## 5 Experimental Results and Discussion
Examples of actual PV power forecasting in watts for the three models in a row of five days are depicted in Figure 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Split** & **Type of Metrics** & **AHW** & **LSTM** & **HM** \\ \hline \multirow{3}{*}{90\% Train} & nRMSE & 0.523 & 0.124 & 0.102 \\  & RankCorr & 0.830 & 0.956 & 0.943 \\  & R\({}^{2}\) & 0.796 & 0.988 & 0.992 \\  & nRMSE & 0.351 & 0.175 & 0.175 \\
10\% Test & RankCorr & 0.890 & 0.941 & 0.931 \\  & R\({}^{2}\) & 0.927 & 0.981 & 0.982 \\ \hline \multirow{3}{*}{85\% Train} & nRMSE & 0.520 & 0.122 & 0.098 \\  & RankCorr & 0.828 & 0.957 & 0.945 \\  & R\({}^{2}\) & 0.790 & 0.988 & 0.992 \\  & nRMSE & 0.353 & 0.183 & 0.189 \\
15\% Test & RankCorr & 0.888 & 0.942 & 0.925 \\  & R\({}^{2}\) & 0.930 & 0.981 & 0.980 \\ \hline \multirow{3}{*}{80\% Train} & nRMSE & 0.524 & 0.118 & 0.095 \\  & RankCorr & 0.824 & 0.957 & 0.948 \\  & R\({}^{2}\) & 0.785 & 0.989 & 0.993 \\  & nRMSE & 0.345 & 0.190 & 0.185 \\
20\% Test & RankCorr & 0.889 & 0.948 & 0.931 \\  & R\({}^{2}\) & 0.927 & 0.977 & 0.979 \\ \hline \multirow{3}{*}{70\% Train} & nRMSE & 0.523 & 0.110 & 0.088 \\  & RankCorr & 0.818 & 0.959 & 0.946 \\  & R\({}^{2}\) & 0.776 & 0.990 & 0.993 \\  & nRMSE & 0.404 & 0.197 & 0.176 \\
30\% Test & RankCorr & 0.881 & 0.942 & 0.933 \\  & R\({}^{2}\) & 0.899 & 0.975 & 0.980 \\ \hline \multirow{3}{*}{65\% Train} & nRMSE & 0.518 & 0.109 & 0.088 \\  & RankCorr & 0.819 & 0.958 & 0.946 \\  & R\({}^{2}\) & 0.775 & 0.990 & 0.993 \\  & nRMSE & 0.447 & 0.186 & 0.163 \\
35\% Test & RankCorr & 0.870 & 0.946 & 0.936 \\  & R\({}^{2}\) & 0.876 & 0.978 & 0.983 \\ \hline \multirow{3}{*}{60\% Train} & nRMSE & 0.517 & 0.110 & 0.090 \\  & RankCorr & 0.819 & 0.957 & 0.944 \\  & R\({}^{2}\) & 0.777 & 0.989 & 0.993 \\  & nRMSE & 0.482 & 0.167 & 0.144 \\
40\% Test & RankCorr & 0.861 & 0.949 & 0.940 \\  & R\({}^{2}\) & 0.854 & 0.982 & 0.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the performance of three PVPF methods (AHW, LSTM, and HM) across various train/test splits of the original dataset. Evaluation based on nRMSE, RankCorr, and R\({}^{2}\). Figure 5c highlights that HM effectively leverages the strengths of both the AHW and LSTM models, enabling it to forecast PV power spikes, as the LSTM model alone does in Figure 5b but with smoother results owing to the contributions of the AHW model in Figure 5a, which leads to superior forecasting accuracy. The LSTM method tends to overestimate the actual data because of its rapid response to fluctuations, often forecasting spikes in power even in the absence of actual peaks (as illustrated in Figure 5b). However, the AHW model exhibits higher inertia, which helps prevent overestimation and results in smoother peaks (as depicted in Figure 5a). As expected, the HM model strikes a balance between the two models by combining their respective advantages (as shown in Figure 5c). The plots in Figure 6 illustrates the correlation between the actual and predicted PV power for all methods using an 80% training dataset and a 20% testing dataset for the entire validation period. Figure 6 The results suggested a significant linear relationship between these variables. Although the AHW, LSTM, and HM methods yielded similar results, the HM method demonstrated an improved performance because the predicted values were closer to the actual values. The AHW and HM methods exhibit higher latencies than the LSTM model. This was confirmed by the large sample of non-null observations predicted with a zero value (i.e., points on the \(y\)-axis) in Figure 6a,c. In contrast, the LSTM model demonstrates higher reactivity, which often results in overestimation of the target values, as seen in the out-of-bound predictions in the range of 20-25 kW and 10-20 kW in Figure 6b. The discrepancies between the predicted and actual values in the ranges-15-25 kW and 5-20 kW are shown in Figure 6b. Additionally, Figure 6c reveals that the AHW and HMs models exhibit fewer deviations from the fit line, which in turn leads to less overestimation.