Package 'MEFM' reference manual

Title:	Monash Electricity Forecasting Model
Description:	This package includes a set of tools for implementing the Monash Electricity Forecasting Model for electricity demand. The package requires the following data as input: half-hourly/hourly electricity demands; half-hourly/hourly temperatures at one or two locations; seasonal demographical and economical data; public holiday data. The formats of the required data are described in the help files.
Authors:	Rob Hyndman [aut, cre, cph] , Shu Fan [aut]
Maintainer:	Rob Hyndman <[email protected]>
License:	GPL (>=2)
Version:	2.2
Built:	2024-11-10 05:45:46 UTC
Source:	https://github.com/robjhyndman/MEFM-package

Monash Electricity Forecasting Model

Description

This package includes a set of tools for implementing the Monash Electricity Forecasting Model based on the paper by Hyndman and Fan (2010).

The package requires the following data as input: half-hourly/hourly electricity demands; half-hourly/hourly temperatures at one or two locations; seasonal demographic and economic data; public holiday data. The formats of the required data are described in the help files.

Author(s)

Rob J Hyndman and Shu Fan.

Maintainer: Rob J. Hyndman <[email protected]>; Shu Fan <[email protected]>

References

R. J. Hyndman and S. Fan (2010) "Density Forecasting for Long-term Peak Electricity Demand", IEEE Trans. Power Systems, 25(2), 1142–1153. http://robjhyndman.com/papers/peak-electricity-demand/

R. J. Hyndman and S. Fan (2014) "Monash Electricity Forecasting Model" Version 2014.1. http://robjhyndman.com/working-papers/mefm/

Examples

# formula for half-hourly model, to be given by the user
formula.hh <- list()
for(i in 1:48)
  formula.hh[[i]] = as.formula(log(ddemand) ~ ns(temp, df=2) + day 
    + holiday + ns(timeofyear, 9) + ns(avetemp, 3) + ns(dtemp, 3) + ns(lastmin, 3) 
    + ns(prevtemp1, df=2) + ns(prevtemp2, df=2) 
    + ns(prevtemp3, df=2) + ns(prevtemp4, df=2) 
    + ns(day1temp, df=2) + ns(day2temp, df=2) 
    + ns(day3temp, df=2) + ns(prevdtemp1, 3) + ns(prevdtemp2, 3) 
    + ns(prevdtemp3, 3) + ns(day1dtemp, 3))

# Formula for annual model, to be given by the user
formula.a <- as.formula(anndemand ~ gsp + ddays + resiprice)

# Create lagged temperature variables
sa <- maketemps(sa,2,48)

sa.model <- demand_model(sa, sa.econ, formula.hh, formula.a)

summary(sa.model$a)
summary(sa.model$hh[[33]]) 

# Simulate future normalized half-hourly data
simdemand <- simulate_ddemand(sa.model, sa, simyears=10)

# Seasonal economic and weather forecast, to be given by user
afcast <- data.frame(pop=1694, gsp=22573, resiprice=34.65, ddays=642)

# Simulate half-hourly data
demand <- simulate_demand(simdemand, afcast)

# Illustrate the results
plot(density(demand$annmax, bw="SJ"),
  main="Density of seasonal maximum demand", xlab="Demand")

# formula for half-hourly model, to be given by the user
formula.hh <- list()
for(i in 1:48)
  formula.hh[[i]] = as.formula(log(ddemand) ~ ns(temp, df=2) + day 
    + holiday + ns(timeofyear, 9) + ns(avetemp, 3) + ns(dtemp, 3) + ns(lastmin, 3) 
    + ns(prevtemp1, df=2) + ns(prevtemp2, df=2) 
    + ns(prevtemp3, df=2) + ns(prevtemp4, df=2) 
    + ns(day1temp, df=2) + ns(day2temp, df=2) 
    + ns(day3temp, df=2) + ns(prevdtemp1, 3) + ns(prevdtemp2, 3) 
    + ns(prevdtemp3, 3) + ns(day1dtemp, 3))

