Solar energy installations

• Analyse data about solar energy installations.
• Save, store and access datasets and use data cleaning techniques
• Sort and filter datasets to make sense of data to answer particular inquiry questions
• Visualise data using online mapping software or as a chart such as a histogram or column graph.

Learning hook

Solar installations seem to be popular in domestic housing. Why do people install solar systems, and is there an upward trend in installation across Australia?

View the video Renewable Energy 101: National Geographic (3 minutes) and discuss how renewable energy can address climate change.

Alternatively, view the video How Do Solar Panels Work? (5 minutes) and discuss the impact of solar panels on household CO2 emissions.

Provide students with a dataset found on Postcode data for small-scale installations. Note: there is a range of datasets of this web page to accommodate students with differing levels of spreadsheeting skills.

In particular, use this dataset provided in csv format:

• Postcode data for small-scale installations – SGU-Solar

Step 1: Dataset download and data cleaning

Download the dataset and open the csv file in a spreadsheet.

Look at the postcodes.

In most spreadsheets this data will show postcodes with one, three and four digits, although postcodes in Australia all have four digits. What has happened to these postcodes?

This is an example of a spreadsheet being programmed to modify the data (hiding things the programmer believes the user does not need to know about – in this case, leading 0s). Mathematically speaking, there’s no difference between 0, 00, 000, and 0000. They all just mean 0. So spreadsheets (and other software) tend to remove the leading 0s, which means postcode 0 is actually 0000, 200 is actually 0200 and so on.

This is an important part of data cleaning. Sometimes you have to convert the data back to its original form to fix errors that spreadsheets and other software introduce in an attempt to be ‘helpful’. Format cells and select ‘custom’ from the drop-down menu and type in 0000 to allow for a 4-digit postcode.

Step 2: Is this dataset useful?

Now let’s look at the first two columns in the dataset. The first is historical installations from 2001 to 2016. The dataset doesn’t seem to have any data for installations prior to 2001, but that’s not because there were no installations. What might be the reason?

It turns out it’s because 2001 was when the Government introduced the mandatory renewable energy target and began tracking renewable energy.

Students may ask, ‘How many solar panels are actually operating currently?’

We can’t exactly tell that from this dataset. This data tracks installations. It doesn’t track people getting rid of their solar panels, or the panels ceasing to work. Installations are a reasonable, but not perfect, measure of how much solar we have.

This question can springboard a useful conversation about the data we want, versus the data we have, and how many data studies work with flawed or missing data, simply because it’s all that is available.

Step 3: Sorting the data

Look at the first column in the dataset. Having it sorted by postcode is logical, but not terribly interesting. Let’s look at the top 20 postcodes – to do that, we can sort the entire table by the second column (how many installations happened between 2001 and 2016), in descending order. In other words, put the largest values up the top.

Table 1: Solar Installations sorted by Previous Years column, largest to smallest

A quick glance shows us that the majority of the top 20 postcodes start with a 4, indicating they’re in Queensland. (To check postcodes refer to a postcode site.)

The top postcode, 4670, covers 53 regions, including Bundaberg. There’s a surprisingly large gap between the top postcodes and the bottom of the top 20, which is interesting. Most of the postcodes in this list that aren’t in Queensland are in Western Australia, except for 3029, which is west of Melbourne, around Hoppers Crossing, and 3977, which is south-east of Melbourne, in the Cranbourne area.

There’s a rich conversation to be had around why these suburbs have so much more solar than other places in Victoria. Toorak, for example, a famously wealthy suburb, comes in at 1701 on the list. Areas with a lot of new housing are more likely to have solar, as it gets put in when the house is built as a way to increase the energy rating of the house.

This is a topic worth exploring! You don’t have to know all the answers, as it’s an opportunity for the students to research and explore, and come up with their own theories for why it might be the case.

Figure 1: Data visualised in Google MyMaps: 20 postcodes with highest number of installations

Step 4: Data analysis

Let’s look at column 4: solar installations in January 2017. How different are the top 20 if you sort the whole table by this column?

Table 2: Solar Installations sorted by January Installations

• Australian Government Clean Energy Regulator: Postcode data for small-scale installations
• Solar PV Maps and Tools

  Understand the Australian solar PV market with live generation data, historical maps and animations, and tools to explore rooftop PV potential and per-postcode market data.

• National Map

  National Map is an online map-based tool to allow easy access to spatial data from Australian government agencies. Mapping data can be added, but needs to include latitude and longitude data.

• Find a postcode

  This postcode finder from Australia Post is a quick and easy way to search and check postcodes for all suburbs and locations around Australia.

• Postcodes in Australia

  Provides postcode ranges for Australian states and territories.

• Graphing tutorial

  Useful tutorial on graph types

• Solar Energy Installations: Assessment checklist: MS Word
• Solar Energy Installations: Assessment checklist: PDF