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Simple models to compute solar global irradiance from the CMSAF product Cloud Fractional Coverage

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  • Badescu, Viorel
  • Dumitrescu, Alexandru

Abstract

Simple models are proposed to compute solar global irradiance by using the hourly Cloud Fractional Coverage (CFC) data provided by the Climate Monitoring Satellite Application Facility (CMSAF). The models are tested against measurements performed in five Romanian weather stations. The cloudy sky models based on CFC (n, for short) are compared with cloudy sky models based on ground-based estimates of point cloudiness (C, for short). Two models were proposed here for clear sky and overcast sky defined as n = 0 and n = 1, respectively. Two types of cloudy sky regression models were built on the basis of these clear sky and overcast sky models. Eight cloudy sky models based on n have been tested in a particular location. The bias error is good or good enough for all cloudiness classes. The spreading error is good for n = 0 ÷ 0.3; good enough for n = 0.3 ÷ 0.7 and poor for n > 0.7. For low zenith angle (Z = 0° ÷ 30°) the bias error of the eight models is generally good enough or poor. Generally, best fit models based on C perform better than best fit models based on n. One model (D1) has been selected for further testing. The sub-model D1TOT has been obtained by fitting the model D1 to all available measured data. The accuracy of sub-model D1TOT is good and good enough for all stations at low and intermediate zenith angles (Z < 70°). The performance of a model based on n is significantly better than that of a model based on C, for all zenith angle classes. D1 sub-models were developed by using data from particular stations. Generally, all sub-models have good or good enough performance when used in stations other than the origin one, for cloudiness classes n < 0.7. In case of skies with n = 0.3 ÷ 0.7, the performance of the sub-models based on n is obviously worse than that of sub-models based on C. For low zenith angles (Z = 0° ÷ 70°), the performance of D1 sub-models is good or good enough, when applied in the origin station or other stations, and it is comparable with that of models based on C.

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  • Badescu, Viorel & Dumitrescu, Alexandru, 2014. "Simple models to compute solar global irradiance from the CMSAF product Cloud Fractional Coverage," Renewable Energy, Elsevier, vol. 66(C), pages 118-131.
    • Handle: RePEc:eee:renene:v:66:y:2014:i:c:p:118-131
    DOI: 10.1016/j.renene.2013.11.068
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    References listed on IDEAS

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    1. Badescu, Viorel & Gueymard, Christian A. & Cheval, Sorin & Oprea, Cristian & Baciu, Madalina & Dumitrescu, Alexandru & Iacobescu, Flavius & Milos, Ioan & Rada, Costel, 2013. "Accuracy analysis for fifty-four clear-sky solar radiation models using routine hourly global irradiance measurements in Romania," Renewable Energy, Elsevier, vol. 55(C), pages 85-103.
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    1. Antonanzas-Torres, F. & Urraca, R. & Polo, J. & Perpiñán-Lamigueiro, O. & Escobar, R., 2019. "Clear sky solar irradiance models: A review of seventy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 374-387.
    2. Kostić, Rastko & Mikulović, Jovan, 2017. "The empirical models for estimating solar insolation in Serbia by using meteorological data on cloudiness," Renewable Energy, Elsevier, vol. 114(PB), pages 1281-1293.
    3. Hussain, C.M. Iftekhar & Norton, Brian & Duffy, Aidan, 2017. "Technological assessment of different solar-biomass systems for hybrid power generation in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1115-1129.
    4. García, Jesús M. & Padilla, Ricardo Vasquez & Sanjuan, Marco E., 2016. "A biomimetic approach for modeling cloud shading with dynamic behavior," Renewable Energy, Elsevier, vol. 96(PA), pages 157-166.
    5. Alonso-Montesinos, J. & Martínez-Durbán, M. & del Sagrado, J. & del Águila, I.M. & Batlles, F.J., 2016. "The application of Bayesian network classifiers to cloud classification in satellite images," Renewable Energy, Elsevier, vol. 97(C), pages 155-161.

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