A Beta Regression Model for Improved Solar Radiation Predictions

Abstract

redicting global solar radiation is an integral part of much environmental modeling. There are several approaches for predicting global solar radiation at a site where no instrumentation exists. One popular approach uses the difference between daily high and low temperature, typically using a nonlinear equation to express the relationship between change in temperature and estimated global solar radiation. Additional variables are usually included in successive steps creating a hierarchy of analysis. The authors propose an alternative beta regression approach to modeling global solar radiation, allowing for the inclusion of multiple environmental predictor variables and strata into one flexible model. The model is applied to several case studies, and results are compared with recently proposed empirical solar radiation models. Beta regression provides a robust, flexible modeling approach for predicting global solar radiation that allows for the addition and removal of independent variables as appropriate and can be interpreted using standard inferential statistics. In addition, the beta regression model provides estimates of uncertainty that can be incorporated into subsequent models and calculations.