A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data
文献类型：SCI/SSCI论文
| 作者 | Lu N. ; Qin J. ; Yang K. ; Sun J. L. |
| 发表日期 | 2011 |
| 关键词 | Global solar radiation Artificial neural network Geostationary satellite Data compression artificial neural-network sunshine duration quality-control data sets surface retrieval models validation simulation irradiance |
| 英文摘要 | Surface global solar radiation (GSR) is the primary renewable energy in nature. Geostationary satellite data are used to map GSR in many inversion algorithms in which ground GSR measurements merely serve to validate the satellite retrievals. In this study, a simple algorithm with artificial neural network (ANN) modeling is proposed to explore the non-linear physical relationship between ground daily GSR measurements and Multi-functional Transport Satellite (MTSAT) all-channel observations in an effort to fully exploit information contained in both data sets. Singular value decomposition is implemented to extract the principal signals from satellite data and a novel method is applied to enhance ANN performance at high altitude. A three-layer feed-forward ANN model is trained with one year of daily GSR measurements at ten ground sites. This trained ANN is then used to map continuous daily GSR for two years, and its performance is validated at all 83 ground sites in China. The evaluation result demonstrates that this algorithm can quickly and efficiently build the ANN model that estimates daily GSR from geostationary satellite data with good accuracy in both space and time. (C) 2011 Elsevier Ltd. All rights reserved. |
| 出处 | Energy |
| 卷 | 36 |
| 期 | 5 |
| 页 | 3179-3188 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0360-5442 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/23355]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Lu N.,Qin J.,Yang K.,et al. A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data. 2011. |
入库方式： OAI收割
来源：地理科学与资源研究所
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