The answer is probably in the data

Note: Second part of "The answer is probability in the data" - Who will guess the title of the third part?

Someone, it must be Heisenberg as he made a theorem out of it, once said: “Uncertainty is absolute”, quite a challenging statement for us mere mortals, always looking for a bit of certainty. We do like certainty, it is reassuring, it is safe. Living outside of our comfort zone is not our forte, it makes us, unsurprisingly, uncomfortable. From that point of view, disruption does not help as it breaks apart what we use to know and takes away the certainty we thought we had. Then again, a small fringe of us, the so called early adopters, are always keen to discover new grounds. This is how we keep making progress and, going back to the beginning of this discussion, it looks like the discreet has once more the upper hand on the continuous; we reach the ceiling, and through a ground breaking process discover a new floor.

Our capability to explore data has been used to identify patterns. A lot of hedge funds companies have been making fortunes in the early days of Big Data, pushing the boundaries to catastrophic results for the unaware (GFC anyone?). This is the trick, Big Data and its tools can tell us what is most likely to happen but how much can we trust what will happen most probably? What if a black swan appears? (Nicholas Taleb has written a great a book on the subject: The Black Swan: The Impact of the Highly Improbable). Some people will never accept the highly probable because it is not certain, some others will use it to increase the speed of their multiple decision making process.

Let’s focus for a while on Building Management Control Systems (BMCS), where data are used to automatically maintain conditions such as temperature, pressure and humidity with the best possible accuracy in buildings. This has been a building centric exercise for decades, where inputs and outputs installed on various assets of a building are connected to field device controllers with local computing abilities to maintain set points by the mean of Proportional-Integral-Derivative (PID) loops. This method allows to maintain accurately temperature, humidity and pressure set points. It creates a lot of dissatisfaction as well, just ask any person working in a building what their views are on air conditioning, they would probably tell you that the acronym BMCS stands for something completely different, like Big Mess Coming Soon. The issue here is, a given temperature of let’s say 21 °C is not perceived in the same way by two different people; one might say “too cold” while the other might say “too warm”, from there how do you reach “perfect”? This is the predicament any professional in the air conditioning industry is entangled with.

Big Data has brought some element of response to help solving this conundrum by correlating data that were not only building centric. It is now possible to maintain conditions in buildings while maximising the percentage of least dissatisfied people living in it; the immediate perk of this approach is the energy savings it generates. The correlation of interval data coming from the BMCS, the utility and the bureau of meteorology, associated with the thermal comfort model, allow to make real time set point adjustments as external conditions change. The continuous slight drift helps maintain the best possible conditions in the building, consuming the least amount of energy and with the least amount of dissatisfied people. Not perfect but not bad.

In my view, this is a step in the right direction despite the relatively rudimentary approach. What I mean by rudimentary is some of the variables are still set manually by the data engineers watching the results of the machine learning algorithm, like the thresholds set as limits for the set points to drift. Nonetheless, these are the first steps of data driven decisions for air conditioning in buildings.

In a not too distant future, all buildings will be able to make data driven decisions, not just on the air conditioning side, but on all aspects, from the way they are built, the life expectancy of their assets, true preventative maintenance, lighting, water, waste, etc… Their only limitation will be how connected they are.

Do you remember the days when a phone was just a phone? We all know what happened, they became mobile and then they became smart as in intelligent. The same is happening to buildings and to a larger extend to cities.

How does this happen?

In a word: App (A self-contained program or piece of software designed to fulfil a particular purpose)

In the same way that smart phone users stay connected to the world by downloading apps sitting in the cloud, buildings and cities will more and more stay connected to the world by using Software as a Service (SaaS) sitting in the cloud. The challenge is enormous, given the incredible power these SaaS will have to make data driven decisions for buildings and cities.

To be continued…