Explicit basis function kernel methods for cloud segmentation in infrared sky images

Photovoltaic systems are sensitive to cloud shadow projection, which needs to be forecasted to reduce the noise impacting the intra-hour forecast of global solar irradiance. We present a comparison between different kernel discriminative models for cloud detection. The models are solved in the prima...

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Autores principales:	Guillermo Terrén-Serrano, Manel Martínez-Ramón
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Cloud segmentation Machine learning Kernel methods Sky imaging Solar forecasting Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/4cd7143a032e4675a62fb9fe311d1f28
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id	oai:doaj.org-article:4cd7143a032e4675a62fb9fe311d1f28
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spelling	oai:doaj.org-article:4cd7143a032e4675a62fb9fe311d1f282021-11-26T04:32:39ZExplicit basis function kernel methods for cloud segmentation in infrared sky images2352-484710.1016/j.egyr.2021.08.020https://doaj.org/article/4cd7143a032e4675a62fb9fe311d1f282021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721006235https://doaj.org/toc/2352-4847Photovoltaic systems are sensitive to cloud shadow projection, which needs to be forecasted to reduce the noise impacting the intra-hour forecast of global solar irradiance. We present a comparison between different kernel discriminative models for cloud detection. The models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. The infrared cloud images have been preprocessed to remove debris, which increases the performance of the analyzed methods. The use of the pixels’ neighboring features also leads to a performance improvement. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation.Guillermo Terrén-SerranoManel Martínez-RamónElsevierarticleCloud segmentationMachine learningKernel methodsSky imagingSolar forecastingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 442-450 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cloud segmentation Machine learning Kernel methods Sky imaging Solar forecasting Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Cloud segmentation Machine learning Kernel methods Sky imaging Solar forecasting Electrical engineering. Electronics. Nuclear engineering TK1-9971 Guillermo Terrén-Serrano Manel Martínez-Ramón Explicit basis function kernel methods for cloud segmentation in infrared sky images
description	Photovoltaic systems are sensitive to cloud shadow projection, which needs to be forecasted to reduce the noise impacting the intra-hour forecast of global solar irradiance. We present a comparison between different kernel discriminative models for cloud detection. The models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. The infrared cloud images have been preprocessed to remove debris, which increases the performance of the analyzed methods. The use of the pixels’ neighboring features also leads to a performance improvement. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation.
format	article
author	Guillermo Terrén-Serrano Manel Martínez-Ramón
author_facet	Guillermo Terrén-Serrano Manel Martínez-Ramón
author_sort	Guillermo Terrén-Serrano
title	Explicit basis function kernel methods for cloud segmentation in infrared sky images
title_short	Explicit basis function kernel methods for cloud segmentation in infrared sky images
title_full	Explicit basis function kernel methods for cloud segmentation in infrared sky images
title_fullStr	Explicit basis function kernel methods for cloud segmentation in infrared sky images
title_full_unstemmed	Explicit basis function kernel methods for cloud segmentation in infrared sky images
title_sort	explicit basis function kernel methods for cloud segmentation in infrared sky images
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/4cd7143a032e4675a62fb9fe311d1f28
work_keys_str_mv	AT guillermoterrenserrano explicitbasisfunctionkernelmethodsforcloudsegmentationininfraredskyimages AT manelmartinezramon explicitbasisfunctionkernelmethodsforcloudsegmentationininfraredskyimages
_version_	1718409839540961280

Explicit basis function kernel methods for cloud segmentation in infrared sky images

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