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Prediction of monthly average global solar radiation based on statistical distribution of clearness index

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  • Ayodele, T.R.
  • Ogunjuyigbe, A.S.O.

Abstract

In this paper, probability distribution of clearness index is proposed for the prediction of global solar radiation. First, the clearness index is obtained from the past data of global solar radiation, then, the parameters of the appropriate distribution that best fit the clearness index are determined. The global solar radiation is thereafter predicted from the clearness index using inverse transformation of the cumulative distribution function. To validate the proposed method, eight years global solar radiation data (2000–2007) of Ibadan, Nigeria are used to determine the parameters of appropriate probability distribution for clearness index. The calculated parameters are then used to predict the future monthly average global solar radiation for the following year (2008). The predicted values are compared with the measured values using four statistical tests: the Root Mean Square Error (RMSE), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and the coefficient of determination (R2). The proposed method is also compared to the existing regression models. The results show that logistic distribution provides the best fit for clearness index of Ibadan and the proposed method is effective in predicting the monthly average global solar radiation with overall RMSE of 0.383 MJ/m2/day, MAE of 0.295 MJ/m2/day, MAPE of 2% and R2 of 0.967.

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  • Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.
    • Handle: RePEc:eee:energy:v:90:y:2015:i:p2:p:1733-1742
    DOI: 10.1016/j.energy.2015.06.137
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    1. Kisi, Ozgur, 2014. "Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach," Energy, Elsevier, vol. 64(C), pages 429-436.
    2. Hejase, Hassan A.N. & Al-Shamisi, Maitha H. & Assi, Ali H., 2014. "Modeling of global horizontal irradiance in the United Arab Emirates with artificial neural networks," Energy, Elsevier, vol. 77(C), pages 542-552.
    3. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    4. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    5. López, G. & Batlles, F.J. & Tovar-Pescador, J., 2005. "Selection of input parameters to model direct solar irradiance by using artificial neural networks," Energy, Elsevier, vol. 30(9), pages 1675-1684.
    6. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2015. "A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance," Energy, Elsevier, vol. 82(C), pages 570-577.
    7. Muzathik, A.M. & Ibrahim, M.Z. & Samo, K.B. & Wan Nik, W.B., 2011. "Estimation of global solar irradiation on horizontal and inclined surfaces based on the horizontal measurements," Energy, Elsevier, vol. 36(2), pages 812-818.
    8. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    9. Varo, M. & Pedrós, G. & Martínez-Jiménez, P. & Aguilera, M.J., 2006. "Global solar irradiance in Cordoba: Clearness index distributions conditioned to the optical air mass," Renewable Energy, Elsevier, vol. 31(9), pages 1321-1332.
    10. Mellit, A. & Kalogirou, S.A. & Shaari, S. & Salhi, H. & Hadj Arab, A., 2008. "Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system," Renewable Energy, Elsevier, vol. 33(7), pages 1570-1590.
    11. Scerri, E. & Farrugia, R., 1996. "Wind data evaluation in the Maltese Islands," Renewable Energy, Elsevier, vol. 7(1), pages 109-114.
    12. 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.
    13. Kumar, Ravinder & Umanand, L., 2005. "Estimation of global radiation using clearness index model for sizing photovoltaic system," Renewable Energy, Elsevier, vol. 30(15), pages 2221-2233.
    14. Akarslan, Emre & Hocaoğlu, Fatih Onur & Edizkan, Rifat, 2014. "A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting," Energy, Elsevier, vol. 73(C), pages 978-986.
    15. Youcef Ettoumi, F. & Mefti, A. & Adane, A. & Bouroubi, M.Y., 2002. "Statistical analysis of solar measurements in Algeria using beta distributions," Renewable Energy, Elsevier, vol. 26(1), pages 47-67.
    16. Rizwan, M. & Jamil, Majid & Kirmani, Sheeraz & Kothari, D.P., 2014. "Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters," Energy, Elsevier, vol. 70(C), pages 685-691.
    17. Sabzpooshani, Majid & Mohammadi, Kasra, 2014. "Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran," Energy, Elsevier, vol. 69(C), pages 571-577.
    18. 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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