Forecasting electricity usage using univariate time series models - Lim, Hock Eam; Yip, Chee Yin (IP Publishing LLC, 2014)
Electricity is one of the important energy sources.A sufficient supply of electricity is vital to support a country’s development and growth.Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before.It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method.In literature, there are mixed evidences on the best forecasting methods of electricity demand.This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.