Historical data of South Australia
A data frame with 124848 half-hourly observations on the following 19 variables.
demanda numeric vector containing half-hourly electricity demand for South Australia.
offseta numeric vector containing half-hourly demand from some industrial customers who are not temperature sensitive (e.g., mines and smelters).
timeofdaya numeric vector giving the time of day (0-47).
datea numeric vector giving the date within the month (1-31).
montha numeric vector giving the month (1-12).
yeara numeric vector giving the year (2000-2014).
daya factor with levels Mon Tue Wed Thu Fri Sat Sun
idatea numeric vector giving the date in days since 1 January 1900.
holidaya factor with levels Normal Day before Holiday Day after.
workdaya character vector with values NWD (Non-WorkDay) and WD (WorkDay).
timeofyeara numerical time series giving the time in days since midnight on 1 January of each year.
Yeara numeric time series giving the time in years.
fyeara numeric vector giving the financial year (starting 1 July).
temp1a numeric vector giving the temperature in Celsius at location 1
temp2a numeric vector giving the temperature in Celsius at location 2.
anndemanda numeric vector giving the total demand in each year.
annoffseta numeric vector giving the total offset demand in each year.
ddemanda numeric vector giving the normalized demand (demand/anndemand).
doffseta numeric vector giving the normalized offset (offset/annoffset).
Historical data for South Australia, including half-hourly demand, temperatures from 2 locations, weekday, weekend, and holiday dates. Only data from October-March were retained for summer analysis and modelling.
R. J. Hyndman and S. Fan (2010) "Density Forecasting for Long-term Peak Electricity Demand", IEEE Trans. Power Systems, 25(2), 1142--1153.
plot(ts(sa[,"demand"],freq=48*seasondays,start=c(2000,7)))
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