Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

作     者：Khaled Ferkous Farouk Chellali Abdalah Kouzou Belgacem Bekkar 

作者机构：LAADI LaboratoryDepartment of Electrical EngineeringZian Achour UniversityDjelfaAlgeria University of GhardaiaGhardaiaAlgeria 

出 版 物：《Clean Energy》 (清洁能源（英文）)

年 卷 期：2021年第5卷第2期

页      面：316-328页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Gaussian process regression wavelets hybrid models forecasting solar radiation solar measurements Ghardaia 

摘      要：Forecasting solar radiation is fundamental to several domains related to renewable energy where several methods have been used to predict daily solar radiation,such as artificial intelligence and hybrid ***,the Gaussian process regression(GPR)algorithm has been used successfully in remote sensing and Earth *** this paper,a wavelet-coupled Gaussian process regression(W-GPR)model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia(Algeria).For this purpose,3 years of data(2013-15)have been used in model training while the data of 2016 were used to validate the *** this work,different types of mother wavelets and different combinations of input data were evaluated based on the minimum air temperature,relative humidity and extraterrestrial solar radiation on a horizontal *** results demonstrated the effectiveness of the new hybrid W-GPR model compared with the classical GPR model in terms of root mean square error(RMSE),relative root mean square error(rRMSE),mean absolute error(MAE)and determination coefficient(R^(2)).