# 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  
Recently, Ref. [153] suggested an integration of CNN and LSTM to establish a framework for forecasting solar irradiance based on Clear-Sky Index that was 1-h ahead. The suggested model was trained on historical solar radiation data and does not consider any outside influences. The model was trained on the NREL solar dataset from Oahu, which consisted of 20 months of data (March 2010 to October 2011). The developed model outperformed the considered baseline models, as evidenced by variations in forecast skill scores of 7.4 to 41%. Before that study, ref. [154] also integrated the CNN and LSTM network (ConvLSTM) for hourly solar irradiation forecasting in the Korean Peninsula. The dataset consisted of 1100 photos taken in a row from 1 April 2011 to 31 December 2015. The simulations observed that the ConvLSTM model generated a higher forecasting accuracy than ANN and RF, with an R\({}^{2}\) value of 0.895 and an RMSE value of 71.334 W/m\({}^{2}\). An innovative hybrid deep architecture based on (ResNet) and LSTM was developed by [155] for short-time solar irradiance forecasting. The model was trained on an eight-year dataset (from 2000 to 2017) available from the NSRDB. The ResNet-LSTM hybrid model lowered forecasting errors by 52.44 and 17.07%, respectively, more than standalone ResNet and LSTM. Furthermore, [102] integrated the use of LSTM and CNN algorithms to extract spatial and temporal information in sequence. The meteorological data from 23 different sites in California were used for model training, such as DHI, GHI, temperature, relative humidity, cloud cover, and precipitation. The model was tested over a whole year, through all four seasons, and under three various types of meteorological conditions. The forecasts made using the developed LSTM-CNN model are more accurate than individual models, like SP, CNN, LSTM, ANN, SVM, and others. In Alice Springs, Australia, [156] proposed an integration of CNN and LSTM (CLSTM) for both short- and long-term forecasting horizons of the half-hourly solar irradiation forecasting model. The model was evaluated for daily, weekly, and n-month (n = 1 to 8) solar irradiance forecasting horizons using data from a 12-year and and eight-month period (from 1 January 2006 to 31 August 2018). The recommended model obtained a higher percentage, which denoted satisfactory performance. As seen in Figure 16, the CLSTM hybrid model in 1-day forecasting for RMSE, MAE, MAPE, and RMSE obtained a higher percentage, which denoted satisfactory performance. Ref. [67] used historical time series solar irradiance data to create a new hybrid deep learning model of LSTM, CNN, and wavelet decomposition for day-ahead forecasting of solar irradiance in North Carolina, USA. The model was validated using the partitioned data (into four different weather categories) obtained from NREL and the NOAA Earth System Research Laboratory at two different locations: Desert Rock Station and Elizabeth City State University. Recent work by [157] suggested a new hybrid model using both MLP networks and LSTM that uses two-branch inputs for hour-ahead solar irradiance forecasting. The dataset used is a time series of historical solar irradiation and auxiliary inputs. Findings showed superior performance compared to other machine learning models, with performances of SVM, BPNN, Random Forest, RNN, and LSTM improved by 19.31, 19.19, 11.68, 20.15, and 13.48%, respectively. For hourly GHI forecasting, [PERSON] [102] developed a hybrid deep learning model that made use of both the LSTM algorithm and the CNN algorithm to extract spatial and temporal information in sequence. The model was trained using the GHI data, in addition to meteorological data from 23 different sites across the state of California, including GHI, temperature, relative humidity, cloud cover, and precipitation data. The model was tested over a whole year, through all four seasons, and under the three types of meteorological conditions.