January 2015
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AutomatedBuildings.com

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Belimo

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Learning from People

By using input from humans, we can greatly improve the systems, and therefore buildings, that serve our occupants.
David Weidberg
David Weidberg,
Client Solutions Manager
Building Robotics

 

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The time is now! With machine learning at your doorstep, what do we do - run, hide or embrace?  No, I am not talking about the Terminator, but I am talking about your existing building having the capacity to evolve and learn from the surplus of data that already exists in your facility and within your occupants.   

While the concept might sound new, in the book How Buildings Learn, Stewart Brand professed the philosophy of ever-adapting buildings long before machine-learning technology existed for buildings.  As he writes, “First we shape our buildings, then, they shape us, then we shape them again – ad infinitum”. He is describing the notion that buildings are not static, but that they are dynamic and continually changing based on occupants, technology and environment.  Machine learning can play a vital role in enabling buildings to adjust to peoples’ needs, thus helping shape the buildings in which we work.   

To the average person, the concept of machine learning is only foreign by name. We interact with machine learning on a daily basis. For example, Amazon, Netflix, Google, and countless other companies use machine learning to personalize your experience on their websites and provide predictive insight into products that you might like based upon your previous inputs.  These companies have invested heavily in machine learning because people respond to curated content in the form of loyalty or purchases.   The reason people use these services is because they provide a tailored experience – one that provides more meaningful content and saves time from browsing through unwanted content.

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Much like people’s experience on the internet, building occupants want meaningful personalization in their workspaces.  The concept of changing temperature or lighting levels at work might seem trivial, but it is something we all control in other environments, including one’s home and car.  For building managers, giving occupants this type of control has always been controversial due to energy concerns and the difficulty of managing different preferences among occupants.  So, how can machine learning help?

Reliable Controls Machine learning creates a perpetual learning environment by using various data streams from the building management system along with the use of human input. Comfy, the software we make at Building Robotics, employs machine learning in exactly this way.  It works by engaging occupants while providing them with an instantaneous temperature change. The machine learning then continues to learn from occupants’ input, providing preferred temperatures based on each persons preferences and trends.  With continuous learning enabled, building systems react immediately and are only used as needed.  This saves energy and therefore allows buildings to run more efficiently.

So, as the Terminator once said, “the more contact I have with humans, the more I learn”.   Interestingly, we can now see the same is true with buildings.  By using input from humans, we can greatly improve the systems, and therefore buildings, that serve our occupants.  So don’t run or hide, but embrace the benefits of a smart building that incorporates machine learning.



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