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Evaluation of different diffuse radiation models for Indian stations and predicting the best fit model

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  • Karakoti, Indira
  • Pande, Bimal
  • Pandey, Kavita

Abstract

In the present study, the non-linear solar radiation models for predicting the monthly average daily diffuse radiation are developed using the measured data on global radiation, diffuse radiation and sunshine hours for 12 locations of India. Statistical method is used to derive these correlations. The developed models are employed to estimate the monthly average daily diffuse radiation. The performance of these correlations is compared with existing model. Accuracy of developed relationships is also tested using statistical indicators viz. Percentage error (PE), root mean square error (RMSE), mean percentage error (MPE) and mean bias error (MBE). The study finds that these statistical parameters have very low values for the proposed models. A cubic correlation of diffuse coefficient with percent possible sunshine gives the best fit. The maximum values of RMSE, MPE and MBE for the proposed third order equation are 4.33%, 8.68% and -1.25% respectively while in the case of existing model these values are 13.28%, 13.39% and -3.83% respectively. Hence, it is possible to apply the cubic equation for the prediction of monthly mean daily diffuse radiation.

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  • Karakoti, Indira & Pande, Bimal & Pandey, Kavita, 2011. "Evaluation of different diffuse radiation models for Indian stations and predicting the best fit model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2378-2384, June.
    • Handle: RePEc:eee:rensus:v:15:y:2011:i:5:p:2378-2384
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    1. Mosalam Shaltout, M.A & Hassan, A.H & Fathy, A.M, 2001. "Study of the solar radiation over Menia," Renewable Energy, Elsevier, vol. 23(3), pages 621-639.
    2. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    3. Noorian, Ali Mohammad & Moradi, Isaac & Kamali, Gholam Ali, 2008. "Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces," Renewable Energy, Elsevier, vol. 33(6), pages 1406-1412.
    4. Soares, Jacyra & Oliveira, Amauri P. & Boznar, Marija Zlata & Mlakar, Primoz & Escobedo, João F. & Machado, Antonio J., 2004. "Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique," Applied Energy, Elsevier, vol. 79(2), pages 201-214, October.
    5. Wong, L. T. & Chow, W. K., 2001. "Solar radiation model," Applied Energy, Elsevier, vol. 69(3), pages 191-224, July.
    6. Elminir, Hamdy K. & Azzam, Yosry A. & Younes, Farag I., 2007. "Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models," Energy, Elsevier, vol. 32(8), pages 1513-1523.
    7. Jacovides, C.P. & Tymvios, F.S. & Assimakopoulos, V.D. & Kaltsounides, N.A., 2006. "Comparative study of various correlations in estimating hourly diffuse fraction of global solar radiation," Renewable Energy, Elsevier, vol. 31(15), pages 2492-2504.
    8. Jiang, Yingni, 2009. "Estimation of monthly mean daily diffuse radiation in China," Applied Energy, Elsevier, vol. 86(9), pages 1458-1464, September.
    9. Paulescu, M. & Schlett, Z., 2004. "Performance assessment of global solar irradiation models under Romanian climate," Renewable Energy, Elsevier, vol. 29(5), pages 767-777.
    10. 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.
    11. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
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    2. 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.
    3. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Ma, Xin & Bai, Hua, 2019. "Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 168-186.
    4. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    5. Karakoti, Indira & Das, Prasun Kumar & Singh, S.K., 2012. "Predicting monthly mean daily diffuse radiation for India," Applied Energy, Elsevier, vol. 91(1), pages 412-425.
    6. Khorasanizadeh, Hossein & Mohammadi, Kasra, 2016. "Diffuse solar radiation on a horizontal surface: Reviewing and categorizing the empirical models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 338-362.
    7. Anamika, & Peesapati, Rajagopal & Kumar, Niranjan, 2016. "Estimation of GSR to ascertain solar electricity cost in context of deregulated electricity markets," Renewable Energy, Elsevier, vol. 87(P1), pages 353-363.
    8. 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.
    9. 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.
    10. Wang, Lunche & Lu, Yunbo & Zou, Ling & Feng, Lan & Wei, Jing & Qin, Wenmin & Niu, Zigeng, 2019. "Prediction of diffuse solar radiation based on multiple variables in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 151-216.
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    12. Bakirci, Kadir, 2015. "Models for the estimation of diffuse solar radiation for typical cities in Turkey," Energy, Elsevier, vol. 82(C), pages 827-838.

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