AI locates nearly all U.S. solar panels by analyzing satellite images

December 20, 2018 //By Nick Flaherty
AI locates nearly all U.S. solar panels by analyzing satellite images
Researchers at Stanford University have used machine learning to analyze over a billion high-resolution satellite images to identify nearly every solar power installation in the contiguous 48 states of the US.

Knowing which Americans have installed solar panels on their roofs is enormously useful for managing the changing US electricity system but until now, all that has been available are essentially estimates.The analysis found 1.47 million installations, which is a much higher figure than either of the two widely recognized estimates. The scientists also integrated US Census and other data with their DeepSolar catalogue to identify factors leading to solar power adoption.

"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," said Ram Rajagopal, associate professor of civil and environmental engineering, who supervised the project with Arun Majumdar, professor of mechanical engineering.

The group's data could be useful to utilities, regulators, solar panel marketers and others. Knowing how many solar panels are in a neighborhood can help a local electric utility balance supply and demand, the key to reliability. The inventory highlights activators and impediments to solar deployment. For example, the researchers found that household income is very important, but only to a point. Above $150,000 a year, income quickly ceases to play much of a role in people's decisions.

On the other hand, 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. For example, in areas with a lot of sunshine and relatively high electricity rates, utility bill savings would exceed the monthly cost of the equipment. The impediment for low- and medium-income households is upfront cost, the authors suspect. This finding shows that solar installers could develop new financial models to satisfy unmet demand.

The team used publicly available data from the US census that cover about 1,700 households each, about half the size of a ZIP code and about four


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