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Remote Sensing Technology and Application  2020, Vol. 35 Issue (3): 615-622    DOI: 10.11873/j.issn.1004-0323.2020.3.0615
    
Simulation of Solar Radiation based on Three Machine Learning Methods
Jing Li(),Songnan Wen()
The College of Geographical and Environmental Science, Northwest Normal University, Lanzhou 730070, China
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Abstract  

Quantitative simulation of solar radiation is essential for understanding climate change 0n the Loess Plateau, Many machine learning methods were developed to estimate solar radiation well, but different machine learning methods have different simulation accuracy in different regions, In order to achieve optimal simulation of solar radiation on the Loess Plateau, this provides more higher precision solar radiation data for crop models, hydrological models, and climate change models. In this study, three machine learning methods, including Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), were applied to estimate solar radiation on the Loess Plateau, three machine learning methods were trained using ground measurements at fourteen radiation sites from 2003 to 2009 and ten radiation sites from 2010 to 2016 and corresponding parameter pressure, cloud fraction, cloud optical thickness, ozone, precipitation water vapor and DEM, slope, and aspect to train the three model, The solar radiation estimates based on three machine learning methods were evaluated using ground measurements at four radiation sites from 2010 to 2016. The validation results show that the RF model has the best simulation effect on the Loess Plateau and surrounding areas. The average deviation is -0.17 MJ·m-2, the root mean square error is 1.48 MJ·m-2, and has a good fit of 0.96. The results show that combined RF model and meteorological data and remote sensing data can effectively solve the problem about solar radiation simulation on the non-radiation observation area of the Loess Plateau, which is of great significance to the research of regional solar radiation.

Key words:  Solar radiation      Random Forest(RF)      Artificial Neural Network(ANN)      Support Vector Machine(SVM)      Remote sensing     
Received:  04 June 2019      Published:  10 July 2020
ZTFLH:  P422.1  
Corresponding Authors:  Songnan Wen     E-mail:  li_jinger@163.com;18893111471@163.com
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Jing Li
Songnan Wen

Cite this article: 

Jing Li,Songnan Wen. Simulation of Solar Radiation based on Three Machine Learning Methods. Remote Sensing Technology and Application, 2020, 35(3): 615-622.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.3.0615     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I3/615

