# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 4 Solar Irradiance Forecasting Based on Deep Learning Techniques
### Solar Irradiance Forecasting Model Based on Deep Hybrid Model
The hybrid model must extract both temporal and spatial aspects of the data [169], which results in longer training times than the LSTM, which takes 983.71 s. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **The Best Case (s)** & **The Worst Case (s)** & **The Average Case (s)** \\ \hline LSTM & 393.01 & 400.57 & 396.27 \\ GRU & 354.92 & 379.57 & 365.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The training time of LSTM vs. GRU [94]. The authors of [170] examined several Deep Neural Network (DNN) models for the one-day forecast of Global Horizontal Irradiance (GHI). The considered DNN models include Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional-GRU (Bi-GRU), Bidirectional-LSTM (BiLSTM), one-dimensional Convolutional Neural Network (CNN1D), and various hybrid configurations, like CNN-BiLSTM and CNN-LSTM. For the development and evaluation of the DNN-based models, the authors utilized a dataset provided by NASA, which consisted of daily GHI recordings from 1 January 2000 to 30 June 2020 at a dry site (Hail, Saudi Arabia). The analysis also focused on identifying the features that influenced the accuracy of the models. However, this work did not take into account other weather features, such as air temperature, wind direction, wind speed, pressure, and relative humidity. Only historical values of daily GHI were used to develop the DNN-based models. The outcomes of the study indicated that the DNN models exhibited generally good performance, with a maximum correlation coefficient of 96% achieved for the daily GHI forecasts. In the Saudi Arabian province, the authors of [171] investigated several models for forecasting solar energy yield. The models considered in the study were Neural Networks (NN), Non-Linear Autoregressive (NAR), Gaussian Process Regression (GPR), Support Vector Machine (SVM), Simple Moving Average (SMA), Simple Average (SA), and Naive (N). The findings demonstrated that all of the models generated accurate estimates for the three years from 2019 to 2021, with the Naive and Simple Moving Average models slightly outperforming the other models. [PERSON], et al. [172] developed a hybrid forecasting model that combined a Slime-Mould algorithm-based feature selection technique, a Convolutional Neural Network (CNN), a Long-Short-Term Memory Neural Network (LSTM), and a final CNN with a Multilayer Perceptron output (referred to as the SCLC algorithm later). The proposed approach was applied to six solar farms in Queensland, Australia, considering daily temporal horizons in six different time increments. The forecasting model proposed in the study outperformed two Deep Learning models (CNN-LSTM and Deep Neural Network) and three Machine Learning models (Artificial Neural Network, Random Forest, and Self-Adaptive Differential Evolutionary Extreme Learning Machines) across all six selected solar farms. The results demonstrated strong performance metrics (r \(>\) 0.9 in all cases) and improvements of more than 10% for daily, monthly, and seasonal periods. [PERSON] et al. [173] present a PV power forecasting approach for long- and short-term memory networks based on Pearson feature selection. The method analyzed the influence of input features on the variance in PV power using Pearson coefficients, and the model was confirmed using case tests. The results revealed that the intensity of insolation radiation, temperature, and humidity were important effect features in the change in PV power. When LSTM was compared to the BP, RB, and TS algorithms, it was discovered that the proposed approach reduced the forecasting error rate by 1.48, 11.4, and 6.45%, respectively, compared to the LSTM, CNN, and BP. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [172] developed a hybrid forecasting model that combined a Slime-Mould algorithm-based feature selection technique, a Convolutional Neural Network (CNN), a Long Short-Term Memory Neural Network (LSTM), and a final CNN with a Multilayer Perceptron output (SCLC algorithm later).