Assessing the transferability of support vector machine model for estimation of global solar radiation from air temperature
Jilong Chen,Guo-Sheng Li,Bei-Bei Xiao,Zhaofei Wen,Ming-Quan Lv,Chundi Chen,Yi Jiang,Xiaoxiao Wang,Shengjun Wu +8 more
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TL;DR: In this article, the authors evaluated the transferability of support vector machines (SVM) for estimation of solar radiation in subtropical zone in China, and showed that solar radiation at one site (estimation site) could be well estimated by SVM model developed at another site (source site), the accuracy of estimation is affected by the distance and temperature difference between two sites, and altitude of source site.
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About: This article is published in Energy Conversion and Management. The article was published on 01 Jan 2015. and is currently open access. The article focuses on the topics: Altitude.
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Figures
Table 2 RMSE (MJ m 2) of SVM models.a 
Fig. 2. Relation between RMSE of SVM (MJ m 2) and the distance between the estimation site and source site. 
Table 1 Detail information of the studied meteorological stations. 
Fig. 1. Location of the studied meteorological stations in China’s subtropical zone (stations are numbered in compliance with Table 1). 
Fig. 3. Spatial variation of determination correlation between RMSE (MJ m 2) of SVM and distance between source site and estimation site. 
Fig. 7. Relation between RMSE (MJ m 2) at the estimation sites where the altitude are h 200 m but lower than that of the estimation site.
Citations
•Book Chapter
Support vector machines: Theory and applications
Theodoros Evgeniou,Massimiliano Pontil +1 more
- 01 Dec 2001
TL;DR: In this article, a summary of the issues discussed during the one day workshop on SVM Theory and Applications organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece is presented.
549
Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China
Junliang Fan,Xiukang Wang,Lifeng Wu,Hanmi Zhou,Fucang Zhang,Xiang Yu,Xianghui Lu,Youzhen Xiang +7 more
TL;DR: Wang et al. as discussed by the authors proposed two machine learning algorithms, i.e., Support Vector Machine (SVM) and a novel simple tree-based ensemble method named Extreme Gradient Boosting (XGBoost), for accurate prediction of daily H using limited meteorological data.
539
Application of support vector machine models for forecasting solar and wind energy resources: A review
TL;DR: In this article, a hybrid support vector machine (SVM) model was proposed to forecast both solar and wind energy resources for most of the locations in the United States, where the authors highlighted main problems, opportunities and future work in this research area.
525
Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China
Junliang Fan,Lifeng Wu,Lifeng Wu,Fucang Zhang,Huanjie Cai,Wenzhi Zeng,Xiukang Wang,Haiyang Zou +7 more
TL;DR: In this article, the performance of 12 empirical model forms and 12 machine learning algorithms for estimating daily solar radiation (Rs) were further evaluated in different climatic zones of China as a case study, i.e., the temperate continental zone (TCZ), temperate monsoon zone (TMZ), mountain plateau zone (MPZ) and (sub)tropical monsoon zones (SMZ).
255
A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation
Kasra Mohammadi,Shahaboddin Shamshirband,Chong Wen Tong,Muhammad Arif,Dalibor Petković,Sudheer Ch +5 more
TL;DR: In this paper, a new hybrid approach by combining the Support Vector Machine (SVM) with Wavelet Transform (WT) algorithm is developed to predict horizontal global solar radiation for both daily and monthly mean scales for an Iranian coastal city.
253
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