Comparison of time series methods for electricity forecasting: A case study in Perlis

This paper attempts to forecast the monthly electricity demand for the state of Perlis using three time series methods namely Box-Jenkins ARIMA, Multiplicative Holt-Winter Exponential Smoothing and Time Series Regression for the seasonal monthly data spanning from September 1996 to February 2004. The study focused only on the domestic sector because it reveals the seasonal nature of the data. The comparison is based on the forecast error which is evaluated from September 2003 onwards (six month period). This study showed that the data series did not reveal any drastic changes of electricity consumption for the forecasted period. The forecast values followed the same trend for every year, along with seasonal variation in data series. This study also found that Regression with seasonal element was the ‘best’ method for short term electricity forecasting in Perlis