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Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product

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  • Chen, Ji-Long
  • He, Lei
  • Chen, Qiao
  • Lv, Ming-Quan
  • Zhu, Hong-Lin
  • Wen, Zhao-Fei
  • Wu, Sheng-Jun

Abstract

Reliable diffuse and direct beam radiation data are critical for many scientific researches and engineering applications. While knowledge of radiation components is not sufficient due to the cost and maintenance and calibration of the measuring equipments. Exploring novel methods for estimating radiation components have therefore considerable significance. In this paper, twenty satellite-based models for estimating radiation components were developed using MOD08-M3 atmospheric product, and evaluated using measured radiation data at 15 sites across China. The best site-specific models for diffuse and direct beam radiation were proposed, with the average RMSE of 0.642 MJ m−2 (9.299%), and 0.736 MJ m−2(10.69%), respectively. General models for radiation components were also recommended for widespread applications. The study provides a simple but efficient alternative to obtaining solar radiation components data at regional scale using MODIS satellite data. For other places of interest, the coefficients of the proposed models can be calibrated following the guidelines provided in this study with relative ease, making our models appropriate for widespread applications.

Suggested Citation

  • Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
    • Handle: RePEc:eee:renene:v:132:y:2019:i:c:p:221-232
    DOI: 10.1016/j.renene.2018.07.151
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    as
    1. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    2. Qin, Jun & Chen, Zhuoqi & Yang, Kun & Liang, Shunlin & Tang, Wenjun, 2011. "Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products," Applied Energy, Elsevier, vol. 88(7), pages 2480-2489, July.
    3. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    4. Cao, Fei & Li, Huashan & Yang, Tian & Li, Yan & Zhu, Tianyu & Zhao, Liang, 2017. "Evaluation of diffuse solar radiation models in Northern China: New model establishment and radiation sources comparison," Renewable Energy, Elsevier, vol. 103(C), pages 708-720.
    5. Shamshirband, Shahaboddin & Mohammadi, Kasra & Khorasanizadeh, Hossein & Yee, Por Lip & Lee, Malrey & Petković, Dalibor & Zalnezhad, Erfan, 2016. "Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 428-435.
    6. Zhang, Xiongwen, 2014. "A statistical approach for sub-hourly solar radiation reconstruction," Renewable Energy, Elsevier, vol. 71(C), pages 307-314.
    7. Li, Huashan & Bu, Xianbiao & Lian, Yongwang & Zhao, Liang & Ma, Weibin, 2012. "Further investigation of empirically derived models with multiple predictors in estimating monthly average daily diffuse solar radiation over China," Renewable Energy, Elsevier, vol. 44(C), pages 469-473.
    8. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    9. Gopinathan, K.K. & Soler, Alfonso, 1995. "Diffuse radiation models and monthly-average, daily, diffuse data for a wide latitude range," Energy, Elsevier, vol. 20(7), pages 657-667.
    10. Pandey, Chanchal Kumar & Katiyar, A.K., 2009. "A comparative study to estimate daily diffuse solar radiation over India," Energy, Elsevier, vol. 34(11), pages 1792-1796.
    11. Zhang, Jin & Xuan, Yimin, 2017. "Performance improvement of a photovoltaic - Thermoelectric hybrid system subjecting to fluctuant solar radiation," Renewable Energy, Elsevier, vol. 113(C), pages 1551-1558.
    12. Li, Huashan & Bu, Xianbiao & Long, Zhen & Zhao, Liang & Ma, Weibin, 2012. "Calculating the diffuse solar radiation in regions without solar radiation measurements," Energy, Elsevier, vol. 44(1), pages 611-615.
    13. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    14. Bakirci, Kadir, 2009. "Models of solar radiation with hours of bright sunshine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2580-2588, December.
    15. Gopinathan, K.K. & Soler, Alfonso, 1996. "Effect of sunshine and solar declination on the computation of monthly mean daily diffuse solar radiation," Renewable Energy, Elsevier, vol. 7(1), pages 89-93.
    16. Zou, Ling & Wang, Lunche & Xia, Li & Lin, Aiwen & Hu, Bo & Zhu, Hongji, 2017. "Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems," Renewable Energy, Elsevier, vol. 106(C), pages 343-353.
    17. Rivero, M. & Orozco, S. & Sellschopp, F.S. & Loera-Palomo, R., 2017. "A new methodology to extend the validity of the Hargreaves-Samani model to estimate global solar radiation in different climates: Case study Mexico," Renewable Energy, Elsevier, vol. 114(PB), pages 1340-1352.
