## 4 Analysis Results
### Prediction Model Based on Neural Network Analysis  
Neural networks are used as representative nonlinear algorithms, which can be learned by adjusting the layer, node, and activation functions. In this work, the hyperbolic tangent was used as the activation function, and the numbers of nodes were set as 1, 5, 10, and 20. Before those models were made, the dataset was preprocessed to remove the outlier data to have more than 40% MAPE in the linear model, which was also applied in linear regression. Figure 14 shows the learning result model with one hidden layer node.  
In Figure 15, the trained results show stable results on the graph. Although a MAPE of 10.25% occurred, this is 0.64% lower than that of the linear regression of 9.61%, but can be judged to be almost similar.  
Figure 16 shows the result of verifying the accuracy by entering the test data into the trained model. The MAPE was 12.17%, which was similar to that of the learning model, without overfitting.  
Figure 17 shows a model that predicts the power generation after calculating eight input variables with five hidden nodes.  
Figure 18 shows that the percentage error is reduced to 8.78% as a result of the five hidden nodes learned.  
Figure 14: Neural network learning model (hidden node 1).  
Figure 15: Neural network analysis results (training data).  
Figure 16: Neural network analysis results (verification data).  
Figure 17: Neural network learning model (hidden node 5).  
Figure 18: Neural network analysis results (training data).  
Figure 19 presents the results of verifying the accuracy of the model by entering test data into the trained model. The percentage error is 10.36% and an error increase of 1.58% occurs; however, it can be confirmed that the percentage error is lower than that of the result of the learning and verification of the linear regression analysis.  
Figure 20 shows a model that predicts the power generation after calculating eight input variables with ten hidden nodes.  
Figure 21 shows that the percentage error is reduced to 6.98% as a result of the ten hidden nodes learned.  
Figure 19: Neural network analysis results (Test data).  
Figure 20: Neural network learning model (hidden node 10).  
Figure 22 presents the results of verifying the accuracy of the model by entering verification data into the trained model. The percentage error is 8.8% and an error increase of 1.82% occurs; however, it can be confirmed that the percentage error is lower than that of the result of the learning and verification of the linear regression analysis and the 5 hidden nodes of the neural network.  
Figure 23 shows a model that predicts the power generation after calculating eight input variables with twenty hidden nodes.  
Figure 21: Neural network analysis results (training data).  
Figure 22: Neural network analysis results (test data).  
Figure 24 shows that the percentage error is reduced to 7.54% as a result of the five hidden nodes learned.  
Figure 25 presents the results of verifying the accuracy of the model by entering verification data into the trained model. The percentage error is 11.35% and an error increase of 3.81% occurs. It is confirmed that overfitting occurred with an error value much higher than that of the linear regression or the neural networks of the hidden nodes 1, 5, and 10.  
Figure 23: Neural network learning model (hidden node 20).  
Figure 24: Neural network analysis results (training data).