Abstract
Buildings play a critical role in energy consumption, representing one of the primary consumers of power. Heating load (HL) and cooling load (CL) are essential for determining the energy efficiency of buildings. Several research projects attempt to address the critical challenge of enhancing energy efficiency in residential buildings, focusing on accurately estimating HL and CL using solutions that implement statistical prediction or typical building control management. This study, however, looked into advanced machine learning (ML) models for sustainable building design based on harnessing the potential of artificial intelligence and explainable AI (AIX) technologies. The proposed model was trained and tested using a dataset of 768 buildings based on feature engineering methods with various ML algorithms (including cutting-edge emotional neural learning (ENN), nonparametric kernel-based probabilistic models known as Gaussian process regression (GPR), and boosted tree (BT) algorithm). In addition, the output of the model was fed to standard building energy performance software (Ecotect) that utilizes the dataset from twelve different building shapes to perform various building energy efficiency analyses. The overall performance of the proposed model was measured using different performance metrics, including MAPE, MAE, RMSE, and PCC to measure the performance of HL- and CL-based building energy efficiency. The performance evaluation results indicate that the M3 variants, especially GPR-M3, consistently outperformed their counterparts across heating and cooling scenarios. The three models indicated reliability for modeling HL and CL. However, for HL, the GPR-M3 model emerged as the best model, outperforming GPR-M1 by 9.2% and GPR-M2 by 3.9%. Similarly, GPR-M3 is superior to CL, with the highest PCC at 0.9858, marking an 8.1% and 1.9% improvement over GPR-M1 and GPR-M2, respectively.