# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 1 Introduction
The advancements in data acquisition and storage technologies have made it possible for meteorological stations and photovoltaic power plants to collect massive amounts of data samples. Nonetheless, establishing a research question is still the best way to use these samples. While some machine learning models are unable to handle high-dimensional inputs or large datasets effectively, others even deal with them at all. However, the theory of deep learning is rapidly developing, especially in unsupervised feature extraction [37]. Moreover, deep learning models represent the most suitable approach for solar irradiance forecasting, especially when it involves a complex and substantial amount of data. In contrast, machine learning models' efficiency decreases as the quantity of input data increases. [PERSON]. [38] investigated the impact of altering the input size on deep learning and ML models. The findings demonstrated that the performance of traditional machine learning models was stagnant after a specified quantity of data, whereas deep learning models continued to be enhanced when they received more training data. Deep learning models thrive in a variety of areas, including image processing, pattern extraction, classification, and forecasting, since they are efficient in learning complex patterns data without human expertise. For example, building thermal load forecasting using deep learning models was utilized in [39]. Before that study, the deep learning model was introduced by [40] for short-term wave energy forecasting. Likewise, deep learning models have been extensively used for various forecasting applications, such as wind speed [41], PV power [42], solar irradiance [43], and others. The known problems associated with Conventional Neural Networks (CNN), such as gradient vanishing and training complexity, can be simply resolved using deep learning networks. To forecast time series, [44] developed a sophisticated neural network, while [45] utilized deep learning to forecast solar radiation at 30 locations in Turkey. Thus, deep learning models are better than machine learning and empirical models in terms of accuracy. This fact is due the structure of the hidden layers; however, training takes more time to complete because there are more parameters to adjust as the architecture becomes more complex. Although deep learning may yield positive outcomes, the training process is typically challenging; therefore, researchers often merge deep learning with feature extraction methods, like Attention Mechanism, to enhance the efficiency of feature extraction. Subsequently, the neural network that was paired with Attention Mechanism was able to perform most effectively, improving neural network interpretability by eliminating irrelevant data and only selecting more important information, as well as differently weighting each feature of the meteorological data. Ref. [33] proposed Convolutional Neural Networks and Generative Adversarial Networks that categorize the weather into 10 categories. Enervative adversarial networks increase the training dataset for every weather category. The simulation outcomes demonstrate that Generative Adversarial Networks have the potential to build high fineness quality specimens that capture the underlying properties of the original data. A data augment based on Generative Adversarial Networks optimizes all categorization models. Thus, GAN is ideal for data creation to augment and rebalance the training dataset. On the other hand, researchers have rarely used GAN to create time-series data (i.e., hourly GHI and meteorological data). Indeed, there is a significant need for sufficient training data for PV power models, which might come from either an actual dataset or augmentations. Despite solar energy being the most prominent renewable energy resource, few review papers have been published on this topic. Thus, ref. [46] presented a review analysis of an ANN-based model used for solar radiation forecasting. Similarly, ref.