Self-organising modular probabilistic neural network for STLF

A new approach to Short Term Load Forecasting (STLF) using probabilistic neural network (PNN) is proposed in this work. The present approach is different from other popular feed forward ANN methods as PNN uses statistical learning theory for learning from examples. The PNN processes the training data only once and therefore, it is very fast compared to back propagation ANN. In addition, PNN parameters and input variables selection is optimized using simulated annealing. The proposed method results in a self organizing adaptive forecast model which adapts with changing load characteristics. Since, the input selection and parameter identification is integrated with the forecaster, the approach becomes utility independent unlike other ANN approaches which are utility dependent. The comparison of present approach and its accuracy with other ANN and conventional approaches establish its superiority.