Assignment Task
In this exercise you should write a code to load, analyse and create data visualisations for energy market in Australia in 2020.
The data set includes multiple CSV files containing the information about electricity demand and wholesale electricity prices in five Australian states (NSW, QLD, Vic, SA, TAS) in 2020. WA is not a part of Australian energy market, so it is not included in the analysis. Each file has information about one month, so there are 12 files per year per each of 5 states – in total, there are 60 files. The data were downloaded from the Australian Energy Market Operator (AEMO)
All data files have the same structure with the following variables:
• REGION – state name.
• SETTLEMENTDATE – day and time for the recording. Until recently all electricity sales in Australia were executed on 30-minute basis. So, every observation represents a 30-minute block.
• TOTALDEMAND – total electricity demand over 30-minute block in measured megawatts
• RRP – average price per one megawatt of electricity during that block of time
• PERIODTYPE – type of recording; its value is the same everywhere and we don’t need it for analysis
While you do data analysis, no interpretations/discussions are required. This is mostly a programming exercise. Still, you should be confident that your results and/or data visualisations are meaningful.
Your tasks are following:
1. Load and prepare data for the analysis. You need to have one dataframe for all data. It is OK to use for-loop for loading data. Loading data is a slow process, so inefficiencies of for-loop are not important. It is NOT OK to load every file by manually typing their names. See below hints on working with multiple files.
2. Calculate electricity “demand per capita”, that is, demand normalised (divided) by state population.
3. Aggregate data to get “daily” information. That is, aggregate 30-minute blocks in one-day blocks: one observation – one day. See below hints on working with date and time data. Electricity demand should be summed up to get a total demand per day. Prices should be averaged as it does not make sense to sum up prices per megawatt.
4. Plot comparison graph of distributions of electricity demand per day for each of five states for original “raw” demand and then for demand per capita.
5. Build historical graph for each of five states for demand and then for demand per capita.
6. Study a relationship between prices and demand per capita for all five states.
7. Aggregate data by the hour of the day for each of five states. One might think that demand is higher during the day and lower during the night. You must investigate that. Make a plot of electricity demand for each state per hour. Then another graph for demand per capita per hour for each state.
8. Each graph should be nicely presented, that is, proper title, axis labels, colour legend. Each graph should be accompanied by appropriate numerical summary.
9. Think about the patterns in the data, differences in the graphs – no need to write anything. This is non-graded step.
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