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Journal articles
Modelling the clear-sky intensity distribution using a sky imager
Abstract : This paper introduces a new empirical formulation of the clear-sky intensity distribution based on images acquired with a sky imager developed at the PROMES-CNRS laboratory (Perpignan, France). Both the formulation and image processing methodology are detailed and stand for key steps in the development of a high quality cloud detection algorithm. The work presented in this paper is a part of a research project which aims at improving solar plant control procedures using direct normal irradiance forecasts under various sky conditions at short-term horizon (5-30 min) and high spatial resolution (∼1 km 2). Modeling the clear-sky intensity distribution in real time allows clear-sky images to be generated. These clear-sky images can then be used to remove the clear-sky background anisotropy on images and so improve cloud detection algorithms significantly. Cloud detection is essential in short-term solar resource forecasting. The new formulation is especially designed for improving performance of the existing models in the circumsolar area. When tested over more than 2200 clear-sky images, corresponding to a solar zenith angle spanning from 24 • to 85 • , the new formulation outperforms a standard approach based on the All-Weather model (Perez et al., 1993) by 15% on the whole sky and more than 20% in the circumsolar area. Application of the methodology for the real-time cloud detection purpose is discussed at the end of the paper.
Cited literature [52 references]
https://hal-univ-perp.archives-ouvertes.fr/hal-01273096
Contributor : Julien Eynard Connect in order to contact the contributor
Submitted on : Thursday, February 11, 2016 - 6:42:09 PM
Last modification on : Thursday, January 11, 2018 - 6:22:15 AM
Long-term archiving on: : Thursday, May 12, 2016 - 5:25:26 PM
Contributor : Julien Eynard Connect in order to contact the contributor
Submitted on : Thursday, February 11, 2016 - 6:42:09 PM
Last modification on : Thursday, January 11, 2018 - 6:22:15 AM
Long-term archiving on: : Thursday, May 12, 2016 - 5:25:26 PM
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