## 2 Artificial Neural Networks Topology
### Dataset Collection  
Meteorological data consisting of solar irradiance and temperature were gathered in real-time from An-Najah Energy Research Center (ERC) [45]. The equipment used to collect the dataset consists of a temperature sensor to measure the PV panel body temperature (namely, the sensor WE710 with \(\pm\)0.25 \({}^{\circ}\)C precision), and a high accuracy solar radiation sensor (namely, the sensor WE300 with \(\pm\)1% precision).  
Despite the precision of the used sensors, the data captured are sometimes unreliable, noisy, or incomplete. This is primarily due to a sensor error percentage or noise in connections. Hence, to overcome the noise in the obtained data, a moving average filter [46] was used, see Figure 6. The meteorological data were collected on an hourly based manner in the period from February 2018 to July 2018. Anomalies in data due to a broken or unfunctional device/sensor were eliminated.  
The targeted output voltage (\(Vr\)) and current (\(Ir\)) are collected by running a MATLAB code that analyses the validated PV model output characteristics [47; 48; 49; 50]. The dynamic behaviour of the proposed PV model has been validated previously by the author for different types of PV modules under variety of solar radiation and temperature conditions. High agreement between the outcomes of the model and the experimental data was achieved. Results and validation of the module can be found in the author previous work published in [50].  
Three different photovoltaic panels were used to obtain data; these are the Astronergy-CHSM6610P panel, the Sharp's-NUS0E3E panel, and Lorentz mono-crystalline panel. Standard testing conditions used when recording the PV panel's manufacturer specifications. Table 1 shows the used PV panel's manufacturer specifications.  
Figure 5: Levenberg-Marquardt (LM) state diagram.  
For the proposed neural networks to be modeled, the collected data were separated into three parts: testing (25%), training (60%), and validation (15%). To have the same range of values, a normalization process [49] is done on all data to be in the range(\(-1\) to \(+1\)) using Equation (9):  
\[A=(B-B_{\text{min}})\left[\frac{A_{\text{max}}-A_{\text{min}}}{B_{\text{max}}-B _{\text{min}}}\right]+A_{\text{min}} \tag{9}\]  
where the normalized value is denoted by \(A\), and \(B\) denotes the non-normalized value. When the result is obtained, it becomes normal after using Equation (10). Table 2 shows typical examples of the used datasets.  
\[B=(A-A_{\text{min}})\left[\frac{B_{\text{max}}-B_{\text{min}}}{A_{\text{max}}- A_{\text{min}}}\right]+B_{\text{min}} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Astronergy-CHSM6610P** & **Sharp’s NUS0E3E** & **Lorentz Mono-Crystalline** \\ \hline Max power (Pm) & 225 W & 180 W & 75 W \\ Voltage when power at max & 29.76 V & 23.7 V & 16.5 V \\ Current when power at max & 7.55 A & 7.6 A & 4.6 A \\ Voltage at Open circuit & 36.88 V & 30 V & 21.0 V \\ Current at Short circuit & 8.27 A & 8.37 A & 5.4 A \\ OCV Temp coefficient & \(-0.129\) V/\({}^{\circ}\)C & \(-104\) mV/\({}^{\circ}\)C & \(-60.8\) mV/\({}^{\circ}\)C \\ SCC Temp coefficient & \(+0.052\)\%/\({}^{\circ}\)C & \(+0.053\)\%/\({}^{\circ}\)C & 3.0 mA/\({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 1: The used PV panel’s manufacturer specifications.  
Figure 6: Average hourly temperature and solar radiation distributions throughout April 2018 (**a**) with moving average filter (**b**) without moving average filter.