Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/133339
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dc.contributor.authorTravieso-González, Carlos M.en_US
dc.contributor.authorCabrera-Quintero, Fidelen_US
dc.contributor.authorPiñan Roescher, Alejandroen_US
dc.contributor.authorCelada Bernal, Sergioen_US
dc.date.accessioned2024-10-02T08:03:09Z-
dc.date.available2024-10-02T08:03:09Z-
dc.date.issued2024en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/133339-
dc.description.abstractThe increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.en_US
dc.languageengen_US
dc.relation.ispartofApplied Sciences (Basel)en_US
dc.sourceApplied Sciences-Basel,v. 14 (13), (Julio 2024)en_US
dc.subject33 Ciencias tecnológicasen_US
dc.subject.otherNumerical Weather Predictionen_US
dc.subject.otherAutomatic Cloud Classificationen_US
dc.subject.otherMachine Learning Techniquesen_US
dc.subject.otherArtificial Neural-Networken_US
dc.subject.otherIrradiance Forecastsen_US
dc.subject.otherSky-Imageren_US
dc.subject.otherRadiation Predictionen_US
dc.subject.otherOptical-Propertiesen_US
dc.subject.otherAnalog Ensembleen_US
dc.subject.otherSatellite Dataen_US
dc.subject.otherSolar Irradianceen_US
dc.subject.otherNowcastingen_US
dc.subject.otherAll-Sky Cameraen_US
dc.subject.otherStatistical Methoden_US
dc.subject.otherRegression Methoden_US
dc.subject.otherSatelliteen_US
dc.titleA Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Useden_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/app14135605en_US
dc.identifier.isi001269285100001-
dc.identifier.eissn2076-3417-
dc.identifier.issue13-
dc.relation.volume14en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngidNo ID-
dc.contributor.daisngidNo ID-
dc.contributor.daisngidNo ID-
dc.contributor.daisngidNo ID-
dc.description.numberofpages56en_US
dc.utils.revisionNoen_US
dc.contributor.wosstandardWOS:Travieso-González, CM-
dc.contributor.wosstandardWOS:Cabrera-Quintero, F-
dc.contributor.wosstandardWOS:Piñán-Roescher, A-
dc.contributor.wosstandardWOS:Celada-Bernal, S-
dc.date.coverdateJulio 2024en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr0,508
dc.description.jcr2,7
dc.description.sjrqQ2
dc.description.jcrqQ2
dc.description.scieSCIE
dc.description.miaricds10,5
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.orcid0000-0003-0948-0840-
crisitem.author.orcid0000-0002-6078-2716-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
crisitem.author.fullNameCabrera Quintero, Fidel-
crisitem.author.fullNamePiñan Roescher, Alejandro-
crisitem.author.fullNameCelada Bernal, Sergio-
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