sa | R Documentation |
Historical data of South Australia
A data frame with 124848 half-hourly observations on the following 19 variables.
demand
a numeric vector containing half-hourly electricity demand for South Australia.
offset
a numeric vector containing half-hourly demand from some industrial customers who are not temperature sensitive (e.g., mines and smelters).
timeofday
a numeric vector giving the time of day (0-47).
date
a numeric vector giving the date within the month (1-31).
month
a numeric vector giving the month (1-12).
year
a numeric vector giving the year (2000-2014).
day
a factor with levels Mon
Tue
Wed
Thu
Fri
Sat
Sun
idate
a numeric vector giving the date in days since 1 January 1900.
holiday
a factor with levels Normal
Day before
Holiday
Day after
.
workday
a character vector with values NWD (Non-WorkDay) and WD (WorkDay).
timeofyear
a numerical time series giving the time in days since midnight on 1 January of each year.
Year
a numeric time series giving the time in years.
fyear
a numeric vector giving the financial year (starting 1 July).
temp1
a numeric vector giving the temperature in Celsius at location 1
temp2
a numeric vector giving the temperature in Celsius at location 2.
anndemand
a numeric vector giving the total demand in each year.
annoffset
a numeric vector giving the total offset demand in each year.
ddemand
a numeric vector giving the normalized demand (demand/anndemand).
doffset
a 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.
http://www.aemo.com.au/Electricity/Data/Price-and-Demand/Aggregated-Price-and-Demand-Data-Files
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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