# 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
The dataset included hourly images from all-sky web cameras, as well as data on temperature, turbidity, precipitation, wind speed, zenith angle, DHI, GHI, and DNI. Based on the findings, the novel model outperformed well-known forecasting models, such as ANN and ARIMA. Prior research conducted by [148] forecasted short-term solar irradiance in the Netherlands based on a DNN model. Satellite photographs of previous irradiance values, historical and prospective temperatures, NWP projections, clear-sky irradiance, and relative humidity data for the next 6 h, as well as historical and prospective temperature, NWP projections, clear-sky irradiance, and relative humidity data for the next 6 h, were used to train the model. In the Netherlands, 30 locations were chosen, with 5 serving as model training sites and the remaining 25 serving as model evaluation sites. The results showed superior performance compared to other techniques. Subsequent work in 2021 by [149] introduced a Comprehensive Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Bi-LSTM, and the Sine Cosine Algorithm (SCA) for solar irradiance forecasting. The time-series data were split into a few periodic intrinsic mode functions (IMFs) using CEEMDAN. Next, the trends of solar irradiation were identified using an auto-correlation function (ACF) and a partial auto-correlation function (PACF). The SCA algorithm was then used to optimize the forecasting of Bi-LSTM. The model was trained using solar radiation data from Dauphin Island, Alabama, USA. The suggested CEN-SCA-Bi-LSTM model outperformed other considered models. As shown in Figure 15, the comparison analyses of all considered models were based on the season ranking, i.e., autumn, winter, summer, or spring. Prior work conducted in 2020 by [150] utilized two unique variants of LSTM, which were attention LSTM and GRU, for daily solar irradiance forecasting in India. The sug  
Figure 15: Comparison analysis of all considered models based on four seasons [149]. gested model was introduced as a Bidirectional LSTM (Bi-LSTM) and was trained using a 36-year dataset (1983-2019) available from the National Aeronautics and Space Administration (NASA). The observed findings indicated superior accuracy performance for Bi-LSTM than other deep learning models. Ref. [151] introduced a sophisticated RNN model integrated with a Chain-Structure Echo State Network (CESN) for hour-ahead solar radiation forecasting. The model was trained with spatial-temporal behavior data and demonstrated rapid learning, as well as a cheaper computational cost than RNN. Based on the findings, the suggested CESN model outperforms backpropagation (BP), ENN, and classical ESN. Further, ref. [152] adopted GAN integrated with CNN for model training to produce realistic future cloud images that were retrieved from the University of Patras, Greece, and consisted of 1.5 million images taken between August 2014 and April 2017. Based on observation, the images produced by the model without the adversarial loss are rather blurry.