# 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 the LSTM Algorithm
They trained and tested the proposed model using data from a Canadian solar farm. Figure 11 depicts the forecast results for a variety of weather conditions, including cloudy sky, scattered clouds, clear sky, and few clouds, using the suggested model. In addition, it outperformed the SVR and FFNN. The LTSM model was employed by [131] for hourly solar irradiance forecasting in Johannesburg, South Africa. The LSTM network was trained based on solar radiation, sunlight duration, relative humidity, and temperature. The dataset available from the National Oceanic and Atmospheric Administration (NOAA) consists of a ten-year duration of weather data from 2009-2019. Simulation findings demonstrated that the proposed LSTM network performed satisfactorily compared to the SVR model with an nRMSE of 3.2%. Subsequently, ref. [132] suggested the implementation of two methods based on LSTM for novel solar irradiance forecasting model on an image-based dataset. In this study, different input features were assigned to achieve variations of 5 to 60 min in advance of solar irradiance forecasting. The first method focused on input features, such as solar irradiance 5 min before, current solar irradiance, and center value. In contrast, the second method focused on solar irradiance 5 min earlier, most recent solar irradiance, center value, variance value, the red-blue comparison method, and the three-step search method. Findings indicated that the LSTM model trained using the second method showed superior forecasting. Before that study, ref. [133] employed LTSM for hourly solar irradiation forecasting using historical and meteorological data from Kharagpur, India. Further, they intended to use  
Figure 11: (**a**) Solar irradiation forecasting in various weather situations. (**b**) Daily solar irradiation forecasting [130]. these data for solar PV power output forecasting. For validation purposes, the forecasting models used 15-year data available from 2000-2014 provided by NSRDB. The suggested model demonstrated superior performance compared to the ANN model, achieving an RMSE value of 57.249 W/m\({}^{2}\). Subsequently, ref. [134] utilized the LSTM network to forecast hourly GHI in Islamabad, Pakistan. The data were provided by the meteorological station at NUST Islamabad over 55 months from 2015-2020. The model training was performed on historical GHI data while considering all meteorological feature inputs, such as relative humidity, wind speed and direction, ambient temperature, DNI, and DHI. A recent study by [135] utilized a new variant of LSTM known as stacked LTSM integrated with principal component analysis (PCA) for solar irradiance forecasting on a 6-month dataset retrieved from a weather station at Morong, the Philippines. Among the input features considered were humidity, station height, wind speed, station temperature, absolute pressure, illuminance, sea level pressure, and ambient temperature. The proposed stacked LSTM model was compared to CNN, Bidirectional-LSTM, and other deep learning models and managed to achieve a performance of R\({}^{2}\) value 0.953 and MAE value 41.738 W/m\({}^{2}\). Previous work by [136] used a deep learning-based univariate LSTM model to conduct short-term solar irradiance forecasting. The model was trained using data on solar radiation collected over two years, starting on 1 January 2014, and originating from a solar monitoring station based at the University of Jaffma's Faculty of Engineering. The outcomes showed that the suggested model outperformed the ARIMA model. Ref. [137] utilized an LSTM for solar irradiance forecasting based on the range of forecasting horizons (intra-hour and intra-day). The dataset used to validate the model was retrieved from SURFRAD, which consists of data from seven stations located across the United States. The suggested approach outperformed typical machine learning models by 71.5%. For short-term forecasting, ref. [138] utilized the LSTM Neural Network model for solar irradiation forecasting.