Development of data-driven models for prediction of daily global horizontal irradiance in Northwest China

Abstract Global horizontal irradiance (Hg) data are the primary information for the design and evaluation of solar systems, which can generate clean thermal or electrical energy for agricultural and industrial production. However, Hg measurements are highly costly and time-consuming, which makes Hg data unavailable in most regions of the world. Therefore, numerous empirical and data-driven models have been established to predict Hg using other commonly available meteorological data. The present study proposed three data-driven models, i.e., extreme learning machine (ELM), random forests (RF) as well as the hybrid genetic algorithm and backpropagation neural networks (GANN), for accurate prediction of daily Hg in Northwest China. Daily minimum, maximum air temperature, sunshine duration and relative humidity were used to train and assess the models. The performance of the three data-driven models were also compared against two widely used empirical models (Angstrom–Prescott and Ogelman models). The results demonstrated that the estimated Hg had statistically significant correlations (P

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