For all its promise of delivering a bold future, the green energy industry is still decidedly low tech when it comes to moving merchandise. Solar panels are still sold door-to-door, a slow and expensive process that has impeded the industry’s growth.
Now, a California startup thinks it has a solution, using sophisticated data science to find consumers likely to adopt solar power and let them know just how much they can save on their electric bills. PowerScout, a machine-learning-enabled eCommerce platform for solar energy, aims to eliminate marketing costs that, according to CEO Attila Toth, can exceed the cost of the actual equipment for some green-power vendors.
“This is very absurd—very crazy,” Toth says. “[I]f somebody is trying to go solar, that person is going to pay more for the sales guy, for the marketing costs, than for the panels themselves, and that’s the reality today.”
PowerScout, which was founded in 2014 and has received $5.2 million in funding, including an award from the Department of Energy’s SunShot Initiative announced this week, uses a mix of data from commercial databases and LIDAR imaging to predict which households are most likely to be interested in using solar energy. The company began sales in the first quarter of this year and has since signed customers in four states, with sales increasing each month, says Toth.
Families with fuel-efficient cars are much more inclined to be interested in powering their homes on green energy, for instance, and other factors like education levels, household size, credit scores—since most solar installations are financed—and income levels all factor in, Toth says.
“Lower-income families, they adopt because this is cost savings every month to the bottom line, zero money down,” while higher-income families adopt more for reasons for prestige, he says. “The middle-income households are the ones where most of the marketing dollars need to spend on convincing.”
And more generally, areas that already have a high degree of solar adoption are likely to see more, he says.
“There’s a herd effect here, so if people keep seeing a lot of solar in the neighborhood, they become increasingly more intrigued, which is natural,” he says. The company can even estimate potential customers’ existing electric bills based on an existing sample set of about 100,000 electric bills and dozens of available data points, he says.
And while there’s no national database of who has or doesn’t have rooftop solar, other than confidential tax credit records, PowerScout’s image analysis tools can help the company figure it out.
Those tools can also help estimate how much energy can be harvested from a home’s rooftop without needing to take measurements in person with a decent degree of accuracy.
“We do the latest convolutional neural network image recognition,” says Toth. “Very few companies in energy do that.”
PowerScout can target direct mail and online marketing to the most promising customers and quickly give them online estimates. Then, those who are interested in rooftop solar can choose a financing plan and get connected to a local installation partner to have it installed, Toth says.
And for those who rent, or otherwise might better benefit from connecting to a shared, community solar project, the platform can still give them a savings estimate and help large-scale solar installation developers acquire the customers they need to sell their generating capacity, he says.
In the future, as smart electric meters tracking precise data on usage become more prominent, potential customers will be able to share more data with PowerScout to get more precise estimates, Toth says.
“Once we have that, we can tailor any clean energy product to your home in a precise fashion without setting foot in your kitchen,” he says. That could include increasingly inexpensive battery storage options, like Tesla’s Powerwall system, he says.
PowerScout’s systems are able to perform all their calculations thanks to the on-demand power of Amazon Web Service’s private cloud systems, making possible computation on a level that wouldn’t have been available just five years ago, Toth says.
“This is probably the largest big data problem of this century, because the electric grid is the largest man-made machine,” he says.