## 4 Simulations
### Reasonableness of Factor Importance Ranking Obtained by NC-RBF-DNN  
This section explores the reasonableness of the importance score ranking of the 16 factors obtained by the NC-RBF-DNN from qualitative and quantitative perspectives. This was achieved using weather data at time t as the input for NC-RBF-DNN and solar panel power generation at time t as the output. After training NC-RBF-DNN on this input and output, the method outlined in Section 3.2 was used to analyze the parameters of the trained NC-RBF-DNN model to determine the importance of each input field. In Figure 10, the \(x\)-axis indicates the importance ranking assigned to the 16 input factors by NC-RBF-DNN, the solid line indicates the importance score calculated by NC-RBF-DNN, and the dotted line indicates the Pearson correlation coefficient between each input factor and the corresponding power generation. Due to differences in the numerical ranges of NC-RBF-DNN and Pearson's correlation coefficient, we present the two scores divided by their respective maximum values along the y-axis to prevent confusion. The solid line revealed two phenomena. First, we observed the division of factors into four groups based on their importance scores. From highest to lowest, these groups were [_surface solar rad down_, _top net solar rad_], [_10-m U wind component_, _10-m V wind component_, _season_, _total cloud cover_, _hour_], [_solar panel model_, _month_, _total column liquid water_, _total column ice water_], and [_surface pressure_, _relative humidity at 1000 mbar_, _surface thermal rad down_, _two-meter temperature_, _total precipitation_]. The score interval that each factor is located in roughly corresponds to its impact on solar power generation. This indicates that the NC-RBF-DNN can indeed reasonably rank the factors based on their importance to the target problem. For instance, the power output of solar panels is mainly determined by the amount of solar radiation, so the two factors associated with solar radiation were placed in the highest importance score group by the NC-RBF-DNN. Next are the factors with the second-highest importance scores, which are those which influence solar radiation. For instance, season and hour are both important reference data that impact solar radiation. The factors _10-m U wind component_, _10-m V wind component_, and _total cloud cover_ are associated with clouds, which strongly affect the total amount of solar radiation that can reach the ground from outer space. The NC-RBF-DNN thus considered their importance scores to be relatively high as well. The factors in the third group had three characteristics. The first was that they influence solar radiation under special circumstances but because our approach considers long-term conditions, their importance was reduced. _Total column liquid water_ and _total column ice water_, for instance, are important factors that influence solar power generation \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Random Forest** & **DNN** & **NC-RBF-DNN** \\ \hline nRMSE & 0.08764 & 0.07935 & 0.07874 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of NC-RBF-DNN versus other models. when it rains. However, because the probabilities of rainfall where the power plants in the target dataset were located were not high, their importance was reduced over the long term. The second characteristic was that they provide overlapping modeling information. For instance, the solar radiation information in the month field overlaps the information in the season field. In the target dataset collected in Australia (in the Southern Hemisphere), 'July' (in the month field) corresponds to 'winter' (in the season field), indicating a reduction in solar radiation and a shorter insolation duration. Similarly, 'January' and'summer' convey the same information--a period of increased solar radiation and extended insolation duration. The third characteristic that we found was that the NC-RBF-DNN gave _solar panel model_ a lower importance score.