A review on global solar radiation prediction with machine learning models in a comprehensive perspective

Global solar radiation information is the basis for many solar energy utilizations as well as for economic and environmental considerations. However, because solar-radiation changes, and measurements are sometimes not available, accurate global solar-radiation data are often difficult or impossible to obtain. Machine-learning models, on the other hand, are capable of conducting highly nonlinear problems. They have many potential applications and are of high interest to researchers worldwide. Based on 232 paper regarding to the machinelearning models for global solar radiation prediction, this paper provides a comprehensive and systematic review of all important aspects surrounding machine-learning models, including input parameters, feature selection and model development. The pros and cons of three input-parameter sources (observation data from a surface meteorological observation station, satellite-based data, numerical weather-predicting re-analyzed data) and three feature selection methods (filter, wrapped, embedded) are reviewed and analyzed in this paper. Using data pre-processing algorithms, output ensemble methods, and model purposes, seven classes of machinelearning models are identified and reviewed. Finally, the state of current and future research on machinelearning models to forecast the global solar radiation are discussed. This paper provides a compact guide of existing model modification and novel model development regarding predicting global solar radiation.