Smart buildings: Predicting your buildings' energy needs

Tue, 2013-06-25 12:32 -- Jesse Berst

We told you earlier about Microsoft's powerful foray into smart building management. It is impressive for its ability to work with myriad devices and myriad building management systems. That kind of integration is oh-so-difficult but oh-so-necessary if we hope to retrofit and connect existing real-estate. I am particularly impressed by the way the system rank-orders the alerts it gets. It knows which alerts from which buildings are most important and sends the maintenance crew to the high-value problems first.

Here's a similar system developed at Columbia University.It likewise prioritizes actions. This system is also notable because it doesn't just record what is happening. It also predicts what the energy and comfort needs of the buildings ahead of time (while there is time to take action). -- Jesse Berst

From the Columbia University Engineering…

Innovative technology developed by Columbia Engineering’s Center for Computational Learning Systems (CCLS) is the driving force—in effect, the brain—behind Di-BOSS™, a new digital building operating system that integrates all building operating systems into one, easy-to-use cockpit control interface for desktops and portable devices, including laptops, tablets, and smart phones. This new machine learning technology, known as Total Property Optimizer (TPO), combines the need to provide comfort and safety for large building managers and tenants with situational awareness, energy savings, and re-commissioning (continuous optimal performance), and provides the smart analytics and communications needed for real-time operations.

“This system is so effective that in the last six months, it has already realized more than a half million dollars in energy savings in over two million square feet of Rudin Management properties in Manhattan, resulting in an astonishing return-on-investment. And this savings was gained in Rudin buildings that were already citywide leaders in energy efficiency,” says Roger Anderson, senior research scientist at CCLS who led the advanced technology team.

TPO computes two kinds of forecasts every hour: a 24-hours-out forecast for energy and comfort throughout the building and a more dynamic “Now-Cast” that predicts two hours ahead where the building will be if no corrective energy and comfort changes in HVAC are made (see Figure A). The Now-Cast is unique to TPO, and designed to enable operators to “steer” the building to comfort, making improved real-time operational corrections that can then save in energy usage. The CCLS Team worked closely with Rudin Management, one of the largest privately held property management companies in New York City, to create this transformational system, and through Columbia Technology Ventures, licensed 19 patents covering the machine learning methods and techniques.

One of the system’s primary features is its ability to continuously track occupancy. “The technology to link the building management system with occupancy to control energy use is a cutting-edge capability,” Anderson observes. “The ability to track occupancy on a large scale and link who is where and when to energy use is a key component to its success.”

Read more about Di-Boss >>


Jesse Berst is the founding Chairman of the Smart Cities Council. Click to subscribe to SmartCitiesNow, the weekly newsletter highlighting smart city trends, technologies and techniques.