# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 3 Method and Model
### Time-Sequential Predictive Algorithm (RNN, LSTM, GRU, and Related Models)  
The different time-sequential predictive models are utilized to validate and compare the capability and performance in predicting the generated solar power. In this study, the RNN, LSTM, and GRU are applied to explore efficiency and accuracy. In addition, the combination of bidirectional training with sequential predictive models is also utilized to evaluate the performance. The general structure of RNN, LSTM, and GRU is shown in Figure 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Correlation Strength** & **Positive Correlation** & **Negative Correlation** \\ \hline Extremely low & 0\(-\)0.09 & 0\(\sim\)\(-\)0.09 \\ low & 0.1\(-\)0.29 & \(-\)0.1\(\sim\)\(-\)0.29 \\ in & 0.3\(-\)0.69 & \(-\)0.3\(-\)0.69 \\ high & 0.7\(-\)0.89 & \(-\)0.7\(\sim\)0.89 \\ Extremely high & 0.9\(-\)1 & \(-\)0.9\(\sim\)\(-\)1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pearson correlation coefficient comparison table. Figure 1: The topological structure of RNN, LSTM, and GRU for short-term prediction: (**a**) the recurrent neural network (RNN), (**b**) the Long short-term memory (LSTM), and (**c**) the recurrent unit neural network (GRU). Recursive neural network (recursive neural network) is regarded as a generalization of the recurrent neural network, which is mainly based on the development of [PERSON]'s research results [49]. Its linear recursion on the time structure derive of the t vector will propagate to t-1, t-2,... Traditional RNNs can't capture long-term memory because the final vector produces coefficients of continuous multiplication, resulting in the input of the starting vector in the forward process. The weak influence of the vector causes the problem of the disappearance of the gradient and causes the long-term memory to be hidden by the short-term memory. The RNNs that are designed to support sequences of arbitrary length can often only handle time sequences that are not too long [50]. The long short-term memory, which is a special type of RNN, has a significant advantage in consecutive events between relevant timestamps [51]. The LSTM has a lot of outperformed applications in renewable energy prediction [52; 53]. The input, output, and forget gate in LSTM memory blocks could update and control the flow of consequent information which are relatively advantageous for the short-term prediction of renewable energy. The LSTM could continuously update the consequent data by utilizing the input, output, and forget gate of memory blocks. In addition, [PERSON] et al. proposed gated recurrent unit (GRU) which combines the input gate and forget gate in LSTM [54]. This lightweight version of LSTM could accelerate execution and reserve computational resources. The LSTM and GRU learning performance are significantly improved than traditional RNN when applied to different data sets and corresponding tasks. In addition to the three models described above, the Bidirectional RNN was proposed by [PERSON] in 1997 [55], which combined regular recurrent neural networks. The Bidirectional RNN extended the training process in the direction of positive and negative times. The results of the two training directions comprehensively state the transmission of recursive neural networks. The prediction value is generally performed in one direction from the front to the back, which can only be predicted through the previous information. The calculation process of the model may need to use the information from the future time. Therefore, the predictions could use the bidirectional to execute two one-way recursive neural networks at the same time and combine operations. The Bidirectional RNN provides the input value to these two recursive directions at the same time and the final output value is jointly determined by these two unidirectional recursive neural networks.