AUDem: Australian electricity demand data
In qgam: Smooth Additive Quantile Regression Models

Description Usage Format Value Examples

Data set on electricity demand from Sidney, Australia. The data has been downloaded from https://www.ausgrid.com.au, and it originally contained electricity demand from 300 customers, at 30min resolution. We discarded 53 customers because their demand was too irregular, and we integrated the demand data with temperature data from the National Climatic Data Center, covering the same period.

1	data(AUDem)

AUDem is a list, where AUDem$meanDem is a data.frame containing the following variables:

doy: the day of the year, from 1 to 365;
tod: the time of day, ranging from 18 to 22, where 18 indicates the period from 17:00 to 17:30, 18.5 the period from 17:30 to 18:00 and so on;
dem: the demand (in KW) during a 30min period, averaged over the 247 households;
dow: factor variable indicating the day of the week;
temp: the external temperature at Sidney airport, in degrees Celsius;
date: local date and time;
dem48: the lagged mean demand, that is the average demand (dem) during the same 30min period of the previous day;

The second element is AUDem$qDem48 which is a matrix with as many rows as AUDem$meanDem. Each rows contains 20 equally spaced empirical quantiles of the lagged individual electricity demand of the 247 customers.

A list where AUDem$meanDem is a data.frame and AUDem$qDem48 a matrix.

library(qgam)
data(AUDem)
  
# Mean demand over the period
plot(AUDem$meanDem$dem, type = 'l')
  
# 20 quantiles of individual demand over 5 days
matplot(seq(0.01, 0.99, length.out = 20), 
        t(AUDem$qDem48[c(1, 50, 75, 100, 250), ]), 
        type = 'l', 
        ylab = "Electricity demand (KW)",
        xlab = expression("Probability level " * "(p)"), 
        lty = 1)