Artificial neural network based electrical load prediction for food retail stores

It has been shown by a number of investigators that artificial neural networks (ANNs) can be more reliable and effective building energy predictors than traditional simulation models. This paper presents the results from comparisons of the predictive accuracy of two commonly used neural networks employed for the prediction of the electrical load of a retail food store. The networks used were the multi-layered perceptron (MLP) and radial basis function (RBF). The MLP network was found to perform better than the RBF network particularly in the prediction of fluctuations of the electrical energy around the base and maximum loads. Further work will be carried out to optimise the structure and prediction accuracy of the two networks. (C) 1998 Elsevier Science Ltd. All rights reserved.