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Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks

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  • Božnar, Marija Zlata
  • Grašič, Boštjan
  • Oliveira, Amauri Pereira de
  • Soares, Jacyra
  • Mlakar, Primož

Abstract

We describe a procedure to build an artificial neural network model of half-hourly values of diffuse solar radiation at the surface that can be repeated for other locations in a region. The model was developed for the location of the Portorož Airport (Slovenia) using data gathered by a standard automatic meteorological station and diffuse solar radiation measured over one year. The model was constructed based on a perceptron artificial neural network, which is a universal approximator for highly nonlinear systems. To date, models of this type have been restricted to a single chosen location. An inland location at Maribor was tested as a benchmark for comparison. It is shown that the Portorož model can be directly transferable without significant quality loss to the inland location of Maribor Airport, which has a different climate. Comparison to the Maribor benchmark model gives a correlation ranging from the initial value of 0.9030 to 0.9004, RMSE increases from 40.5 to 43.7 Wm−2, coefficient of variation of the RMSE increases from 38% to 41%; values for the initial location of Portorož are 0.9453, 28.7 Wm−2, 26.4%. To the best of our knowledge, this report describes the first such regional model that is spatially transferable.

Suggested Citation

  • Božnar, Marija Zlata & Grašič, Boštjan & Oliveira, Amauri Pereira de & Soares, Jacyra & Mlakar, Primož, 2017. "Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks," Renewable Energy, Elsevier, vol. 103(C), pages 794-810.
    • Handle: RePEc:eee:renene:v:103:y:2017:i:c:p:794-810
    DOI: 10.1016/j.renene.2016.11.013
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    1. Dal Pai, Alexandre & Escobedo, João Francisco & Dal Pai, Enzo & de Oliveira, Amauri Pereira & Soares, Jacyra Ramos & Codato, Georgia, 2016. "MEO shadowring method for measuring diffuse solar irradiance: Corrections based on sky cover," Renewable Energy, Elsevier, vol. 99(C), pages 754-763.
    2. Shamshirband, Shahaboddin & Mohammadi, Kasra & Khorasanizadeh, Hossein & Yee, Por Lip & Lee, Malrey & Petković, Dalibor & Zalnezhad, Erfan, 2016. "Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 428-435.
    3. Božnar, Marija Zlata & Grašič, Boštjan & Mlakar, Primož & Soares, Jacyra & de Oliveira, Amauri Pereira & Costa, Tássio Santos, 2015. "Radial frequency diagram (sunflower) for the analysis of diurnal cycle parameters: Solar energy application," Applied Energy, Elsevier, vol. 154(C), pages 592-602.
    4. Badescu, Viorel & Gueymard, Christian A. & Cheval, Sorin & Oprea, Cristian & Baciu, Madalina & Dumitrescu, Alexandru & Iacobescu, Flavius & Milos, Ioan & Rada, Costel, 2012. "Computing global and diffuse solar hourly irradiation on clear sky. Review and testing of 54 models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1636-1656.
    5. Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
    6. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    7. Soares, Jacyra & Oliveira, Amauri P. & Boznar, Marija Zlata & Mlakar, Primoz & Escobedo, João F. & Machado, Antonio J., 2004. "Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique," Applied Energy, Elsevier, vol. 79(2), pages 201-214, October.
    8. Marques Filho, Edson P. & Oliveira, Amauri P. & Vita, Willian A. & Mesquita, Francisco L.L. & Codato, Georgia & Escobedo, João F. & Cassol, Mariana & França, José Ricardo A., 2016. "Global, diffuse and direct solar radiation at the surface in the city of Rio de Janeiro: Observational characterization and empirical modeling," Renewable Energy, Elsevier, vol. 91(C), pages 64-74.
    9. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    10. Jacovides, C.P. & Tymvios, F.S. & Assimakopoulos, V.D. & Kaltsounides, N.A., 2006. "Comparative study of various correlations in estimating hourly diffuse fraction of global solar radiation," Renewable Energy, Elsevier, vol. 31(15), pages 2492-2504.
    11. Oliveira, Amauri P. & Escobedo, João F. & Machado, Antonio J. & Soares, Jacyra, 2002. "Correlation models of diffuse solar-radiation applied to the city of São Paulo, Brazil," Applied Energy, Elsevier, vol. 71(1), pages 59-73, January.
    12. Janjai, S. & Pankaew, P. & Laksanaboonsong, J., 2009. "A model for calculating hourly global solar radiation from satellite data in the tropics," Applied Energy, Elsevier, vol. 86(9), pages 1450-1457, September.
    13. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
    14. 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.
    15. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
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    4. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
    5. Mohammadi, M. & Lakestani, M. & Mohamed, M.H., 2018. "Intelligent parameter optimization of Savonius rotor using Artificial Neural Network and Genetic Algorithm," Energy, Elsevier, vol. 143(C), pages 56-68.
    6. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.

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