# Formula for annual model, to be given by the user
formula.a <- as.formula(anndemand ~ gsp + ddays + resiprice)

# Create lagged temperature variables
sa <- maketemps(sa,2,48)

sa.model <- demand_model(sa, sa.econ, formula.hh, formula.a)

summary(sa.model$a)
summary(sa.model$hh[[33]]) 

# Simulate future normalized half-hourly data
simdemand <- simulate_ddemand(sa.model, sa, simyears=10)

# Seasonal economic and weather forecast, to be given by user
afcast <- data.frame(pop=1694, gsp=22573, resiprice=34.65, ddays=642)

# Simulate half-hourly data
demand <- simulate_demand(simdemand, afcast)

# Illustrate the results
plot(density(demand$annmax, bw="SJ"),
  main="Density of seasonal maximum demand", xlab="Demand")

Estimate the electricity demand models

Description

Estimate the half-hourly/hourly and seasonal demand models.

Usage

demand_model(hhdata, adata, hhoptformula, aoptformula)
demand_model(hhdata, adata, hhoptformula, aoptformula)

Arguments

`hhdata`	The historical half-hourly/hourly demand, temperature and seasonality data
`adata`	The historical seasonal (annual, summer, winter or quarterly) demographic and economic data
`hhoptformula`	The formula for each half-hourly/hourly demand model
`aoptformula`	The formula for seasonal demand model

Details

Estimate the demand model using the historical data, use additive model for half-hourly/hourly demand and linear model for seasonal demand, log demand is used for half-hourly/hourly model.

Value

`hh`	half-hourly/hourly demand models
`hhfits`	fitted values of half-hourly/hourly models
`hhres`	half-hourly/hourly model residuals
`a`	seasonal model
`afits`	fitted values of seasonal model
`fits`	fitted values of the entire model
`res`	entire model residuals

Author(s)

Rob J Hyndman and Shu Fan

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

# formula for half-hourly model, to be given by the user
formula.hh <- list()
for(i in 1:48)
  formula.hh[[i]] = as.formula(log(ddemand) ~ ns(temp, df=2) + day 
    + holiday + ns(timeofyear, 9) + ns(avetemp, 3) + ns(dtemp, 3) + ns(lastmin, 3) 
    + ns(prevtemp1, df=2) + ns(prevtemp2, df=2) 
    + ns(prevtemp3, df=2) + ns(prevtemp4, df=2) 
    + ns(day1temp, df=2) + ns(day2temp, df=2) 
    + ns(day3temp, df=2) + ns(prevdtemp1, 3) + ns(prevdtemp2, 3) 
    + ns(prevdtemp3, 3) + ns(day1dtemp, 3))

# formula for annual model, to be given by the user
formula.a <- as.formula(anndemand ~ gsp + ddays + resiprice)

# create lagged temperature variables
sa <- maketemps(sa,2,48)

sa.model <- demand_model(sa, sa.econ, formula.hh, formula.a)

summary(sa.model$a)
summary(sa.model$hh[[33]]) 
# formula for half-hourly model, to be given by the user
formula.hh <- list()
for(i in 1:48)
  formula.hh[[i]] = as.formula(log(ddemand) ~ ns(temp, df=2) + day 
    + holiday + ns(timeofyear, 9) + ns(avetemp, 3) + ns(dtemp, 3) + ns(lastmin, 3) 
    + ns(prevtemp1, df=2) + ns(prevtemp2, df=2) 
    + ns(prevtemp3, df=2) + ns(prevtemp4, df=2) 
    + ns(day1temp, df=2) + ns(day2temp, df=2) 
    + ns(day3temp, df=2) + ns(prevdtemp1, 3) + ns(prevdtemp2, 3) 
    + ns(prevdtemp3, 3) + ns(day1dtemp, 3))

# formula for annual model, to be given by the user
formula.a <- as.formula(anndemand ~ gsp + ddays + resiprice)

# create lagged temperature variables
sa <- maketemps(sa,2,48)

sa.model <- demand_model(sa, sa.econ, formula.hh, formula.a)

summary(sa.model$a)
summary(sa.model$hh[[33]])

Create lagged temperature variables

Description

The function is used to create lagged temperature variables for model estimation.

