Adaptively trained neural networks and their application to electric load forecasting

A training procedure that adapts the weights of a trained layered perceptron type artificial neural network to training data originating from a slowly varying nonstationary process is proposed. The procedure for adaptive dating assures proper response to previous training data by seeking to minimize a weight sensitivity cost function while, at the same time, minimizing the mean square error normally ascribed to the layered perceptron. The process is illustrated through application to an interpolation problem and by its use on an electric load forecasting problem with data collected from the power industry.<
>