3 Emerging Technology Trends in Building Automation 

By August 17, 2017Product News

Generally speaking, we see technology trends progress at a much faster rate in consumer products like cell phones and televisions than we do in the building automation industry. This is mostly because the building automation industry is slow to adapt to new technology, wanting to see it proven in other fields before implementing it in large-scale commercial applications. Here are a few technology trends that we see in your daily lives that we are starting to see or will soon see in building automation.

Cloud Computing

Cloud ComputingCloud computing has been prevalent in consumer products for nearly a decade. Perhaps the most common example we see in our day to day lives is in intelligent personal assistants on our phones, such as Siri. The technology to run this natural language user interface does not exist in each device, rather it exists in a cluster of computers in a data center. Each time an individual makes a request of Siri, it is sent to a data center, analyzed, and a solution is returned to the individual device. This happens so quickly that it may seem that each device is equipped with its own personal assistant.

For the purposes of building automation systems, we are already starting to see many front-end user interfaces being moved to the cloud. Rather than having a physical head-end computer that is used to access the BAS in each facility, there are now hubs that receive and transmit data to the cloud. This allows the facility operators the ability to access their system on any internet connected device.

Moreover, by moving this data to the cloud, we are able to analyze how buildings are operating with more capable machines, giving facility managers insights into how to operate their HVAC and lighting systems more efficiently. Accumulating this “big data” and its subsequent insights for hundreds or thousands of buildings then enables BAS companies to utilize machine learning algorithms for research into the patterns and commonalities of well-run buildings.

Machine Learning + Artificial Intelligence

Machine LearningYes, this is where Skynet begins, but rather than using science fiction movies as examples of machine learning, let’s try a harmless, real life music app that most people have come across at some time or another.

Pandora is a free music streaming application that makes automated recommendations based on the music listeners have previously “liked”. First, users choose a genre, artist, or song they like and a “station” is created. Based on particular features of that genre, artist, or song, Pandora plays what it perceives to be similar tracks. Users then indicate, in the app, whether or not they like this song. As listeners feed Pandora more information about their preferences, the app “learns” what kind songs they will and will not like.

Unlike cloud computing, machine learning and artificial intelligence are not nearly as common in today’s building automation systems. Some systems have features built into their software that represent the beginnings of this technology, but none have been able to capture the true benefits we foresee for large environments.

Much like Pandora, your BAS needs some initial input to effectively start learning. This initial input will likely come in the form of amps, voltage, temperature, etc. from your various field devices. One of the greatest advantages we gain from machine learning is the ability to recognize patterns and anomalies. It’s in this advantage where we find our first application for machine learning in building automation.

As your BAS is able to start distinguishing what is “normal” for your system through pattern recognition, it can also start detecting abnormalities. For example, your system might be able to notice if an air handling unit has a progressive decrease in amperage over a long period of time and notify you to make a preventative maintenance call on the fan belt. This is only possible because your system was previously given data that indicated what the normal amperage was.

The other, perhaps even more exciting, use case for machine learning in building automation is in predictive analysis. By combining historical weather and BAS data with future weather forecasts, your building automation system of the future will be able to predict how our facility is going to run the next day. Facility engineers will be able to fast forward through the following day and see how each piece of equipment behaves and how much energy is used at various times of the day. They can then make adjustments to optimize the functions of their mechanical systems.

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