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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:
Learn more at https://www.brainboxai.com/.
If you have an iPhone, subscribe to the podcast HERE, and if you have an Android phone, subscribe HERE.
Check out our handy calculators HERE.
By Bryan Orr4.9
10031,003 ratings
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:
Learn more at https://www.brainboxai.com/.
If you have an iPhone, subscribe to the podcast HERE, and if you have an Android phone, subscribe HERE.
Check out our handy calculators HERE.

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