# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 5 Discussion and Analysis of Performance
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**Author** & **Year** & **Model** & **Result** & **Advantage** & **Limitation** \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] [175]} & \multirow{6}{*}{2018} & \multirow{6}{*}{RNN} & \multirow{6}{*}{The model generated substantial forecasting with an RMSE of 30 W/m2.} & The main structure of RNN is the internal feedback and feedforward connections between the neurons. & \multirow{6}{*}{Inability to deal with long-term data dependencies because of vanishing gradient and gradient explosion issues.} \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] [127]} & \multirow{6}{*}{2020} & \multirow{6}{*}{GRU} & The model generated substantial forecasting with an RMSE of 30 W/m2.} & It is computationally cheaper because it requires lesser parameter training and memory, hence faster execution. & It is a struggle to capture long-term dependencies in sequences. \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & \begin{table}
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**Author** & **Year** & **Model** & **Result** & **Advantage** & **Limitation** \\ \hline [PERSON] and [PERSON] [102] & 2021 & CNN and LSTM & LSTM & LSTM-CNN model is more accurate in a range of about 37-45\% compared to the benchmark models. & It handles temporal and spatial dependencies in time-series data and extracts non-linear features in data. & It takes longer to train than a single model because it needs to extract both spatial and temporal features from the data. & It takes longer to train than other models because it needs to extract both spatial and temporal features from the data. \\ \hline [PERSON] and [PERSON] [150] & 2020 & Attention Mechanism Mechanism to perform most effectively. & The neural network that was paired with the Attention Mechanism was able to perform most effectively. & The Attention Mechanism works deep learning models to engage in enhanced feature extraction for a sequence of inputs. & The pairwise calculations required by Attention Mechanisms can result in increased computational complexity, leading to longer training and inference times. \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [172] & Hybrid forecasting model & The results demonstrated strong performance metrics (\(r>0\) in all cases) and improvements of their performance metrics (\(r>0\) in all cases) and improvements of their forecasting models to enhance the accuracy of deep learning networks. & The hybrid model focuses on combining different methods while models because it needs to extract both spatial and temporal features from the data. \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [33] & 2019 & The simulation outcomes demonstrated that Generative Adversarial Networks have the potential to build high fineness quality specimens that catch the underlying properties of the original data. & It makes more training data available by adding new data devised from training data. & Training GAN can be computationally expensive and resource-intensive, particularly for larger and more complex models. \\ \hline \hline \end{tabular}
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Table 8: _Cont._Deep learning techniques have robust performance, especially when dealing with spatial and temporal features. However, small datasets may not offer appropriate or adequate representative training data, which may result in model overfitting. They are not sufficient when dealing with extreme weather types because they do not have sufficient data for training. Data augmentation is a way to make deep learning, and especially deep networks, work better. This process makes more training data available by adding new data made of either old data or new copies of old data that have been changed.