Modelling the clear-sky intensity distribution using a sky imager

Abstract

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

Cloud motion estimation using a sky imager

“…The HDR image generation is briefly described in the following section. The clear-sky images are generated via the methodology detailed by Chauvin et al in [7]. Secondly, the continuous forecasting scheme introduced by Bernecker et al could be adapted to our application.…”

“…the 3-D rotation matrix that align the camera with the zenith, is obtained by comparing the position of the Sun as detected on the image with the real one calculated using the SG2 algorithm [19]. More details about the calibration process can be found in [7]. Once the camera is calibrated, it is possible to interpolate the pixels' values from the sensor coordinate system to the world coordinate system, where the zenith corresponds to the center of the image and the top of the image faces north.…”

“…In a previous work [6], a cloud detection algorithm was developed to accurately evaluate the sky conditions above the camera, namely the cloud map. This algorithm relies on the generation of clear-sky images [7] that are used to reduce the clear-sky background non-uniformity and therefore improve the detection of clouds. In the end, clouds are detected using a classical thresholding technique applied on the corrected sky images.…”

Irradiance Variability Quantification and Small-Scale Averaging in Space and Time: A Short Review

“…• the analysis of power spectra and non-linear characteristics of PV power and solar irradiance time series [16,17,32,35,42,43,48,[64][65][66][67][68], • the comparison of power fluctuations from specific PV plants with corresponding irradiance measurements [25,43,57,69], • the characterization of power variability as a function of PV plant size [13,25,36,44], • the assessment of the effects of partial shadowing on performance reduction from single modules to entire PV systems [51,[70][71][72], • the development of methods to infer irradiance and PV power estimates from whole sky images [47,[73][74][75][76][77][78][79][80][81], • the proposition of classification schemes and measures to quantify variability [25,38,39,[82][83][84][85][86][87], • the estimation of spatial smoothing, including correlation structures and decorrelation length scales of irradiance and PV power, as well as their increments…”

ELIFAN, an algorithm for the estimation of cloud cover from sky imagers

“…Ghonima et al (2012) and Shields et al (2013) give rich and interesting reviews of sky imager systems and their applications. Several algorithms have now been proposed that enable us to retrieve an estimation of cloud cover (e.g., Long and DeLuisi, 1998;Li et al, 2011;Ghonima et al, 2012;Martinis et al, 2013;Silva and Echer, 2013;Cazorla et al, 2015;Kim et al, 2016;Krinitskiy and Sinitsyn, 2016) or solar irradiance (Pfister et al, 2003;Chu et al, 2014;Chauvin et al, 2015;Kurtz and Kleissl, 2017), to classify the type of observed clouds (e.g., Heinle et al, 2010;Kazantzidis et al, 2012;Xia et al, 2015;Gan et al, 2017), or to track them (Peng et al, 2015;Cheng, 2017;Richardson et al, 2017). Sky imagers have also been specifically used for the detection of cirrus or thin clouds (Li et al, 2012) and contrail studies (Schumann et al, 2013).…”