Being a multi-passionate professional, one of the other fields (besides data) that I am passionate about are social and environmental causes. Hence, why I found the day 4 dataset very interesting.

Day 4 datasets were all about solar energy generation. We were given 2 principal datasets:

  • Small scale solar systems, also called ‘Small Generation Units’ (SGU); typically solar installations on rooftops.
  • Medium to large scale solar systems including commercial enterprises generating their own electricity using solar as well as large scale solar farms (AEMO registered generators).

And there was one rule: no Alteryx Allowed, which means all the data wrangling had to be done in Tableau itself or Tableau Prep.

My Initial Story

The first question that came to mind while browsing the dataset was: “does political preference influence the solar capacity of a given electorate?”. I began with the hunt for election results data for each state (state elections only). I knew that this process would take me some time therefore I took a step back and thought more carefully about whether the data would allow me to answer such a question? My theory was that electorates with higher votes for the Greens will typically generate more solar power per household, but this assumption may not hold because the reality is that the motives for going solar, in many/(some?) cases are not environmental but financial.

The story that I ultimately settled for

I decided to compare each state’s current solar capacity and potential and determine their ability to go 100% solar.

Here were my key findings:

  • 9% of Australia’s electricity is currently generated through solar.
  • Without adjusting for weather conditions, Australia has the potential of fulfilling close to 80% of its power needs using solar alone.
  • In Queensland, most of the solar installations are located outside of South-East Queensland, which surprised me as I believed that solar adoption was more common in the Southeast.

You can find the dashboard here:

Next Steps

Here are some of the next steps that I would like to incorporate into this dashboard:

  • Adjust solar capacity and potential based on the photovoltaic power of the area in which the generation unit is located.
  • Add locations of large-scale generators on the map as well as the locations of proposed solar farms. This data will also allow the display of capacity projections, using a line chart embedded in the tooltip of the capacity metric card.

Stay tuned for solar data viz – part 2. Thank you for reading this article. Take care!

Fabrice Joseph
Author: Fabrice Joseph

Originally from Mauritius, Fabrice moved to Australia to complete a Bachelor of Commerce (Accounting) at the University of Queensland. Since graduating, Fabrice accumulated 7+ years of experience in primarily management accounting roles and a couple of entrepreneurial projects. Having encountered data in the accounting profession, Fabrice had developed a passion for analysing data, extracting insights and findings ways to improve his workflow. Such is his passion for data that he has data projects as hobbies. Data School was the natural next step for him to launch his career in data. Besides data, Fabrice has other interests such as yoga, reading books on ancient history and philosophy.