Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

Abstract

Suboptimal least squares or iteratively reweighted least-squares (IRWLS) procedures for estimating the parameters of a seasonal multiplicative autoregressive (AR) model encountered during power system load forecasting are proposed. The method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squares or the IRWLS to estimate the optimal seasonal AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method. >