Appraisal of energy loss reduction in green buildings using large-scale experiments compiled with swarm intelligent solutions

Abstract

Today, the issue of energy efficiency is a major one in global politics. The external environment, particularly the wind speed and outside air temperature, determines the thermal burden the cold outside air places on a building's interior. The heat load of a building is influenced by several factors, including the wall’s heat transfer coefficients (W/mK), the coating material (W/mK), the inside temperature (°C), and the outside temperature (°C), and the temperature of external surface (°C). In this investigation, we undertake a comprehensive assessment, evaluation, and comparing the performance of two unique artificial approaches (BSA and COA) utilized for anticipating heat loss in green buildings; the optimum way is then identified depending on the R2 and RMSE criteria. The outcomes demonstrate that BSA and COA have R2 values of (0.97038 and 0.90158) and (0.9919 and 0.94239) in the training and testing phases. Additionally, the RMSE values for BSA and COA in the training and testing stages are (0.02541 and 0.08616) and (0.01336 and 0.06662), correspondingly. Also, the estimated MAEs (0.019055 and 0.0097193) denote a low level of training error for both methods. Regarding R2, RMSE and MAE values, the COA predicts energy loss more accurately.