**5. Results and Discussion**

As noted previously and depicted in Figure 7, the two feature-scoring approaches generated very similar results. Therefore, the learning performance was almost equivalent using both approaches. We omitted the results of the information gain to reduce duplication.

The results of the prediction error, illustrated in Figure 8, reveal that all prediction models behave in a similar manner. The DL-based model gave the minimum error with the minimum set of features (approximately seven features). The DL error was steady, with almost over all feature sets' cardinalities ranging from almost two features up to the full cardinality. Thus, it can be concluded that, when using only a few features or looking for a very stable prediction regardless of the features, DL is preferable.

In contrast, PR's prediction was the best when the feature set was greater than 10 features. This illustrates the advantageous properties of PR in the extraction of marginally useful knowledge, even from extremely irrelevant features. MSE kept steadily reducing after adding more features. With regard to MSE, PR is the most optimal choice in this case, as it had the lowest value.

As expected, LR had the highest error associated, with erros found over various selected cardinalities. LR is not capable of modeling non-linear relationships. The generated power is nonlinear in this problem. Thus, LR is not a suitable and adequate fit for the model.

LASSO, XGBOOST, SVM, and RF behaved in a similar manner. RF was the worst in terms of MSE in the cases with a single feature. This is intuitive, due to the nature of the algorithm. To build more decision trees, RF requires more features. Thus, one feature was not sufficient to extract sufficient and relevant knowledge in this case. However, SVM was extremely steady after selecting 13 features. This is due to the fundamental nature of SVM, which works by selecting a set of support vectors to maximize the margin. These support vectors are the same beyond the thirteenth feature. This is another way of indicating the proper number of selected features.

Figure 9 illustrates the actual active power versus the predicted one from December 2019 to February 2020 using a PR model. Thus, we can observe that the model can reasonably predict the generated power. However, there are still obstacles to some predictions, due to sudden voltage dips in the original dataset. The latter occured because we applied a transient three-phase voltage dip to gauge the performance of the system under study. The active power output from the whole PV system before the fault was 4000 W. After the occurrence of a fault, a transient peak of 5800 W was instantly observed for the active

power generation. Within a short interval, and according to the Saudi grid code [47], the transient was cleared. The solar PV system controller action was sustained to cope with the fault, after which the power oscillations were damped out and the system restored to its regular operation. Therefore, immediately after the fault was cleared, the solar PV system entered a voltage regulation mode [48,49], and the active power generated at the solar PV terminals started to reach the rated values. **p** output's mirrored characteristics are a sign of the controlled converter action, which is only limited by the converter's nominal current rating.

#### **Figure 9.** Results.
