New approach for solar tracking systems based on computer vision, low cost hardware and deep learning

被引：50

作者：

Carballo, Jose A. ^{[2
,3
]}

Bonilla, Javier ^{[1
,2
]}

Berenguel, Manuel ^{[2
,3
]}

Fernandez-Reche, Jesus ^{[1
]}

Garcia, Gines ^{[1
]}

机构：

[1] CIEMAT Plataforma Solar Almeria, Ctra Senes S-N Tabernas, Almeria 04200, Spain

[2] UAL PSA CIEMAT Joint Ctr, CIESOL Res Ctr Solar Energy, Almeria, Spain

[3] Univ Almeria, Ctra Sacramento S-N, Almeria 04120, Spain

来源：

RENEWABLE ENERGY | 2019年 / 133卷

关键词：

Solar energy; Sun tracking; Computer vision; Deep learning; Convolutional neural networks; ENERGY;

D O I：

10.1016/j.renene.2018.08.101

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almeria (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：1158 / 1166

页数：9

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