An optimized deep learning based hybrid model for prediction of daily average global solar irradiance using CNN SLSTM architecture

Global horizontal irradiance prediction is essential for balancing the supply-demand and minimizing the energy costs for effective integration of solar photovoltaic system in electric power grid. However, its stochastic nature makes it difficult to get accurate prediction results. This study aims to develop a hybrid deep learning model that integrates a Convolutional Neural Network and Stacked Long Short-Term Memory (CNN-SLSTM) to predict the daily average global solar irradiance using real time meteorological parameters and daily solar irradiance data recorded in the study site. First, we have selected 14 significant relevant features from the dataset using recursive feature elimination techniques. The hyperparameters of the developed models are optimized using metaheuristic algorithm, a Slime Mould Optimization method. The efficacy of the model performance is evaluated using tenfold cross validation techniques. By using statistical performances metrics, the predictive performance of the developed model is compared with Gated Recurrent Unit, LSTM, CNN-LSTM, SLSTM and machine learning regressor models like Support Vector Machine, Decision Tree, and Random Forest. From the experimental results, the developed CNN-SLSTM model outperformed other models with a MSE, R² and Adj_R² of 0.0359, 0.9790 and 0.9789, respectively.