Planon: From Smart to Learning Buildings with Machine Learning


Organisations and more specifically, their facility and real estate managers are constantly looking to improve our work environment and the buildings that we work in. They want to be cost-efficient, increase productivity, and create a healthy and attractive workplace for their employees.

The Internet of Things and Smart Buildings are providing interesting opportunities to improve our work environments. Achieving this, however, is a big challenge for organisations. What Smart Building solutions are organisations looking for? What is the real value to organisations? How will organisations realise these benefits?

From a reactive to a proactive approach through “machine learning”

The ability of buildings to measure every action or change in behaviour by the building or its occupants is changing rapidly. Nowadays, affordable sensors are available that measure for example space occupancy, air quality, usage of specific spaces or the state of building installations. Data collected from these sensors provides information about these items. We can use this data to make improvements to the work environment, building or user experience.

For example, when sensor measurements show that a meeting room that was reserved is actually not in use, it can immediately become available for a new meeting. In addition, when sensor measurements show that a specific toilet area is used less than expected, the cleaning schedule can be adjusted. However, these useful examples are based on an “If This Then That” scenario, meaning that if an event occurs we react to that event. This is a reactive approach rather than a proactive approach, so can we really call this “smart”?

[Admin: This post is related to the 11.01.17 post about designing smarter buildings that learn. To see other related posts, use the Planon tag or Smart Buildings tag.]

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