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Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium

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  • Bertrand, Cédric
  • Vanderveken, Gilles
  • Journée, Michel

Abstract

Solar energy production is directly correlated to the amount of radiation received at a given location. Appropriate information on solar resources is therefore very important for designing and sizing solar energy systems. Concentrated solar power projects and photovoltaic tracking systems rely predominantly on direct normal irradiance (DNI). However, the availability of DNI measurements from surface observation stations has proven to be spatially too sparse to quantify solar resources at most potential sites. Satellite data can be used to calculate estimates of direct solar radiation where ground measurements do not exist. Performance of decomposition models of various complexity have been evaluated against one year of in situ observations recorded on the roof of the radiometric tower of the Royal Meteorological Institute of Belgium in Uccle, Brussels. Models were first evaluated on a hourly and sub-hourly basis using measurements of global horizontal irradiance (GHI) as input. Second, the best performing ground-based decomposition models were used to extract the direct component of the global radiation retrieved from Meteosat Second Generation (MSG) images. Results were then compared to direct beam estimations provided by satellite-based diffuse fraction models and evaluated against direct solar radiation data measured at Uccle. Our analysis indicates that valuable DNI estimation can be derived from MSG images over Belgium regardless of the satellite retrieved GHI accuracy. Moreover, the DNI retrieval from MSG data can be implemented on an operational basis.

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  • Bertrand, Cédric & Vanderveken, Gilles & Journée, Michel, 2015. "Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium," Renewable Energy, Elsevier, vol. 74(C), pages 618-626.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:618-626
    DOI: 10.1016/j.renene.2014.08.042
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    1. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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    3. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    4. Bessafi, Miloud & Oree, Vishwamitra & Khoodaruth, Abdel & Chabriat, Jean-Pierre, 2020. "Impact of decomposition and kriging models on the solar irradiance downscaling accuracy in regions with complex topography," Renewable Energy, Elsevier, vol. 162(C), pages 1992-2003.
    5. Moretón, R. & Lorenzo, E. & Pinto, A. & Muñoz, J. & Narvarte, L., 2017. "From broadband horizontal to effective in-plane irradiation: A review of modelling and derived uncertainty for PV yield prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 886-903.
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    8. Feiyan Chen & Zhigao Zhou & Aiwen Lin & Jiqiang Niu & Wenmin Qin & Zhong Yang, 2019. "Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods," Energies, MDPI, vol. 12(1), pages 1-19, January.
    9. Hassan, Muhammed A. & Abubakr, Mohamed & Khalil, Adel, 2021. "A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals," Renewable Energy, Elsevier, vol. 167(C), pages 613-628.
    10. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    11. Hemmati, S. & Doshi, N. & Hanover, D. & Morgan, C. & Shahbakhti, M., 2021. "Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle," Applied Energy, Elsevier, vol. 283(C).
    12. Linares-Rodriguez, Alvaro & Quesada-Ruiz, Samuel & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2015. "An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery," Energy, Elsevier, vol. 91(C), pages 264-273.

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