Teaching–learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building

Abstract

Early assessment of the energy performance of buildings (EPB) is focused in this study. This task is carried out by predicting the cooling load (CL) in a residential building. To this end, due to the drawbacks of neural computing approaches (e.g., local minima), a novel metaheuristic technique, namely teaching–learning-based optimization (TLBO) is employed to modify a multi-layer perceptron neural network (MLPNN). The complexity of the proposed model is also optimized by a trial and error process. Evaluating the results revealed a high efficiency for this scheme. In this sense, the prediction error of the MLPNN was reduced by around 20%, and the correlation between the measured and forecasted CLs rose from 0.8875 to 0.9207. It was also deduced that the TLBO outperforms two benchmark optimizers of cuckoo optimization algorithm (COA) and league championship algorithm (LCA) in terms of both modeling accuracy and network complexity. Moreover, the TLBO-MLP emerged as the most time-effective hybrid as it required considerably lower computation time than COA-MLP and LCA-MLP. Regarding these advantages, the proposed model can be promisingly used for early assessment of EPB in practice.