Machine Learning Method Based on Symbiotic Organism Search Algorithm for Thermal Load Prediction in Buildings

Abstract

This research investigates the efficacy of a proposed novel machine learning tool for the optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) metaheuristic algorithm to a well-known model, namely an artificial neural network (ANN), a sophisticated optimizable methodology is developed for estimating heating load (HL) in residential buildings. Moreover, the SOS is comparatively assessed with several identical optimizers, namely political optimizer, heap-based optimizer, Henry gas solubility optimization, atom search optimization, stochastic fractal search, and cuttlefish optimization algorithm. The dataset used for this study lists the HL versus the corresponding building conditions and the model tries to disclose the nonlinear relationship between them. For each mode, an extensive trial and error effort revealed the most suitable configuration. Examining the accuracy of prediction showed that the SOS–ANN hybrid is a strong predictor as its results are in great harmony with expectations. Moreover, to verify the results of the SOS–ANN, it was compared with several benchmark models employed in this study, as well as in the earlier literature. This comparison revealed the superior accuracy of the suggested model. Hence, utilizing the SOS–ANN is highly recommended to energy-building experts for attaining an early estimation of the HL from a designed building’s characteristics.

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings

“…As far as the ANNs are concerned, they have shown high integration competency to be trained via metaheuristic techniques. Nejati, Zoy [39] introduced a hybrid of ANN with symbiotic organism search (SOS) for energy performance assessment, and compared the suggested model with four benchmark metaheuristic strategies, namely Henry gas solubility optimization (HGSO), political optimizer (PO), heap-based optimizer (HBO), atom search optimization (ASO), cuttlefish optimization algorithm (CFOA), and stochastic fractal search (SFS). After careful assessment of accuracy, they concluded the superiority of the SOS-ANN method for the mentioned objective.…”

Predictive analysis of concrete slump using a stochastic search-consolidated neural network

“…For instance, Ye et al [ 57 ] used the SFS to optimize adaptive neuro-fuzzy inference system (ANFIS) applied to the prediction of blast-induced air overpressure. As for ANNs, Moayedi et al [ 58 ] and Nejati et al [ 59 ] proved the competency of this algorithm in tunning weights and biases of ANN for predicting landslide susceptibility and buildings’ energy performance, respectively. From these studies, it can be derived that the SFS has a reliable performance in dealing with intricate and high-dimensional problems.…”