Hybrid models for global solar radiation prediction: a case study

ABSTRACT

This paper presents a comparison between different prediction models for solar radiation application. The present study assessed the performance of multi-layer perceptron (MLP) as well as boosted decision tree, and used a new combinition of these models with linear regression for the prediction of daily global solar irradiation (DGSR). The performance of the studied models was validated using a real dataset measured at the Applied Research Unit for Renewable Energies (URAER) situated in the south of Algeria. Different input combinations have been analysed in order to select the relevant input parameters for DGSR prediction. The results acheived show that the MLP model perfoms better than the others models in terms of statistical indicators: normalised root mean square error (0.033) and R² (97.7%).

KEYWORDS:

• Global solar radiation
• prediction
• multi-layer perceptron
• boosted decision tree
• artificial neural networks

Disclosure statement

No potential conflict of interest was reported by the authors.