Ensemble of radial basis neural networks with k-means clustering for heating energy consumption prediction

For the prediction of heating energy consumption of university campus, neural network ensemble is proposed. Actual measured data are used for training and testing the models. Improvement of the prediction accuracy using k-means clustering for creating subsets used to train individual radial basis function neural networks is examined. Number of clusters is varying from 2 to 5. The outputs of ensemble members are aggregated using simple, weighted and median based averaging. It is shown that ensembles achieve better prediction results than the individual network.