# 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 Other Deep Learning  
Ref. [144] introduced a Multi-Reservoir Echo State Network (MR-ESN) and deep Echo State Network architecture-based solar irradiance forecasting model (ESN). The model was designed to forecast a wide range of periods (1 and multiple hours ahead). The California Irrigation Management Information System collects solar irradiance data from Seeley Owens Lake South, Davis, Salinas North, and Blythe NE Markleeville. The suggested MR-ESN model achieved a lower forecasting error than the benchmark models evaluated, such as Elman Neural Networks, BP, and ESN, according to quantitative simulation results (ENN). Furthermore, as illustrated in Figure 12, MR-ESN has a far greater RMSE forecasting accuracy than ESN. Table 4 further illustrates that as the forecasting horizon lengthens, model forecasting accuracy declines. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Forecasting Step**} & **Evaluation** & \multirow{2}{*}{**RMSE (ESN)**} & **RMSE (MR-ESN)** & \multirow{2}{*}{**Reduction (\%)**} \\  & **Period** & & & \\ \hline \hline
1-step & 1 & 147.258 & 121.671 & 17.38 \\  & 2 & 132.787 & 63.014 & 52.55 \\  & 3 & 196.057 & 168.158 & 14.23 \\  & 4 & 119.763 & 98.987 & 17.35 \\ \hline
2-step & 1 & 222.684 & 169.543 & 23.86 \\  & 2 & 171.536 & 70.068 & 59.15 \\  & 3 & 273.373 & 224.234 & 17.98 \\  & 4 & 154.126 & 113.687 & 26.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ratio comparison of ANN and RNN models using a 10-minute data sampling frequency in terms of forecasting accuracy [144]. Furthermore, ref. [145] developed RNN using the sliding window technique for short-term solar irradiation forecasting of meteorological data from an Alabama weather station. The forecasting was generated for variations in sampling intervals, such as 1 h, 30 min, and 10 min, based on two factors: outside dry-bulb temperature and time. The model was trained using data from 7 days (22-28 May 2016) and validated on a single day (29 May 2016). According to the results in Table 5, both ANN and RNN have respectable accuracy levels, with RNN significantly exceeding ANN. Further, Figure 13 illustrates the forecasting accuracy of both ANN and RNN models with and without the sliding window techniques. Previous work by [45] used a Deep Neural Network (DNN) for solar irradiance forecasting in Turkey. Meteorological input data used included maximum temperature, sunshine duration, cloud type, and lowest temperature, as well as an astronomical feature known as extra-terrestrial rotation. The model training and testing were performed on a 7-year dataset gathered from 34 stations between 2001 and 2007. The results showed highly accurate forecasting results of 0.98 for the coefficient of determination. Further, ref. [146] suggested a new deep learning model integrated with Embedding Clustering (EC) and Deep Belief Networks (DBN) for daily solar irradiance forecasting. The dataset was gathered from 30 meteorological stations located across China and validated based on daily input features (for mean) for wind speed, maximum and minimum dry-bulb temperature, relative humidity, sunshine duration, and global solar radiation. A dataset from the preceding 22 years was used to train the model (1994 to 2015). The suggested hybrid model generated an excellent forecasting score, with an RMSE of 0.282 W / m\({}^{2}\) and an MAE of 0.137 W/m\({}^{2}\). Figure 14 shows the comparative probabilistic density error curves for the models. The improved performance of the developed model emphasizes its global acceptability for solar irradiance forecasting. Figure 14: The probability density curves of hourly errors in four Chinese stations [146]. Figure 13: Comparison between findings of (**a**) ANN and (**b**) RNN forecasting [145]. The Hierarchical Multi-Modal (H-MDL) and the Wavelet Decomposition Multi-Modal were introduced as two deep learning-based multi-modal solar irradiance forecasting models [147]. The proposed models were trained on a 12-year dataset gathered via NREL.