Potential of kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support for predicting daily global solar radiation in humid regions

Abstract The knowledge of global solar radiation is of vital importance for the design and use of solar energy systems. This study evaluated the potential of two new powerful machine learning models, i.e., kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support, for accurately estimating daily global solar radiation in humid regions. These two models were also compared with the multilayer perceptron, M5 model tree, random forest and multivariate adaptive regression spline models, using five input combinations of daily meteorological data during 2001–2015 from four weather stations in the (sub)tropical humid regions of South China. The results showed that, when lack of complete meteorological data, machine learning models using the ratio of actual and theoretical sunshine duration, maximum and minimum temperatures obtained satisfactory daily global solar radiation estimates. Generally, the kernel-based nonlinear extension of Arps decline model offered the best prediction accuracy among the studied models, followed by the gradient boosting with categorical features support. The multilayer perceptron model exhibited the smallest average percentage increase in the root mean square error during testing over the training values, followed by the kernel-based nonlinear extension of Arps decline model. Both the kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support were successfully applied to develop general models for daily global solar radiation prediction (differences in root mean square error

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