As the new smart buildings system came online, a surge of data rushed in from sensor-enabled equipment throughout the Microsoft headquarters. Within moments, the system pointed to exhaust fans that had been mistakenly left running for a year, costing the company $66,000.
This initial launch was part of a large project to harness the power of big data for energy efficiency, which helped Microsoft save millions of dollars and led to a commercial smart buildings solution. Over three years, Microsoft integrated scattered data from 30,000 sensors of different eras into a centralized energy efficiency system, producing billions of data points each week.
Why Big Data and Sustainability?
Big data refers to the unprecedented volume of data now available. Such data come from sources including the Internet, enterprise software, personal devices, and digital components that are part of equipment from kitchen appliances to manufacturing machinery. An IBM report suggests the amount of data produced in 2015 and 2016 is nearly 10 times what had been produced in human history until then.
Big data are changing the terms of competition in many aspects of business, from marketing to R&D.
Sustainability and big data can compete for organizational resources, but they can also complement each other. In this series, New Data Technologies for Sustainability, we explore some of the opportunities for organizations to leverage big data and related technologies to create social and environmental value.
The Original Win-Win, Updated
Using resources efficiently and minimizing waste is fundamental to sustainability. Such efficiency is also where sustainability contributes most directly to the bottom line.
Big data can take decisions about resource use to the next level. Relevant data are produced by a new generation of “smart” meters and sensors, which can track in real time the consumption of energy and other resources, transmit various kinds of information, and handle two-way communication with central systems. Such data can spot sources of waste that would otherwise be invisible or require costly observation by staff.
Smart meters and sensors can monitor almost any operational process involving equipment. For example, Pirelli, the Italian tire company, developed an analytical tool that helps manage the maintenance of their tires in vehicle fleets. Built-in sensors in the tires send real-time information about their status, allowing fleet managers to maintain them in optimal pressure and operational condition. The system saves fuel, extends tire life, and reduces the need for manual checks by personnel.
Using big data for energy efficiency is a particularly exciting area, important for organizational budgets and the environment. There’s a thriving marketplace for monitoring services, with companies such as IBM, MCS, and Microsoft offering facility management solutions.
Journey to Efficient Energy Use
Here’s how CircuitMeter, an Ontario-based start-up, has seen organizations improve their energy efficiency.
CircuitMeter has developed low-cost smart meters that allow organizations to track and analyze energy use at the circuit level: for example, a specific area in an office building or a machine in an industrial facility.
Paul Mertes, CircuitMeter’s CEO, describes the path he sees for organizations his company works with.
First, the organization discovers relatively obvious sources of waste.
“We’ve never seen a building,” says Mertes, “where there weren’t mistakes in the control system that led to things working, sometimes 24/7, that really shouldn’t.” Heating systems operating in the heat of July, exhaust fans that fail to shut down during the night – there is always an ‘I had no idea!’ kind of story.
The data also highlight needless energy use from machines or lights that are controlled by people. CircuitMeter can provide weekly reports that show energy use over time by floor or by production area. Providing this information to users, says Mertes, helps “get energy efficiency as part of the ethos of people that work there.”
Once unnecessary energy use has been targeted, companies can turn their efforts to inefficient use – important functions that utilize excessive energy.
Such inefficiencies can come from machine malfunctions, which don't interrupt operations but could significantly increase its energy consumption. In one case, a company client discovered such a rooftop unit that spent 40% extra energy.
Companies can also use equipment that is not optimally sized to tasks. Mertes recalls: “We have a big retailer. In their distribution center, they discovered that the motors of the conveyers that move the goods were oversized. And they didn’t know that they had a problem with these motors because they work fine. They just use a lot more energy than they need to.” The retailer decided to replace its 10 horsepower motors with 3 horsepower motors, reducing equipment and energy costs.
The final goal: an optimized network that is continuously monitored. Continuous analysis of energy data can help organizations immediately detect problems that surface, or quickly assess the implications of changes in their energy use.
Redefining Industrial Engineering
To Mertes, big data in the energy field means a new branch of industrial engineering and cost accounting. “Taking a block of energy data like monthly use for a factory or a building, breaking it down to individual systems and machines every three seconds — this is a massive amount of data.” Making the most of these data requires innovation in methodologies such as lean engineering and Six Sigma, which organizations use to design and control operational processes.
Dr. Fengqi You, an engineering professor at Cornell University, says that big data will indeed take an increasing role in optimizing business operations. “There are many success stories of leveraging big data analytics for improving energy efficiency, building ‘smart’ systems, and reducing waste and costs,” he says. “The age of big data has already arrived.”
About This Series
Artificial Intelligence, fintech, Internet of Things – businesses today face an environment of continuous disruption. These trends can distract managers from sustainability – but they can also support a company's sustainability efforts.
In the New Data Technologies for Sustainability series, NBS offers guidance on aligning your company’s sustainability initiatives with the transition to big data and related technologies.
Interested in data technologies and sustainability? We would love to hear from you. What would you like to learn about? How do you use big data, Artificial Intelligence, and other technologies to support your organization's sustainability efforts? Please email thoughts and suggestions to email@example.com.