###### Abstract
The enhancement in performance occurs when a model is tested using data from the training set compared to data from the testing set that the model has never been exposed to before [12]. Numerous advanced generative models were introduced in previous publications; for instance, Random Oversampling (RO), a basic generative model, generates new data by replicating random minority class specimens [3]. Although the RO process is straightforward, its perfect reproduction of training instances might lead to overfitting because the model is repeatedly exposed to the same data. A different strategy, Synthetic Minority Oversampling Technology (SMOTE) [13], tries to avoid overfitting by generating artificial data connecting specimens of minority classes. Moreover, SMOTE may produce noisy samples because the distinction between minority and majority category clusters is not always apparent [14]. There have been some previous efforts to build generative models using an agent-based or Markov chain approach, but these methods have strict requirements for the data to satisfy; for instance, the first-order Markov property [15]. Auto-encoders are generative models consisting of an encoder and a decoder. They are frequently used to reconstruct images in image processing as close to the original [16]. Using an auto-encoder may result in the same issue as RO since the recently founded data only retain the distribution of specimens from the pristine data and do not conserve the variety of samples produced. A new robust generative model known as the \"generative adversarial network\" was recently developed by [17]; it is an excellent resolution to solve difficulties in the preceding techniques and is a well-known technique for data augmentation that creates new data by examining the distribution of the existing data. Additionally, the size or number of training data categories is not a restriction for GAN-based data augmentation. It is worth noting that although typical GAN models prioritize data production with adequate variability, they also prioritize consistency [18]. Since then, numerous GAN variants have been proposed for use in diverse situations. While GANs were first developed for picture generation, they are now used to generate time series data. Adversarial networks can now process intricate time-series data patterns. Since their inception, GANs have been used in many different study areas, from speech and language processing to image and vision computing and beyond [19]. The capabilities of excellent GAN modeling allow it to generate new data with a distribution identical to pristine data while preserving the diversification of produced data to create new specimens with a distribution similar to the pristine information while preserving diversification of produced data [19]. Furthermore, modeling complex data with certain implicit distributions does not require the data to conform to any pre-existing assumptions [15]. Convolutional neural networks and generative adversarial networks have been proposed in [1] to classify the weather into ten categories. Generative adversarial networks augment the training dataset for all weather categories. The simulation results show that the generative adversarial networks have the ability to generate high-quality data that capture the underlying features of the original data. The data augmentation based on convolutional generative adversarial networks optimizes whole categorization models. Thus, a convolutional GAN is ideal for data generation and augments the training dataset, which is the basis for our study. In this study, the core of the generative model is understanding the distributions of previous solar radiation data under various weather circumstances to generate sufficient training data for the forecasting models; this will also be explained in Section 2.5. On the other hand, researchers have rarely used convolutional GANs to create time series solar radiation data.