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On the use of niching genetic algorithms for variable selection in solar radiation estimation

Author

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  • Will, A.
  • Bustos, J.
  • Bocco, M.
  • Gotay, J.
  • Lamelas, C.

Abstract

Prediction of climatic variables, in particular those related to wind and solar radiation, has developed a huge interest in recent years, mainly due to its applications to renewable energy. In many cases there is a large number of factors that influence the climatic variable of interest, and the researcher chooses the most relevant ones (based on previous knowledge of the region, availability, etc.) and runs a series of experiments combining the available data in order to find the combination that provides the best prediction.

Suggested Citation

  • Will, A. & Bustos, J. & Bocco, M. & Gotay, J. & Lamelas, C., 2013. "On the use of niching genetic algorithms for variable selection in solar radiation estimation," Renewable Energy, Elsevier, vol. 50(C), pages 168-176.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:168-176
    DOI: 10.1016/j.renene.2012.06.039
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    References listed on IDEAS

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    1. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    2. Marco Ratto, 2008. "Analysing DSGE Models with Global Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 115-139, March.
    3. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
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    Citations

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    Cited by:

    1. 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.
    2. Gokan, Toshitaka & Kichko, Sergey & Thisse, Jacques-François, 2019. "How do trade and communication costs shape the spatial organization of firms?," Journal of Urban Economics, Elsevier, vol. 113(C).
    3. Mohammadi, Kasra & Shamshirband, Shahaboddin & Kamsin, Amirrudin & Lai, P.C. & Mansor, Zulkefli, 2016. "Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 423-434.
    4. 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.
    5. Yao, Wanxiang & Zhang, Chunxiao & Hao, Haodong & Wang, Xiao & Li, Xianli, 2018. "A support vector machine approach to estimate global solar radiation with the influence of fog and haze," Renewable Energy, Elsevier, vol. 128(PA), pages 155-162.
    6. Mohammadi, Kasra & Shamshirband, Shahaboddin & Petković, Dalibor & Khorasanizadeh, Hossein, 2016. "Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1570-1579.
    7. Almorox, Javier & Bocco, Mónica & Willington, Enrique, 2013. "Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina," Renewable Energy, Elsevier, vol. 60(C), pages 382-387.
    8. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    9. Samuel Chukwujindu, Nwokolo, 2017. "A comprehensive review of empirical models for estimating global solar radiation in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 955-995.

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