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Review

The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review

1
Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India
2
Department of Computer Science and Engineering, Defense Institute of Advanced Technology, Pune 411025, India
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Departamento de Enxeñería Eléctrica, EEI, Campus de Lagoas-Marcosende, Universidade de Vigo, 36310 Vigo, Spain
4
Department of Civil and Architectural Engineering, Aarhus University, 8000 Aarhus, Denmark
*
Author to whom correspondence should be addressed.
Academic Editor: Galih Bangga
Energies 2021, 14(23), 8119; https://doi.org/10.3390/en14238119
Received: 15 October 2021 / Revised: 19 November 2021 / Accepted: 1 December 2021 / Published: 3 December 2021
A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too. View Full-Text
Keywords: cloud detection; renewable energy; cloud tracking; solar irradiance cloud detection; renewable energy; cloud tracking; solar irradiance
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MDPI and ACS Style

Sawant, M.; Shende, M.K.; Feijóo-Lorenzo, A.E.; Bokde, N.D. The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review. Energies 2021, 14, 8119. https://doi.org/10.3390/en14238119

AMA Style

Sawant M, Shende MK, Feijóo-Lorenzo AE, Bokde ND. The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review. Energies. 2021; 14(23):8119. https://doi.org/10.3390/en14238119

Chicago/Turabian Style

Sawant, Manisha, Mayur Kishor Shende, Andrés E. Feijóo-Lorenzo, and Neeraj Dhanraj Bokde. 2021. "The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review" Energies 14, no. 23: 8119. https://doi.org/10.3390/en14238119

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