library(rnn)
df <- read.csv("power_dataset.csv")
train <- df[1:2016,] # train set from 5-16 June
test <- df[145:dim(df)[1],] # test set from 6-18 June
# prepare data to train a model
trainX <- train[1:1872,]$power # using only power column now
trainY <- train[1873:dim(train)[1],]$power
# data formatting acc. to rnn as [samples, timesteps, features]
tx <-  array(trainX,dim=c(NROW(trainX),144,1))
ty <-  array(trainY,dim=c(NROW(trainY),144,1))
model <- trainr(X=tx,Y=ty,learningrate = 0.04, hidden_dim = 10, numepochs = 100)

The sample dimension of X is different from the sample dimension of Y.