https://github.com/robjhyndman/mefm-package
R package to implement the MEFM
https://github.com/robjhyndman/mefm-package
Last synced: 6 months ago
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R package to implement the MEFM
- Host: GitHub
- URL: https://github.com/robjhyndman/mefm-package
- Owner: robjhyndman
- Created: 2013-11-07T10:40:09.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2024-06-13T00:31:26.000Z (about 1 year ago)
- Last Synced: 2024-12-08T23:25:32.600Z (6 months ago)
- Language: R
- Size: 30.5 MB
- Stars: 55
- Watchers: 13
- Forks: 32
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
#MEFM package
The R package *MEFM* includes a set of tools for implementing the Monash Electricity Forecasting Model based on the paper by [Hyndman and Fan (2010)](http://robjhyndman.com/papers/peak-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.
Some documentation of the underlying model is provided at [robjhyndman.com/publications/mefm/](https://robjhyndman.com/publications/mefm/)
## Installation
You can install the latest version from
[Github](https://github.com/robjhyndman/MEFM-package)
```s
# install.packages("devtools")
library(devtools)
install_github("robjhyndman/MEFM-package")
```
## Usage
```s
library(MEFM)
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")
```
## License
This package is free and open source software, licensed under GPL (>= 2).