## 2 Related Works
### Related Works: Solar Power Forecasting
[65] and [PERSON] and [PERSON] [13] predicted solar radiation using an artificial neural network. [PERSON] et al. [14] constructed a hybrid model combining neural networks and fuzzy logic to predict solar radiation in Spain based on satellite images and cloud cover data. [PERSON] et al. [15] predicted solar radiation in certain areas using a multilayer perceptron, with latitude, longitude, elevation, month, sunshine ratio, rainfall, and humidity as inputs. [PERSON] and [PERSON] [66] established a model combining an EnergyPLAN system with neural networks. The EnergyPLAN system outputs the information of multiple power grids at the same time, and a trained neural network can effectively monitor several power grids. Numerous researchers are advocating for the adoption of deep learning (DL) models as an alternative to machine learning models, with the aim of improving the accuracy of solar power generation predictions. [PERSON] and [PERSON] [67] reported that long-short-term memory (LSTM) was superior to conventional multi-layer perceptron models in predicting solar power generation. [PERSON] et al. [68] reported that their DL model, based on encoding, outperforms regression techniques. [PERSON] et al. [69] added bidirectionality to LSTM to extend prediction results over longer durations. Some researchers have claimed that the relationship between weather factors and solar power generation is too complex for any single deep learning model, prompting the development of composite prediction models. [PERSON] [70] integrated the Theta statistical method with LSTM and a Gated Recurrent Unit recurrent neural network (GRU) to improve prediction accuracy. [PERSON] et al. [71] integrated a feedforward deep learning model, LSTM, and GRU to capture the characteristics of solar power generation over various timeframes. [PERSON] et al. [16] developed a hybrid model based on a deep belief network, AutoEncoder, and LSTM to forecast the power output of solar power plants. [PERSON] et al. [18] merged multiple types of DL models to monitor and predict the power generation, soiling loss, and performance ratio of a large-scale power plant. [PERSON] et al. [17] created a hybrid model using LSTM and a convolutional NN (CNN) to forecast short-term variations in photovoltaic power output. Note that the methods mentioned above involve digitizing weather data and then inputting the numerical results into the model for prediction. By contrast, some scholars have recently begun exploring the possibility of predicting solar power generation by analyzing images of the sky. [PERSON] et al. [72] proposed the use of convolutional NNs to extract features from images for use in predicting solar power generation. [PERSON] et al. [73] and [PERSON] et al. [74] respectively proposed the SUNSET model and ConvLSTM model, both of which have become benchmarks by which new methods are developed and tested. [PERSON] et al. [75] developed a transfer learning framework based on the SUNSET model and ConvLSTM to reduce the amount of data required for model building. Some scholars have also tried combining images with other data. [PERSON] et al. [76] combined numerical weather data with data from sky images to improve the accuracy of their model. Accuracy is not the only concern. As discussed, the sensors that collect data for prediction are subject to errors, introducing instability to solar power forecasting. Researchers have sought to overcome this issue in three ways. The first approach involves preprocessing the data to ensure that incorrect data are not input to the model. For instance, [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [30], and [PERSON] and [PERSON] [77] all used wavelet models to first denoise the original solar radiation time series before inputting the denoised data into various models for power generation forecasting. These studies all verified the validity of their methods using historical data from real-world power plants.