I am trying to forecast electricity consumption in GWh for 2 years ahead (from June 2013 ahead), using R (the forecast package). For that purpose, I tried regression with ARIMA errors. I fitted the model using the
auto.arima function, and I used the following variables in the
xreg argument in the
- Heating and Cooling Degree Days,
- Dummies for all 12 months and
- Moving holidays dummies (Easter and Ramadan)
I have several questions regarding the model:
1) Is it correct to use all 12 dummies for monthly seasonality, since when I tried to include 11, the function returned error. The
Auto.arima function returned the model ARIMA(0,1,2)
2)The model returned the following coefficients (I won't specify all of them as there are too many coefficients):
ma1 ma2 HDD CDD January February March April -0.52 -0.16 0.27 0.12 525.84 475.13 472.57 399.01
I am trying to determine the influence of the temperature component over electricity load. In percentages, (interpreting the coefficients just as with the usual regression) the temperature components (
CDD) account for 11,3% of the electricity consumption. Isn't this too little, considering the fact that the electricity consumption is mostly influenced by the weather component? On the other hand, taking look at the dummies' coefficients, it turns out that the seasonality accounts for the greater part of the load. Why is this? Is the model completely incorrect?
I tried linear regression, and the temperature component accounts for 20%, but it is still a low percentage. Why is this?
3) I am obviously making some mistakes in the use of
forecast.Arima or the plot function parameters since when I plot the forecasts, I get a picture of the original time series which is continued (merged) with the forecasts for the whole time series period (from 2004 until 2015). I don't know how to explain this better, I tried to paste the picture, but it seems I cannot paste pictures here.