LoadCurve: Load curve of an habitation

In extremefit: Estimation of Extreme Conditional Quantiles and Probabilities

Load curve of an habitation

Description

The data frame provides electric consumption of an habitation in France over one month.

Usage

data("LoadCurve")

Format

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.

Source

Electricite Reseau Distribution France

Examples

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)

extremefit documentation built on April 11, 2025, 6:10 p.m.