Usage

maketemps(x, temp_sites, periods = 48)
maketemps(x, temp_sites, periods = 48)

Arguments

`x`	Data frame including temperature observations from 1 or 2 weather stations
`temp_sites`	The number of weather stations, select between 1 and 2
`periods`	The periods within a day, choose between 48 (half-hourly data) and 24 (hourly data)

Value

The complete data frame that can be used for model estimation.

Author(s)

Rob J Hyndman and Shu Fan

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

sa <- maketemps(sa,2,48)

sa <- maketemps(sa,2,48)

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

Historical demographic & economic data for South Australia

Description

Annual data for South Australia including population, GSP, residential electricity price, total electricity price and cooling/heating degree days

Format

A multivariate time series with the following columns:

pop: State population (thousands of people)
gsp: Gross State Product chain volume estimate (in 2008-2009 millions of dollars)
resiprice: Residential price index (2008-2009 cents per kWh)
totalprice: Total price (2008-2009 cents per kWh)
anndemand: Annual electricity demand (GW).
ddays: Cooling degree days with a threshold of 18.5 degrees Celsius.

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(sa.econ)
plot(sa.econ)

The number of days in a season

Description

The number of days in a season, set to 182 for a summer season.

Details

A "season" is taken to be the period over which a model is estimated. In Hyndman & Fan (2010), the season was 1 November to 31 March and so seasondays=151. The default value in the package is seasondays=182; i.e., six months. The value of seasondays can be changed by the user.

Temperature and demand Simulation

Description

Simulate the half-hourly/hourly temperature and demand

Usage

simulate_ddemand(model, hdata, simyears = 1000, delta = 5)
simulate_ddemand(model, hdata, simyears = 1000, delta = 5)

Arguments

`model`	The demand models
`hdata`	The half-hourly/hourly demand, temperature and seasonality data
`simyears`	The length of years of simulation
`delta`	The value of blockdays offset limit for bootstrap

Details

Simulate the half-hourly/hourly temperature and demand using the historical data and the half-hourly/hourly demand model

Value

An object of class simdemand, basically a list including elements

`hhfit`	Simulated half-hourly demand
`hhres`	Simulated half-hourly residuals
`ores`	Simulated half-hourly offset demand
`a`	Seasonal model

Author(s)

Rob J Hyndman and Shu Fan

References

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

Simulate the electricity demand for the next season

Description

Simulate the half-hourly/hourly, seasonal peak electricity demand for the next season

Usage

simulate_demand(sim, afcast, nyears = length(sim$hhfit)/seasondays/48, periods = 48)
simulate_demand(sim, afcast, nyears = length(sim$hhfit)/seasondays/48, periods = 48)

Arguments

`sim`	The simulated half-hourly demand (normalized against seasonal average demand)
`afcast`	The demographic and economic forecasts for the next season
`nyears`	The length of years of simulation
`periods`	The periods within a day, choose between 48 (half-hourly data) and 24 (hourly data)

Details

Simulate/forecast the half-hourly/hourly, seasonal peak electricity demand for the next season by incorporating the seasonal demographic & economic forecasts (to be provided by the user)

Value

`demand`	The forecasted half-hourly demand
`annmax`	The forecasted seasonal maximum demand

Author(s)

Rob J. Hyndman and Shu Fan

References

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

Package 'MEFM'

Help Index

Monash Electricity Forecasting Model

Description

Author(s)

References

Examples

Estimate the electricity demand models

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Create lagged temperature variables

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Historical data for model estimation

Description

Format

Details

Source

References

Examples

Historical demographic & economic data for South Australia

Description

Format

References

Examples

The number of days in a season

Description

Details

Temperature and demand Simulation

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Simulate the electricity demand for the next season

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also