Adaptive online load forecasting via time series modeling

Reliable short-term load forecasting by computer is a topic of considerable interest. In this paper, a reliable online method to accomplish short-term load forecasting is presented. It automatically adapts to changing conditions and requires very little human intervention. It makes use of time series models, automatically adjusting the model parameters, to forecast the load. Unique in this method are (i) the autocorrelation optimization used for incorporating daily and weekly periodicities, and (ii) the identification method used for adapting the time series model parameters, which adapts not only the model parameter values, but also the structure and order of the time series model itself.