We were really fascinated by the Planets API, but also wanted to think of a hack that was outside of the box and in an area we could all enjoy and learn more. We stumbled upon solar power (particularly, energy resource allocation) and knew that was what we wanted to do. As Computer Science, Aerospace, and Chemical Engineering majors, coming up with a project that touched on all our fields was very tough, but tackling the problem of solar efficiency through satellite data analysis was something we could all use our expertise while all learning something new.

What it does

Since 2/3 of us were competing for the first time (and 2/3 are in their first Computer Science class), we decided that we would use the API to create a user interface that allows the user to enter a geoJSON polygon, and receive thousands of data points related to solar positioning and cloud cover. This data is used to make calculations about how much solar energy the area of land has the potential to harness, and can even be compared side by side to another location. All these interactions are handled by a seamless GUI.

How we built it

We handled the GUI, API requests, and interaction of parts in Python with Gtk, and handled the data analysis in MATLAB.

Challenges we ran into

We had a very VERY hard time making Python and MATLAB hold hands.

Accomplishments that we're proud of

Making Python and MATLAB hold hands, watching the whole thing come together, through the use of MATLAB Python engines.

What we learned

How to find an area given a random curvature of the earth. How to properly interact with a GUI using threading. How to calculate energy output by a solar cell. How angle affects solar radiance.

What's next for Super Solar

Adding additional data points, features, and user interactions!

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