> Hi,
> I am would like to ask few questions.
> I am trying to forecast hourly electricity prices by 24 hours ahead.
> I have hourly data starting from 2015*12*18 to 2017-10-24
> and I have defined the data as time series as written in the code below.
>
> Then I am trying do neural network with 23 non-seasonal dummies and 1
> seasonal dummy.
> But I don’t know whether training set is enough.( Guess it is 50 hours in
> here?)
>
> The problem is that I couldn’t 24 for output here. How can I make such
> forecast?
> And my MASE score (6.95 in the Test set) is not good. Could be related to
> shortness of training set?
>
> The Code:
>
> library(zoo)
> library(readxl)
> setwd("C:/Users/emrek/Dropbox/2017-2018 Master Thesis/DATA")
> epias <- read_excel("eski.epias.xlsx")
>
>
> nPTF <- epias$`PTF (TL/MWh)`
> nSMF<- epias$`SMF(TL/MWh)`
> nC<- epias$`TT(MWh)`
> nEAK<- epias$`EAK-Toplam (MWh)`
> nTP<- epias$`Toplam (MWh)`
>
> times <- seq(from=as.POSIXct("2015-12-18 00:00:00"),
> to=as.POSIXct("2017-10-24 23:00:00"), by="hour")
> mydata <- rnorm(length(times))
>
> PTF <- zoo(nPTF, order.by=times )
> SMF <- zoo(nSMF, order.by=times )
> C <- zoo(nC, order.by=times )
> EAK <- zoo(nEAK, order.by=times )
> TP<- zoo(nTP, order.by=times )
> SH <- (EAK-TP)
>
> epias <- cbind(PTF,C,SH)
> View(epias)
>
> #neural networks
> library(forecast)
> set.seed(201)
> epias.nn <- nnetar(PTF, repeats = 50, p=23, P=1, size =12)
> summary(epias.nn$model[[1]])
>
> epias.pred <- forecast(epias.nn, h= 24)
> accuracy(epias.pred, 24)
>
> plot(PTF, ylim=c(0,500) , ylab= , xlab= , bty="l", xaxt="n",
> xlim=c(as.POSIXct("2017-10-20 00:00:00"),as.POSIXct("2017-10-25 23:00:00")) ,
> lty=1 )
>
> lines(epias.pred$fitted,lwd = 2,col="blue")
>
>
> Best Regards,
> --
> Emre
>
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