## 2 Material and Methods
### Multi-Criteria Decision Support System (MCDSS) for Photovoltaic Set Identification
#### 2.3.2 Data Pre-Processing  
Data pre-processing handles the mapped input data and supports the multi-criteria decision-making models with structured information. Data pre-processing is divided into sub-steps, which are (i) analysis of available photovoltaic potential, (ii) calculation of demanded photovoltaic potential, and (iii) assessment of the feasibility of photovoltaic system installation. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Band** & \multicolumn{2}{c}{**Model**} & **Area (m\({}^{\star}\))** & **Weight (kg)** & **V\({}_{\infty}\) (V)** & **I\({}_{\infty}\) (A)** & **V\({}_{\infty}\) (V)** & **I\({}_{\infty}\) (A)** & **Power (W)** & **Eff (\%)** & **Price (USD)** \\ \hline RENESOLA & [53] & RS635 ME3 & 2.58 & 29.0 & 49.5 & 13.78 & 41.5 & 12.90 & 535 & 21 & 116.20 \\ \hline UP SOLAR & [54] & UPM375 MFH & 1.82 & 19.0 & 41.5 & 11.57 & 34.6 & 10.93 & 375 & 21 & 128.77 \\ \hline UP SOLAR & [54] & UPB450P & 2.17 & 28.0 & 49.5 & 11.60 & 41.3 & 10.88 & 450 & 22 & 115.28 \\ \hline CANADIAN & [55] & CS6W535 MS & 2.56 & 27.6 & 49.0 & 13.85 & 41.1 & 13.02 & 535 & 21 & 125.27 \\ \hline CANADIAN & [55] & CS6W550 MS & 2.56 & 27.6 & 49.6 & 14.00 & 41.7 & 13.20 & 550 & 21 & 127.28 \\ \hline SCHUTEN & [56] & ST365/120 & 1.81 & 20.5 & 41.2 & 11.29 & 33.9 & 10.75 & 365 & 20 & 127.27 \\ \hline SCHUTEN & [56] & STM995/120 & 1.81 & 20.5 & 42.0 & 11.65 & 35.6 & 11.05 & 395 & 22 & 137.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of the data for a PV database. The analysis of installed photovoltaic potential is the first step of data pre-processing. It focuses on two fundamental analyses for determining the best photovoltaic set, which are (i) defining solar irradiation at the location on the ground where the photovoltaic system will be installed and (ii) determining the average temperature at the location where the photovoltaic system will be installed. The definition of solar irradiation at the location on the ground where the photovoltaic system will be installed is based on Equations (1)-(4), which are available on different platforms such as Weather Spark [57] and Solar Electricity Handbook [58]. Figure 2 demonstrates the potential solar irradiation on the inclined plane of the Sao Paulo region, Brazil, throughout April 2024. For this report, the geographical coordinates of Sao Paulo are \(-23.548\) degrees latitude, \(-46.636\) degrees longitude, and \(2523\) ft elevation. With the determination of the irradiation potential, it is necessary to obtain the average temperature of the region. The temperature of the area where the system will be installed strongly influences photovoltaic energy generation, so it must be calculated before estimating the photovoltaic potential of the region. In addition to ambient temperature, it is necessary to assess the temperature of the photovoltaic module, as the higher the temperature of the solar system, the lower its efficiency. The average temperature can be obtained from the Weather Spark and Fabhabs platforms, and the photovoltaic module temperature can be obtained from Equation (5). Figure 3 shows the average temperature of the Sao Paulo region, Brazil, throughout April 2024. \[T_{cel}=T_{a}+\left(\frac{T_{NOCT}-20}{0.8}\right)\cdot~{}~{}I_{\%} \tag{5}\]  
where \(T_{cel}\) is the operating temperature of the photovoltaic cell, \(T_{\alpha}\) is the ambient temperature, \(T_{NOCT}\) is the value of the operating temperature of the photovoltaic cell provided by the module datasheets, and \(I_{\%}\) is the percentage obtained from the behaviour of irradiation at the top of the atmosphere relative to its maximum value. After estimating the operating temperature of the modules, it is possible to calculate the percentage of losses due to temperature, which has a value of \(-0.40\%\) in power for each \({}^{\circ}\)C above \(25~{}^{\circ}\)C. Therefore, the photovoltaic potential of the region is given by Equation (6).