Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/191
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dc.contributor.authorKalogirou, Soteris A.-
dc.contributor.authorEftekhari, Mahroo-
dc.contributor.authorPinnock, D. J.-
dc.date.accessioned2009-05-27T10:59:44Zen
dc.date.accessioned2013-05-17T05:22:49Z-
dc.date.accessioned2015-12-02T10:11:25Z-
dc.date.available2009-05-27T10:59:44Zen
dc.date.available2013-05-17T05:22:49Z-
dc.date.available2015-12-02T10:11:25Z-
dc.date.issued2001-
dc.identifier.citationBuilding Services Engineering Research and Technology, Vol. 22, No. 2, 83-93 (2001)en_US
dc.identifier.issn1477-0849-
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/191-
dc.description.abstractThe objective of this research is to investigate air flow distribution inside a light weight test room which is naturally ventilated using artificial neural networks. The test room is situated in a relatively sheltered location and is ventilated through adjustable louvres. Indoor air temperature and velocity are measured at four locations and six different levels. The outside local temperature, relative humidity, wind velocity and direction are also monitored. The collected data are used to predict the air flow across the test room. A multi-layer feedforward neural network was employed with three hidden slabs. Satisfactory results with correlation coefficients equal to 0.985 and 0.897, for the indoor temperature and combined velocity, respectively have been obtained when unknown input data, not used for network training, were used as input. Both values are satisfactory especially if the fact that combined velocity readings were very unstable is considered. The work presented in this paper primarily aims to show the suitability of neural networks to perform such predictions. In order to make the method more usable the training database needs to be enriched with readings from actual measurements from a number of applications.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.publisherSageen
dc.rightsCopyright © 2001 by SAGE Publications.en_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.titleArtificial neural networks for predicting air flow in a naturally ventilated test roomen_US
dc.typeArticleen_US
dc.collaborationHigher Technical Institute Cyprusen_US
dc.subject.categoryMechanical Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1191/014362401701524145en_US
dc.dept.handle123456789/54en
cut.common.academicyear2019-2020en_US
item.grantfulltextnone-
item.openairetypearticle-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.parentorgFaculty of Engineering and Technology-
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