# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 4 Experiments and Discussion
### Result of Feature Selection  
Since the selection of feature variables will have the most obvious impact on the model prediction effect, the items with values less than \(\pm 1.0\) after the analysis of feature variable correlation were eliminated at the beginning of the experiment, and the monitoring records on the site were used to implement the first experimental plan. Finally, using the performance indicator \(r^{2}=0.849865\) of experimental program A-1. From this experiment, it can be found that the prediction effect by using the monitoring records on the site as the experimental sample is not good and still needs to be improved. Therefore, to make the performance of the model prediction more accurate, the subsequent experimental programswill be tried with different feature data samples. It is expected that the optimal results of the experiment can be obtained, then find the feature variables of the optimal model to extend subsequent research. In the process of trying to improve the above experimental program. First, we combine the external meteorological with the on-site monitoring system information and obtain the effectiveness index \(r^{2}\) = 0.853013 of the experimental program A-2 after training. When we compared it with the monitoring system that just referenced the on-site project, the features have been strengthened a lot. Secondly, the external environment is combined with the on-site monitoring system information and obtains the performance indicator \(r^{2}\) = 0.860999 of the experimental program A-3 after training. Thirdly, above the external meteorological and environment is combined with the on-site monitoring system information and obtains the performance indicator \(r^{2}\) = 0.865523 of the experimental program A-3 after training. Finally, it can be found that increasing the model's feature category can effectively improve the model's prediction situation by comparing the results of the above-mentioned experiments. As the performance indicators of the experimental program, A-4 still have room for improvement, the results of the previous Pearson product-moment correlation coefficient analysis were used as the basis for screening, and the correlation coefficient was divided into 6 levels \(\pm\) 0.2\(\sim\)0.3 according to the correlation coefficient. Figure 2 illustrates the resulting map of the Pearson correlation coefficient analysis. The list of feature variables of the data set used after Pearson correlation coefficient analysis is shown in Table 3. According to the comparison of the results of the above 6 programs (A-5-A-10), when the experimental program A-5 is used to delete all the feature variables of the correlation coefficient +0.2\(--\)0.2, the performance indicator \(r^{2}\) = 0.880833 can be obtained, and the correlation of the feature variables is further compared. When the coefficient screening range is expanded to \(\pm\)0.3, only the experimental program A-10 can obtain a better performance indicator \(r^{2}\) = 0.845699 when all the feature variables of the correlation coefficient +0.3\(-\)0 are deleted. So, according to the above project of the results of the experiment, and finally the correlation coefficient +0.3\(-\)\(-\)0.2, feature variables are aggregated into the experimental program A-11, after training and prediction, the best performance indicator of the experiment at this stage is taken \(r^{2}\) = 0.890179. There are 14 items of feature variables which are recording time, inverter device temperature, solar cell module temperature, atmospheric temperature, horizontal insolation, module corresponding angle insolation, relative humidity of external weather information, nitrogen oxides, ozone, non-methane hydrocarbons, nitrogen dioxide, relative humidity, temperature, and acid rain value of external environment information.