Software will be crucial to lower the cost of solar energy.
Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology.
The rise of data tools—algorithms, machine learning, sensors—are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun.
It shouldn’t come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business. But when it comes to the solar industry, it could end up being data that ultimately pushes down the cost of solar enough so that it can compete with fossil fuel energy. In that way, it’s not just a way for solar companies to make money (though it’s that, too), but it would also be a way to transition the world from generating energy that emits tons of planet-warming carbon emissions.
At a solar industry conference in Las Vegas this week, companies across the solar industry are talking about their latest data technology and plans.
On Wednesday, a young startup called PowerScout, based in Oakland, Calif., announced that it has raised a seed round of $5.2 million to expand its technology that uses big data, analytics, and e-commerce to find smarter ways to sell solar panels to consumers. The company’s software amasses tons of data on current and potential solar customers, and uses machine learning to predict which of them are likely to buy solar panels.
Machine learning is an artificial intelligence technique that involves feeding data to algorithms so that the machine learning-powered algorithms get better at figuring our patterns in the data. Turns out it can be helpful for selling solar panels.
Selling solar panels to consumers has emerged as a tricky and expensive problem for companies. Particularly, as some markets, like in California, have been successful enough that many of the early adopters already have solar. That means in those places, increasingly, solar companies will have to convince more mainstream customers, who are generally more price-sensitive, to buy solar panels.
PowerScout CEO Attila Toth tells Fortune from the conference in Las Vegas that solar is “still being sold door-to-door just like vacuum cleaners in the 1950s.” That means consumers end up paying more, because of the high costs of the sales process itself, instead of just for the panels.
The company, whose funding includes millions of dollars from the Department of Energy, has also partnered with Google’s GOOG 0.78% Project Sunroof, which is using data to build similar solar software.
Other startups are using data to make it easier to finance the cost of installing solar. Last week a San Francisco-based company, kWh Analytics, introduced a new solar software product that uses data from global solar projects to convince insurance companies to back a production guarantee for solar projects. The guarantee drives down the interest rate for money raised for solar systems, and thus lowers the cost of financing them.
Solar data has historically “been fairly disorganized,” and the industry has little information about who builds quality solar projects, says kWh Analytics CEO Richard Matsui. The company counts Google, which has bought over 2.5 gigawatts over clean energy, as a client. This summer kWh Analytics raised a $5 million round of funding from Anthemis Group and power giant Engie.
Solar data startups aren’t just launching and raising funds from venture capitalists. Big companies are buying them, too.
Last month electronics giant Flex, through its solar gear subsidiary NEXTracker, revealed that it had bought a machine learning startup called BrightBox Technologies. The three-year-old company, based in Berkeley, Calif., developed software to optimize heating and cooling systems in buildings.
No comments:
Post a Comment