Development of a hybrid computational intelligent model for daily global solar radiation prediction

Providing an accurate and reliable solar radiation prediction is highly significant for optimal design and management of thermal and solar photovoltaic systems. It is massively essential for producing renewable and clean energy production. In the current research, an exploration of newly developed computation intelligent models based on the hybridization of Covariance Matrix Adaptive Evolution Strategies (CMAES) with Extreme Gradient Boosting (XGB) and Multi-Adaptive Regression Splines (MARS) models for building robust predictive models for daily scale solar radiation. The proposed hybrid models are examined at four meteorological stations (i.e., Boromo, Dori, Gaoua, and Po) located in the Burkina Faso region, Sub-Sahara Africa. The prediction matrix is constructed using several related climate parameters. Based on the statistical and graphical evaluation, the proposed models demonstrated an outstanding predictability performance. The computational experiments showed that the proposed framework consistently enhanced prediction accuracy by 17.2%, 4.9%, 39.8%, and 44.5%, at Boromo, Dori, Gaoua, and Po stations using the Root Mean Squared Error over the testing phase against the existing methods proposed in the literature. The resulting method has the potential to be used as a surrogate tool for solar radiation prediction on the stations. The findings of this study contribute to a growing body of literature towards advancing the study of machine learning methods for predicting renewable energy production.

Automated summary

In this research, a hybrid model combining Covariance Matrix Adaptive Evolution Strategies (CMAES) with Extreme Gradient Boosting (XGB) and Multi-Adaptive Regression Splines (MARS) was developed to predict daily solar radiation. The model was tested at four meteorological stations in Burkina Faso and showed outstanding predictability performance, achieving significantly higher accuracy compared to existing methods.