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Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria

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  • Olatomiwa, Lanre
  • Mekhilef, Saad
  • Shamshirband, Shahaboddin
  • Petković, Dalibor

Abstract

In this paper, the accuracy of a soft computing technique is investigated for predicting solar radiation based on a series of measured meteorological data: monthly mean minimum temperature and, maximum temperature, and sunshine duration obtained from a meteorological station located in Iseyin, Nigeria. The process was developed with an adaptive neuro-fuzzy inference system (ANFIS) to simulate solar radiation. The ANFIS network has three neurons in the input layer, and one neuron in the output layer. The inputs are monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin), and monthly mean sunshine duration (n¯). The performance of the proposed system is obtained through the simulation results. The ANFIS results are compared with experimental results using root-mean-square error (RMSE) and coefficient of determination (R2). The results signify an improvement in predictive accuracy and ANFIS capability to estimate solar radiation. The statistical characteristics of RMSE=1.0854 and R2=0.8544 were obtained in the training phase and RMSE=1.7585 and R2=0.6567 in the testing phase. As a result, the proposed model deemed an efficient techniques to predict global solar radiation for practical purposes.

Suggested Citation

  • Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    • Handle: RePEc:eee:rensus:v:51:y:2015:i:c:p:1784-1791
    DOI: 10.1016/j.rser.2015.05.068
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    1. Ramedani, Zeynab & Omid, Mahmoud & Keyhani, Alireza & Shamshirband, Shahaboddin & Khoshnevisan, Benyamin, 2014. "Potential of radial basis function based support vector regression for global solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1005-1011.
    2. Bakirci, Kadir, 2009. "Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey," Energy, Elsevier, vol. 34(4), pages 485-501.
    3. 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.
    4. Yohanna, Jonathan K. & Itodo, Isaac N. & Umogbai, Victor I., 2011. "A model for determining the global solar radiation for Makurdi, Nigeria," Renewable Energy, Elsevier, vol. 36(7), pages 1989-1992.
    5. Bahel, V. & Srinivasan, R. & Bakhsh, H., 1987. "Statistical comparison of correlations for estimation of global horizontal solar radiation," Energy, Elsevier, vol. 12(12), pages 1309-1316.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Wu, Ji & Chan, Chee Keong & Zhang, Yu & Xiong, Bin Yu & Zhang, Qing Hai, 2014. "Prediction of solar radiation with genetic approach combing multi-model framework," Renewable Energy, Elsevier, vol. 66(C), pages 132-139.
    14. 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.
    15. 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.
    16. Bahel, V. & Bakhsh, H. & Srinivasan, R., 1987. "A correlation for estimation of global solar radiation," Energy, Elsevier, vol. 12(2), pages 131-135.
    17. 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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