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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Would you drive a Ferrari for a trip to the grocery store?


TRIRIGAFEEDIA is proud to present its first guest post by Dennis Salguero, IBM TRIRIGA platform developer. In his article, Dennis guides us through the nuts and bolts of data science and looks ahead at the best way to tackle cognitive IoT.

Understanding Cognitive IoT: Would you drive a Ferrari for a trip to the grocery store?

By Dennis Salguero

As IBM focuses more on being an IoT and Cognitive entity, many of us are being asked to think of ways to use machine learning within our teams and products. The recent partnership with The Weather Company also presents various opportunities for a cognitive approach. But along with this new focus comes a dizzying combination of terms: analytics, data science, machine learning, predictive algorithms, cognitive programming, etc. In this article, I will try to present a fundamental approach to all of these items and what they may mean for you and your teams within IBM.

If you are already a data scientist or Watson specialist at IBM, then this article is probably not for you; I would expect that you already understand these concepts. This article is for everyone else: the sales teams, the programmers, the DBAs, the trainers, and so on. There are very few corners of IBM that won’t be touched by a cognitive focus and many of us have been asked to think about it in some way or another. Let this article be an introductory guide to further your learning — however deep it may need to be in your given position — and help you understand what a cognitive approach means to you. If you finish this article and want to dig deeper, I provide some learning resources at the end too.

Let’s establish some norms before we begin. While it can be somewhat of a moving target, the en vogue term for what we are about to discuss is data science. Throughout this article, you can take data science, machine learning, algorithmic programming, predictive models, etc., to all mean the same thing. In the cases where the terms need to be differentiated, I will make that clear. Also, when I refer to Watson, I am referring to the Watson Analytics version that is available to all of us as an online tool. Obviously, Watson gets more complex than that web package, but Watson Analytics is an excellent way to get working with these concepts and can make for great demos for your teams and/or clients.

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