Modelling Electricity Demand in Sudan Using Multiplicative Seasonal ARIMA and HOLT-WINTERS

Abstract

Reliable forecast of energy demand represents a starting point in policy development and improvement of production and distribution facilities. This paper predicts the electricity demand in Sudan during the period from January 2006 to December 2016. For the purposes of these forecasts, multiplicative seasonal ARIMA and Holt-Winters models were used. The results show that the Holt-Winters exponential smoothing method, as opposed to one. An autoregressive model has been included in both the standard and the seasonal Holt-Winters methods to model the residuals and thus improve the forecast accuracy.