Segmentation Algorithms for Ground-Based Infrared Cloud Images

被引：0

作者：

Terren-Serrano, Guillermo ^{[1
]}

Martinez-Ramon, Manel ^{[1
]}

机构：

[1] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA

来源：

2021 IEEE PES INNOVATIVE SMART GRID TECHNOLOGY EUROPE (ISGT EUROPE 2021) | 2021年

关键词：

Cloud Segmentation; Machine Learning; Solar Forecasting; Sky Imager; CLASSIFICATION; STATISTICS; SUPPORT; MODELS;

D O I：

10.1109/ISGTEUROPE52324.2021.9639922

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds. Global Solar Irradiance (GSI) forecasting allows smart grids to optimize the energy dispatch, preventing energy shortages caused by occlusion of the sun. This investigation compares the performances of machine learning algorithms (not requiring labelled images for training) for rea-ltime segmentation of clouds in images acquired using a ground-based infrared sky imager. Real-time segmentation is utilized to extract cloud features using only the pixels in which clouds are detected.

引用

页码：7 / 12

页数：6

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