# Solar Power Prediction Modeling Based on Artificial Neural Networks under Partial Shading
## 4 Learning Parameter and Simulation
The regression analysis results in Figure 3a-e are explained as follows. In the graph, the data represent the actual value, while the fitting represents the predicted values. First, from a scatter plot analysis perspective, it can be observed that scatter points are distributed around the regression line. Secondly, from a regression line analysis perspective, it can be observed that the slope of the regression line in the graph is close to 1, indicating a high level of accuracy in the predictions. Lastly, from an error analysis perspective, the difference between the data and the fitting remains relatively constant. The important aspect to consider when training with five parameters is the diode saturation current. The diode saturation current is typically very small, on the order of \(1\times 10^{-10}\), which can cause difficulties in the artificial neural network training process. To address this issue, the calculated diode saturation current is transformed by taking the logarithm before proceeding with the training. After the training is completed, the trained results can be visualized in a three-dimensional graph by reversing the logarithmic transformation. This  
Figure 4-8 show the mesh plots for the data used to train the artificial neural network; the data represented by the blue dots are the data from the KC200 GT model [18]. The training data are generated by simulating various combinations of temperature and irradiation to create input data. In the previous research, each function composed of temperature and irradiance was separately presented in graphs representing temperature and irradiance. When combining both temperature and irradiance into a single graph, a three-dimensional graph like Figures 4-8 is plotted, showing the relationship between the two variables. It can be seen in Figures 4-8 that two sets of data match in the three-dimensional graphs, implying successful training for each parameter. The KC200 GT model is a multi-crystalline solar module. The detailed parameters of the KC200 GT module under normal operating conditions are \(I_{ph}=8.214\) A, \(I_{o}=9.825\times 10^{-8}\)A, \(n=1.3\), \(R_{sh}=415.405\)\(\Omega\) and \(R_{s}=0.221\)\(\Omega\)[9; 19]. The training data were compared with the I-V characteristics curve of the KC200 GT model under standard conditions, as shown in Figure 9 [7]. Figure 8: Comparison between measurement and ANN data of photocurrent \(n\). Figure 6: Comparison between measurement and ANN data of photocurrent \(R_{s}\). Figure 7: Comparison between measurement and ANN data of photocurrent \(R_{sh}\). For Figure 9, the values provided in the KC200 GT data sheet are used. Given the parameters and some errors in the graph-extraction process, the measurement and training data are consistent. Even if the temperature is varied as input in the artificial neural network, the same results as the data sheet KC200 GT are obtained. Figure 10 represents the SPICE model of an ANN model using MATLAB/Simulink. Typically, when implementing a SPICE model for a photovoltaic module, it consists of a DC current source, diode, shunt resistor and series resistor. However, in the SPICE model depicted in Figure 10, the conventional SPICE model is incorporated including the DC current source, diode, shunt resistor, series resistor, into the simulation block highlighted in red. These trained components are calculated within the ANN block to produce the outputs. As a result, the circuit is constructed using only the controlled current source and voltage measurement. A photovoltaic system under partial shade conditions can be designed using an ANN model that has high accuracies under standard conditions, as shown in Figure 11. The KC200 GT model has 54 cells per module. To design a solar photovoltaic system under partial shading conditions and compare it with existing experimental data, a model with two bypass diodes is configured with 27 cells per module, while a model with three bypass diodes is configured with 18 cells per module [18; 20].