# 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
The forecasts made using the developed LSTM-CNN model are more accurate, having a range of about 37-45%, than standalone models, like SP, CNN, LSTM, ANN, SVM, and others. Ref. [158] suggested an evolutionary attention-based LSTM-based time-series forecasting model to address the problem of attention dispersion that plagues the typical LSTM algorithm. Varying in terms of time steps, the Attention Mechanism can give characteristics to time-series weights. The LSTM-based approach of forecasting has difficulty interpreting complex relationships between features that only use weights. A Self-Attention Mechanism is an Attention Mechanism that extracts deeper information from a row of data. The suggested method aimed to maximize the benefits of the features of the extended input sequences. The Attention Mechanism performed better than the standard LSTM. Following that approach, ref. [159] proposed a probabilistic forecasting model that integrated residual modeling and an RNN. An LSTM-based point forecasting method was utilized for deterministic forecasting and calculating the residual distributions. The input features considered include temperature, cloud cover, dew point, east sea-level pressure,  
Figure 16: The performance of the CLSTM model for 1-day GSR forecasting (**a**) RMSE (%) and (**b**) MAPE (%) [156]. relative humidity, wind speed, hour of the day, and month of the year. The model was based on data from the MIDC official website dating back ten years. The suggested model outperformed ELM, RF, SVR, and LSTM in terms of accuracy. Subsequently, ref. [160] utilized six variants of RNN techniques, which are RNN, GRU, Content-Based Attention, Luong Attention, Self-Attention-Based RNN, and LSTM for solar radiation forecasting. A comparative analysis was conducted, using a 37-year dataset, between the two independent sites. The models were trained on historical time-series data of solar radiation with no exogenous characteristics, and forecasts were produced on several forecasting horizons. Based on the findings, it was revealed that the Attention Mechanism significantly improved memory-based RNN. Another work by [161] suggested a two-stage deep solar radiation forecasting model based on the CNN model to encode a frame from a sky-video to restore a full-sky image, and a two-tier LSTM model was used for monitoring. The model training was performed on a free dataset from two sites in Tucson, Arizona, and Golden, Colorado. Results showed that the suggested model can forecast a range of time ranges of 1-4 h in advance. An ensemble model of (XGBF-DNN) [162] was employed for hourly solar radiation forecasting by integrating Deep Neural Networks and Extreme Gradient Boosting Forest. The suggested hybrid model was evaluated at three sites in India, which were Gangtok, Jaipur, and Delhi, in terms of temperature, pressure, relative humidity, wind speed, clear sky index, wind direction, time of day, and month number. Figure 17 depicts the comparison analysis of the proposed model against benchmarks, such as smart persistence, SVR, RF, DNN, and XGBoost which resulted in superior performance at each site location during the winter, summer, monsoon, and autumn seasons. Recent work by [163] suggested a hybrid deep learning model that utilized both CNN and Bi-LSTM for excellent mid-term solar radiation forecasting. The CNN architecture captured available solar irradiance features, and Bi-LSTM manipulated the time-series data dependencies. The evaluation and comparison of the suggested model were performed against other established DL models at three different geographical areas within similar latitudes that received similar amounts of solar radiation. It was demonstrated that the hybrid DL model was durable and outperformed existing DL models for forecasting mid-term solar radiation in terms of accuracy.