# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 3 Method and Model
### Normalization and Feature Data Analysis  
The data set collected in this research is from 1 November 2018 to 31 January 2020, a total accumulation of 457 days. As the monitoring system and sensors malfunction or fail during the data collection process, interrupted and abnormal data often appear. Therefore, we must sort the data fields of the Environmental Protection Administration and meteorological station by using the transformation of the matrix in the process of data normalization and combine them with the monitoring system data of the research site, at last individually deleted abnormal feature data and insufficient columns. In addition, because the generating process of solar power needs daylight, we adopt to have the data just collect which from 6 a.m. to 17 p.m., then analyzes the results of the association between each feature data through the Pearson Correlation. After abandonment, the feature data which had a worse association was used as the training sample, the testing sample, and the prediction sample of the power generation prediction.  
In statistics, correlation coefficients are used to measure the strength of a linear association between two variables and are denoted by r. Furthermore, the prediction dataset of solar power generation is the continuous sample data of the time series, which records the feature data per hour. This research work will use Pearson product-moment correlation coefficient to measure the linear correlation between two variables X and Y, through covariance of the two variables divided by the product of their standard deviations, and it is between \(+1\) and \(-1\), which is defined as follows:  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{y}}=\frac{E\big{[}(X-\mu_{x} )\big{(}Y-\mu_{y}\big{)}\big{]}}{\sigma_{X}\sigma_{y}} \tag{1}\]  
We can get the sample correlation coefficient r based on the equation defined as overall correlation coefficient, and \(\overline{X}\), \(\overline{Y}\) respectively is the mean of the products of the standard scores of the \(X\), \(Y\) as follows:  
\[r=\frac{\sum_{i=1}^{n}\big{(}X_{i}-\overline{X}\big{)}\big{(}Y_{i}-\overline{ Y}\big{)}}{\sqrt{\sum_{i=1}^{n}\big{(}X_{i}-\overline{X}\big{)}^{2}}\sqrt{ \sum_{i=1}^{n}\big{(}Y_{i}-\overline{Y}\big{)}^{2}}} \tag{2}\]  
Therefore, the Pearson product-moment correlation coefficient can be used to observe whether the current data is approximately linear or the scattered data. When the calculated data is extremely messy, the correlation coefficient will approach zero; When the data consist of a straight line that approaches a positive slope, the correlation coefficient will approach 1.0. On the other hand, if the data consist of a straight line that approaches a negative slope, the correlation coefficient will approach \(-1.0\).  
Finally, as shown in Table 2, according to the positive and negative values of the correlation coefficient, it respectively indicates positive and negative correlations, and the size of the coefficient value represents the strength of the correlation. If we do not consider the correlation of the positive and negative, we can divide the value. If it less than 0.1, it represents almost no correlation, among 0.1\(-\)0.29 represents low correlation, among 0.3\(-\)0.69 represents medium correlation, among 0.7\(-\)0.89 represents high correlation, and among 0.9\(-\)1 represents almost complete correlation.