The machines are coming – indeed, they are already here – and they are talking to each other. But rather than being the villains in a science fiction movie, these machines are here to help. They connect our homes, cars and workplaces to the Internet in the quest to make life better.
In late 2013, the research firm Gartner estimated that the “Internet of Things” – the vast network of automated pieces of hardware that are individually connected to the Internet – would consist of 26 billion devices by the year 2020, a number that does not even include laptops, tablets and smartphones (another 7.3 billion). Hundreds of millions of these devices will be located in buildings.
Facilities are only beginning to harness the possibilities of such incredible interconnectedness. Arrays of sensors on building systems like HVAC and lighting are now part of the Internet of Things. They are not just monitoring status and alerting facilities to malfunctions; they are constantly capturing data and sending it to the cloud. But managing the sheer volume of this data presents its own challenge.
“As you add more sensors, you add more power,” says David Kollmorgen, an international director with JLL who leads the firm’s business intelligence practice. “But you also add a lot more data. Each layer makes the data set grow exponentially, not linearly.”
The usefulness of this data extends beyond variance analyses and if/then scenarios, Kollmorgen adds: “What we’re starting to see is data scientists looking at these huge clusters to identify patterns that we haven’t even thought about yet.”
This is the true value of “big data” – a collection of information so vast as to be indecipherable without the aid of advanced computer-driven analytics. Facilities are just approaching the frontier of these capabilities. But a cottage industry has already developed to help companies capitalize now. Learn how you can take advantage.
Measure More to Manage Better
“We are taking big data in the direction of managing operations and utilities,” says Logan Soya, founder and CEO of Aquicore. Soya’s company uses web-enabled sensors to rapidly collect consumption and equipment operation data.
“Buildings are going from a dozen data points on a utility bill to real-time data reported every 30 seconds,” he says. For a simple building, that extrapolates to over 12 million data points in a year. For a more complex building with multiple submeters or a large portfolio, the figure quickly escalates into the billions.
“The sensors are BAS-agnostic,” explains Soya, “and it’s a cloud-based platform, which means a manager at any building can have decision-making data within a few hours of installation.”
For one client, that data revealed that the energy consumption curve of one building showed an unexpectedly high base load. Further analysis then flagged the heat pumps as the source of the abnormality. Upon examination, it was discovered that the relays between the pumps to the BAS were not connected properly, which meant they had been running continuously at night despite the indication from the BAS that they were shutting down as scheduled.
“The solution was simple and cost practically nothing,” notes Soya, “but it would have taken forever to find without the analytics.” As a result, the building will save $80,000 in annual expense, far exceeding the upfront cost of the sensors.
This data has tremendous value even when there is no obvious malfunction to correct, Soya adds.
“We have seen buildings raise their ENERGY STAR ratings by 50 points and save hundreds of thousands of dollars as our tools help them optimize scheduling,” Soya says, noting that the value extends even further when the data feed indicates increased stress on equipment, often indicating an impending shutdown.
“It’s like giving a car a full dashboard when it has never even had a speedometer before,” Soya says. “A lot of buildings are still working with static monthly bills, so there is a lot of opportunity out there.”
A Few Degrees of Freedom
The usefulness of BAS data doesn’t stop at tracking energy consumption to learn from the data feed. In fact, some systems flip that paradigm and learn from their human operators.
“Machine learning is a big part of the story. There is a lot of repetition and pattern recognition involved in looking at data, and it just isn’t scalable,” says Lindsay Baker, VP of business development for Building Robotics, a building controls company that grew out of UC Berkeley’s computer science department. The firm’s breakthrough product is Comfy, an interactive temperature control system built on a machine-learning platform.
Comfy works in a counterintuitive way – it gives individual workers a measure of control over the temperature in their space. Workers are given access to a web portal or iPhone app that allows them to dispatch one of three messages: “Warm my space,” “I’m comfortable,” or “Cool my space.” This control, which is connected to the BAS on the back end, responds immediately to the request.
“If someone wants cooler space, that person’s zone will get about 10 minutes of cool air,” Baker explains. “People are usually amazed at the response, which makes them feel like the building is listening to them.”