International audienceThis paper deals with an image processing methodology based on a sky-imaging system developed at the PROMES-CNRS laboratory. It is a part of a project which aims at improving solar plant control procedures using Direct Normal Irradiance (DNI) forecasts under various sky conditions at short term horizon (5-30 minutes) and high spatial resolution (~1 km²). The work presented in this paper is about the improvement of the cloud cover estimation, which is the main step in DNI forecasting. First, an overview of the existing sky-imaging systems and the current cloud detection algorithms is presented. Next, the experimental setup is introduced. Then, the methodology used to estimate the cloud cover is detailed. Finally, the paper ends with some results and discussion
International audienceThis 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
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