Comparison of very short-term load forecasting techniques

Three practical techniques-fuzzy logic (FL), neural networks (NN), and autoregressive models-for very short-term power system load forecasting are proposed and discussed in this paper. Their performances are evaluated through a computer simulation study. The preliminary study shows that it is feasible to design a simple, satisfactory dynamic forecaster to predict very short-term power system load trends online. FL and NN can be good candidates for this application.