Reducing our reliance on the electric grid is something that appeals to most people around the world, with the exception of utility companies that see their stranglehold over our energy supplies slipping away.

So how do we know exactly how many solar panels have been installed and where? This would be good information to have for planning energy security and future improvements to the local electric grids right?

Solar panels now account for over 10% of total electricity generation in some U.S. states, such as California. But policy-makers, utility companies, and engineers still find it difficult to put an accurate number on the country’s total solar power installation, let alone to describe what factors make solar power thrive in certain areas and not others.

This image of the DeepSolar interactive map shows solar panel distribution by county in the San Francisco Bay Area.
DeepSolar/Stanford University (

Now, researchers at Stanford University have developed a new tool and accompanying open access website that identifies solar panels from high-resolution satellite data using automated image analysis, giving them unprecedented insight into the societal trends that drive solar power adoption. Their work appears December 19 in the journal Joule.

This image shows the interactive map of the United States on the DeepSolar website.
DeepSolar/Stanford University (

The tool, dubbed DeepSolar by its developers, including co-first-author doctoral students Jiafan Yu and Zhecheng Wang, scans high-resolution images covering the entire United States for solar panels, registers their locations, and calculates their sizes.

Previous algorithms were so slow that they would have needed at least a year of computational time to find every solar panel across the United States, but DeepSolar requires a fraction of that time.

co-senior author Ram Rajagopal, a civil engineering professor at Stanford.

With these methods, we can not only maintain and update a high-fidelity database of solar installations, but also correlate them at the census-tract level with the amount of incoming solar radiation as well as non-physical factors such as household income and education level.

co-senior author Arun Majumdar, a mechanical engineering professor at Stanford and co-Director of the Precourt Institute for Energy.

All told, the authors located 1.47 million individual solar installations nationwide, including rooftop setups, solar farms, and utility-scale systems. Before DeepSolar, Rajagopal and Majumdar say, the decentralization of solar power meant that there was no comprehensive way to catalog the photovoltaic panels strewn atop homes and businesses, limiting understanding of American solar deployment at an aggregate level.

One area where DeepSolar could make an immediate impact is in guiding upgrades meant to make the American power grid more compatible with solar sources, which are intermittent due to daily and seasonal fluctuations in incoming sunlight.

Now that we know where the solar panels are, or are likely to be in the future, we can feed that infor
This image of the DeepSolar interactive map shows solar panel distribution by county in the region surrounding Chicago.
DeepSolar/Stanford University ( into questions of modeling the electricity system and predicting where storage units and substations should go.


It could also come in handy for pointing out areas that are ripe for new solar deployment. The researchers used their results to extract correlations between solar installation levels and population density, household income, and other variables, creating a model that can predict which geographic regions are most likely to adopt solar technology based on socioeconomic factors.

Utilities, companies that install solar panels, even community planners that are thinking about sustainability, they all can benefit from this high-resolution spatial data and a website where they can explore and analyze the different trends involved.


Moving forward, the researchers plan to expand the DeepSolar database to include solar installations in other countries with suitably high-resolution satellite images. They also intend to add in features that can calculate a solar panel angle and orientation from image analysis alone, allowing for more complete and accurate estimation of power-generating capacity in addition to the basic location and size data already collected.


About Gordon Smith
Gordon's expertise in the area of industrial energy efficiency and alternative energy. He is an experienced electrical engineer with a Masters degree in Alternative Energy technology. He is the co-founder of several renewable energy media sites including Solar Thermal Magazine.

No tags for this post.

Leave a Reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.