to large operations costs. Furthermore, one faulty HVAC system can cost over $6,500/employee/year in employee productivity. Efficient operation of a building depends on the reliable environment, process and behavioral data. However, the availability of such data is still lacking. Many of HVAC systems have undiagnosed issues, which can cause crippling failure that can disable an operation. Furthermore, our buildings often have serious contamination issues, which are not just affecting the energy efficiency of the buildings, but also our health. This problem is particularly significant in hospitals and clinics, which spend over $6.5 B spent on energy and are affected by over $45 B annual direct operation costs due to contamination due to malfunctioning air distribution systems. There’s an untapped opportunity of innovation.

What it does

WeavAir harnesses advanced sensor technology and predictive analytics to: 1) reduce HVAC maintenance costs and equipment failure, 2) reduce building operation costs, 3) improve health and productivity of employees. We develop patent-pending modules that attach to vents to measure the air coming out of these systems. WeavAir system can also reduce the liability, helping building owners comply with green building standards and building code while improving the workers’ health and productivity. The technology can be used for quality control of industrial processes as well as manufacturing contamination control. It is suitable for integration in a variety of building types including but not limited to healthcare, commercial, industrial and residential buildings.

How I built it

WeavAir solution was built using C firmware running on Atmel MCU and real time data processing algorithms running on AWS Cloud

Challenges I ran into

Sensor calibration and correction were the biggest challenges that were overcome with signal correction and pattern recognition system

Accomplishments that I'm proud of

Improvement in accuracy by over 30%

What I learned

Sensors need to be calibrated using variable data set

What's next for WeavAir

Pilot testing in commercial and healthcare buildings in Canada, US, Taiwan and Korea (partners already identified).

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