THERMAL SCIENCE

International Scientific Journal

MULTISTAGE ENSEMBLE OF FEEDFORWARD NEURAL NETWORKS FOR PREDICTION OF HEATING ENERGY CONSUMPTION

ABSTRACT
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.
KEYWORDS
PAPER SUBMITTED: 2015-01-22
PAPER REVISED: 2015-08-22
PAPER ACCEPTED: 2015-09-07
PUBLISHED ONLINE: 2015-09-26
DOI REFERENCE: 10.2298/TSCI150122140J
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2016, VOLUME 20, ISSUE 4, PAGES [1321 - 1331]
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© 2017 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence