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RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria

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  • Lanre Olatomiwa

    (University of Malaya
    Federal University of Technology)

  • Saad Mekhilef

    (University of Malaya)

  • Shahaboddin Shamshirband

    (University of Malaya)

  • Dalibor Petkovic

    (University of Niš)

Abstract

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration ( $$ \bar{n} $$ n ¯ ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877.

Suggested Citation

  • Lanre Olatomiwa & Saad Mekhilef & Shahaboddin Shamshirband & Dalibor Petkovic, 2015. "RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 1055-1068, June.
    • Handle: RePEc:spr:nathaz:v:77:y:2015:i:2:d:10.1007_s11069-015-1641-x
    DOI: 10.1007/s11069-015-1641-x
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    1. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    2. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
    3. Azoumah, Y. & Yamegueu, D. & Ginies, P. & Coulibaly, Y. & Girard, P., 2011. "Sustainable electricity generation for rural and peri-urban populations of sub-Saharan Africa: The "flexy-energy" concept," Energy Policy, Elsevier, vol. 39(1), pages 131-141, January.
    4. Halawa, Edward & GhaffarianHoseini, AmirHosein & Hin Wa Li, Danny, 2014. "Empirical correlations as a means for estimating monthly average daily global radiation: A critical overview," Renewable Energy, Elsevier, vol. 72(C), pages 149-153.
    5. Hasan, M.H. & Mahlia, T.M.I. & Nur, Hadi, 2012. "A review on energy scenario and sustainable energy in Indonesia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2316-2328.
    6. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
    7. Ming, Tingzhen & de_Richter, Renaud & Liu, Wei & Caillol, Sylvain, 2014. "Fighting global warming by climate engineering: Is the Earth radiation management and the solar radiation management any option for fighting climate change?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 792-834.
    8. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    9. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    10. Akikur, R.K. & Saidur, R. & Ping, H.W. & Ullah, K.R., 2013. "Comparative study of stand-alone and hybrid solar energy systems suitable for off-grid rural electrification: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 738-752.
    11. Jiang, Yingni, 2009. "Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models," Energy, Elsevier, vol. 34(9), pages 1276-1283.
    12. Bajpai, Prabodh & Dash, Vaishalee, 2012. "Hybrid renewable energy systems for power generation in stand-alone applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2926-2939.
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    1. Lanre Olatomiwa & Saad Mekhilef & Shahaboddin Shamshirband & Dalibor Petkovic, 2020. "Retraction Note to: Potential of support vector regression for solar radiation prediction in Nigeria," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3865-3866, September.

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