A Self-Selecting Neural Network for Short-Term Load Forecasting

Abstract

An original application of a special type of neural network called radial basis function network toward systematization of neural network forecaster design is made. The network itself is used to select its input variables and parameters. The network has a characteristic of convergence to the lowest possible training error for a given set of network parameters and input variables. The advantage of this network lies in selection of input variables on the basis of network performance, and this selection includes the load time series and weather variables. The training of the network is considerably fast and does not need monitoring of training process for nonconvergence and parameter tuning during design and testing. The design method is tested for short-term load forecast on hourly basis for one of the systems, and excellent results were obtained. The comparison with existing feed forward neural network forecaster shows that the proposed forecaster outperforms the former.