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Feedforward neural network models are created for prediction of heating   energy consumption of a university campus. Actual measured data are used for   training and testing the models. Multistage neural network ensemble is   proposed for the possible improvement of prediction accuracy. Previously   trained feed-forward neural networks are first separated into clusters, using   k-means algorithm, and then the best network of each cluster is chosen as a   member of the ensemble. Three different averaging methods (simple, weighted   and median) for obtaining ensemble output are applied. Besides this   conventional approach, single radial basis neural network in the second level   is used to aggregate the selected ensemble members. It is shown that heating   energy consumption can be predicted with better accuracy by using ensemble of   neural networks than using the best trained single neural network, while the   best results are achieved with multistage ensemble

Multistage ensemble of feedforward neural networks for prediction of heating energy consumption