###### Abstract  
The accuracy of solar radiation forecasting depends greatly on the quantity and quality of input data. Although deep learning techniques have robust performance, especially when dealing with temporal and spatial features, they are not sufficient because they do not have enough data for training. Therefore, extending a similar climate dataset using an augmentation process will help overcome the issue. This paper proposed a generative adversarial network model with convolutional support vector regression, which is named (GAN-CSVR) that combines a GAN, convolutional neural network, and SVR to augment training data. The proposed model is trained utilizing the Multi-Objective loss function, which combines the mean squared error and binary cross-entropy. The original solar radiation dataset used in the testing is derived from three locations, and the results are evaluated using two scales, namely standard deviation (STD) and cumulative distribution function (CDF). The STD and the average error value of the CDF between the original dataset and the augmented dataset for these three locations are 0.0208, 0.1603, 0.9393, and 7.443981, 4.968554, and 1.495882, respectively. These values show very significant similarity in these two datasets for all locations. The forecasting accuracy findings show that the GAN-CSVR model produced augmented datasets that improved forecasting from 31.77% to 49.86% with respect to RMSE and MAE over the original datasets. This study revealed that the augmented dataset produced by the GAN-CSVR model is reliable because it provides sufficient data for training deep networks. 2023 13 12768 18 July 2023 10 November 2023 14 November 2023 14 November 2023 128 November 2023 128 November 2023 128 November 2023  
estimates; researchers must also consider the availability of solar radiation data. As a result, they are becoming more interested in studies that gather weather variables and the availability of photovoltaic forecasting models to effectively estimate the volatility and uncertainty of photovoltaic power output owing to varied and changing climatic conditions [5; 6; 7; 8]. Accurate and reliable data are essential for training and validating the models to provide accurate forecasting. Missing data in the historical dataset might lead to incomplete training, resulting in lower forecasting accuracy. Erroneous data, such as faulty sensor readings or outliers, can alter the model's grasp of patterns and correlations, resulting in false forecasting. PV power forecasting models consider various input features such as solar irradiance, temperature, cloud cover, and humidity. If any of these features have missing or incorrect data, the model may be unable to effectively represent the underlying relationship between the inputs and the PV power output. This missing data can lead to biased or untrustworthy forecasts. For instance, gaps in time series analysis due to missing data might reduce the precision of identifying trends and detecting seasonality. It is possible to impute or replace missing data using the right procedures, but it might be difficult to do so accurately if there are many missing data points [9]. Therefore, data augmentation has indeed been required when implementing deep learning techniques with a restricted training dataset. Data augmentation is a technique for boosting deep learning performance, particularly deep networks. It produces new data generated from present data or new copies of old data that have been altered to make more training data available. Artistically, data augmentation contributes a kind of disturbance or noise to the datasets, which are both considered undesirable features in statistical modeling and must be removed using filters [10; 11]. The impact of data augmentation in deep learning is to regularize the model and reduce overfitting throughout deep training, which increases the generalizability and prevalence of the learned models.