|Autors: Hensel, Stefan., Marinov, M. B., Schwarz, R., Toplalov, I. P.|
Title: Ground Sky Imager Based Short Term Cloud Coverage Prediction
Keywords: Cloud coverage, High dynamic range images, Prediction algorithms, Short term irradiance prediction.
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%.
Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science