Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration
文献类型:期刊论文
作者 | Chen, Ji-Long1,2,3,4; Li, Guo-Sheng3,4; Wu, Sheng-Jun1,2 |
刊名 | ENERGY CONVERSION AND MANAGEMENT |
出版日期 | 2013-11-01 |
卷号 | 75页码:311-318 |
ISSN号 | 0196-8904 |
关键词 | Daily Solar Radiation Sunshine Duration Models Support Vector Machine Liaoning Province |
DOI | 10.1016/j.enconman.2013.06.034 |
英文摘要 | Estimation of solar radiation from sunshine duration offers an important alternative in the absence of measured solar radiation. However, due to the dynamic nature of atmosphere, accurate estimation of daily solar radiation has been being a challenging task. This paper presents an application of Support vector machine (SVM) to estimation of daily solar radiation using sunshine duration. Seven SVM models using different input attributes and five empirical sunshine-based models are evaluated using meteorological data at three stations in Liaoning province in China. All the SVM models give good performances and significantly outperform the empirical models. The newly developed model, SVM1 using sunshine ratio as input attribute, is preferred due to its greater accuracy and simple input attribute. It performs better in winter, while highest root mean square error and relative root mean square error are obtained in summer. The season-dependent SVM model is superior to the fixed model in estimation of daily solar radiation for winter, while consideration of seasonal variation of the data sets cannot improve the results for spring, summer and autumn. Moreover, daily solar radiation could be well estimated by SVM1 using the data from nearby stations. The results indicate that the SVM method would be a promising alternative over the traditional approaches for estimation of daily solar radiation. (C) 2013 Elsevier Ltd. All rights reserved. |
资助项目 | National Key Technology Research and Development Program[2012BAC21B01] ; Geological Survey program of China Geological Survey[GZH201200503] ; Special foundation for scientific research on public Interest[1212010611402] ; Special foundation for scientific research on public Interest[201111023] ; Chongqing Science and Technology Key project[cstc2012ggB20001] |
WOS研究方向 | Thermodynamics ; Energy & Fuels ; Mechanics ; Physics |
语种 | 英语 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:000326661400033 |
源URL | [http://119.78.100.138/handle/2HOD01W0/366] |
专题 | 生态过程与重建研究中心 |
作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 401122, Peoples R China 2.Chinese Acad Sci, Three Gorges Inst Ecol Environm, Chongqing 401122, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.China Geol Survey, Key Lab Coastal Wetland Biogeosci, Qingdao 266071, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Ji-Long,Li, Guo-Sheng,Wu, Sheng-Jun. Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration[J]. ENERGY CONVERSION AND MANAGEMENT,2013,75:311-318. |
APA | Chen, Ji-Long,Li, Guo-Sheng,&Wu, Sheng-Jun.(2013).Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration.ENERGY CONVERSION AND MANAGEMENT,75,311-318. |
MLA | Chen, Ji-Long,et al."Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration".ENERGY CONVERSION AND MANAGEMENT 75(2013):311-318. |
入库方式: OAI收割
来源:重庆绿色智能技术研究院
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