demand_model | R Documentation |
Estimate the half-hourly/hourly and seasonal demand models.
demand_model(hhdata, adata, hhoptformula, aoptformula)
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 |
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.
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 |
Rob J Hyndman and Shu Fan
R. J. Hyndman and S. Fan (2010) "Density Forecasting for Long-term Peak Electricity Demand", IEEE Trans. Power Systems, 25(2), 1142–1153.
simulate_ddemand
,
simulate_demand
,
sa
,
sa.econ
# 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]])
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.