# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 7 Conclusions  
A comprehensive review of numerous deep learning-based models used to forecast solar irradiance and accessible in the literature is provided in this paper. These models' sophisticated architectures make it easier to extract high-level, non-linear features from the solar data. This study elaborates on several popular deep learning models, such as LTSM, DBN, CNN, RNN, RGU, GAN, and an Attention Mechanism based on solar irradiance forecasting, and improves the training of models by increasing the training data and avoiding overfitting models. These models also include their associated architecture, strengths, and limitations. In addition, the influential factors of forecasting model accuracy, such as forecasting horizons, weather classifications, input feature optimization, and assessment measures, are presented. Based on the literature review, the existing research demonstrates the superiority of deep learning-based solar irradiance forecasting models compared to conventional machine learning approaches. In most cases, the forecasting accuracy performance is further enhanced by incorporating the hybrid of deep learning models. Thus, the hybrid model, which extracts both temporal and spatial data features, enhances accuracy even further and should be preferred. Additionally, extending the data are an approach that improves the performance of deep learning, particularly deep networks. It involves augmenting the training data by adding new data generated from existing data or creating modified copies of old data. Furthermore, conducting correlation analysis between features and irradiation and removing redundant information in the feature set leads to reduced model complexity and improved forecasting. Based on the literature provided, hybrid LSTM and CNN models demonstrated superior performances compared to those of other models considered. The main findings of this study can be summarized as follows:  
1. The forecasting horizon is considered the key factor for improving accuracy in forecasting models. The performance of a solar irradiance forecasting model typically degrades as the forecasting horizon lengthens. Therefore, forecasting models should be considered based on the selected forecasting horizon. 2. The use of a single deep learning model is limited. Our findings show that the implementation of hybrid models and using different models can improve accuracy and are preferred over basic deep learning models. The training duration should be considered during performance evaluation, since the hybrid model requires the extraction of both temporal and spatial features of data; therefore, this model has a longer training time compared to other models. 3. Model complexity is also a key factor. While hybrid models perform better than individual models, their computational time is generally higher. There is a trade-off between the accuracy of the model and its computational complexity and time. Therefore, using high-performance processors and large data frameworks may be difficult. 4. Changes in the weather influence how effectively a forecasting model performs. For example, some forecasting models generate lower accuracy for sky circumstances, while others have substantially better performance. Therefore, it is important to prioritize weather classification to ensure accuracy in forecasting. 5. The implementation of deep learning algorithms in solar irradiance forecasting has been widely explored. Some of the most used algorithms include LSTM, CNN, and DBN, while rarely used algorithms include GRU, ESN, and RNN. These models, however, have trade-offs because they can be highly precise while also being computationally expensive. In conclusion, deep learning techniques are still in their infancy, and their potential to resolve challenging time-series forecasting challenges should be further investigated.