Abstract
The paper describes a systematic approach for a precise short-time cloud coverage prediction based on an optical system. We present a distinct pre-processing stage that uses a model based clear sky simulation to enhance the cloud segmentation in the images. The images are based on a sky imager system with fish-eye lens optic to cover a maximum area. After a calibration step, the image is rectified to enable linear prediction of cloud movement. In a subsequent step, the clear sky model is estimated on actual high dynamic range images and combined with a threshold based approach to segment clouds from sky. In the final stage, a multi hypothesis linear tracking framework estimates cloud movement, velocity and possible coverage of a given photovoltaic power station. We employ a Kalman filter framework that efficiently operates on the rectified images. The evaluation on real world data suggests high coverage prediction accuracy above 75%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sun, S., et al.: Short term cloud coverage prediction using ground based all sky imager. In: IEEE International Conference on Smart Grid Communications (2014)
Chow, C.W., et al.: Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed. Solar Energy 85(11), 2881–2893 (2011)
Kleissl, J.: Solar Energy Forecasting and Resource Assessment. Academic Press, Oxford (2013)
Marquez, R.; Coimbra, C.: Short term DNI forecasting with sky imaging techniques. In: Proceedings of the American Solar Energy Society, Rayleigh, NC (2012)
Kömm, T.: Entwicklung einer Wolkenkamera zur Kurzzeitvorhersage von Solarenergie. University of Offenburg, Offenburg (2016)
Kleissl, J., Urquhart, B.: Sky imager cloud position study field campaign report. University of California, San Diego (2016)
Cazorla, A., Olmo, F.J., Alados-Arboledas, L.: Development of a sky imager for cloud cover assessment. J. Opt. Soc. Am. A 25(1), 29–39 (2008)
Gauchet, C., Blanc, P., Espinar, B., Charbonnier, B., Demengel, D.: Surface solar irradiance estimation with low-cost fish-eye camera. In: Workshop on Remote Sensing Measurements for Renewable Energy, Risoe, Denmark (2012)
Geyer, C., Daniilidis, K.: A unifying theory for central panoramic systems and practical implications. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 445–461. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45053-X_29
Paniagua, C., Puig, L., Guerrero, J.J.: Omnidirectional structured light in a flexible configuration. Sensors 13(10), 13903–13916 (2013)
Mei, C., Rives, P.: Single view point omnidirectional camera calibration from planar grids. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA 2007, Rome, Italy (2007)
Puig, L., Bastanlar, Y., Sturm, P., Guerrero, J.J., Barreto, J.: Calibration of central catadioptric cameras using a DLT-like approach. Int. J. Comput. Vis. 93(1), 101–114 (2011)
Barreto, J.P., Araujo, H.: Geometric properties of central catadioptric line images and their application in calibration. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1327–1333 (2005)
Scaramuzza, D., Martinelli, A., Siegwart R.: A flexible technique for accurate omnidirectional camera calibration and structure from motion. In: Proceedings 4th IEEE International Conference on Computer Vision Systems, ICVS 2006, NY, USA (2006)
Scaramuzza, D., Martinelli, A., Siegwart, R.: A toolbox for easily calibrating omnidirectional cameras. In: Proceedings of IEEE International Confernce on Intelligent Robots and Systems, IROS 2006, Beijing, China (2006)
Corke, P.: Robotics: Vision and Control: Fundamental Algorithms in MATLAB, 2nd edn. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54413-7
Hensel, S., Marinov, Marin B., Schwarz, R.: Fisheye camera calibration and distortion correction for ground based sky imagery. In: Proceedings of XXVII International Scientific Conference Electronics – ET 2018, Sozopol, Bulgaria, 13–15 September 2018 (2018)
Reda, I., Andreas, A.: Solar position algorithm for solar radiation application, Technical report, National Renewable Energy Laboratory (2008)
Asparuhova, K., Djamiykov, T., Spasov, I.: Research and design of effective positioning algorithm for solar tracking system. In: IX National Conference with International Participation (ELECTRONICA), Sofia (2018)
Kittler, R., Darula, S.: Cie general sky standard defining luminance distributions. In: Proceedings of Conference eSim 2002. The Canadian Conference on Building Energy Simulation (2002)
Hensel, S., Marinov, Marin B., Schwarz, R., Ganev, B.: Algorithms for cloud segmentation with ground-based camera images. In: Proceedings of International Conference Balkan Light 2018, Varna, Bulgaria, 20–22 September 2018 (2018)
Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 1(5), 32–38 (1957)
Acknowledgments
This paper has been produced within the framework of the ERASMUS + project Geothermal & Solar Skills Vocational Education and Training (GSS-VET).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Hensel, S., Marinov, M.B., Schwarz, R., Topalov, I. (2019). Ground Sky Imager Based Short Term Cloud Coverage Prediction. In: Poulkov, V. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-23976-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-23976-3_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23975-6
Online ISBN: 978-3-030-23976-3
eBook Packages: Computer ScienceComputer Science (R0)