LoadCurve {extremefit} | R Documentation |
The data frame provides electric consumption of an habitation in France over one month.
data("LoadCurve")
The data is the electric consumption of an habitation in Kilovolt-amps (kVA) every 10 minutes during one month. The habitation has a contract that allows a maximum power of 6 kVA.A list of 2 elements.
$data : a data frame with 24126 observations for 2 variables
Time
the number of day since the 1st of January, 1970.
Value
the value of the electric consumtion in kVA.
$Tgrid : A grid of time to perform the procedure.
Electricite Reseau Distribution France
data("LoadCurve") X<-LoadCurve$data$Value days<-LoadCurve$data$Time Tgrid <- seq(min(days), max(days), length = 400) new.Tgrid <- LoadCurve$Tgrid ## Not run: #For computing time purpose # Choice of the bandwidth by cross validation. # We choose the truncated Gaussian kernel and the critical value # of the goodness-of-fit test 3.4. # As the computing time is high, we give the value of the bandwidth. #hgrid <- bandwidth.grid(0.8, 5, 60) #hcv<-bandwidth.CV(X=X, t=days, new.Tgrid, hgrid, pcv = 0.99, # kernel = TruncGauss.kernel, CritVal = 3.4, plot = FALSE) #h.cv <- hcv$h.cv h.cv <- 3.444261 HH<-hill.ts(X, days, new.Tgrid, h=h.cv, kernel = TruncGauss.kernel, CritVal = 3.4) Quant<-rep(NA,length(Tgrid)) Quant[match(new.Tgrid, Tgrid)]<-as.numeric(predict(HH, newdata = 0.99, type = "quantile")$y) Date<-as.POSIXct(days*86400, origin = "1970-01-01", tz = "Europe/Paris") plot(Date, X/1000, ylim = c(0, 8), type = "l", ylab = "Electric consumption (kVA)", xlab = "Time") lines(as.POSIXlt((Tgrid)*86400, origin = "1970-01-01", tz = "Europe/Paris"), Quant/1000, col = "red") ## End(Not run)