Daily electricity demand for Victoria, Australia, during 2014 is contained in elecdaily. The data for the first 20 days can be obtained as follows.

daily20 <- head(elecdaily,20)

Plot the data and find the regression model for Demand with temperature as an explanatory variable. Why is there a positive relationship?

Produce a residual plot. Is the model adequate? Are there any outliers or influential observations?

Use the model to forecast the electricity demand that you would expect for the next day if the maximum temperature was 1515 and compare it with the forecast if the with maximum temperature was 3535. Do you believe these forecasts?

Give prediction intervals for your forecasts. The following R code will get you started:

autoplot(daily20, facets=TRUE) daily20 %>% as.data.frame() %>% ggplot(aes(x=Temperature, y=Demand)) + geom_point() + geom_smooth(method="lm", se=FALSE) fit <- tslm(Demand ~ Temperature, data=daily20) checkresiduals(fit) forecast(fit, newdata=data.frame(Temperature=c(15,35)))

Plot Demand vs Temperature for all of the available data in elecdaily. What does this say about your model?