Fig.1  Radiation sites in Loess Plateau
训练误差验证误差
模型参数MBE/MJ·m-2RMSE/MJ·m-2RMBE/MJ·m-2RMSE/MJ·m-2R
RF最大特征数8、最大深度100.252.430.92-0.171.480.96
SVM核函数为RBF、gamma为0.02、C为100000.162.660.841.552.630.92
ANN训练函数为trainlm、神经网络结构为8-20-1-1.344.270.73-1.144.010.75
Table1  Parameter、training and verification error of Model
Fig.2  Comparision between observed and measured value of monthly average solar radiation
Fig.3  Verification error of simulation
1 Sun H, Zhao N, Zeng X, et al. Study of Solar Radiation Prediction and Modeling of Relationships between Solar Radiation and Meteorological Variables[J]. Energy Conversion and Management, 2015, 105: 880-890. .
doi: 10.1016/j.enconman.2015.08.045
2 Chen J L, Li G S, Wu S J. Assessing the Potential of Support Vector Machine for Estimating Daily Solar Radiation Using Sunshine Duration[J]. Energy Convers Manage, 2013,75:311-318.
3 Shi Guoping, Qiu Xinfa, Zeng Yan. Distributed Simulation of Three Solar Radiation Starting Data in China[J]. Geographical Science, 2013, 33(4): 385-392.
3 施国萍, 邱新法, 曾燕. 中国三种太阳辐射起始数据分布式模拟[J]. 地理科学, 2013, 33(4): 385-392.
4 Li Weiwei, Letu Husi, Chen Hongbin. Calculation of Surface Solar Radiation Under Different Cloud Conditions Using MODIS Data[J]. Remote Sensing Technology and Application, 2017,32(4): 643-650.
4 黎微微, 胡斯勒图, 陈洪滨. 利用 MODIS 资料计算不同云天条件下的地表太阳辐射[J]. 遥感技术与应用, 2017,32(4): 643-650.
5 Journée M, Bertrand C. Improving the Spatio-temporal Distribution of Surface Solar Radiation Data by Merging Ground and Satellite Measurements[J]. Remote Sensing of Environment, 2010, 114(11): 2692-2704.
6 Zhang T, Stackhouse Jr P W, Gupta S K, et al. The Validation of the GEWEX SRB Surface Shortwave Flux Data Products Using BSRN Measurements: A Systematic Quality Control, Production and Application Approach[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2013, 122: 127-140.
7 Huang G, Liang S, Lu N, et al. Toward a Broadband Parameterization Scheme for Estimating Surface Solar Irradiance: Development and Preliminary Results on MODIS Products[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(21): 12180-12193.
8 Gueymard C A, Myers D R. Evaluation of Conventional and High-performance Routine Solar Radiation Measurements for Improved Solar Resource, Climatological Trends, and Radiative Modeling[J]. Solar Energy, 2009, 83(2): 171-185.
9 Angstrom A. Solar and Terrestrial Radiation. Report to the International Commission for Solar Research on Actinometric Investigations of Solar and Atmospheric Radiation[J]. Quarterly Journal of the Royal Meteorological Society, 1924, 50(210): 121-126.
10 Li R, Zhao L, Wu T, et al. Temporal and Spatial Variations of Global Solar Radiation over the Qinghai-Tibetan Plateau during the Past 40 Years[J]. Theoretical and Applied Climatology, 2013, 113(3-4): 573-583.
11 Mummadisetty B C, Puri A, Sharifahmadian E, et al. A hybrid Method for Compression of Solar Radiation Data Using Neural Networks[J]. International Journal of Communications, Network and System Sciences, 2015, 8(6): 217. .
doi: 10.4236/ijcns.2015.86022
12 Belaid S, Mellit A. Prediction of Daily and Mean Monthly Global Solar Radiation Using Support Vector Machine in an Arid Climate[J]. Energy Conversion and Management, 2016, 118: 105-118.
13 Chen R, Ersi K, Yang J, et al. Validation of Five Global Radiation Models with Measured Daily Data in China[J]. Energy Conversion and Management, 2004, 45(11-12): 1759-1769.
14 Peng Z, Letu H, Wang T, et al. Estimation of Shortwave Solar Radiation Using the Artificial Neural Network from Himawari-8 Satellite Imagery over China[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2020, 240: 106672.
15 Zhu X, Qiu X, Zeng Y, et al. A Remote Sensing Model to Estimate Sunshine Duration in the Ningxia Hui Autonomous Region, China[J]. Journal of Meteorological Research, 2015, 29(1): 144-154.
16 Sun H, Gui D, Yan B, et al. Assessing the Potential of Random Forest Method for Estimating Solar Radiation Using Air Pollution Index[J]. Energy Conversion and Management, 2016, 119: 121-129.
