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An enhanced cloud segmentation algorithm for accurate irradiance forecasting

机译:一种增强云分段算法,用于准确辐照度预测

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摘要

This paper presents a novel approach to calculate cloud cover under any illumination conditions for short-term irradiance forecasting. A sky-imaging system, in parallel with an irradiance measurement, is set up to collect a database of sky images and irradiance readings. The colour space operations and various image segmentation methods are investigated to improve the visual contrast of the cloud component. Experimental results shows the effectiveness of applying the Normalized Blue-to-Red Ratio (NBRR) colour operation for high-illumination condition and the Red/Green channel for the low-illumination condition. The pre-processed sky images are then fed into Minimum Cross Entropy (MCE) adaptive thresholding segmentation, which effectively differentiates the sky and cloud components for all-sky images. The cloud fraction calculation is employed for the segmented images as an indication of cloud coverage. The proposed method demonstrates a positive linear correlation between cloud fraction and real-time irradiance data.
机译:本文介绍了一种新的方法来计算云覆盖在短期辐照度预测下的任何照明条件下。建立了一个天空成像系统,与辐照度测量并行,以收集天空图像和辐照度读数的数据库。研究了颜色空间操作和各种图像分割方法,以改善云组分的视觉对比度。实验结果表明,在低照明条件下对高照明条件和红色/绿色通道施加归一化蓝色对红色比(NBRR)颜色操作的有效性。然后将预处理的天空图像馈入最小跨熵(MCE)自适应阈值分割,这有效地区分了全天图像的天空和云组分。云分数计算用于分段图像作为云覆盖的指示。所提出的方法显示云分数和实时辐照数据之间的正线性相关性。

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