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A biomimetic approach for modeling cloud shading with dynamic behavior

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  • García, Jesús M.
  • Padilla, Ricardo Vasquez
  • Sanjuan, Marco E.

Abstract

Clouds are a complex phenomenon which is the result of a strong interaction between multiple variables. Modeling its behavior through physical principles is a task that requires time and is computationally demanding. One of the main effects caused by clouds are the shadows produced over the earth's surface, a phenomenon inherently complex due its origin. This paper proposes a computationally low-demanding model for imitating (not predicting) the behavior of cloud shading by applying a biomimetic approach. This analogy relays on using a bacterial colony growth behavior. The aim of this paper is to establish a methodology to develop a cloud-shading model useful for transient analysis in solar fields. The proposed model is evaluated qualitative and quantitative by comparing it with a model based on fractal surfaces and with real sky images. The qualitative evaluation indicates that shadows created by the proposed model change its shapes and move as seen in the real phenomenon. On the other hand, the quantitative assessment is accomplished through the Fast Fourier Transform analysis. This analysis indicates that the proposed model is able to achieve the performance shown by real cloud images.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:96:y:2016:i:pa:p:157-166
    DOI: 10.1016/j.renene.2016.04.070
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    References listed on IDEAS

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    1. Pérez-Burgos, Ana & Román, Roberto & Bilbao, Julia & de Miguel, Argimiro & Oteiza, Pilar, 2015. "Reconstruction of long-term direct solar irradiance data series using a model based on the Cloud Modification Factor," Renewable Energy, Elsevier, vol. 77(C), pages 115-124.
    2. Cheng, Hsu-Yung & Yu, Chih-Chang, 2015. "Multi-model solar irradiance prediction based on automatic cloud classification," Energy, Elsevier, vol. 91(C), pages 579-587.
    3. Li, Mengying & Chu, Yinghao & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2016. "Quantitative evaluation of the impact of cloud transmittance and cloud velocity on the accuracy of short-term DNI forecasts," Renewable Energy, Elsevier, vol. 86(C), pages 1362-1371.
    4. Tapakis, R. & Charalambides, A.G., 2014. "Enhanced values of global irradiance due to the presence of clouds in Eastern Mediterranean," Renewable Energy, Elsevier, vol. 62(C), pages 459-467.
    5. de Andrade, Ricardo Cesar & Tiba, Chigueru, 2016. "Extreme global solar irradiance due to cloud enhancement in northeastern Brazil," Renewable Energy, Elsevier, vol. 86(C), pages 1433-1441.
    6. 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.
    7. Marek Brabec & Viorel Badescu & Marius Paulescu, 2014. "Cloud shade by dynamic logistic modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1174-1188, June.
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    Cited by:

    1. García, Jesús & Soo Too, Yen Chean & Padilla, Ricardo Vasquez & Beath, Andrew & Kim, Jin-Soo & Sanjuan, Marco E., 2018. "Dynamic performance of an aiming control methodology for solar central receivers due to cloud disturbances," Renewable Energy, Elsevier, vol. 121(C), pages 355-367.
    2. Abutayeh, Mohammad & Padilla, Ricardo Vasquez & Lake, Maree & Lim, Yee Yan & Garcia, Jesus & Sedighi, Mohammadreza & Soo Too, Yen Chean & Jeong, Kwangkook, 2019. "Effect of short cloud shading on the performance of parabolic trough solar power plants: motorized vs manual valves," Renewable Energy, Elsevier, vol. 142(C), pages 330-344.
    3. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez-Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2022. "Transient simulation of a control strategy for solar receivers based on mass flow valves adjustments and heliostats aiming," Renewable Energy, Elsevier, vol. 185(C), pages 1221-1244.

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