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Ground Sky Imager Based Short Term Cloud Coverage Prediction

  • Stefan HenselEmail author
  • Marin B. Marinov
  • Raphael Schwarz
  • Ivan Topalov
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 283)

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%.

Keywords

Cloud coverage High dynamic range images Prediction algorithms Short term irradiance prediction 

Notes

Acknowledgments

This paper has been produced within the framework of the ERASMUS + project Geothermal & Solar Skills Vocational Education and Training (GSS-VET).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Department for Electrical EngineeringUniversity of Applied Sciences OffenburgOffenburgGermany
  2. 2.Department of ElectronicsTechnical University of SofiaSofiaBulgaria

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