## 2 Materials and Methods
### The Proposed GAN-CSVR Model
#### 2.5.2 Step 2: Augmenting Data Utilizing GAN-CSVR Algorithm  
In this step, a new algorithm is proposed that combines a GAN, CNN, and SVR, which is called the GAN-CSVR algorithm, to augment data. As shown in Figure 1, the GAN consists of two neural networks competing with each other; the generator (G) and discriminator (D) have been trained using a backpropagation technique. These two networks work together aggressively to improve each other. During the training phase, [PERSON] tries to learn and create \"fake\" samples of input in order to fool [PERSON], while [PERSON] attempts to accurately identify \"fake\" or \"genuine\" inputs. The model converges until the discriminator can no longer detect the difference between the samples. The convolutional GAN (Conv-GAN) [30] has successfully been utilized to create specimens for data augmentation, which is one major mission for the GAN, and serves as the foundation of this model. However, when applied to solar irradiance data generation, it often suffers from several issues, such as the loss of critical discriminative features and big variations between generated specimens and input specimens, which might not help train a deeper network. Consequently, the traditional Conv-GAN is enhanced by removing the fully connected CNN layer and substituting it  
Figure 1: The structure of the proposed GAN-CSVR model. with a basic linear layer SVR to decrease the number of features that need to be learned without a significant number of training examples to produce the output specimens. This kind of linear layer SVR is effective for limiting the variance of the samples produced, is robust in discarding outliers, and has excellent generalization capability. The SVR is utilized in this study as the ultimate layer of the GAN-CSVR model because of its own recent superiority in solar irradiance forecasting and energy demand problems [43; 44; 45; 46; 47]. The combination of the GAN, CNN, and SVR in the hybrid model constructed to augment the dataset allows the CNN to capture domestic pattern features and popular properties that repeat in the time series at various periods. Thus, the proposed GAN-CSVR model can produce data precisely by extracting features from meteorological data that influence the production of specimens. The generator and the discriminator use a neural network with two convolutional layers (conv1D) together with two Max pooling layers, one convolutional layer (conv1D) with RELU activation function, a single flatten layer, and SVR as the last layer, as shown in Figure 2. The 1D convolutional layers start learning the input time series data to best grasp the dependence between features to create a symmetric feature map and increase the number of rows of the input twofold by a simple linear operation simultaneously. After these three 1D convolutional layers, the output is filtered by an SVR layer that linearly separates the patterns of the original input features, which can augment a dataset to fit as many features as possible. The discriminator is updated using training data and augmented data, while the generator is updated using the output of the discriminator as feedback. This iterative process allows the generator to improve its ability to produce realistic data. The generator is compiled with the optimizer and the Multi-Objective loss function to eliminate a gradient vanishing problem in the generator. The loss function convergence helps the discriminator to reach a stable state where it can effectively distinguish between real and fake samples, which leads to producing extremely identical samples that have the diversity representing the underlying properties of the original data. Adam is used as an optimizer for the generator and discriminator. The pseudocode of GAN-CSVR is shown in Figure 3.  
Figure 2: Components of generator and discriminator. Consequently, the GAN-CSVR model seeks to maintain representational characteristics without overfitting.