## 4 Processes and Methods
### Data Features and Characteristics  
In the context of optimizing SPVSs, the study explores the application of machine learning techniques for predicting DC power output based on critical environmental and meteorological parameters. A set of eight influential input variables is identified:  
* Irradiance Average (X1): This parameter quantifies the average solar radiation received, which is fundamental for estimating energy output potential.
* Temperature Average (X2): The average temperature is crucial as it significantly impacts the efficiency of solar panels.
* Air Pressure Relative (X3) and Air Pressure Absolute (X4): These parameters are essential for understanding atmospheric conditions, which can alter PV performance.
* Relative Humidity (X5) and Absolute Humidity (X6): Both metrics are vital in assessing moisture content, affecting the cooling of solar panels and their overall efficiency.
* Wind Speed (X7): This factor influences the thermal regulation of SPVS panels, potentially enhancing their performance.
* Wind Direction (X8): Understanding Wind Direction contributes to evaluating the environmental flow impacting solar irradiance on the panels.  
Figure 1: Solar Facility at GUtech.  
Figure 2: Irradiance and temperature sensor of the Solar Facility at GUtech.  
As previously mentioned, this research employs various regression techniques, including MLR and advanced machine learning models, to analyze the relationships between these parameters and DC power output.  
The analysis presented highlights notable correlations (autocorrelation) between various independent variables that may affect the modeling of the performance of SPVSs. Specifically, a strong positive correlation was observed between DC current and DC power, with correlation coefficients approaching 1 for both string configurations. This indicates that as DC current increases, the DC power output also increases proportionally, reflecting a direct relationship between these two variables. Conversely, the relationship between DC current and DC voltage was characterized by a weak negative correlation, suggesting that while current increases, voltage tends to decrease slightly. This inverse connection may be attributed to system impedance, which impacts voltage drops in the circuit.  
Interestingly, the study also revealed that the data from the Shams Solar Facility did not conform to normal distribution patterns, as indicated by statistical measures such as skewness and kurtosis. With over 36,000 observations for each parameter, the findings challenge the common assumption in many SPVS modeling efforts that environmental factors are independent and normally distributed. This non-normality underscores the complexity of SPVS performance and suggests that researchers should exercise caution in applying traditional modeling techniques that may overlook the intricate relationships among environmental variables. Furthermore, the absence of significant correlations between factors like air pressure and wind direction indicates that not all environmental variables exert a meaningful influence on SPVS performance, further complicating the modeling landscape.