Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model

Optimal utilisation of the sun's freely available energy to generate electricity requires efficient predictive models of global solar radiation (GSR). These are necessary to provide solar energy companies an early and effective market entry to support renewable energy integration into electrical grids. We propose a hybrid deep learning CNN-REGST method where a Convolutional Neural Network is integrated with a dual-stage Stacked Regression (Level-O Learner and Level-O predictor) followed by a Support Vector Machine (Level-1 Learner) with its hyperparameters optimised using the HyperOpt function to predict the daily GSR with high accuracy. Six solar energy farms in Queensland, Australia, are selected as testing sites and the predictive features from Global Climate Models and observations, derived using marine predator algorithm, are employed to build the CNN-REGST prediction model. We include a feature selection process based on meta-heuristic methods to select the optimal predictors used as inputs for the resulting CNN-REGST model. Our hybrid model is rigorously evaluated to analyze its performance over a yearlong, and all four season data. We also compare the proposed CNN-REGST model with several deep learning (i.e., CNN, Long-term Short-term Memory Network LSTM, Deep Neural Network DNN) and conventional ML approaches (Extreme Learning Machine ELM, Stacked Regression REGST, Random Forest Regression RFR, Gradient Boosting Machine GBM, Multivariate Adaptive Regression Splines MARS) using the same test datasets. The simulations carried out show that the proposed hybrid model is significantly accurate in GSR predictions compared with the deep learning and the ML models as well as a commonly used persistence model. We conclude that the CNN-REGST prediction model could be a useful scientific ploy incorporated in modern solar energy monitoring technologies to utilize a greater proportion of sustainable energy resources captured from the sun into consumer electricity for conventional-renewable hybrid energy grid systems.