# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 4 Results and Discussion  
The present research aimed to provide a reliable early estimation of the heating load in energy-efficient building systems through several machine learning solutions, namely MLPr, LLWL, AMT, RF, ENet, and RBFr models. These approaches are well known. After running all these machine learning techniques, the best outputs can be selected as the most trustworthy solutions in the early estimation of heating load in energy-efficient residential buildings. Therefore, to find the most appropriate predictive networks, the proposed AI models (e.g., MLPr, LLWL, AMT, RF, ENet, and RBFr models) are evaluated and compared. The results of employed machine learning-based solutions proposed here, and their performances are evaluated through Tables 3 and 4. The overall scoring for the performances of the proposed technique is provided further in Table 5.  
As is illustrated Figures 10 and 11, AMT, RF, and MLPr models provided significant accuracy in predicting heating load in energy-efficient buildings, however, the RF-based model can be nominated as more reliable than other developed estimations of machine learning-based techniques. The values 10, 25, 30, 5, 20, and 15 were calculated as the total scores for the LLWL, ATM, RF, ENet, MLPr, and RBFr techniques, respectively. These scores prove the superiority of the RF when compared with other nominated models. The amounts of R\({}^{2}\), MAE, RMSE, RAE (%), and RRSE (%) in the RF model for the training dataset were 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The amounts of R\({}^{2}\), MAE, RMSE, RAE (%), and RRSE (%) in the RF model for the testing dataset were 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. This indicates the higher reliability of the generated RF method compared to other techniques. The weakest estimation model results were from ENet solution where the R\({}^{2}\), MAE, RMSE, RAE (%), and RRSE (%) were 0.8915, 3.2332, 4.5678, 35.3566, and 45.2993 in the training dataset, respectively, and the R\({}^{2}\), MAE, RMSE, RAE (%), and RRSE (%) were 0.896, 3.2585, 4.4683, 35.4392, and 44.2052 for the testing dataset, respectively. According to the total scoring, the best performance from Table 4 was found to be RF. Right after RF model, the next best estimation network was obtained for the AMT technique. The R\({}^{2}\), MAE, RMSE, RAE (%), and RRSE (%) for the AMT training dataset were 0.9985, 0.4096, 0.5449, 4.4788, and 5.4036, respectively. The R\({}^{2}\), MAE, RMSE, RAE (%), and RRSE (%) for the AMT testing dataset were 0.9981, 0.4869, 0.6236, 5.2956, and 6.1693, respectively. The results of network reliability based on the R\({}^{2}\) performance of all proposed models, for both training and testing, are provided in Figures 10 and 11. As stated earlier, both of the RF models could provide a more reliable predictive network with higher accuracy when compared to other proposed techniques. The results of network output for the proposed RF are illustrated in Figures 10d and 11d. Having the provided information, the predictive network of RF proved to be slightly better than other proposed techniques and was superior in making a better regression relationship among the estimated and actual values.