THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

online first only

Support vector machine for the prediction of heating energy use

ABSTRACT
Prediction of the building energy use for heating is very important for adequate energy planning. In this paper the daily district heating use of one university campus was predicted using the Support Vector Machine (SVM) model. SVM is the artificial intelligence method that has recently proved that it can achieve comparable, or even better prediction results than the much more used artificial neural networks. The proposed model was trained and tested on the real, measured data. The model accuracy was compared with the results of the previously published models (various neural networks and their ensembles) on the same database. The results showed that the SVM model can achieve better results than the individual neural networks, but also better than the conventional and multistage ensembles. It is expected that this theoretically well known methodology finds wider application, especially in prediction tasks.
KEYWORDS
PAPER SUBMITTED: 2017-05-26
PAPER REVISED: 2017-10-25
PAPER ACCEPTED: 2017-12-11
PUBLISHED ONLINE: 2018-04-28
DOI REFERENCE: https://doi.org/10.2298/TSCI170526126S
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