## 5 Results and Discussion  
Figure 8 illustrates the hourly error in the prediction for each techniques. The final results of the study comparing the performance of the proposed deep ANN with the other baseline approaches is shown in Table 11. It is very clear from the results that the proposed deep ANN and XGB were more accurate in prediction than the shallow ANN and MLR models. The observations reinforce the finding from the MCA analysis carried out on the literature illustrating the proposed model's acceptance. However, the result from Table 11 also emphasises that a simple shallow ANN would perform as weak as an MLR model unless it is adequately tuned. It is shown that hyperparameter tunning allowed the shallow ANN to achieve much higher accuracy, with an exceptional coefficient of determination of 97.5%.  
The MLR prediction model graph showed that the estimated values followed a linear pattern and did not adequately represent the actual values. MLR machine learning method achieved an average RMSE value of 635 W and 17% accuracy. Similarly, the shallow ANN prediction model achieved an average RMSE value of 637 W and 18% accuracy. Therefore, it is clear that a lack of hidden layers and hyperparameter tuning significantly reduces the ANN model's predicting performance. On the other hand, the XGB prediction model's estimated value was much closer to the expected values. XGBoost machine learning  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **RMS Error (Watts)** & **Coefficient of Determination (\%)** \\ \hline Deep ANN & 111.20 & 97.5 \\ XGB & 270.85 & 84.9 \\ MLR & 634.65 & 17.2 \\ Shallow ANN & 636.74 & 16.6 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The result of the simulation.  
Figure 8: The graph shows the predication from each techniques: MLR (**top left**), XGB (**top right**), shallow ANN (**bottom left**), and deep ANN (**bottom right**).  
method achieved an average RMSE value of 271 W and 85% accuracy. Even better, the deep ANN model's estimated values were able to resemble the original data set to a greater extent. The deep ANN machine learning method achieved an average RMSE value of 111 W and 98% accuracy. Overall, this trend in the results is consistent with the inferences obtained from the MCA in Section 3.2 and the machine learning model's design objectives highlighted in Section 4.2.1.  
The line graph in Figure 9 reveals a better visual representation of all models' performance. The deep ANN model achieved the highest accuracy of all the techniques examined in this study, indicated by the fact that it had the lowest RMSE curve compared to the rest. The increased accuracy is due to its ability to model the randomness in the model and deal with input noises, unlike linear regression methods, which are only suited for linear modelling. The XGB models are also susceptible to noisy data, evident from the accurate prediction results obtained. However, despite performing well in predicting energy consumption values, deep ANN took a significant longer computation time (2738 s or 45 min and 38 s) to build the model compared to 4 s and 28 s for MLR, XGB respectively. Based on the accuracy required and computational availability, the type of prediction model is chosen.