TY - JOUR
TI - Multistage ensemble of feedforward neural networks for prediction of heating energy consumption
AU - Jovanović Radiša Ž
AU - Sretenović Aleksandra A
AU - Živković Branislav
JN - Thermal Science
PY - 2016
VL - 20
IS - 4
SP - 1321
EP - 1331
PT - Article
AB - 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.