# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 4 Experiments and Discussion  
The main sources of research data used in this research are ambient air quality from the Environmental Protection Administration, weather-station observation data from Central Weather Bureau, and on-site monitoring system data of solar power plants. In the process of data collection, it has been discovered that the original data of the trilateral parties are stored and presented in different ways. So, the first step is converting the data into the array to stacking, and then merges all data according to the same timeline, and then through the artificial cross match. Finally, record the situation which is lost or unusual, and then the data will be eliminated and consolidated for a database.  
At the beginning of this study, many difficulties were encountered in the collection of data. For example, the data fields were not completely, and multiple data sources were needed to compare with each other to fill in the missing fields. Due to the lack of fields, we tried many data sources, such as some government agencies and other data sources that provide real-time and historical climate inquiries. Although it seems that the completeness is relatively high, most of them are presented in a graphical way, which is slightly time-consuming in data conversion. In the data provided, only temperature and rainfall have clear values, and the rest must be found in other data sources or calculated manually. In this case, we have to find more data sources for data collection.  
Therefore, this research data will use the historical data collected by the above trilateral parties from 7 November 2018 to 28 January 2020 (total 448 days) between 6 o'clock and 17 o'clock each day (total 12 h), it is used as the data of model of neural network. During the data collection period, the date when the abnormal data is found by manually checked is 39 days. Finally, the total data after the abnormal data is eliminated is 409 days.  
Finally, through the consolidated data, 80% of the early data (from 7 November 2018, to 31 October 2019, a total of 3948 records) recorded in the file are used as training data for the model to training, and the other 20% data (from 1 November 2019 to 28 January 2020, a total of 960 records) are used for testing, and then the testing data will be taken out for 10 days (1-10 January 2020, a total of 120 records) data is used as a visualization to present the forecast results.  
Since this study is divided into two stages to execute multiple experimental schemes, in stage 1, it compares and descript model's effect which used different feature variables; In stage 2, testing the influence of performance indicator which choose the different model. In this study, the above-mentioned experimental results uniformly use the coefficient of determination \(r^{2}\) (R-Squared) as the evaluation standard of the performance indicator, and the value between [0,1] is obtained by the above-mentioned function calculation to determine the model fitting effect of is good or bad. Inside the function, the numerator is the summation of the difference of squares of original and predicted values, the denominator is the summation of the difference of squares of original and average values.  
\[r^{2}=1-\frac{\sum_{i}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i}(\overline{y}_{i}-y_{i })^{2}} \tag{6}\]