## 4 Simulation Results
### Performance Comparison of Proposed Work with other Forecasting Strategies  
In analyzing the connection between the condition of the day with the model accuracy, the MAE, RMSE and NRMSE values obtained from several forecasting models are plotted in the histogram bars as shown in Figures 10-12. Figure 10 illustrates the changing bars of several forecasting models on sunny days according to the specific performance metrics (MAE, RMSE and NRMSE). On sunny days, the solar power data is not greatly impacted by the noise as it experiences less fluctuation. However, this work applies the WT to remove the noisy data on sunny days. The result of this work shows the significant implementation of the WT to eliminate noisy signals due to the performance improvement offered. This is revealed in Figure 10, where the heights of all blue bars (WT-ANFIS, WT-RBFNN and WT-MLP) for the MAE, RMSE and NRMSE are lower than all green bars (ANFIS, RBFNN and MLP). It is concluded that the application of a denoising tool will increase the performance of the forecasting models. Figure 9: Actual versus forecasted curve of average solar power for WT-ANFIS-HFPSO on cloudy days. Figure 10: Comparison of forecasting models in terms of the MAE, RMSE and RMSE on sunny days. The performance of a forecasting model can be further improved by using an optimization algorithm. This work integrates the WT-ANFIS model with two optimization algorithms known as the FF and PSO algorithms. By referring to the values of the MAE, RMSE and NRMSE in Figure 10, the utilization of the WT-ANFIS with an individual PSO and FF has not increased the accuracy of the WT-ANFIS. This is may due to the premature convergence of the PSO algorithm, as well as the condition of the FF algorithm that is trapped at local minima. Hence, it is obvious that the employment of an optimization algorithm to a forecasting model on sunny days has not improved the model's accuracy. To mitigate this matter, the FF and PSO algorithms were hybridized and the result of this hybridization has improved the performance of WT-ANFIS. This can be proven from Figure 10, where the dark orange bar (WT-ANFIS-HFPSO) has provided the lowest MAE, RMSE and NRMSE values than other forecasting strategies. Figure 11: Comparison of forecasting models in terms of the MAE, RMSE and RMSE on partially cloudy days. Figure 12: Comparison of forecasting models in terms of the MAE, RMSE and RMSE on cloudy days. The changing bars in Figures 11 and 12 signify the MAE, RMSE and NRMSE values of various forecasting models on partially cloudy days and cloudy days. The solar power data on partially cloudy days and cloudy days presents higher volatility and fluctuation. These phenomena lead to the existence of noise in data. To overcome the noise problem, the WT is applied to the data before it is introduced to the forecasting models. According to Figures 11 and 12, the implementation of the WT has improved the performance of the forecasting model. This can be proven according to the MAE, RMSE and NRMSE values, where the heights of all blue bars which represent the WT-ANFIS, WT-RBFNN and WT-MLP models are lower than all green bars that signify the ANFIS, RBFNN and MLP models. In sum, an improved forecasting model on cloudy days and partially cloudy days can be obtained from the utilization of a WT to remove noisy data. Furthermore, the PSO and FF algorithms are applied to the WT-ANFIS to improve the performance of the forecasting model. In Figures 11 and 12, the employment of a PSO has increased the performance of the WT-ANFIS. This is revealed by the fact that the light orange bar (WT-ANFIS-PSO) for the RMSE, MAE and RMSE in both figures is lower than the dark blue bar (WT-ANFIS). On the other hand, the integration of the FF algorithm has not improved the performance of the WT-ANFIS for cloudy days and partially cloudy days. This is may due to the condition of the FF algorithm that is trapped in the local minima.