基于MODIS遥感产品和神经网络模拟太阳辐射
Simulation of Solar Radiation Based on Neural Network and MODIS Remote Sensing Products
查看参考文献30篇
|
文摘
|
现有的神经网络模拟太阳辐射的模型很少考虑云、气溶胶、水汽对太阳辐射的影响,采用MODIS提供的气溶胶、云、水汽高空大气遥感产品和常规气象数据,输入LM(Levenberg-Marquardt)算法优化后的BP(Back-Propagation)神经网络模型(简称LM-BP)模拟了和田、西宁、固原、延安4个辐射站点的太阳辐射月均值。验证结果表明:神经网络模型中加入气溶胶、云、水汽之后,4个辐射站点的R~2均大于0.90,且各项误差指标均小于仅用常规气象站点数据模拟的太阳辐射结果。 |
|
其他语种文摘
|
Climate change is a major global issue of common concern of the international community, over the past century, the earth experienced a temperature rise, while solar radiation is an indicator of climate change. At the same time, solar radiation data is an important parameter about crop models, hydrological models and climate change models, many Artificial Neural Network ensemble models are developed to estimate solar radiation using routinely measured meteorolological variables, but it do not consider cloud, aerosol, and water vapor influence on solar radiation. In this article, we use cloud, aerosols, atmospheric precipitable water vapor from MODIS atmosphere remote sensing products and conventional meteorological data including air pressure, Air temperature, vapor pressure, relative humidity, and sunshine duration, and we analyze the relationship between solar radiation and meteorological data. In terms of conventional meteorological data, we make the selection of variables, the redundant variables are proposed. Then, BP artificial neural network model optimized by LM (Levenberg-Marquardt) algorithm (referred to as LM-BP) is used to stimulate solar radiation. This LM algorithm has fast local convergence feature about Gauss-Newton method, but also has global search feature about gradient descent method, which allows error along the direction of deterioration to search, and greatly improving the convergence rate and generalization ability of the network. Therefore, this article use LM-BP model to predict monthly mean daily global solar radiation from 2010 to 2013 about Hetian, Xining, Guyuan, Yan ' an radiating station using only conventional meteorological data(referred to as A) and using MODIS atmosphere remote sensing products binding conventional meteorological data(referred to as A +) respectively. Then, we validate performance of the model with measured data about radiation station. The results show that cloud amount, cloud optical thickness, aerosol optical depth, and atmospheric precipitable water vapor these factors are added to the established model, the degree of matching simulated solar radiation and actual observations is more higher. And correlation determination (R~2) for 4 radiation station are 0.90 or higher, while error indicators are small. This article showed that the use of LM-BP neural network model, combining with remote sensing data and conventional meteorological data to simulate solar radiation is a reasonable and effective way to simulate solar radiation. |
|
来源
|
地理科学
,2017,37(6):912-919 【核心库】
|
|
DOI
|
10.13249/j.cnki.sgs.2017.06.013
|
|
关键词
|
太阳辐射
;
MODIS
;
神经网络
;
LM-BP
;
云
;
气溶胶
;
水汽
|
|
地址
|
西北师范大学地理与环境科学学院, 甘肃, 兰州, 730070
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-0690 |
|
学科
|
大气科学(气象学) |
|
基金
|
国家自然科学基金项目
;
西北师范大学青年教师科研能力提升计划项目
|
|
文献收藏号
|
CSCD:6015787
|
参考文献 共
30
共2页
|
|
1.
Lu Ning. A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data.
Energy,2011,36(5):3179-3188
|
CSCD被引
3
次
|
|
|
|
|
2.
施国萍. 中国三种太阳辐射起始数据分布式模拟.
地理科学,2013,33(4):385-392
|
CSCD被引
7
次
|
|
|
|
|
3.
查良松. 我国地面太阳辐射量的时空变化研究.
地理科学,1996,16(3):41-46
|
CSCD被引
5
次
|
|
|
|
|
4.
赵东. 我国近50年来太阳直接辐射资源基本特征及其变化.
太阳能学报,2009,30(7):946-952
|
CSCD被引
25
次
|
|
|
|
|
5.
曾燕. 起伏地形下黄河流域太阳直接辐射分布式模式.
地理学报,2005,60(4):680-688
|
CSCD被引
27
次
|
|
|
|
|
6.
Chen Jilong. Estimation of monthly-mean global solar radiation using MODIS atmospheric product over China.
Journal of Atmospheric and Solar-Terrestrial Physics,2014,110:63-80
|
CSCD被引
3
次
|
|
|
|
|
7.
Sun Huaiwei. Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables.
Energy Conversion and Management,2015,105:880-890
|
CSCD被引
7
次
|
|
|
|
|
8.
Teke A. Evaluation and performance comparison of different models for the estimation of solar radiation.
Renewable and Sustainable Energy Reviews,2015,50:1097-1107
|
CSCD被引
8
次
|
|
|
|
|
9.
Wu Ji. Prediction of solar radiation with genetic approach combing multi-model framework.
Renewable Energy,2014,66:132-139
|
CSCD被引
3
次
|
|
|
|
|
10.
Yao Wanxiang. New decomposition models to estimate hourly global solar radiation from the daily value.
Solar Energy,2015,120:87-99
|
CSCD被引
5
次
|
|
|
|
|
11.
Manzano A. A single method to estimate the daily global solar radiation from monthly data.
Atmospheric Research,2015,166:70-82
|
CSCD被引
4
次
|
|
|
|
|
12.
和清华. 我国太阳总辐射气候学计算方法研究.
自然资源学报,2010,25(2):308-319
|
CSCD被引
48
次
|
|
|
|
|
13.
Shamim M A. An improved technique for global solar radiation estimation using numerical weather prediction.
Journal of Atmospheric and Solar-Terrestrial Physics,2015,129:13-22
|
CSCD被引
3
次
|
|
|
|
|
14.
周开利.
神经网络模型及其MATLAB仿真程序设计,2005
|
CSCD被引
196
次
|
|
|
|
|
15.
Lam J C. Solar radiation modelling using ANNs for different climates in China.
Energy Conversion and Management,2008,49(5):1080-1090
|
CSCD被引
1
次
|
|
|
|
|
16.
Wan K K W. An analysis of thermal and solar zone radiation models using an Angstrom-Prescott equation and artificial neural networks.
Energy,2008,33(7):1115-1127
|
CSCD被引
6
次
|
|
|
|
|
17.
Ozgoren M. Estimation of global solar radiation using ANN over Turkey.
Expert Systems with Applications,2012,39(5):5043-5051
|
CSCD被引
5
次
|
|
|
|
|
18.
Senkal O. Modeling of solar radiation using remote sensing and artificial neural network in Turkey.
Energy,2010,35(12):4795-4801
|
CSCD被引
2
次
|
|
|
|
|
19.
曹双华. 小波分析在太阳辐射神经网络预测中的应用研究.
东华大学学报:自然科学版,2004,30(6):18-22
|
CSCD被引
5
次
|
|
|
|
|
20.
李净. 利用LM-BP神经网络估算西北地区太阳辐射.
干旱区地理,2015,38(3):438-445
|
CSCD被引
7
次
|
|
|
|
|
了解气象卫星更多信息，请访问