# 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")
Run the code above in your browser using DataLab