.png){kind=icon}
返回
Detecting clear sky images
.png){kind=icon}
OA
AI
DOI：10.1016/j.solener.2019.02.069.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many solar forecast algorithms based on ground based sky imagery apply the red-blue ratio (RBR) method to classify image pixels as clear or cloudy, by comparing the current image with the corresponding image from a clear sky library (CSL). The CSL needs to be updated regularly due to changes in clear sky readings over time caused by aerosols and imager dome properties. This clear sky library is typically created by visually scrutinizing daily sky videos and selecting appropriate clear sky periods. This practice takes a significant amount of time and manual intervention can result in human errors. To avoid this, an automated CSL algorithm (ACSL) was developed which filters each image for clear sky features including maximum green pixel brightness, average RBR, and red channel difference by pixel with respect to the previous image. The root mean square difference (RMSD) between the image RBR of the manually created CSL and the ACSL for November and April 2013 at UC San Diego were observed to be less than 6% over the full range of solar zenith angles. The ACSL was found to be more representative of clear conditions than its manual counterpart.
Keyword:
Algorithm
Sky imager
Clear sky
Automated
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
6.2W
机构
引用论文
Coupling sky images with radiative transfer models: a new method to estimate cloud optical depthOAAI
OAAI分享收藏Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego.png)
SOLAR ENERGY
SOLAR ENERGYIF6.6
PREAI
分享收藏Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbedSOLAR ENERGYOAAI
SOLAR ENERGYIF6.6
OAAI分享收藏 没有更多内容
没有更多内容