## 4 Simulation and Results
Performance evaluation of the proposed methods is presented in Table 2 in terms of R, MSE, RMSE, and MAE. Based on the results presented in the Table 2, it can be seen that the best prediction was related to the prediction of the heating load by the MLP method with the highest value of R (0.9993) and minimal errors in the form of MSE (0.2335), RMSE (0.4832), and MAE (0.4118). However, in predicting the cooling load, the SVR method with a large amount of R (0.9878) and lowest errors in the terms of MSE (3.024), RMSE (1.7389), and MAE (1.4762) made a good prediction. Highest values of MSE and RMSE errors of prediction were also related to the MLP method in the prediction of cooling load. The use of machine learning methods and their results are highly dependent on the type of input data. It is observed that there is a difference between the results of predicting the cooling load and heating load by each of the networks and the heating load is predicted with high accuracy. This difference is due to the poor correlation between the input data and the amount of cooling load relative to the heating load. To evaluate the effectiveness of the proposed methods in this paper, it is necessary to compare the results obtained with the results of other studies. Comparisons should be made with caution using similar datasets. To this end, a number of studies were selected for comparison in which similar data was used for predicting the cooling and heating loads. To express the effectiveness of the data type in the accuracy of the results, the results of several studies conducted to predict cooling and heating loads using different data were compared with the results obtained in this paper. Table 3 makes this comparison. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Heating Load**} & \multicolumn{3}{c}{**Cooling Load**} \\ \hline  & **R** & **MSE** & **RMSE** & **MAE** & **R** & **MSE** & **RMSE** & **MAE** \\ \hline MLP & 0.9993 & 0.2335 & 0.4832 & 0.4118 & 0.9824 & 6.896 & 2.626 & 2.0973 \\ SVR & 0.9979 & 0.7838 & 0.8853 & 0.7780 & 0.9878 & 3.024 & 1.7389 & 1.4762 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of accuracy and error for proposed methods in heating and cooling load prediction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **References** & **Heating Load (R)** & **Cooling Load (R)** \\ \hline Used data in this paper & MLP in this paper & 0.9993 & 0.9824 \\  & SVR in this paper & 0.9979 & 0.9878 \\  & DNN [14] & 0.9805 & 0.9976 \\  & GBM [14] & 0.9853 & 0.9853 \\  & GPR [14] & 0.9984 & 0.9913 \\  & MPMR [14] & 0.8802 & 0.8955 \\  & ANN [15] & 0.9980 & 0.9840 \\  & CART [15] & 0.9960 & 0.9810 \\  & GLR [15] & 0.9950 & 0.9830 \\  & CHAID [15] & 0.9950 & 0.9810 \\  & GA-ANN [18] & 0.9800 & - \\  & PSO-ANN [18] & 0.9720 & - \\  & ICA-ANN [18] & 0.9700 & - \\  & ABC-ANN [18] & 0.9730 & - \\ \hline Different data & GRNN [28] & - & 0.9640 \\  & PENN [20] & - & 0.9500 \\  & MLR [20] & - & 0.7510 \\  & AR [20] & - & 0.8370 \\  & ARX [20] & - & 08640 \\  & MNR (initial prediction) [20] & - & 0.8990 \\  & MNR (final calibration) [20] & - & 0.9580 \\  & ANN [21] & 0.9900 & - \\  & Decision tree [22] & 0.92 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of cooling and heating loads prediction results with other works. The comparison made in Table 3, shows the accuracy and efficiency of the proposed methods in this paper for forecasting the cooling and heating loads of the building. The use of machine learning applications and the selection of the applicable method for energy predicting and energy efficiency measures in residential buildings are significantly effective in saving energy consumption. The selected methods were able to realize the purpose of the paper with their high accuracy and achieve this important goal. Finally, it should be noted that the proposed solutions can also be used for real-world data.