en عمومى General پژوهشي Research <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times="">Planning, management, and energy conservation all benefit from accurate predictions of building energy usage. The secret to ensuring energy systems&#39; performance and sustainability is continuously improving and enhancing forecasting models&#39; effectiveness. In this context, the current research presents, after studying and evaluating several kinds of HL forecasting models, a new enhanced hybrid approach of machine learning application for predicting residential buildings&#39; heating load (HL). The suggested hybrid model, GWO-ANIFS, combines the support vector regression (GWO) and group technique of ANFIS models. The forecasting models used the building&rsquo;s technical characteristics as input factors, and the HL was chosen as the network&#39;s output variable. The findings showed that the suggested ANFIS approach with a 100-person population size was the best approach for forecasting building energy because it had the highest R² (0.97905 and 0.9789) and the lowest error amunts in the forms of MSE (0.012433), RMSE (0.1115), and MAE (0.088128) for predicting HL.</span></span></span> ANFIS, Residential buildings, Metaheuristic, Cooling-load. 109 122 http://aisesjournal.com/browse.php?a_code=A-10-278-1&slc_lang=en&sid=1 Mohamed Salem salemm@usm.my 1003194753284600170 1003194753284600170 Yes School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal 14300, Penang, Malaysia