ANALYSIS OF AN ADAPTIVE TIME-SERIES AUTOREGRESSIVE MOVING-AVERAGE (ARMA) MODEL FOR SHORT-TERM LOAD FORECASTING

In this paper, an adaptive ARMA (autoregressive moving-average) model is developed for short-term load forecasting of a power system For sho rt-term load forecasting, the Box-Jenkins transfer function approach h as been regarded as one of the most accurate methods. However, the Box -Jenkins approach without adapting the forecasting errors available to update the forecast has limited accuracy. The adaptive approach first derives the error learning coefficients by virtue of minimum mean squ are error (MMSE) theory and then updates the forecasts based on the on e-step-ahead forecast errors and the coefficients. Due to its adaptive capability, the algorithm can deal with any unusual system condition. The employed algorithm has been tested and compared with the Box-Jenk ins approach. The results of 24-hours- and one-week-ahead forecasts sh ow that the adaptive algorithm is more accurate than the conventional Box-Jenkins approach, especially for the 24-hour case.