# Prediction Model for the Performance of Different PV Modules Using Artificial Neural Networks
## 2 Experimental Setup and Major Parameters of the PV Module  
During this research, six different types of PV panels were used to collect data using a Prova 1011 solar system analyzer, namely, a polycrystalline panel (100 W), a monocrystalline panel (100 W), a flexible mono-panel (100 W), a thin-film amorphous panel (100 W), a CIGS solar panel (90 W), and a flexible back-contact mono-panel (30 W). Figure 1 shows the six PV modules' visual images while collecting the dataset.  
The Prova 1011 solar system analyzer was used to measure the PV module performance (IV curve), FF, P\({}_{m}\) (V\({}_{m}\) and I\({}_{m}\)), and efficiency under different test conditions. The instrument connects with a wireless irradiance meter and thermometer to collect the irradiance and cell temperature under the test conditions for the PV modules, as shown in Figure 2. Moreover, the device can calculate the efficiencies and the maximum power under test conditions by saving the specification of the PV module (area, V\({}_{oc}\), and I\({}_{sc}\)) by connecting the device with a special software program for the device. After that, a considerable number of IV curves at different irradiance and temperature for the six PV modules are extracted from the device, and the software is used during the MATLAB program's training process.  
\begin{table}
\begin{tabular}{c c c} \hline
**Name of Author** & **Type of Method** & **Accuracy (\%)** \\ \hline [PERSON] et al. [14] & Online—Variable Resistor & 69 \\ [PERSON] et al. [15] & & 78 \\ [PERSON] et al. [16] & Online—Capacitive load & 80 \\ [PERSON] et al. [18] & Online—Electronic load & 91.6 \\ [PERSON] et al. [20] & Online—DC-DC converter & 63 \\ [PERSON] et al. [23] & Offline—Numerical models & 90.5–99 \\ [PERSON] et al. [24] & Offline—Evolutionary algorithms & 78–98.6 \\ [PERSON] et al. [25] & & \\ [PERSON] et al. [33] & Offline—Artificial neural networks & 98.5 \\ [PERSON] et al. [34] & & 99 \\ [PERSON] et al. [35] & & 99 \\ [PERSON] et al. [38] & Offline—recurrent neural networks & 90 \\ \hline \end{tabular}
\end{table}
Table 1: The accuracy of some of the methods that extracted the IV curve or output power.  
Figure 1: (**a**) Polycrystalline panel, (**b**) monocrystalline panel, (**c**) flexible mono-panel, (**d**) thin-film amorphous panel, (**e**) CIGS solar panel, and (**f**) flexible back-contact mono-panel.  
Figure 2: Set-up for extracting IV curves by Prova 1011 solar system analyzer.  
An IV curve can represent the relationship between the current (the vertical axis of the curve) and voltage (the horizontal axis of the curve) at a specific irradiance and temperature, as shown in Figure 3. Analyzing the figure shows several essential points in the IV curve. As shown in Equation (1), the \(\mathrm{I_{sc}}\) represents the highest current produced when the voltage is zero.  
\[\mathrm{I}\left(\mathrm{at}\;\mathrm{V}=0\right)=\mathrm{I_{sc}} \tag{1}\]  
Secondly, as shown in Equation (2), \(\mathrm{V_{oc}}\) represents the highest voltage produced when the current is zero.  
\[\mathrm{V}\left(\mathrm{at}\;\mathrm{I}=0\right)=\mathrm{V_{oc}} \tag{2}\]  
Other essential points and parameters for this research include the maximum power point (MPPT). This represents the point in the IV curve where the rectangle area below the IV curve is the maximum. At the same time, the efficiency is the ratio between \(\mathrm{P_{m}}\) and the input power (\(\mathrm{P_{in}}\)). \(\mathrm{P_{in}}\) is the product of the solar cell irradiation of the incident light. Lastly, the FF is the ratio between \(\mathrm{P_{m}}\) and the product of \(\mathrm{I_{sc}}\) and \(\mathrm{V_{oc}}\). These important parameters can be given by Equations (3)-(5) [40,41]:  
\[\mathrm{P_{m}}=\;\mathrm{I_{m}}\cdot\mathrm{V_{m}} \tag{3}\]  
\[\mathrm{\eta} =\mathrm{P_{m}}\;/\mathrm{P_{in}} \tag{4}\] \[\mathrm{FF}=\frac{\mathrm{P_{m}}}{\mathrm{I_{sc}}\cdot\mathrm{V_{oc }}} \tag{5}\]