## 4 Comparative Analysis  
The proposed AUTO-encoder-based Neural Network (AUTO-NN) was evaluated among three standard models, namely Radial Belief Neural Network (RBNN), Deep Belief Network (DBN), and Artificial Neural Network (ANN), based on these parameters. For the purpose of analysis, \(RMSE\) (Root Mean Square Error), \(nRMSE\) (Normalized Root Mean  
Figure 3: Flowchart for proposed AUTO-NN for forecasting. Square Error), _MAE_ (Mean Absolute Error), _MaxAE_ (Maximum Absolute Error), and _MAPE_ (Mean Absolute Percentage Error) were selected as the parameters. Here MATLAB is the tool used to analyze the results and the proposed and existing models. \[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(Y(i)-Y^{\prime}(i)^{2}}{N}} \tag{30}\]  
where \(Y(i)\) is the actual generated PV output and \(Y^{\prime}(i)\) is the predicted PV output. \(N\) is the total estimated points in the forecasting time period. \[nRMSE=\frac{1}{max(Y)}\sqrt{\frac{\sum_{i=1}^{n}(Y(i)-Y^{\prime}(i)^{2}}{N}} \tag{31}\]  
\[MaxAE=max\big{|}Y(i)-Y^{\prime}(i)\big{|} \tag{32}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\big{|}Y(i)-Y^{\prime}(i)\big{|} \tag{33}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{\big{|}Y(i)-Y^{\prime}(i)\big{|}}{Capacity} \tag{34}\]  
where Capacity--capacity of each PV array for power generation. Figure 4 presents the comparison of _RMSE_ between the existing methods and the proposed method, in which the number of epochs used for analysis is given on the X-axis, and the _RMSE_ values in percentage are on the Y-axis. When compared, the existing RBNN, ANN, and DBN methods achieved _RMSE_ of 64%, 62.2%, and 60.52%, respectively. Comparatively, the proposed AUTO-NN method achieved an _RMSE_ of 58.72%, which is 6.72, 4.4, and 2.3 % lesser than the RBNN, ANN, and DBN methods. Figure 5 presents the comparison of _nRMSE_ between the existing RBNN, ANN, and DBN methods and the proposed AUTO-NN method. The X-axis displays the number of epochs used for analysis, and the Y-axis gives the _nRMSE_ values obtained in percentage. The existing RBNN, ANN, and DBNN methods achieved nRMSE of 67.48%, 66.58%, and 64.74%, respectively. In comparison, the proposed AUTO-NN method achieved an nRMSE of 62.7%, which is 5.36% lesser than RBNN, 4.5% lesser than ANN, and 2.02% lesser than the DBN method. Figure 4: Comparison of _RMSE_. Figure 6 shows the comparison of _MaxAE_ between the existing RBNN, ANN, and DBN methods and the proposed AUTO-NN method, where the X-axis shows the number of epochs, and the Y-axis shows the _MaxAE_ values obtained in percentage. When compared, the existing RBNN, ANN, and DBN methods achieve 53.48%, 51.66%, and 50.54%, while the proposed AUTO-NN method achieves 48.04%, which is 5.44% better than RBNN, 3.62% better than ANN and 2.5% better than DBN. Figure 7 shows the comparison of _MAE_ between the existing RBNN, ANN, and DBN methods and the proposed AUTO-NN method, where the X-axis shows the number of epochs, and the Y-axis shows the MAE values obtained in percentage. When compared, the existing RBNN, ANN, and DBN methods achieve 57.26%, 56.2%, and 51.16%, while the proposed AUTO-NN method achieves 48.66%, which is 9.4% better than RBNN, 8.46% better than ANN and 3.5% better than DBN. Figure 5: Comparison of _nRMSE_. Figure 6: Comparison of _MaxAE_. Figure 8 shows the comparison of _MAPE_ between the existing RBNN, ANN, and DBN methods and the proposed AUTO-NN method where the X-axis shows the number of epochs, and the Y-axis shows the _MAPE_ values obtained in percentage. When compared, the existing RBNN, ANN, and DBN methods achieve 57.96%, 54.12%, and 48.96%, while the proposed method achieves 46.76%, which is 11.2% better than RBNN, 8.64% better than ANN and 2.2% better than DBN. The overall comparison between the existing and proposed model has shown in the following Table 2  
Figure 8: Comparison of _MAPE_. \[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(Y(i)-Y^{\prime}(i)^{2}}{N}} \tag{30}\]0 Comparison of _MAE_.