    18. Hyun-Jin Lee & Shin-Young Kim & Chang-Yeol Yun, 2017. "Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea," Energies, MDPI, vol. 10(5), pages 1-12, April.
    19. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
    20. Marques Filho, Edson P. & Oliveira, Amauri P. & Vita, Willian A. & Mesquita, Francisco L.L. & Codato, Georgia & Escobedo, João F. & Cassol, Mariana & França, José Ricardo A., 2016. "Global, diffuse and direct solar radiation at the surface in the city of Rio de Janeiro: Observational characterization and empirical modeling," Renewable Energy, Elsevier, vol. 91(C), pages 64-74.
    21. Boland, John & Huang, Jing & Ridley, Barbara, 2013. "Decomposing global solar radiation into its direct and diffuse components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 749-756.
    22. Tiwari, Arunendra K. & Kalamkar, Vilas R., 2018. "Effects of total head and solar radiation on the performance of solar water pumping system," Renewable Energy, Elsevier, vol. 118(C), pages 919-927.
    23. Wu, Yujie & Wang, Jianzhou, 2016. "A novel hybrid model based on artificial neural networks for solar radiation prediction," Renewable Energy, Elsevier, vol. 89(C), pages 268-284.
    24. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    25. Li, Huashan & Ma, Weibin & Wang, Xianlong & Lian, Yongwang, 2011. "Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study," Renewable Energy, Elsevier, vol. 36(7), pages 1944-1948.
    26. Xu, Xiaojun & Du, Huaqiang & Zhou, Guomo & Mao, Fangjie & Li, Pingheng & Fan, Weiliang & Zhu, Dien, 2016. "A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products," Energy, Elsevier, vol. 111(C), pages 117-125.
    27. Benghanem, M. & Joraid, A.A., 2007. "A multiple correlation between different solar parameters in Medina, Saudi Arabia," Renewable Energy, Elsevier, vol. 32(14), pages 2424-2435.
    28. Trabea, A.A, 1999. "Technical note a multiple linear correlation for diffuse radiation from global solar radiation and sunshine data over Egypt," Renewable Energy, Elsevier, vol. 17(3), pages 411-420.
    29. Jin, Zhou & Yezheng, Wu & Gang, Yan, 2004. "Estimation of daily diffuse solar radiation in China," Renewable Energy, Elsevier, vol. 29(9), pages 1537-1548.
    30. Jiang, Yingni, 2009. "Estimation of monthly mean daily diffuse radiation in China," Applied Energy, Elsevier, vol. 86(9), pages 1458-1464, September.
    31. Wang, Kun & He, Ya-Ling & Qiu, Yu & Zhang, Yuwen, 2016. "A novel integrated simulation approach couples MCRT and Gebhart methods to simulate solar radiation transfer in a solar power tower system with a cavity receiver," Renewable Energy, Elsevier, vol. 89(C), pages 93-107.
    32. Lemos, Leonardo F.L. & Starke, Allan R. & Boland, John & Cardemil, José M. & Machado, Rubinei D. & Colle, Sergio, 2017. "Assessment of solar radiation components in Brazil using the BRL model," Renewable Energy, Elsevier, vol. 108(C), pages 569-580.
    33. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Potential of four different machine-learning algorithms in modeling daily global solar radiation," Renewable Energy, Elsevier, vol. 111(C), pages 52-62.
    34. Jamil, Basharat & Akhtar, Naiem, 2017. "Estimation of diffuse solar radiation in humid-subtropical climatic region of India: Comparison of diffuse fraction and diffusion coefficient models," Energy, Elsevier, vol. 131(C), pages 149-164.
    35. Zhou, Zhigao & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Niu, Zigeng, 2018. "Innovative trend analysis of solar radiation in China during 1962–2015," Renewable Energy, Elsevier, vol. 119(C), pages 675-689.
    36. Božnar, Marija Zlata & Grašič, Boštjan & Oliveira, Amauri Pereira de & Soares, Jacyra & Mlakar, Primož, 2017. "Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks," Renewable Energy, Elsevier, vol. 103(C), pages 794-810.
    37. El-Sebaii, A.A. & Al-Hazmi, F.S. & Al-Ghamdi, A.A. & Yaghmour, S.J., 2010. "Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia," Applied Energy, Elsevier, vol. 87(2), pages 568-576, February.
    38. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
    39. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
    40. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparative analysis of diffuse solar radiation models based on sky-clearness index and sunshine period for humid-subtropical climatic region of India: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 329-355.
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