%0 Journal Article
%T SHORT-TERM ELECTRICITY LOAD FORECASTING
%A Norizan Mohamed
%A Maizah Hura Ahmad
%A Suhartono
%J Journal of Sustainability Science and Management
%D 2011
%I 
%X The purpose of this study was to develop the best model for forecasting Malaysia load demand. In this study, a half-hourly electricity load demand of Malaysia for one year, from September 01, 2005 until August 31, 2006 measured in Megawatt (MW) was used. The double-seasonal ARIMA model was considered due to the existence of two seasonal cycles in the load data. Analysis was done by using SAS package. The best model was selected based on the mean absolute percentage error (MAPE), autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. The ARIMA(0,1,1)(0,1,1)48(0,1,1)336 with in-sample MAPE of 0.9906% was selected as the best model. Comparing the one-step and k-step ahead out of sample forecasts performance, it was found that the MAPE for the one-step ahead out-sample forecasts from any horizon were all less than 1% . It can be concluded that the one-step ahead out-sample forecasts were more accurate. There was a reduction in MAPE percentages for all lead time horizons considered, ranging 89% to 96%. Furthermore, a time-series plot of out-samples of actual load data, k-step ahead and one-step ahead out-sample forecasts showed that one-step ahead out-sample forecasts followed the actual load data more closely than k-step ahead out-sample forecasts. Therefore it is proposed that the ACF and PACF plots must be considered in proving the best model for load demand. It is also proposed that the one-step ahead out-sample forecasts must also be considered in forecasting load, especially in Malaysia load data.
%K Load forecasting
%K double seasonal ARIMA model
%K ACF and PACF plots
%K one-step ahead forecasts and k-step ahead forecasts
%U http://jssm.umt.edu.my/files/2012/05/Short-term-Electricity-Load-Forecasting.pdf