Having a reliable approximation of heating load (HL) and cooling load (CL) is a substantial task
for evaluating the energy performance of buildings (EPB). Also, the appearance of soft computing
techniques has made many traditional methods antiquated. Thus, the main effort of this study was
to evaluate the capability of several learning methods for appraising the HL and CL of a residential
building. To this end, a proper dataset consisting of eight influential factors was provided. To simplify
the problem, we executed feature validity by using a correlation-based feature subset selection
(CfsSubsetEval) technique. The results of this process showed that wall area, overall height, orientation
and glazing area have the most significant impact on the HL and CL simulation. After preparing the
suitable dataset, sixteen learning methods namely, elastic net (EN), Gaussian process regression (GPR),
least median of squares regression (LMSR), multiple linear regression (MLR), multi-layer perceptron
regression (MPR), multi-layer perceptron (MLP), radial basis function regression (RBFR), sequential
minimal optimization regression (SMOR), functions XNV, lazy K-star, lazy LWL, rules decision table
(RDT), M5Rules, alternating model tree (AMT), directional path consistency (DPC), and Random Forest
(RF) were developed in Weka environment to forecast the HL and CL variables. Referring to the
results, it was concluded that RF, lazy K-star, RDT and AMT outperform other predictive models.
Also, comparing the results with the results of the previous studies showed that the applied feature
reduction not only did not disturb the learning process but also has enhanced the performance of
models. Also, due to the excellent accuracy of the MLP, a formula was derived from the optimized
structure of it to predict the HL and CL variables.
