@inproceedings{HenselMarinovSchwarzetal.2019,
  author    = {Stefan Hensel and Marin B. Marinov and Raphael Schwarz and Ivan Topalov},
  title     = {Ground Sky Imager Based Short Term Cloud Coverage Prediction},
  series = {FABULOUS 2019: Future Access Enablers for Ubiquitous and Intelligent Infrastructures},
  editor    = {Vladimir Poulkov},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-23975-6 (Print)},
  issn      = {1867-8211 (Print)},
  doi       = {10.1007/978-3-030-23976-3\_33},
  pages     = {372 -- 385},
  year      = {2019},
  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\%.},
  language  = {en}
}