*4.3. Comparison of the Results with the Cases Introduced by Other Works*

Naderloo (2020) [34] developed a neural network and RSM methods to predict solar radiation and found out that RSM was superior to the ANFIS model in terms of performance, speed, and simplicity. The correlation coefficients and mean square errors of each method were considered for comparison of the ANFIS (0.993 and 0.0005), ANN (0.996 and 0.00029), and RSM (0.996 and 0.00027). Benmouiza et al. (2019) [35] analyzed the performance of the ANFIS model and found out that RMSE is 102.57 W/m2 for 32 fuzzy rules and 129.13 for 243 fuzzy rules models, and the correlation coefficients were found to be 0.923 and 0.905 for these models, respectively. Mohammadi et al. (2016) [36] developed an ANFIS model for solar radiation based on RMSE during training and testing phases with 1, 2 and 3 fuzzy input parameters. They found that when the number of inputs is increased, the RMSE decreases, and the prediction accuracy enhances. Similarly, in our study, three ANFIS models were developed including five, nine and eleven fuzzy rules based on the sub clustering algorithm. The RMSE of ANFIS model with nine rules gave the best results with minimum error of solar PV generation. The results are presented in Table 3 for comparison. The RMSE was found to be 66.98 for the training process of ANFIS model with nine fuzzy rules, and RMSE was found at 113.52 for ANFIS model with nine rules, and 68.47 for the ANFIS model with eleven fuzzy rules. Aldair et al. (2018) [37] developed ANFIS controllers to determine the stand-alone PV system for which two input variables: the radiation and temperature were considered for the ANFIS model development. The difference between our model and Aldair's [37] model is that our model was established based on more variables.


**Table 3.** The PV power output comparison of ANFIS and RSM models.

The ANFIS and RSM methods developed are highly efficient and effective under different weather conditions especially when the temperature is around 25 ◦C regardless of the radiation variation. In our study, all PV designs determined for KAU hospital were simulated under the local climate conditions of Jeddah and detailed analysis was carried out and presented in the previous sections of this work. For PV system simulation, the Solar-GIS program [43] and the PVGIS program [44] were used to validate the simulation results obtained from the Solar-GIS program. Figure 8 shows the comparison of monthly electricity generation obtained from both the PV-GIS and the Solar-GIS programs for a 25 MW PV system.
