Eur. Phys. J. Plus (2022) 137: 162
https://doi.org/10.1140/epjp/s13360-021-02263-5

Regular Article

New soft computing model for multi-hours forecasting of global solar radiation

¹ Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria
² Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement des Energies Renouvelables (CDER), 01000, Adrar, Algeria
³ Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, 10034, Tamanrasset, Algeria

^a gue.mawloud@gmail.com

Received: 18 September 2021
Accepted: 5 December 2021
Published online: 24 January 2022

Abstract

The growing development of photovoltaic technology has explored the role of effective solar irradiance forecasting in grid security and stability However, due to its non-stationary nature and complexity, make its estimating extremely difficult. The scope of this work is to deal with this issue by introducing a new machine learning forecasting architecture for multi-hours ahead in multiple-site in Algeria. Specifically, we proposed a new decomposition-based ensemble-forecasting model. The developed forecasting strategy based on a new multi-scale decomposition algorithm named Iterative Filtering (IF) used as a pre-processing stage of the historical solar radiation data combined with Gaussian Process Regression (GPR) as an essence predictor to build an IF-GPR model. Hourly global solar radiation data of two years from different cities with diverse solar radiation profiles are used to validate the full potential of the newly proposed IF-GPR model. The performance of the proposed IF-GPR is rigorously assessed utilizing effective metrics and comparing its performance with the reference model. The forecasting results demonstrate the potential of the hybridization IF-GPR methodology for multi-hour forecasting up to four hours ahead. The forecasting errors in terms of normalized root-mean-square error for four hours ahead are as follows: 0.7, 2.45, 5.496, and 9.76 for the Algiers site; 0.373, 1.34, 2.81 and 11.22 for the Ghardaia region, while the attained results for the Adrar site are equal to: 0.525, 1.36, 2.73, and 4.73. Furthermore, the proposed IF method outperforms the recently introduced decomposition algorithm, complete ensemble empirical mode decomposition with adaptive noise, in boosting the forecasting ability of a stand-alone model.