IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v55y2013icp853-859.html
   My bibliography  Save this article

Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast

Author

Listed:
  • Escrig, H.
  • Batlles, F.J.
  • Alonso, J.
  • Baena, F.M.
  • Bosch, J.L.
  • Salbidegoitia, I.B.
  • Burgaleta, J.I.

Abstract

Considering that clouds are the greatest causes to solar radiation blocking, short term cloud forecasting can help power plant operation and therefore improve benefits. Cloud detection, classification and motion vector determination are key to forecasting sun obstruction by clouds. Geostationary satellites provide cloud information covering wide areas, allowing cloud forecast to be performed for several hours in advance. Herein, the methodology developed and tested in this study is based on multispectral tests and binary cross correlations followed by coherence and quality control tests over resulting motion vectors. Monthly synthetic surface albedo image and a method to reject erroneous correlation vectors were developed. Cloud classification in terms of opacity and height of cloud top is also performed. A whole-sky camera has been used for validation, showing over 85% of agreement between the camera and the satellite derived cloud cover, whereas error in motion vectors is below 15%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:55:y:2013:i:c:p:853-859
    DOI: 10.1016/j.energy.2013.01.054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544213000856
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2013.01.054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Olmo, F.J & Vida, J & Foyo, I & Castro-Diez, Y & Alados-Arboledas, L, 1999. "Prediction of global irradiance on inclined surfaces from horizontal global irradiance," Energy, Elsevier, vol. 24(8), pages 689-704.
    2. Sabziparvar, Ali A. & Shetaee, H., 2007. "Estimation of global solar radiation in arid and semi-arid climates of East and West Iran," Energy, Elsevier, vol. 32(5), pages 649-655.
    3. Bakirci, Kadir, 2009. "Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey," Energy, Elsevier, vol. 34(4), pages 485-501.
    4. Muzathik, A.M. & Ibrahim, M.Z. & Samo, K.B. & Wan Nik, W.B., 2011. "Estimation of global solar irradiation on horizontal and inclined surfaces based on the horizontal measurements," Energy, Elsevier, vol. 36(2), pages 812-818.
    5. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    6. 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.
    7. 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.
    8. Lu, Ning & Qin, Jun & Yang, Kun & Sun, Jiulin, 2011. "A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data," Energy, Elsevier, vol. 36(5), pages 3179-3188.
    9. Janjai, S. & Pankaew, P. & Laksanaboonsong, J., 2009. "A model for calculating hourly global solar radiation from satellite data in the tropics," Applied Energy, Elsevier, vol. 86(9), pages 1450-1457, September.
    10. Cavallaro, Fausto, 2010. "Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems," Applied Energy, Elsevier, vol. 87(2), pages 496-503, February.
    11. Gueymard, Christian A., 2005. "Importance of atmospheric turbidity and associated uncertainties in solar radiation and luminous efficacy modelling," Energy, Elsevier, vol. 30(9), pages 1603-1621.
    12. 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.
    13. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
    14. Katiyar, A.K. & Pandey, Chanchal Kumar, 2010. "Simple correlation for estimating the global solar radiation on horizontal surfaces in India," Energy, Elsevier, vol. 35(12), pages 5043-5048.
    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.
    16. Robaa, S.M., 2008. "Evaluation of sunshine duration from cloud data in Egypt," Energy, Elsevier, vol. 33(5), pages 785-795.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    2. Fei Mei & Yi Pan & Kedong Zhu & Jianyong Zheng, 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. 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.
    5. Shitao Wang & Mingjian Sun & Yi Shen, 2022. "Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed," Energies, MDPI, vol. 15(23), pages 1-15, November.
    6. Terrén-Serrano, Guillermo & Martínez-Ramón, Manel, 2021. "Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting," Applied Energy, Elsevier, vol. 288(C).
    7. Alonso-Montesinos, J. & Monterreal, R. & Fernández-Reche, J. & Ballestrín, J. & Carra, E. & Polo, J. & Barbero, J. & Batlles, F.J. & López, G. & Enrique, R. & Martínez-Durbán, M. & Marzo, A., 2019. "Intra-hour energy potential forecasting in a central solar power plant receiver combining Meteosat images and atmospheric extinction," Energy, Elsevier, vol. 188(C).
