The ENTOUCH Blog

07. 31. 2017

Why Machine Learning and Predictive Analytics Are Crucial for Building Operations

Smart buildings cannot exist without predictive analytics and machine learning in today’s data-reliant world.

The Internet of Things has the capacity to supercharge the savings and value in energy management and building operations. However, simply collecting data and reviewing it manually is insufficient to derive real value, explains Rob Patterson of RTInsights.com. This is where machine learning and predictive analytics come into play, resulting in better efficiency, productivity and control over your smart building operations.

Machine Learning Allows Systems to Identify Optimum Functioning Levels

The foundation of building operations relies on the ability to review current systems and past data for trends and correlations. As explained by this graphic, published by Memoori Smart Building Research, automated buildings of the past were engineering intensive, manually tuned and static.

building operations predictive analytics.jpg

In other words, they collected data, but this data did not necessary result in long-term savings beyond automated systems, using timers. However, machine learning allows systems to actively learn and tune their systems to the ideal setting.

Machine Learning Produces Expectations for Building System Performance in New Conditions

The ability to learn more about the building’s environment means the system can create set expectations for how systems should perform in new or changing conditions. This may apply to changes in temperatures, humidity and resource availability. Not having the ability to adapt to changes limits the system functions and benefits. Fortunately, smart building systems can predict how changes will impact building operations and adjust them to continue seamlessly.

Predictive Analytics Detect Indicators of Problems Immediately

When a problem arises in equipment, one of two things may happen. The equipment deteriorates further and becomes unrepairable, or it may shut down. Unfortunately, detecting the subtle changes in function as the problem begins is impossible without systems analyzing equipment and sensor data.

For example, a malfunctioning HVAC unit may still cool a room, but the subtle change could be that it requires longer times to cool the same space to a pre-set temperature.

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Yet, predictive analytics can identify this change and look for other indicators of problems. As a result, the system can trigger an automated alert to building operations’ maintenance team members or the energy management platform account manager. Thus, the problem can be fixed before it spirals out of control and permanently damages the equipment.

This Reduces the Chances of Failing Equipment Impacting the Occupant Experience

 Damaged equipment is only part of the equation. When equipment malfunctions severely, it can increase the fire risk and risk of damage or injury to occupants and assets within your facility. Fortunately, machine learning and predictive analytics reduce this risk by effectively eliminating the threat before it can come to fruition. This level of real-time control and management further optimizes building operations and reduces the impact errors or problems may have on the occupant experience.

Find out what data we provide in our quarterly business reports to our clients.  Vist this link to download the QBR.

Both Predictive Analytics and Machine Learning Are Essential to Leveraging the Power of Smart Buildings

The smart building revolution is continuing to shatter the standards set by traditional energy management systems. However, smart buildings cannot successfully self-manage and self-monitor their operations without both machine learning and predictive analytics technologies. So, your company needs to make sure that your existing energy management system includes these technologies. If not, contact ENTOUCH today to find out how you can take advantage of a connected system with machine learning and predictive analytics at its core.

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