Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/3339
DC FieldValueLanguage
dc.contributor.authorCharalambides, Alexandros G.-
dc.contributor.authorTapakis, Rogiros-
dc.contributor.otherΧαραλαμπίδης, Αλέξανδρος Γ.-
dc.contributor.otherΤαπάκης, Ρογήρος-
dc.date.accessioned2015-03-16T11:50:37Z-
dc.date.accessioned2015-12-08T07:53:40Z-
dc.date.available2015-03-16T11:50:37Z-
dc.date.available2015-12-08T07:53:40Z-
dc.date.issued2013-09-
dc.identifier.citationSolar Energy, 2013, vol. 95, pp. 392-430en_US
dc.identifier.issn0038092X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/3339-
dc.description.abstractThe penetration and acceptance of Renewable Energy Systems (RESs) has already taken place in our lives. Solar energy is the feedstock/source for various applications of RES, and thus, the knowledge of the intensity of the incident solar irradiance is essential for monitoring the performance of such systems. A lot of experimental work and modeling has already been conducted for calculating solar irradiance due to various factors, such as location and season. The major unpredictable factor in defining the solar irradiance and the performance of solar systems is the presence of clouds in the sky. So far, various researchers proposed several models for estimating solar irradiance in correlation with cloud coverage and cloud type. This paper reviews the up-to-date research in automatic cloud detection and classification. It initiates with a brief introduction to clouds types and classification. Then, a detailed description of the equipment used for the measurements is provided, either ground based or satellite integrated. Finally, it concludes with an analysis of the existing algorithms for cloud classification, including a presentation of the up-to-date experimental results.en_US
dc.language.isoenen_US
dc.relation.ispartofSolar Energyen_US
dc.rights© Elsevieren_US
dc.subjectCloudsen_US
dc.subjectAutomatic cloud classificationen_US
dc.subjectSolar irradianceen_US
dc.titleEquipment and methodologies for cloud detection and classification: A reviewen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsSubscriptionen_US
dc.reviewPeer Revieweden
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.solener.2012.11.015en_US
dc.dept.handle123456789/77en
dc.relation.volume95en_US
cut.common.academicyear2013-2014en_US
dc.identifier.spage392en_US
dc.identifier.epage430en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.languageiso639-1en-
crisitem.journal.journalissn0038-092X-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Chemical Engineering-
crisitem.author.deptDepartment of Chemical Engineering-
crisitem.author.facultyFaculty of Geotechnical Sciences and Environmental Management-
crisitem.author.facultyFaculty of Geotechnical Sciences and Environmental Management-
crisitem.author.orcid0000-0002-0374-2128-
crisitem.author.orcid0000-0002-4957-4772-
crisitem.author.parentorgFaculty of Geotechnical Sciences and Environmental Management-
crisitem.author.parentorgFaculty of Geotechnical Sciences and Environmental Management-
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