The City of Duluth, Minnesota recently launched a new web-based solar energy mapping tool for its residents and businesses that will help lower the barriers to solar development in the city. The app, called “Duluth Shines,” helps users evaluate their rooftop’s solar potential and estimate the size and cost of a solar installation. The tool is generating new interest in solar opportunities and has received coverage in local and regional news.

The Great Plains Institute was part of the team that helped develop the mapping tool with Duluth and other partners. This blog provides a description of how the mapping tool was created, including a summary of the methodology used.

Turning statewide data into location-specific, rooftop solar potential

GPI’s role in developing the app was to determine the electric energy production potential of Duluth rooftops (or other potential solar installation site, such as a back yard) based on statewide solar energy data created by the University of Minnesota in 2015. Our aim was to benchmark a conversion ratio and model that could predict the amount of electric energy that could be produced by a set of solar panels (a solar array), using current solar energy photovoltaic (PV) technology, from the high resolution (1-meter) insolation data provided by the statewide data set.

Aerial view of Duluth with annual insolation solar potential (light gold: high potential; dark: low potential)

To accomplish this, we used real-world solar energy production data from existing solar installations in Duluth to determine how much solar energy could be produced on a given rooftop, then compared this to the mapped data from the statewide solar map and dataset. The statewide data, in the form of a high-resolution solar map, shows the total annual sunshine that falls on each square meter of the state, and accounts for local shading from trees, buildings and topography, and local cloud conditions. While this information provides accurate information on which sites have a good solar resource, it didn’t tell us how much energy could be produced from a solar array at those sites. Actual production could vary depending on a variety of conditions and equipment selection. Moreover, the solar map is a 2-dimensional representation of the solar potential, while a solar installation is done in a 3-dimensional world and might be affected by the pitch of the solar array and factors such as the slope and direction of a rooftop.

Moving into 3-D for more accurate estimates

To tackle the challenge of translating the 3-dimensional solar energy information to estimate solar energy production potential on a 3-dimensional rooftop, we worked with surface elevation model data imagery that accompanies the statewide solar data set. We estimated how the slope and direction of the rooftops (determined from 3-dimensional LiDAR data and aerial imagery) might affect energy production from rooftop solar panels. Then we created a benchmark to test whether the solar map could be an accurate predictor of electric production, by using production information from eight existing rooftop solar sites ranging from 2 kilowatts (kW) to 30 kW, and including a variety of installation types (fixed and tracking, flush mounted, and pitched) and roofs (pitched and flat). We measured the 2-dimensional footprint of these solar arrays in order to compare production with the mapped resource and with the site assessment based on the surface elevation and aspect. From this we evaluated the sensitivity of a production-to-resource ratio across all these variables. We found that the sensitivity of the production estimate across all the variables of actual solar installations was within a reasonable range, and thus that a conversion ratio could reasonably estimate solar energy production for a potential installation site.

Three views of a St. Louis County building in downtown Duluth, from left to right: Raw insolation GIS data; Satellite imagery with newly installed solar panels; GPI’s GIS analysis of identified solar panels.

Staff at Ecolibrium3 assembled production data and installation specifications from city and county government installations and from residential and commercial property owners. The University of Minnesota Duluth incorporated the resulting benchmarked conversion ratio into the web-based map.


We found that our Duluth benchmark enabled us to reliably predict how much solar energy could be produced on a specific solar installation site. We look forward to applying this methodology to solar assessments and tools in other areas, and to hearing about how residents and businesses in Duluth, MN used their new tool to make informed decisions about investing in solar.

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