# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 5 Discussion and Analysis of Performance
Furthermore, a hybrid model is expected to provide better accuracy than single deep learning models. However, it takes longer to train than other models because it needs to extract both spatial and temporal features from the data; therefore, the use of hybrid models was introduced. Several researchers have presented their work, including [49], whose author used a hybrid of model CNN and LSTM. Additionally, they integrated the use of LSTM and CNN algorithms to extract spatial and temporal information in sequences. The model training utilized meteorological data from 23 different sites in California, including DHI, GHI, temperature, relative humidity, cloud cover, and precipitation. The developed LSTM-CNN model was tested throughout a whole year, covering all four seasons and under three types of meteorological conditions. Similarly, [19] presented the use of both LSTM and RNN, [176] developed an integrated GA with the DNN for model optimization (GRU, LSTM, and RNN) of solar irradiance forecasting, and [102] developed an LSTM-CNN model to extract spatial and temporal features. The forecasts produced via the LSTM-CNN model demonstrated higher accuracy than individual models, such as SP, CNN, LSTM, ANN, SVM, and others. In another study, [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 (referred to as the SCLC algorithm later). The Attention Mechanism allows deep learning models to engage in enhanced feature extraction for a sequence of inputs. Although deep learning may yield positive outcomes, the training process is typically challenging; therefore, researchers often combine an Attention Mechanism to enhance the efficiency of feature extraction. Such an example can be seen in [177], whose authors used the Attention Mechanism with two LSTM for short-term PV power forecasting. Firstly, the output of both networks was integrated with the Attention Mechanism. Secondly, the integrated output was merged into a fully connected layer for forecasting output. The suggested model was then validated using the dataset of four seasons with variations in forecasting horizons against four other considered models. Based on our observations, the proposed model outperformed other conventional LSTMs, as well as all considered models for more than 15-min solar irradiance forecasting; the neural network that was paired with the Attention Mechanism performed most effectively, improving neural network interpretability by eliminating irrelevant data and only selecting more important information, as well as differently weighting each feature of the meteorological data. In addition, the correlation between features and irradiation in the feature set was analyzed to reduce model complexity and improve forecasting. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [174] suggested a hybrid approach that combined a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) to predict global sun radiation (GSR). Firstly, a CNN-LSTM extracted optimal topological and structural characteristics inherent in predictive features; an MLP-based predictive model then generated GSR forecasts. Meteorological features were used to predict GSR in six solar farms in Queensland, Australia. To improve the efficiency of the proposed CMLP model, a Hybrid-Wrapper feature selection technique based on a Random Forest-Recursive Feature Elimination (RF-RFE) scheme was employed to eliminate redundant predictor features. Table 8 presents a summary of the related studies.