AUTOREGRESSIVE MODELS IN SHORT TERM LOAD FORECAST: A COMPARISON OF AR AND ARMA

Short-term load forecasting plays an important role in
planning and operation of power system. The accuracy of this
forecasted value is necessary for economically efficient
operation and also for effective control. This paper describes a
comparison of autoregressive moving average (ARMA) and
autoregressive (AR) Burg’s and modified covariance
(MCOV) methods in solving one week ahead of short term
load forecast. The methods are tested based from historical
load data of National Grid of Malaysia and load demand in
New South Wales, Australia. The accuracy of discussed
methods are obtained and reported.