Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model. (April 2016)

• Record Type: Journal Article
• Authors: Shamshirband, Shahaboddin; Mohammadi, Kasra; Khorasanizadeh, Hossein; Yee, Por Lip; Lee, Malrey; Petković, Dalibor; Zalnezhad, Erfan
• Is Part Of: Renewable & sustainable energy reviews. Volume 56(2016:Apr.)
• Page Start: 428
• Page End: 435
• Publication Date: 2016-04
• Subjects: Diffuse solar radiation -- Support vector machine -- Wavelet transform algorithm -- Combined model -- Estimation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2015.11.055
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Diffuse solar radiation is a fundamental parameter highly required in several solar energy applications. Despite its significance, diffuse solar radiation is not measured in many locations around the world due to technical and fiscal limitations. On this account, determining the amount of diffuse radiation alternatively based upon precise and reliable estimating methods is indeed essential. In this paper, a coupled model is developed for estimating daily horizontal diffuse solar radiation by integrating the support vector machine (SVM) with wavelet transform (WT) algorithm. To test the validity of the coupled SVM–WT method, daily measured global and diffuse solar radiation data sets for city of Kerman situated in a sunny part of Iran are utilized. For the developed SVM–WT model, diffuse fraction (cloudiness index) is correlated with clearness index as the only input parameter. The suitability of SVM–WT is evaluated against radial basis function SVM (SVM–RBF), artificial neural network (ANN) and a 3rd degree empirical model established for this study. It is found that the estimated diffuse solar radiation values by the SVM–WT model are in favourable agreements with measured data. According to the conducted statistical analysis, the obtained mean absolute bias error, root mean square error and correlation coefficient are 0.5757 MJ/m 2, 0.6940 MJ/m 2 and 0.9631, respectively. While for the SVM–RBF ranked next the attained values are 1.0877 MJ/m 2, 1.2583 MJ/m 2 and 0.8599, respectively. In fact, the study results indicate that SVM–WT is an efficient method which enjoys much higher precision than other models, especially the 3rd degree empirical model.