# 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
A hybrid model of four distinguished deep learning-based techniques was designed by [164] using LTSM and CNN based on Bayesian  
Figure 17: The comparison between performances of actual and predicted GHI in Gangtok for different seasons (**a**) Winter, (**b**) Summer, (**c**) Monsoon and (**d**) Autumn [162]. Optimization (BO) for short-term PV power generation forecasting. The study analyzed feature selection for both benchmark models and selected five predictive features to be used to directly forecast results (1 to 24 h ahead). The data originated from a PV facility in Shandong, China. While Bi-LSTM and CNN-Bi-LSTM models were more practical for 1-hour forecasting, while LSTM-CNN and CNN-Bi-LSTM models were more suitable for 24-hour forecasting. This work demonstrated that the integration of Bayesian optimized optimal weights offered a satisfactory forecasting performance for commercial PV plants and could minimize error rates by up to 32.80% more than the benchmark model. Recently, ref. [165] described a multivariate technique for forecasting solar power generation of PV systems in the very short term using the LSTM algorithm. The model is validated using the standard parameter and the Mean Absolute Error (MAE), which was 0.0565. The implementation of LSTM for multivariate extremely short-term solar PV power forecasting was supported by potential results, which outperformed the univariate technique. LTSM and fully connected (FC) layers were introduced into an LSTM-FC deep learning system to optimize forecasting accuracy [166]. Due to the dual-branch input, the model considered was not limited to analysis of how meteorological data affects power generation, but also considered time continuity and periodic dependencies in terms of accuracy performance. The studies used meteorological data, as well as historical continuous and periodic data, which were then combined into different input forms for comparative evaluation of LSTM-FC relative to other benchmark models, like SVM, GBDT, GRNN, FFNN, and LSTM. Meanwhile, the LSTM-FC model outperformed the others in terms of forecasting accuracy, with a RMSE that was 11.79% greater than that of SVM. Accordingly, ref. [167] investigated the implementation of a hybrid Deep Learning approach by applying RNN, LTSM, and GRU for solar energy forecasting. Real meteorological data (year 2016-2018) from the Errachidia region were used to validate the model. Six metrics, including MAE, MSE, RMSE, ME, R2, and NRMSE, were used in the analysis of the results to measure forecast precision and error margin for real-time photovoltaic forecasts to improve grid management and safety. In terms of cost efficiency, it was found that RNN and LSTM performed much better than GRU because they could preserve long-term dependencies in time-series data. Subsequent work by [168] concentrated on a new deep learning-based multi-step ahead approach using LTSM for feature extraction to enhance the accuracy forecasting of the GHI. The sine-cosine algorithm based on the swarm evolutionary optimization approach was employed to automate the neural network architecture. A three-phase modification model was developed using three datasets gathered from three solar stations in the Eastern USA. This approach was expected to encourage a diversified and reduce premature convergence in the optimization method. The experimental findings demonstrated excellent performance using the MAE, RMSE, and Pearson metrics in comparison to alternative forecasting models. Ref. [67] used LSTM and GRU for forecasting techniques to estimate the efficiency of learning data in the best, worst, and average circumstances. Table 6 compares the training time of LSTM and GRU in the three scenarios. This table yields the superiority of GRU compared to LSTM because the longest training time for GRU was shorter than the best training time for LTSM. Further, Table 7 compares the training times of both LSTM and CNN-LSTM.