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Belkin Gadget Will Reveal How Much Energy Your Devices Use

A project at Belkin could lead to itemized electric bills—showing how much juice your toaster or hair dryer uses.
August 1, 2013

If you use a credit card or a cell phone, chances are you get a monthly statement detailing each purchase or call. This may soon expand to your utility bills, too: a project in the works at electronics company Belkin makes it possible to see how much electricity you’re spending on everything from the TV in your living room to the washing machine in your basement.

Belkin appliance
Powerful stuff: Belkin’s Echo Electricity aims to track how much power is used by the various appliances in the home. Here, an iPad runs a demo electricity-monitoring app in the home of Sidhant Gupta, a Belkin researcher.

Called Belkin Echo Electricity, it’s a small device that connects to your utility meter and pays attention to the electromagnetic interference, or “noise,” emitted by electrical appliances plugged in to wall outlets. It is currently being tested in a handful of U.S. homes, and Belkin plans to install 10,000 of them over the next year in places ranging from military housing to apartments to hotels. Eventually, utilities could build the device into home meters or you could simply plug one into an outlet in your house.

Echo Electricity is just one of a growing number of efforts from startups, utilities, and large companies alike designed to prod us into saving energy by offering tips or comparisons with neighbors, or upgrading to Internet-connected devices. But its focus on figuring out precisely what you’re spending your money to power could help it stand out from the crowd.

Echo Electricity builds on technology acquired in 2010 from an energy-monitoring startup called Zensi together with the doctoral work of University of Washington PhD candidate Sidhant Gupta, whose advisor, Shwetak Patel, was a Zensi founder.

Gupta, also a research consultant to Belkin, says the device uses a sensor to track the electromagnetic interference “signatures” that different appliances emit over power lines when turned on, turned off, or changed from one state to another. A Wi-Fi chip within the device uploads this data to the Web, where machine-learning algorithms can analyze it to tell what appliances are on and how much power they’re consuming at any given time. At home, Gupta visualizes this data with a demo app running on an iPad that shows details like what percentage of power is being used by his kitchen lighting, his laptop, or his cable boxes.

Kevin Ashton, a Belkin general manager who was CEO of Zensi, predicts that commercialization is a couple of years away, but he believes that showing people more granular information about how much energy they use (and waste) will encourage conservation. “The more visible we can make waste, the more we can take advantage of everyone’s natural waste aversion and help you become less wasteful,” he says.

Rather than selling the device straight to consumers, Belkin is focused on working with companies such as utility providers and cable providers, enabling them to offer customers itemized bills.

Ashton says Echo Electricity can currently account for 90 percent of a user’s electricity bill. While it is good at identifying how much energy a refrigerator is using, and even, for example, when the door has been opened, identifying more complex electronics, like pieces of a home entertainment system, is trickier, he says.

Hampden Kuhns, CEO and founder of Load IQ, a startup based in Reno, Nevada, that also tracks energy consumption, says that part of the challenge with something like Echo Electricity will be determining the power draw of many smaller, less common appliances. “I don’t doubt they can do a good job of isolating the loads and saying this one is different from that one,” he says. “But it’s quite a challenge to say this is a light and this is a TV and this is an oven.”

Belkin hopes crowdsourcing will help improve Echo Electricity’s performance: the company is running a contest through the big-data site Kaggle, asking data scientists to pore over data collected thus far to offer improvements to its machine-learning algorithms. The first-place winner will get $14,000 for the assistance.

And Gupta and Ashton sound confident that, in time, it will be possible toget a very detailed look at all the devices in a home or business. Ashton hopes that ultimately the technology will be able to distinguish different makes, models, and brands of appliances—even different types of light bulbs.

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