Solar Panel Installation Analysis

Technologies Used: Census API, NREL Database, GMAPs API, SciPy. Stas, Python, Pandas, Matlab, Seaborn, Jupyter Notebook

Summary: On this group project, we analyzed data from solar panel installations and the US Census to determine whether any significant relationships existed.

We imported US Census data and merged it with over one million data points on solar panel installations from NREL. We used the GMAPs API with heat map layering to produce visualizations which showed where solar panel installations took place. This dynamic interactive map allowed us to zoom in at the region, state, county, and city levels to understand the volume of installations based on the area of interest. We then used SciPy to run ANOVA and 2 sample t tests. We found that those 45 years of age and older, with family incomes of $40,000 or more, and that live in suburban areas are more likely to install solar panels. In addition, we also found that there was a sharp increase of solar panel installations starting in 2008. This is most likely due to the solar panel installation tax credits introduced in the Expanded Federal Tax Credits Emergency Economic Stabilization Act of 2008.