Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms.

Since cooling and heating loads are regarded as significant parameters to examine the energy performance of buildings, the need to predict and analyze them for the residential buildings seems to be undeniable. Hence, the present paper wants to optimize the multi-layer perceptron neural network using several optimization methods to predict the heating and cooling of energy-efficient buildings. The dataset used in this study consists of eight independent factors: surface area, wall area, roof area, relative compactness, overall height, orientation, glazing area, and glazing area distribution. To prove the reliability and accuracy of the obtained results, test and training data are also considered. According to the statistical results, biogeography-based optimization has the highest value of R2 and the lowest values of RMSD, normalized RMSD, and MAE in both training data and test data for cooling and heating loads. Hence, the forecasting accuracy of the proposed MLP neural network optimized with the BBO optimization algorithm with the RMSD, R2, and MAE of 2.82, 0.920, 2.15 in the training phase of heating load and with the RMSD, R2, and MAE of 3.18, 0.887, 2.97 in the training phase of the cooling load is much better than those of the other models. • The effect of eight common building parameters on heating and cooling load is studied. • MLP neural network is used to predict the heating and cooling loads of a residential building. • Through different optimization algorithms, optimal parameters of the MLP model are determined. • Biogeography-based optimization outperforms other optimization algorithms in improving neural network. • A good agreement was observed between the results of training and experimental data. [ABSTRACT FROM AUTHOR]