# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 1 Introduction
[47] analyzed a total of 87 journals that implement ANN for solar power forecasting. Before that study, a review analysis of machine learning-based solar radiation forecasting models was conducted [48]. Recent work by [49] investigated solar irradiance forecasting using the SVM application. In 2019, ref. [50] examined the application of 70 sky solar irradiance models. Following that study, ref. [49] provided a review paper on physical models used to predict solar irradiation. Next, ref. [13] presented deep learning-based solar radiation forecasting models. According to statistics, deep learning-based models has been extensively explored for forecasting model, but there is still no available publication that concentrated on investigating the generation of meteorological data and its effect on the forecasting accuracy of PV power. Most previous studies focused on developing forecasting models, which suffer from inaccuracy, particularly for extreme weather types, for which models use small datasets that do not give enough representative training specimens for Deep Neural Networks. As this review study focuses on generating and balancing meteorological data to improve forecasting performance, it also paves the way for future research in a variety of areas, including photovoltaic power forecasting, wind power forecasting, electricity consumption forecasting in the medium term and long term, etc., by expanding and embracing the modern technique suggested in this work through data generation. In contrast, most studies of photovoltaic power, wind power, and energy consumption concentrate on the short-term forecast, disregarding medium- and long-term forecasts because of a lack of training data for models. In sum, neural networks and deep learning-based models have been extensively explored for solar irradiance forecasting. Although there have been many review papers published recently on solar irradiance forecasting, such as [1; 2; 3; 4; 5; 6], there is a lack of a comprehensive review paper that studies the significance of deep learning models for solar irradiance forecasting. A deep analysis of the effects of deep feature extraction on forecasting performance has been conducted, as many past studies on solar irradiance forecasting tended to overlook how to successfully extract features from meteorological data. This review paper focuses on reviewing recent and effective deep learning models that have achieved notable performance. Additionally, this study has covered forecasting impacts that are often neglected by existing reviews. It provides a comprehensive review that covers various aspects of solar energy forecasting related to deep learning models. It can also serve as a valuable reference point for researchers developing deep-learning solar energy solutions. The main aim of this paper is to address the limitations of existing reviews in terms of covering aspects such as the main variables influencing solar irradiance forecast, including the forecasting horizon, input parameter optimization, weather classification, and others. Furthermore, this paper outlines the application of CNN, RNN, GRU, AM, LSTM, DBN, Attention Mechanism, and GAN for solar irradiance forecasting. It presents factors that influence the accuracy of the solar irradiance forecasting models, in addition to providing a background introduction and discussing prominent deep learning techniques. The review emphasizes the limitations of existing solutions and provides recommendations for future work.