# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 5 Discussion and Analysis of Performance  
The performances of existing deep learning-based solar irradiance forecasting models are analyzed and discussed in this section. Currently, the LTSM is deemed to be the most used model for solar irradiance forecasting due to it being one type of Recurrent Neural Network that preserves data for future use, as well as providing extra storage capacity to the network. It is extensively utilized to forecast solar irradiance because it can speed up the convergence of non-linear forecasts and reveal the long-term relationships between time series. In one of the studies, ref. [107] proposed an LSTM model for 1-day ahead solar irradiance forecasting at 21 locations across the USA and Europe based on remote-sensing data. The suggested model demonstrated over 52.2% more accuracy than the smart persistence model. Following that approach, ref. [65] used LTSM for 1-hour ahead solar irradiance forecasting at three locations in the USA. The findings presented the lowest RMSE forecasting of 41.37 W/m\({}^{2}\). Subsequently, different mechanisms and new variants were introduced to enhance the model's performance. Therefore, ref. [150] presented two different variants of LSTM, known as Bi-LSTM and attention-based LSTM, for daily solar irradiance forecasting at two locations in India. The LTSM architecture was composed of a gating mechanism and memory cells, which assisted in learning long-term data dependencies. Thus, it could be asserted that LTSM and its variants performed adequately for solar irradiance time-series data and forecasting accuracy. Before that study, GRU was also extensively applied in the literature [141]. The benefits of GRU is that it is computationally cheaper because it requires less parameters training and memory; hence, faster execution. On the contrary, LTSM is computationally expensive, as it requires multiple training stages, though it generates effective accuracy forecasting. In addition, ref. [49] presented a CNN model for 10-minute ahead direct normal irradiance forecasting. In this study, ground-based cloud images were utilized to extract temporal and spatial features for model training and demonstrated an increase of 17.06% in accuracy forecasting over a smart persistent model. This result occurred due to the structure of CNN, which was made up of convolutional and pooling layers that were particularly efficient in features extraction. Therefore, it worked extremely effectively for image feature extraction (including converting it to two-dimensional data) for solar irradiance data. The connections of CNN and feature-sharing properties minimize the model parameter training and time, and it has a strong ability to extract spatial correlations from weather data, such as cloud cover, and other factors. Furthermore, the DBN was also widely used for solar irradiance forecasting [146]. The structure of DBN was inherited from the restricted Boltzmann machine, in which a layer-by-layer unsupervised training method was employed. Therefore, it was suitable for extracting input features that were explicitly detectable in renewable energy forecasts. Moreover, another used deep learning model for solar irradiance forecasting was RNN. For example, ref. [175]introduced a multi-horizon GHI forecasting model using RNN that resulted in an average RMSE of 18.57 W/m\({}^{2}\) for variations in forecasting horizons. The main structure of RNN were the internal feedback and feedforward connections between the neurons. These connections acted as memory functions for the RNN, allowing efficient processing of time-series solar data. The performance of standalone models is limited in cases where a hybrid model focuses on combining different methods while exploiting the benefits of individual forecasting models to enhance the accuracy of deep learning networks. This observation is true because a single model sometimes may not be able to fully extract necessary features and satisfactorily perform all tasks.