TY - JOUR
TI - Support vector machine for the prediction of heating energy use
AU - Sretenović Aleksandra A
AU - Jovanović Radiša Ž
AU - Novaković Vojislav M
AU - Nord Nataša M
AU - Živković Branislav
JN - Thermal Science
PY - 2018
VL - 22
IS - 14
SP - 1171
EP - 1181
PT - Article
AB - Prediction of a 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 model. Support vector machine 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 support vector machine 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.