# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 2 Related Work  
In recent years, some data mining and artificial intelligence technologies have been applied to the field of energy applications [28; 29; 30], especially the prediction of renewable solar energy [31; 32; 33; 34; 35]. A lot of renewable energy forecast problems have been solved by utilizing the multilayer perceptron neural network, the radial basis function neural network, and the support vector machine [31; 32; 33; 34; 35]. In addition, the emerging artificial intelligence of deep learning has the capacity for improvement in learning hierarchical features over previous AI models. The Deep learning algorithm could provide an efficient representation of the data set and improving generalization [36; 37; 38]. According to [39], we can find the models and prediction results established using machine learning or deep learning methods based on the forecast models applied to solar power generation in recent years, which can be roughly divided into physical models [40], statistical models [41], and the mixed model [42; 43]. Among them, the physical model is mainly based on the analysis and construction of the mathematical model of the energy conversion process of the solar cell module. Generally, the linear reflection of the temperature of the solar cell module and the solar radiation intensity is used as the main parameter, and the forecast by the local weather station is used. The information uses mathematical formulas to calculate and predict power generation. Therefore, a certain degree of understanding of the physical characteristics of solar cell modules is required to increase the accuracy of prediction. Furthermore, it is a statistical model, which mainly includes two categories: time series [44] and statistical learning. Time series is mainly established by collecting and studying historical monitoring data values and then finding suitable data structures (such as autocorrelation, trend, or seasonality). Models, the more commonly used model methods include SLT, TLSM, ARMA, and ARIMA, etc. ; and statistical learning predictive models include MLR, SVM [45], and manual labor that can input multiple feature parameters or can be verified based on the passage of time. Artificial Neural Network (ANN) can improve the prediction accuracy through the pre-processing and post-processing of historical data. Among them, the models developed by ANN include ENN, RNN, GRNN, RBFNN, DRNN, GRU, and LSTM, etc. Among the many models, LSTM is one of the most widely used by people. Finally, explain the hybrid model (PHANN), which is mainly constructed based on any combination of physical models and statistical models. They can be a combination of physical methods and statistical methods, such as GA or CSRM combined with ANN; they can also be pure statistical methods, such as ARIMA and SVM, ARMA and KNN, or FKM and RBFNN, to try to get the best predictive effect. By reviewing the above-mentioned prediction model categories for solar power, and considering that during the energy conversion process of solar photovoltaic, the intensity change of output power is mainly based on the passage of time, this research further searches for the period from 2017 to 2019, based on the research situation of categorized neural deep learning technology and applied to the power generation prediction of solar power plants, among which the experimental results of each item are shown in Table 1.  
According to the research of [PERSON] [46] and others, five recursive nerves based on historical power generation and LSTM were designed to predict the hourly PV output power, although the final experimental results did not use any weather information data. But historical data based on deep learning can still reduce the prediction error rate compared with other methods (MLR, BRT). Moreover, [PERSON] [47] and others designed a 6-layer feed-forward deep neural network (DNN) and applied it to a grid-connected solar power plant in Seoul, South Korea, and based on the weather information from the previous day. The daily power generation