Predicting building energy consumption based on meteorological data

The reliability of building energy prediction
results is often threatened by lack of comprehensive and
continuous data, especially when dealing with older buildings
that are not furnished with building energy management
systems. In order to investigate the performance of building
energy prediction models under limited data, this paper utilises
four distinct machine learning methods - decision tree (DT),
support vector machine (SVM), random forest (RF) and voting
regression (VR) to predict energy consumption of the Chemistry
building of a prominent higher institution, based on just
meteorological data. The results indicate that SVM is unable to
accurately predict building energy consumption based on the
prescribed input variables alone. However, in general, DT, RF
and VR offered far more reliable and accurate energy
consumption prediction outcomes with the same training and
testing data sets. More specifically, RF outperformed all other
included methods. It was also observed that the extension of the
time span for the training data sets offered insignificant
improvement to the prediction accuracy as postulated by some
earlier studies. With regards to overall generalisation capability,
VR outperformed all approaches, with outcomes from RF also
marginally better than those from DT.