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Ground Sky Imager Based Short Term Cloud Coverage Prediction

  • 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 rectifiedThe 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%.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/3793
Bibliografische Angaben
Title (English):Ground Sky Imager Based Short Term Cloud Coverage Prediction
Conference:4th EAI International Conference (FABULOUS 2019), Sofia, Bulgaria, March 28-29, 2019
Author:Stefan HenselStaff MemberORCiDGND, Marin B. Marinov, Raphael Schwarz, Ivan Topalov
Year of Publication:2019
Place of publication:Cham
Publisher:Springer
Page Number:12
First Page:372
Last Page:385
Parent Title (English):FABULOUS 2019: Future Access Enablers for Ubiquitous and Intelligent Infrastructures
Editor:Vladimir Poulkov
ISBN:978-3-030-23975-6 (Print)
ISBN:978-3-030-23976-3 (Online)
ISSN:1867-8211 (Print)
ISSN:1867-822X (Online)
DOI:https://doi.org/10.1007/978-3-030-23976-3_33
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt
Comment:
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 283)