Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions

Due to the advancement and wide adoption/application of solar-based technologies, the prediction of solar irradiance has attracted research attention in recent years. In this study, the predictive performance of machine learning models is compared with that of deep learning models for both global solar radiation (GSR) and diffuse solar radiation (DSR) prediction. Different studies have proposed the use of different models for solar radiation prediction. While some used machine learning models, the use of deep learning algorithms were considered by others. Although these algorithms were concluded to be appropriate for solar radiation prediction, variation in their performances brings about an intriguing quest to compare and determine the most appropriate algorithm. The three most common deep learning models in the literature namely; artificial neural network, convolutional neural network, and recurrent neural network (RNN) are considered within the scope of this study. Also, two traditional machine learning models namely polynomial regression and support vector regression (SVR) is considered as well as an ensemble machine learning model called random forest. These models have been applied to four different locations in Nigeria and the typical meteorological year data for 12 years in an hourly time step was used to train/test the model developed. Results from this study show that deep learning models have a better GSR and DSR prediction accuracy in comparison to machine learning models. However, the duration for training and testing the machine learning models (except SVR) is shorter than that of deep learning models making it more desirable for low computational applications. The application of RNN for GSR prediction in Yobe (with an r value of 0.9546 and root means square error/mean absolute error of 82.22 W/m(2)/36.52 W/m(2)) had the overall best model performance of all the models developed in this study. This study contributes to the existing literature in this field as it highlights the disparities between machine learning and deep learning algorithms application for solar radiation forecast.