In this section, the result of prediction by using both training datasets and testing datasets will be showed and each of their predicted and actual value will be compared. The percentage of difference and the mean square error of predicted and actual value will be calculated.
Figure 4.16 and the table in Appendix C record the predicted and actual value of Ground ISR using training datasets. The reason that training datasets
has been reused to predict the Ground ISR is to verify the function and reliability of the ANN model before proceeding to do the inference of Ground ISR to other unknown sites. An ANN model which can function well should be able to give a good prediction with the datasets which is used to trained it. From the table in Appendix C, the average percentage of difference between prediction and actual Ground ISR value is 5.6%, which is less than 6%. The 6% of difference is the average percentage of difference between original value of Satellite ISR (one of the ANN inputs) and Ground ISR (the value that ANN designed to predict). If the average percentage of difference between prediction and actual Ground ISR value is less than 6%, it means that the trained ANN model is functioning and perform well to predict Ground ISR when the Satellite ISR is used as ANN input data. In other words, the difference between Satellite ISR and its corresponding Ground ISR is reduced.
Figure 4.16: The comparison between predicted and actual value of Ground ISR by using the training datasets after the ANN training was done.
Figure 4.17, Figure 4.18 and Table 4.4 show the graphs and results of the prediction of the ANN model. From the Table 4.4, the average percentage of difference between prediction and actual Ground ISR value by using testing datasets is 5.8%, which is less than 6%. It means that the trained ANN model is functioning and perform well to predict Ground ISR and the difference between Satellite ISR and its corresponding Ground ISR is reduced. Furthermore, by referring to the table in Appendix B, the percentage of different of actual Satellite ISR and Ground ISR is varied from range of 21.5% to 0% whereas the
percentage of difference of actual Ground ISR and Predicted Ground ISR using testing datasets is reduced to range of 10.6% to 0.5%. The range of percentage of difference is reduced with the application of ANN model developed to predict the Ground ISR of a specific site.
Figure 4.17: The comparison between predicted and actual value of Ground ISR by using the testing datasets. (The highlighted red spot showed the
optimal ISR with maximum and minimum percentage of difference.)
Figure 4.18: Percentage of difference between predicted and actual Ground ISR among the prediction. (The red bar is the maximum value whereas the
green bar is the minimum value of percentage of difference.)
1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Optimal ISR
Number of Prediction
Comparison between Predicted and Actual Value of Optimal ISR
Actual Ground ISR Predicted Ground ISR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
% of Difference 3.2 3.8 8.7 5.6 4.0 7.8 6.3 7.8 10. 3.6 3.5 6.3 8.6 3.9 0.5 4.2 7.3 8.7 Actual ISR 1.8 1.7 1.8 1.8 1.8 1.9 1.9 1.9 1.9 1.7 1.9 1.9 1.6 1.7 1.7 1.6 1.6 1.6
1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Optimal ISR
Perccentage of DIfference (%)
Percentage of Difference among the Prediction
Figure 4.17, Figure 4.18 and Table 4.4 show the graphs and results of the prediction of the ANN model. From the Table 4.4, the average percentage of difference between prediction and actual Ground ISR value by using testing datasets is 5.8%, which is less than 6%. It means that the trained ANN model is functioning and perform well to predict Ground ISR and the difference between Satellite ISR and its corresponding Ground ISR is reduced. Furthermore, by referring to the table in Appendix B, the percentage of different of actual Satellite ISR and Ground ISR is varied from range of 21.5% to 0% whereas the percentage of difference of actual Ground ISR and Predicted Ground ISR using testing datasets is reduced to range of 10.6% to 0.5%. The range of percentage of difference is reduced with the application of optimized ANN model to predict the Ground ISR of a specific site.
Table 4.4: Results of ANN model predicting Ground ISR and comparison with actual value. (The highlighted row showed the results with maximum and minimum percentage of difference.)
Name No. of
Prediction
Monthly Irradiance (W/m2)
Actual ISR
Predicted ISR
Percentage of Difference (%)
AxisBC (Selangor)
1 147.560 1.82 1.761 3.2
2 150.531 1.72 1.654 3.8
3 169.055 1.76 1.606 8.7
4 145.265 1.81 1.708 5.6
5 138.095 1.82 1.747 4.0
6 118.724 1.94 1.788 7.8
7 132.487 1.89 1.771 6.3
8 143.865 1.87 1.724 7.8
9 128.192 1.93 1.726 10.6
10 149.169 1.74 1.677 3.6
11 126.747 1.86 1.795 3.5
12 118.148 1.91 1.789 6.3
AyerKeroh 13 149.346 1.6 1.738 8.6
(Melaka) 14 146.112 1.68 1.746 3.9
15 145.854 1.71 1.701 0.5
16 149.274 1.61 1.677 4.2
17 140.061 1.6 1.717 7.3
18 137.219 1.58 1.717 8.7
(Average) 5.8
Mean Squared Error 0.0128
Root Mean Squared Error 0.1132
Moreover, in the Table 4.5, the Mean Square Error (MSE), Root Mean Square Error (RMSE) and average percentage of difference are listed out to evaluate the ANN model developed. It can be observed that the MSE and RMSE of the actual and predicted Ground ISR which are computed by training datasets and testing datasets has reduced as compared to the errors of actual Satellite ISR and Ground ISR. Thus, it can be concluded that, the ANN model developed is able to use Satellite ISR with monthly irradiance of the sites to infer Ground ISR where the sites do not have ground-based measurement solar irradiance datasets.
Table 4.5: Summary of errors before and after prediction of Ground ISR using ANN model developed.
Original Satellite ISR and Ground ISR
Actual Ground ISR and Predicted Ground ISR using training datasets
Actual Ground ISR and Inferred Ground ISR using testing datasets
MSE 0.0152 0.0136 0.0128
RMSE 0.1234 0.1165 0.1132
Average
Percentage of Difference (%)
6 5.6 5.8
4.6 Suggestions to improve the performance of ANN model based on