# 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  
The application of the LSTM algorithm in recent research into the development of solar irradiance forecasting models is justified according to its ability to address time series-based forecasting problems. Subsequently, the LSTM model was utilized by [65] to forecast solar irradiation 1 h in advance at three location sites in the USA, which were located in Hawaii, Atlanta, and New York. The input features that were used to generate the model were temperature, relative humidity, sun zenith angle, cloud type, perceptible water, dew point, wind direction, and clear sky index. According to their analysis, the suggested LSTM model exhibits an RMSE for the sites under consideration in the range of 45.84 and 41.37 W/m\({}^{2}\). In Seoul, South Korea, ref. [125] utilized LSTM to estimate hourly, daily, and annual solar irradiation. They used the Korea Meteorological Administration (KMA) database to obtain solar data from 2001 to 2017. The model data training was performed using the previous solar irradiance dataset. Following that approach, ref. [43] utilized an LSTM model for hourly solar irradiation forecasting of the following day. Input features considered included the month, hour, and days of the month; wind speed; relative humidity; visibility; dew point; weather conditions; and temperature. To validate the model, the dataset from the Measurement and Instrumentation Data Center (MIDC) was used. Table 2 shows that the proposed model performs better in terms of accuracy than other established models with an RMSE value of 76.245 W/m\({}^{2}\). Figure 10 emphasizes the comparison between real and LSTM forecasted solar irradiance. Recently, ref. [126] built a deep LSTM network for solar irradiance forecasting based on various horizons (3/6/24 h ahead) in the dry regions of India. Among the feature inputs considered were dew point, diffuse horizontal irradiance (DHI), global horizontal irradiance (GHI), direct normal irradiance (DNI), wind speed, pressure, temperature, wind direction, and relative humidity. A five-year dataset (2010-2014) from the NSRB in the Thar Desert was used to validate the model. The generated model had MAPE values ranging from 6.79 to 10.47%, which indicate excellent performance. Ref. [107] utilized satellite data to estimate solar irradiance 1 day in advance using an LSTM model. The model was tested \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Testing RMSE** \\ \hline Persistence & 209.2509 \\ LR & 230.9867 \\ BPNN & 133.5313 \\ LSTM & 76.245 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results for the proposed models using the MIDC dataset [43]. Figure 10: The comparison between actual irradiance and forecasted irradiance [43]. using data from 21 remote sensing sites, 16 of which were in mainland Europe, while 5 were in the United States. The input features considered were maximum temperature, minimum temperature, air pressure, cloud cover, specific humidity, and other meteorological data. According to the empirical results, the generated LSTM outperformed other persistent models tested with a forecast skill score of 52.2%. Recent work by [127] used LSTM for hourly solar irradiance forecasting for 1 day in advance. The input features considered were temperature, wind speed, sky cover, humidity, and precipitation. The dataset for training was retrieved from the Korea Meteorological Administration, which consists of weather variations recorded at different targeted site locations. It was shown that the model used can generate substantial forecasting with an RMSE of 30 W/m\({}^{2}\). Ref. [128] forecasted solar irradiance 1 h in advance using GRU and LSTM. They used a mono-variate technique, which forecasts solar irradiance using historical time series. Their proposed GRU and LSTM models were superior to other conventional machine learning models for solar irradiation forecasting. Another study by [129] forecasted hourly solar radiation utilizing a deep LSTM network.