## 2 Characterization of Wind-Solar Power Generation for Earth Regions
### The Renewable Power System  
Solar and wind power generation systems have proven to be significant generation resources. Still, their random nature, variability, intermittency, and geographical limitations make the analyses associated with their production capacities complex. Therefore, predicting the generation capacities becomes important once the existing resources are estimated. To this end, the main considerations taken for estimating the generation capacities of these systems are detailed below. Solar PV technology offers the potential to convert sunlight into electricity directly, yet its performance still falls short of desired levels, prompting ongoing scientific and engineering focus. Solar radiation, wind speed, temperature, humidity, and dust influence PV panel efficiency, which remains central to energy system reliability and economic viability. However, predicting power output is complex due to the non-deterministic relationship between PV production and variable environmental conditions. Regression, statistical analysis, and machine learning are commonly employed to address forecasting challenges. Recent advancements in machine learning, data science, and artificial neural networks are increasingly utilized to improve accuracy in predicting PV power generation [44]. To construct a model that accurately predicts the electrical production that a given type of solar panel can achieve once the PDFs followed by the existing environmental conditions in each analyzed region are known, it is necessary to have a predictive model. Such a model can be obtained from experimental data on panel performance and, from there, using one of the techniques to reach the desired model. That is, data preprocessing is first carried out, then input variables affecting panel electrical generation are selected; in  
Figure 3: Extraction of two days generated (**a**) irradiance, (**b**) wind speed, and (**c**) synthetic curves for temperature. In red is one of the historical registered values (year 2001) for comparison. this case, solar irradiation, ambient, and module temperature are set as the objective. Other variables may have an effect, such as wind speed and humidity [45]. The generated power of a PV panel depends on the cell efficiency, the panel's efficiency, its area, and the sun irradiance:  
\[PPV=\eta_{cell}\cdot\eta_{panel}\cdot A\cdot G \tag{2}\]  
A linear model is used as a simplified version of the proposed model by [PERSON] and [PERSON] [44]. Solar panel performance relies on solar radiation and temperature. As [PERSON] and [PERSON] [44] propose, the cell efficiency depends on the temperature, and the cell temperature depends on its efficiency. This iterative process is simplified by using a correction coefficient for the most common crystalline silicon-based applications [46; 47]:  
\[\eta_{panel}=\eta_{ref}\cdot\left(1-\beta\cdot\left(T_{c}-T_{ref}\right)\right) \tag{3}\]  
where \(\eta_{ref}\) is the reference efficiency of the solar panel, \(\beta\) is the correction coefficient, \(T_{c}\) is the solar cell temperature, and \(T_{ref}\) is the temperature of reference. The correction for temperature changes reflects how cell efficiency affects the overall panel performance. The solar panel temperature \(T_{c}\) is determined by a linear formula that considers the ambient temperature and the solar irradiation by:  
\[T_{c}=T_{a}+\left(\frac{G}{G_{NOCT}}\right)\cdot\left(T_{NOCT}-20\right) \tag{4}\]  
where \(T_{a}\) is the ambient temperature (\({}^{\circ}\)C), \(T_{NOCT}\) is the nominal cell operating temperature (\({}^{\circ}\)C), \(G\) is the measured solar radiation intensity (W/m\({}^{2}\)), and \(G_{NOCT}\) is 800 W/m\({}^{2}\). \[PPV=P_{M}\cdot\left(\frac{I}{I_{STC}}\right)\cdot\eta_{panel} \tag{5}\]  
where \(P_{M}\) is the maximum rated power output power in standard conditions, and \(I_{STC}\) is the solar irradiance in standard conditions. Thus, the achieved electrical generation can be estimated based on Equation (4) and the data of the atmospheric variables mentioned above (Section 2.1).