Researchers at Stanford University now estimate that there are 1.47 million solar panels in use across the contiguous 48 states, a number that’s higher than any other previous estimate. But what’s really cool about this stat is the way they got it.
The Stanford scientists built an A.I. called DeepSolar to count up solar panels from space. The machine-learning system “analyzes satellite imagery to identify the GPS locations and sizes of solar photovoltaic (PV) panels,” according to its website.
“We can use recent advances in machine learning to know where all these assets are, which has been a huge question, and generate insights about where the grid is going and how we can help get it to a more beneficial place,” says Ram Rajagopal, an associate professor of civil and environmental engineering who helped lead the project, in a press statement.
DeepSolar’s data reveals that despite falling PV panel prices, income still plays a large role in determining who is and who isn’t likely to invest in solar panels. According to the study, “low- and medium-income households do not often install solar systems even when they live in areas where doing so would be profitable in the long term.” Researchers theorized that the upfront costs of solar installation are still hindrances, even if solar could save money down the road.