# Prediction Model for the Performance of Different PV Modules Using Artificial Neural Networks
## 3 Proposed ANNs Model for Predicting the Performance of the Six PV Modules  
Artificial neural networks (ANNs) are non-algorithm information-processing systems that use previously collected data to train the networks to predict specific variables such as current, efficiency, output power, and solar radiation [42]. Every network, in general, has three types of layers, input, hidden, and output layers [42,43]. There are two generalized regression neural networks (GRNNs) that predict the IV curve. The output for the first GRNN is used as an input variable in the second GRNN; the second network predicts the current of one of the six PV modules at specific conditions. The architecture, and the number of neurons in the input, hidden, and output layers, are the  
Figure 3: IV curve for a PV module. same for both networks. Moreover, each training dataset has one neuron for every input with different values and weights. The input neurons in this proposed model feed their values to the neurons in the hidden layer. After multiplying the values with a target, the final value is transferred to the neurons in the pattern layer. In this layer, the final values are added by weights from each hidden neuron, and the result is used as the predicted value [30]. In this research, the mechanism recorded IV curves for the proposed ANNs method using a Prova 1011 Solar System Analyzer. The device extracted a considerable number of IV curves at different irradiances and temperatures for the six PV modules. Most of these IV curve data were sent for the training process for the proposed ANNs method. Simultaneously, some of them were used during the testing process of the method. In the proposed model, the first GRNN is trained by IV curve data for six different types of PV modules at different irradiances and cell temperatures, with FF, P\({}_{\text{max}}\), I\({}_{\text{sc}}\), V\({}_{\text{oc}}\), voltage, current, and the product of I\({}_{\text{sc}}\) and V\({}_{\text{oc}}\). These factors are used as input for the training process in the first network. The output for the first GRNN represents the relation between current and voltage. The training method's inputs contain specialized information for one of the six PV modules covered by the device. Figure 4 shows the first proposed GRNN used in the model. The testing process for the first GRNN starts by obtaining the current and voltage for the PV module by stepping their highest value down. These parameters are used as an input for the testing process with irradiance, cell temperature (T\({}_{\text{c}}\)), FF, P\({}_{\text{max}}\), a product of I\({}_{\text{sc}}\) and V\({}_{\text{oc}}\), I\({}_{\text{sc}}\), and V\({}_{\text{oc}}\) to predict a parameter representing the relation between the \(x\)-axis and the \(y\)-axis. Meanwhile, the second GRNN predicts the current for one of the six types of modules at specific irradiance and temperature. The inputs for the training process are irradiance, T\({}_{\text{c}}\), voltage, a product of I\({}_{\text{sc}}\) and V\({}_{\text{oc}}\), FF, P\({}_{\text{max}}\), I\({}_{\text{sc}}\), V\({}_{\text{oc}}\), and the output parameter from the first GRNN. The inputs for the testing process for the network are the same as the training process. Figure 5 shows the second GRNN proposed in this model. The proposed model starts by entering the name of the PV module with the specifications of the module. A \"for loop\" is used to upload the training dataset for the same PV module, depending on the type of PV module chosen. After this, the test conditions for the PV module (irradiance and T\({}_{\text{c}}\)) should be specified. Then, the datasheet for the PV module is used to calculate I\({}_{\text{sc}}\) and V\({}_{\text{oc}}\) under test conditions using Equations (6) and (7) as below [42]:  
\[\text{V}_{\text{oc-T}}=\text{V}_{\text{oc-stc}}-((\text{T}_{\text{c}}-\text{T }_{\text{a}}))\times 0.123) \tag{6}\]  
Figure 4: The first proposed GRNN flowchart to predict the relation between the X and Y axes.