    8. Alonso, J. & Batlles, F.J., 2014. "Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery," Energy, Elsevier, vol. 73(C), pages 890-897.
    9. 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.
    10. Myeongchan Oh & Chang Ki Kim & Boyoung Kim & Changyeol Yun & Yong-Heack Kang & Hyun-Goo Kim, 2021. "Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery," Energies, MDPI, vol. 14(8), pages 1-18, April.
    11. Cristian Crisosto & Eduardo W. Luiz & Gunther Seckmeyer, 2021. "Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images," Energies, MDPI, vol. 14(3), pages 1-11, February.
    12. 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.
    13. Dambreville, Romain & Blanc, Philippe & Chanussot, Jocelyn & Boldo, Didier, 2014. "Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model," Renewable Energy, Elsevier, vol. 72(C), pages 291-300.
    14. Yongju Son & Yeunggurl Yoon & Jintae Cho & Sungyun Choi, 2022. "Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 14(8), pages 1-24, April.
    15. 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.
    16. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2015. "A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance," Energy, Elsevier, vol. 82(C), pages 570-577.
    17. 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.
    18. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    19. 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.
    20. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khalil, Samy A. & Shaffie, A.M., 2016. "Evaluation of transposition models of solar irradiance over Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 105-119.
    2. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    3. Khalil, Samy A. & Shaffie, A.M., 2013. "A comparative study of total, direct and diffuse solar irradiance by using different models on horizontal and inclined surfaces for Cairo, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 853-863.
    4. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2015. "A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance," Energy, Elsevier, vol. 82(C), pages 570-577.
    5. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    6. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    7. Bikhtiyar Ameen & Heiko Balzter & Claire Jarvis & James Wheeler, 2019. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks," Energies, MDPI, vol. 12(1), pages 1-28, January.
    8. Alonso, J. & Batlles, F.J., 2014. "Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery," Energy, Elsevier, vol. 73(C), pages 890-897.
    9. Mostafavi, Elham Sadat & Ramiyani, Sara Saeidi & Sarvar, Rahim & Moud, Hashem Izadi & Mousavi, Seyyed Mohammad, 2013. "A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran," Energy, Elsevier, vol. 49(C), pages 204-210.
    10. Khorasanizadeh, H. & Mohammadi, K., 2013. "Introducing the best model for predicting the monthly mean global solar radiation over six major cities of Iran," Energy, Elsevier, vol. 51(C), pages 257-266.
    11. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    12. 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.
    13. 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.
    14. Dahmani, Kahina & Dizene, Rabah & Notton, Gilles & Paoli, Christophe & Voyant, Cyril & Nivet, Marie Laure, 2014. "Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model," Energy, Elsevier, vol. 70(C), pages 374-381.
    15. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    16. El-Sebaii, A.A. & Al-Hazmi, F.S. & Al-Ghamdi, A.A. & Yaghmour, S.J., 2010. "Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia," Applied Energy, Elsevier, vol. 87(2), pages 568-576, February.
    17. Dos Santos, Cícero Manoel & De Souza, José Leonaldo & Ferreira Junior, Ricardo Araujo & Tiba, Chigueru & de Melo, Rinaldo Oliveira & Lyra, Gustavo Bastos & Teodoro, Iêdo & Lyra, Guilherme Bastos & Lem, 2014. "On modeling global solar irradiation using air temperature for Alagoas State, Northeastern Brazil," Energy, Elsevier, vol. 71(C), pages 388-398.
    18. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    19. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    20. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong, 2010. "Estimating daily global solar radiation by day of year in China," Applied Energy, Elsevier, vol. 87(10), pages 3011-3017, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:55:y:2013:i:c:p:853-859. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.