## 7 Discussion  
The results of the regression models for predicting both DC current and DC power in SPVSs reveal several important insights about the relationships between the predictor variables and the outcomes. The models--LR, LAR, RR, and Elastic Net--offer different perspectives on how environmental and meteorological factors such as irradiance, temperature, humidity, Wind Speed, and pressure influence the system's performance. Among the eight predictor variables, temperature (X2) and Relative Humidity (X5) consistently emerge as influential in both the DC current and DC power models across all regression methods. This is evident from the low RMSE and high R-squared values for models that include these variables, particularly the combination of X2 and X5. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{2}{l}{**DC\_PowerString1**} & \multicolumn{5}{c}{**Regression Estimates**} \\ \hline
**Regression** & **Predictor Variables** & **RMSE** & **R Sq. ** & **Constant** & **b1** & **b2** \\ \hline \multirow{10}{*}{Linear} & X2, X3 & 197.138 & 0.9295 & \(-\)35,865.24 & 60.19 & 34.01 \\  & X2, X5 & 191.401 & 0.9335 & \(-\)535.44  
For DC current, the X2 and X5 model achieves the lowest RMSE (0.648) and the highest R-squared value (0.9352), indicating that temperature and humidity play a critical role in determining the system's current output. This pattern is also observed for DC power, where the X2 and X5 combination yields the best performance, with an RMSE of 191.401 and R-squared of 0.9335. The coefficients for these variables indicate positive or negative relationships with DC current or power, but the exact magnitude and direction vary depending on the model used. For instance, in LR, the coefficient for temperature (X2) is around 0.21, while Relative Humidity (X5) shows a smaller but significant effect. In LR models, the coefficients reveal direct relationships between the predictor variables and the response variable. However, one limitation is the presence of multicollinearity, where the predictor variables may be highly correlated with one another, leading to instability in the coefficient estimates. This issue is somewhat reflected in the varied coefficients across different variable combinations, with temperature and humidity showing fluctuating values. The R-squared values for LR models are generally high, ranging from 0.9327 to 0.9347 for DC current and from 0.9025 to 0.9335 for DC power. LAR performs better than LR by slightly lowering the RMSE and improving variable selection. The model shrinks some coefficients to zero, particularly in the case of less important predictors such as Wind Speed (X7) or pressure (X3) in some combinations. This helps simplify the model without sacrificing predictive performance. The X2 and X5 combination, again, results in the best fit (RMSE = 0.645 for DC current and 190.768 for DC power) while also performing feature selection by setting some coefficients to zero, indicating that other variables like Wind Direction and Absolute Humidity may not be as impactful for prediction. Furthermore, RR provides an alternative by applying regularization that penalizes large coefficients, thus reducing the impact of multicollinearity. The R-squared values in RR are comparable to those in Lasso and LR, but the coefficients are generally more stable due to the regularization. For instance, the model including X2 and X5 shows the highest R-squared (0.9373 for DC power) with a moderate increase in RMSE compared to Lasso. Although Ridge does not set coefficients to zero like Lasso, it manages to stabilize them, resulting in more robust predictions. Moreover, Elastic Net, which combines the penalties of both Lasso and Ridge, delivers a balanced approach. It retains the feature selection capabilities of Lasso while managing multicollinearity like Ridge. This is evident from the fact that Elastic Net consistently produces high R-squared values and low RMSEs across the various models, often comparable to Lasso but with slightly more stable coefficients.