The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.

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.