Predicting daily global solar radiation in various climatic regions of China based on hybrid support vector machines with meta-heuristic algorithms. (20th January 2023)

• Record Type: Journal Article
• Authors: Wu, Zongjun; Cui, Ningbo; Gong, Daozhi; Zhu, Feiyu; Li, Yanling; Xing, Liwen; Wang, Zhihui; Zhu, Bin; Chen, Xi; Wen, Shengling; Zha, Yuxuan
• Is Part Of: Journal of cleaner production. Volume 385(2023)
• Publication Date: 2023-01-20
• Subjects: Global solar radiation -- Intelligent learning model -- Ant colony optimization -- Cuckoo search -- Grey wolf optimization -- Hybrid models; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.135589
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Accurate prediction of global solar radiation (Rs ) is vital for investment decisions and solar energy distribution. In this study, three hybrid models (ACO-SVM, CS-SVM, and GWO-SVM) based on ant colony optimization (ACO), cuckoo search (CS) and grey wolf optimization (GWO) algorithms were proposed to optimize support vector machine (SVM) for predicting Rs in four climate zones of China (temperate continental zone TCZ, mountain plateau zone MPZ, temperate monsoon zone TMZ, and subtropical monsoon zone SMZ). They were compared with the standalone backpropagation neural network model, decision tree, and support vector machines. The results demonstrated that among the standalone models, support vector machines performed best with the highest accuracy in Rs estimation in each climate zone of China, followed by the decision tree and backpropagation neural network models, with a coefficient of determination (R 2 ) in 0.707–0.882, 0.694–0.881, and 0.681–0.850, respectively. In contrast, the hybrid models exhibited higher accuracy than standalone support vector machines in four climatic regions of China, with the coefficient of determination (R 2 ) increasing by 5.361%, 5.476%, 7.382%, and 10.965%, respectively. Among hybrid models, GWO-SVM performed better than CS-SVM, and both had higher accuracy than ACO-SVM, with the coefficient of determination (R 2 ) in 0.809–0.927, 0.804–0.926, and 0.793–0.930, respectively. Therefore, the hybrid models (ACO-SVM, CS-SVM, and GWO-SVM), especially GWO-SVM and CS-SVM, can significantly improve the accuracy for predicting Rs in various regions of China. Highlights: For standalone models, the SVM had higher prediction accuracy than DT and BP. Three hybrid models (ACO-SVM, CS-SVM and GWO-SVM) were proposed to predict daily Rs in different climatic zones of China. The accuracy of hybrid models was higher than the standalone SVM model. The GWO-SVM model performed better than the CS-SVM model, and the accuracy of both were better than ACO-SVM model.