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Sky camera imagery processing based on a sky classification using radiometric data

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  • Alonso, J.
  • Batlles, F.J.
  • López, G.
  • Ternero, A.

Abstract

As part of the development and expansion of CSP (concentrated solar power) technology, one of the most important operational requirements is to have complete control of all factors which may affect the quantity and quality of the solar power produced. New developments and tools in this field are focused on weather forecasting improving both operational security and electricity production. Such is the case with sky cameras, devices which are currently in use in some CSP plants and whose use is expanding in the new technology sector. Their application is mainly focused on cloud detection, estimating their movement as well as their influence on solar radiation attenuation indeed, the presence of clouds is the greatest factor involved in solar radiation attenuation. The aim of this work is the detection and analysis of clouds from images taken by a TSI-880 model sky. In order to obtain accurate image processing, three different models were created, based on a previous sky classification using radiometric data and representative sky conditions parameters. As a consequence, the sky can be classified as cloudless, partially-cloudy or overcast, delivering an average success rate of 92% in sky classification and cloud detection.

Suggested Citation

  • Alonso, J. & Batlles, F.J. & López, G. & Ternero, A., 2014. "Sky camera imagery processing based on a sky classification using radiometric data," Energy, Elsevier, vol. 68(C), pages 599-608.
    • Handle: RePEc:eee:energy:v:68:y:2014:i:c:p:599-608
    DOI: 10.1016/j.energy.2014.02.035
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    as
    1. Oak, Neeraj & Lawson, Daniel & Champneys, Alan, 2014. "Performance comparison of renewable incentive schemes using optimal control," Energy, Elsevier, vol. 64(C), pages 44-57.
    2. Amadei, C.A. & Allesina, G. & Tartarini, P. & Yuting, Wu, 2013. "Simulation of GEMASOLAR-based solar tower plants for the Chinese energy market: Influence of plant downsizing and location change," Renewable Energy, Elsevier, vol. 55(C), pages 366-373.
    3. Kisi, Ozgur, 2014. "Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach," Energy, Elsevier, vol. 64(C), pages 429-436.
    4. Li, Huanan & Mu, Hailin & Zhang, Ming & Gui, Shusen, 2012. "Analysis of regional difference on impact factors of China’s energy – Related CO2 emissions," Energy, Elsevier, vol. 39(1), pages 319-326.
    5. López, G. & Batlles, F.J. & Tovar-Pescador, J., 2005. "Selection of input parameters to model direct solar irradiance by using artificial neural networks," Energy, Elsevier, vol. 30(9), pages 1675-1684.
    6. Gómez, Antonio & Zubizarreta, Javier & Dopazo, César & Fueyo, Norberto, 2011. "Spanish energy roadmap to 2020: Socioeconomic implications of renewable targets," Energy, Elsevier, vol. 36(4), pages 1973-1985.
    7. Tverberg, Gail E., 2012. "Oil supply limits and the continuing financial crisis," Energy, Elsevier, vol. 37(1), pages 27-34.
    8. Martínez-Chico, M. & Batlles, F.J. & Bosch, J.L., 2011. "Cloud classification in a mediterranean location using radiation data and sky images," Energy, Elsevier, vol. 36(7), pages 4055-4062.
    9. Zarzalejo, Luis F. & Ramirez, Lourdes & Polo, Jesus, 2005. "Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index," Energy, Elsevier, vol. 30(9), pages 1685-1697.
    10. Jiang, Yingni, 2009. "Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models," Energy, Elsevier, vol. 34(9), pages 1276-1283.
    11. Batlles, F.J. & Rubio, M.A. & Tovar, J. & Olmo, F.J. & Alados-Arboledas, L., 2000. "Empirical modeling of hourly direct irradiance by means of hourly global irradiance," Energy, Elsevier, vol. 25(7), pages 675-688.
    12. Tovar, J & Olmo, F.J & Batlles, F.J & Alados-Arboledas, L, 2001. "Dependence of one-minute global irradiance probability density distributions on hourly irradiation," Energy, Elsevier, vol. 26(7), pages 659-668.
    13. Escrig, H. & Batlles, F.J. & Alonso, J. & Baena, F.M. & Bosch, J.L. & Salbidegoitia, I.B. & Burgaleta, J.I., 2013. "Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast," Energy, Elsevier, vol. 55(C), pages 853-859.
    14. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    15. Zidanšek, Aleksander & Ambrožič, Milan & Milfelner, Maja & Blinc, Robert & Lior, Noam, 2011. "Solar orbital power: Sustainability analysis," Energy, Elsevier, vol. 36(4), pages 1986-1995.
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    3. Mathieu David & Joaquín Alonso-Montesinos & Josselin Le Gal La Salle & Philippe Lauret, 2023. "Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera," Energies, MDPI, vol. 16(20), pages 1-18, October.
    4. Kamadinata, Jane Oktavia & Ken, Tan Lit & Suwa, Tohru, 2019. "Sky image-based solar irradiance prediction methodologies using artificial neural networks," Renewable Energy, Elsevier, vol. 134(C), pages 837-845.
    5. Alonso-Montesinos, J. & Batlles, F.J., 2015. "The use of a sky camera for solar radiation estimation based on digital image processing," Energy, Elsevier, vol. 90(P1), pages 377-386.
    6. Alonso-Montesinos, J. & Batlles, F.J., 2015. "Solar radiation forecasting in the short- and medium-term under all sky conditions," Energy, Elsevier, vol. 83(C), pages 387-393.
    7. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.
    8. 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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