Global solar radiation modeling using different machine learning and empirical models in Northeast China

Reliable global solar radiation ( R s ) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, R s measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate R s , this study developed a novel machine learning model, called a Gaussian exponential model (GEM), for daily global R s estimation. The GEM was compared with four other machine learning models and two empirical models to assess its applicability using daily meteorological data from 1997–2016 from four stations in Northeast China. The results showed that the GEM with complete inputs had the best performance. Machine learning models provided better estimates than empirical models when trained by the same input data. Sunshine duration was the most effective factor determining the accuracy of the machine learning models. Overall, the GEM with complete inputs had the highest accuracy and is recommended for modeling daily R s in Northeast China.