Green building’s heat loss reduction analysis through two novel hybrid approaches

One of the key reasons for performance discrepancy between a building's intended usage and actual operation is Heat Loss, which describes envelope efficiency during in-use circumstances. In this setting, ANN models’ use energy analysis green buildings has become more established. This research aims to anticipate heat loss utilizing two artificial neural network-based methodologies (ANN). particular, TLBO BBO are used contrasted. Additionally, RMSE, MAE, R2 calculate an absolute error predicting gauge accuracy findings. The suggested TLBO-MLP standard reliable method with positive outcome (RMSE = 0.01012 0.05216, 0.99536 0.9651). Also, according training ranges [−0.0006078, 0.01133] [−0.00040708, 0.010181] testing [0.0004724, 0.068666] [0.0021984, 0.057688] BBO-MLP TLBO-MLP, respectively, shows that reaches lower range can predict higher it could properly forecast building technologies. Even so, provides satisfactory (R2 0.9943 0.95175, RMSE 0.01122 0.06112). To increase precision calculating buildings, specifically integrating them optimization algorithms, further study required.