###### Abstract
Indeed, there is a significant necessity for sufficient training data for PV power models, which might come from either an actual dataset or augmentations. Short-term solar radiation estimates have been shown to enhance yearly energy consumption for commercial operations in building microgrids. Sudden changes in solar radiation, known as ramp events, hold particular relevance for short-term forecast horizons, which can be captured with greater precision. Changes in solar radiation that happen quickly and are very strong could make PV power become less reliable and reduce in quality. As a result, the results of short-term forecasting may be utilized to determine the I confirm the meaning is retained most effective PV power ramp rates [20]; therefore, the forecasting period in this study was set at 30 min in advance. Forecasting solar radiation facilitates the integration of PV power units into the electrical grid, scheduling energy storage systems, and optimizing energy transmission, thus reducing energy loss [21]. By anticipating PV power generation, it reduces reserve capacity and generation costs, thus preventing electrical energy system disruption [22]. Throughout the years, solar radiation forecasting has gained much interest from academics and businesses. Recently, machine learning methods have been used to investigate the problem of estimating solar radiation forecasting. Among these approaches are the boosted decision tree regression (BDTR) model, which was used to predict solar radiation based on data gathered in Malaysia [23], a multi-objective shark algorithm, and a fuzzy method for forecasting solar radiation [24]. In [25], predicted solar radiation also has used artificial neural network (ANN) and random forest (RF) models. However, deep learning techniques are rapidly developing, particularly in unsupervised feature extraction. Deep learning models are crucial for estimating solar radiation, especially when dealing with a complex problem with a huge amount of data. In contrast to machine learning models, their efficiency remains constant as the amount of input data increases. Technological advancements in data collection and generation have enabled meteorological stations and photovoltaic power plants to collect massive amounts of data samples [26]. While some machine learning algorithms cannot manage high-dimensional inputs or huge datasets, others cannot handle them. The hybrid model focuses on combining standalone methods to exploit the benefits of individual forecasting models, which enhance the accuracy of deep learning networks [27]. For example, ref. [28] proposed a hybrid model consisting of a long short-term memory neural network (LSTM), multi-layer perceptron model (MLP), convolutional neural network (CNN), Gaussian progress model (GPR), and graph convolutional network (GCN) to forecast ozone concentration. Ref. [29] also introduced a hybrid deep learning model that consists of a support vector machine (SVM), Gaussian regression process (GPR), and convolutional neural network (CONN) for rainfall forecasting. What distinguishes this study from previous studies and motivates us to investigate the generation of solar radiation data and its effect on the forecasting accuracy of PV power is that most previous studies focus on developing forecasting models utilizing small datasets, which suffer from inaccuracy, and small datasets do not provide enough specimens to train deep neural networks. To the best of our knowledge, no research has been carried out that considers the generation of solar radiation data that may be trained to improve the performance accuracy of forecasting models.