ausenergyviz

Python and R scripts to download and visualise Australian electricity market data.

Data Import Scripts

These are in the python/ folder and require the scipy and netCDF4 packages. They take a single command-line argument, the path to the data directory. The script cron.sh demonstrates typical usage.

Data Directory Layout

aemo_data/
    AEMO_GENERATORS.csv: generator metadata used by import_aemo.py and
        the R package

    bom_stations.csv: a list of BoM stations, used by download_bom.py

    bom_recent/
        *.csv - CSV file containing weather observations for each
            city, downloaded from the BoM web site by download_bom.py

    dispatch_5min/
        *.zip - dispatch data downloaded by download_aemo.py containing
            an individual 5-minute data point

    dispatch_daily/
        *.zip - dispatch data downloaded by download_aemo.py containing
            a day of data (from archive folder on AEMO web site)

    dispatch_dvd/
        *.zip - bulk/historical AEMO data, not downloaded by script, but
            read by import_aemo.py when creating a new CDF file

    dispatch_swis/
        *.csv - dispatch data for South West Interconnected System (WA),
            downloaded by download_aemo.py

    pricedemand/
        *.csv - regional price and demand data from AEMO web site,
            downloaded by download_aemo.py

    cdf/
        dispatch.cdf: created by import_aemo.py after processing all
            zip files from AEMO

R Package: ausenergyviz

The ausenergyviz/ folder contains an R package to access the CDF data downloaded by the Python scripts.

> library(devtools)
> install("ausenergyviz", args="--no-multiarch")
> install.packages("reshape2")
> install.packages("latticeExtra")
> install_github("rCharts", "ramnathv")

Shiny Web App

The webapp/ folder contains a web application written in Shiny to visualise electricity market data. To launch it, run:

> library(shiny)
> runApp("webapp", launch.browser=T)