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 short-term load forecasting, the Box-Jenkins transfer function approach has 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 square error (MMSE) theory and then updates the forecasts based on the one-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-Jenkins approach. The results of 24-hours- and one-week-ahead forecasts show that the adaptive algorithm is more accurate than the conventional Box-Jenkins approach, especially for the 24-hour case.