17 Benali L, Notton G, Fouilloy A, et al. Solar Radiation Forecasting Using Artificial Neural Network and Random Forest Methods: Application to Normal Beam, Horizontal Diffuse and Global Components[J]. Renewable Energy, 2019, 132: 871-884.
18 Liang Yitong, Liu Kequn, Xia Zhihong. Estimation of Solar Radiation Using FY-2C Satellite Data[J]. Meteorological Science and Technology, 2009, 37(2): 234-238.
18 梁益同, 刘可群, 夏智宏. 利用FY-2C卫星资料估算太阳辐射研究[J]. 气象科技, 2009, 37(2):234-238.
19 Deo R C, Şahin M. Forecasting Long-term Global Solar Radiation with an ANN Algorithm Coupled with Satellite-derived (MODIS) Land Surface Temperature (LST) for Regional Locations in Queensland[J]. Renewable and Sustainable Energy Reviews, 2017, 72: 828-848.
20 Fallahi S, Amanollahi J, Tzanis C G, et al. Estimating Solar Radiation Using NOAA/AVHRR and Ground Measurement Data[J]. Atmospheric Research, 2018, 199: 93-102.
21 Yao W, Zhang C, Hao H, et al. A Support Vector Machine Approach to Estimate Global Solar Radiation with the Influence of fog and Haze[J]. Renewable Energy, 2018, 128: 155-162.
22 Li Jing, Wang Dan, Feng Wei. Simulation of Solar Radiation based on MODIS Remote Sensing Products and Neural Networks[J]. Geographical Science, 2017, 37(6): 912-919.
22 李净, 王丹, 冯姣姣. 基于 MODIS 遥感产品和神经网络模拟太阳辐射[J]. 地理科学, 2017, 37(6): 912-919.
23 Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1): 5-32.
24 Breiman L. Bagging Preditors[J]. Machine Learning, 1996, 24(2):123-140.
25 Fang Kuangnan, Wu Jianbin, Zhu Jianping, et al. Review of Random Forest Methods Research[J]. Journal of Statistics and Information, 2011, 26(3): 32-38.
25 方匡南, 吴见彬, 朱建平, 等. 随机森林方法研究综述[J]. 统计与信息论坛, 2011, 26(3):32-38.
26 Zhao Lingling, Weng Suming, Zeng Huajun, et al. Nuclear Method for Pattern Analysis [M]. Beijing: Mechanical Engineering Press, 2006.赵玲玲, 翁苏明, 曾华军, 等. 模式分析的核方法[M]. 北京: 机械工程出版社,2006.
27 Gao Wei. Principles and Simulation Examples of Artificial Neural Networks [M]. Beijing: Mechanical Engineering Press, 2003.
27 高隽.人工神经网络原理及仿真实例[M].北京: 机械工程出版社,2003.
28 Vapnik V N. The Nature of Statistical Learning Theory [M].NY: Springer-Verlag, 1995.
29 Cortes C,Vapnik V. Support-vector Network [J]. Machine Learning,1995,20(3):273-297.
30 Quej V H, Almorox J, Ibrakhimov M, et al. Empirical Models for Estimating Daily Global Solar Radiation in Yucatán Peninsula, Mexico[J]. Energy Conversion and Management, 2016, 110: 448-456.
31 Meenal R, Selvakumar A I. Assessment of SVM, Empirical and ANN based Solar Radiation Prediction Models with Most Influencing Input Parameters[J]. Renewable Energy, 2018, 121: 324-343.
32 Feng Jiaojiao, Wang Weizhen, Li Jing, et al. Analysis of Solar Radiation Simulation and Time-space Change in East China based on BP Neural Network[J]. Remote Sensing Technology and Application, 2018, 33(5): 881-889.
32 冯姣姣, 王维真, 李净, 等. 基于BP神经网络的华东地区太阳辐射模拟及时空变化分析[J]. 遥感技术与应用, 2018, 33(5):881-889.
33 Wei Y, Zhang X, Hou N, et al. Estimation of Surface Downward Shortwave Radiation over China from AVHRR Data based on Four Machine Learning Methods[J]. Solar Energy, 2019, 177: 32-46.
[1] . A New Direct Solution of Range-Doppler model for SAR Image Location[J]. , , (): 0 .
[2] Rui YANG Su Yang. U-Net neural networks and its application in high resolution satellite image classification[J]. Remote Sensing Technology and Application, 0, (): 0 .
[3] . An improved Hyperspectral Image Clasification Algorithm Based On Multinomial Logistic Regression[J]. Remote Sensing Technology and Application, 0, (): 0 .
[4] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 13 -14 .
[5] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 14 .
[6] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 16 .
[7] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 22 -32 .
[8] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 1 -7 .
[9] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 8 -10 .
[10] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 33 -34 .