Blake Standen and Omar Tabba of BrainBox AI join the HVAC School podcast to talk about commercial HVAC predictive control. This conversation focuses on applying artificial intelligence to existing building systems, including the HVAC, to predict performance under certain weather and occupancy conditions. If you have controls that can predict performance and adapt, then you can maximize energy savings.

A commercial HVAC system may include air handlers, chillers, boilers, RTUs, and all sorts of energy-consuming technologies. These systems also have controls that help direct the infrastructure, and artificial intelligence can help optimize the controls, make performance predictions based on forecast data, and make those controls communicate with foreign controls from other companies (such as via BACnet). 

BrainBox AI uses a cloud to collect and hold the data it needs to predict what a building will do and help control the infrastructure. Controls react to errors, and the goal of BrainBox AI is to predict errors before they happen. For example, AI can help solve short cycling under certain weather conditions. However, buildings that use pneumatics rather than digital controls and older systems may not be good candidates for AI solutions.

One of AI's challenges is that it requires multiple layers of training: you're training the controls engineers, facilities staff, AND the AI itself. Another challenge of AI is that people don't fully understand that it's not the type of automation that takes people's jobs; we can minimize those perceptions with education. 

Blake, Omar, and Bryan also discuss:

• Machine learning vs. artificial intelligence
• Accuracy of prediction models
• Virtual testing environments
• Apathy as a challenge
• What happens when controls go offline or are adjusted?
• Tethered services
• Application programming interface (API)
• Controlling comfort and energy consumption vs. greenhouse gas emissions
• BrainBox's global partnership with ABB

 

Learn more at https://www.brainboxai.com/. 

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