Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6731697 | Energy and Buildings | 2015 | 11 Pages |
Abstract
For prediction of heating energy consumption of a university campus, various artificial neural networks are used: feed forward backpropagation neural network (FFNN), radial basis function network (RBFN) and adaptive neuro-fuzzy interference system (ANFIS). Actual measured data are used for training and testing the models. For each neural networks type, three models (using different number of input parameters) are analyzed. In order to improve prediction accuracy, ensemble of neural networks is examined. Three different combinations of output are analyzed. It is shown that all proposed neural networks can predict heating consumption with great accuracy, and that using ensemble achieves even better results.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
RadiÅ¡a Ž. JovanoviÄ, Aleksandra A. SretenoviÄ, Branislav D. ŽivkoviÄ,