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The application of Bayesian network classifiers to cloud classification in satellite images

Author

Listed:
  • Alonso-Montesinos, J.
  • Martínez-Durbán, M.
  • del Sagrado, J.
  • del Águila, I.M.
  • Batlles, F.J.

Abstract

The need to reduce the impact of traditional electricity generation necessitates an increase in the optimization of alternative systems that produce less environmental contamination. Renewables play a key role, with solar energy considered one of the most important energy supply sources. Solar power plants have to be perfectly designed to optimize electricity generation, and their placement must be as suitable as possible for the meteorological conditions. Clouds are the most mitigating factor in solar energy production and their study is decisive in locating the plant. Apart from the importance of studying clouds before building the solar plants, cloud detection is equally decisive in adapting plant operation to cloud types during solar power plant operation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:97:y:2016:i:c:p:155-161
    DOI: 10.1016/j.renene.2016.05.066
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    References listed on IDEAS

    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. 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.
    3. Tverberg, Gail E., 2012. "Oil supply limits and the continuing financial crisis," Energy, Elsevier, vol. 37(1), pages 27-34.
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    5. 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.
    6. Bosch, J.L. & Batlles, F.J. & Zarzalejo, L.F. & López, G., 2010. "Solar resources estimation combining digital terrain models and satellite images techniques," Renewable Energy, Elsevier, vol. 35(12), pages 2853-2861.
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    Cited by:

    1. Alonso-Montesinos, J. & Polo, Jesús & Ballestrín, Jesús & Batlles, F.J. & Portillo, C., 2019. "Impact of DNI forecasting on CSP tower plant power production," Renewable Energy, Elsevier, vol. 138(C), pages 368-377.
    2. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    3. 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.
    4. Cheong Kim & Francis Joseph Costello & Kun Chang Lee & Yuan Li & Chenyao Li, 2019. "Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis," IJERPH, MDPI, vol. 16(23), pages 1-18, November.

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