sa: Historical data for model estimation

In robjhyndman/MEFM-package: Monash Electricity Forecasting Model

Historical data for model estimation

Description

Historical data of South Australia

Format

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).

Details

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.

Source

http://www.aemo.com.au/Electricity/Data/Price-and-Demand/Aggregated-Price-and-Demand-Data-Files

References

R. J. Hyndman and S. Fan (2010) "Density Forecasting for Long-term Peak Electricity Demand", IEEE Trans. Power Systems, 25(2), 1142–1153.

Examples

plot(ts(sa[,"demand"],freq=48*seasondays,start=c(2000,7)))

robjhyndman/MEFM-package documentation built on June 14, 2024, 11:20 a.m.