A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

Abstract

Recent studies have witnessed remarkable merits of metaheuristic algorithms in optimization problems. Due to the significance of the early analysis of the thermal load in energy-efficient buildings, this work introduces and compares four novel optimizer techniques—the firefly algorithm (FA), optics-inspired optimization (OIO), shuffled complex evolution (SCE), and teaching–learning-based optimization (TLBO)—for an accurate prediction of the heating load (HL). The models are applied to a multilayer perceptron (MLP) neural network to surmount its computational shortcomings. The models are fed by a literature-based dataset obtained for residential buildings. The results revealed that all models used are capable of properly analyzing and predicting the HL pattern. A comparison between them, however, showed that the TLBO-MLP with the coefficients of determination 0.9610 vs. 0.9438, 0.9373, and 0.9556 (respectively, for FA-MLP, OIO-MLP, and SCE-MLP) and the root mean square error of 2.1103 vs. 2.5456, 2.7099, and 2.2774 presents the most reliable approximation of the HL. It also surpassed several methods used in previous studies. Thus, the developed TLBO-MLP can be a beneficial model for subsequent practical applications.