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Potential of support vector regression for solar radiation prediction in Nigeria

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  • Lanre Olatomiwa
  • Saad Mekhilef
  • Shahaboddin Shamshirband
  • Dalibor Petkovic

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 (T max ), monthly mean minimum temperature (T min ) 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. Copyright Springer Science+Business Media Dordrecht 2015

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  • Lanre Olatomiwa & Saad Mekhilef & Shahaboddin Shamshirband & Dalibor Petkovic, 2015. "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:p:1055-1068
    DOI: 10.1007/s11069-015-1641-x
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    Cited by:

    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.
    2. Olatomiwa, Lanre & Mekhilef, Saad & Huda, A.S.N. & Ohunakin, Olayinka S., 2015. "Economic evaluation of hybrid energy systems for rural electrification in six geo-political zones of Nigeria," Renewable Energy, Elsevier, vol. 83(C), pages 435-446.
    3. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
    4. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    5. Giwa, Adewale & Alabi, Adetunji & Yusuf, Ahmed & Olukan, Tuza, 2017. "A comprehensive review on biomass and solar energy for sustainable energy generation in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 620-641.
    6. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    7. Al-Shammari, Eiman Tamah & Keivani, Afram & Shamshirband, Shahaboddin & Mostafaeipour, Ali & Yee, Por Lip & Petković, Dalibor & Ch, Sudheer, 2016. "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm," Energy, Elsevier, vol. 95(C), pages 266-273.

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