Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/4405
DC FieldValueLanguage
dc.contributor.authorKalogirou, Soteris A.-
dc.date.accessioned2013-03-04T10:35:38Zen
dc.date.accessioned2013-05-17T10:30:17Z-
dc.date.accessioned2015-12-09T12:08:13Z-
dc.date.available2013-03-04T10:35:38Zen
dc.date.available2013-05-17T10:30:17Z-
dc.date.available2015-12-09T12:08:13Z-
dc.date.issued2009-
dc.identifier.citationAdvances in Building Energy Research, 2009, vol. 3, no. 1, pp. 83-119en_US
dc.identifier.issn17562201-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4405-
dc.description.abstractThe major objective of this chapter is to illustrate how artificial neural networks (ANNs) and genetic algorithms (GAs) may play an important role in modelling and prediction of the performance of various energy systems in buildings. The chapter initially presents artificial neural networks and genetic algorithms and outlines an understanding of how they operate by way of presenting a number of problems in the different disciplines of energy applications in buildings including environmental parameters, renewable energy systems, naturally ventilated buildings, energy consumption and conservation, and HVAC systems. The various applications are presented in a thematic rather than a chronological or any other order. Results presented in this chapter are testimony of the potential of artificial neural networks and genetic algorithms as design tools in many areas of energy applications in buildingsen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofAdvances in Building Energy Researchen_US
dc.rights© Taylor & Francisen_US
dc.subjectArtificial neural networksen_US
dc.subjectGenetic algorithmsen_US
dc.subjectEnvironmental parametersen_US
dc.subjectRenewable energy systemsen_US
dc.subjectNaturally ventilated buildingsen_US
dc.subjectEnergy consumption and conservationen_US
dc.subjectHVAC systemsen_US
dc.titleArtificial neural networks and genetic algorithms in energy applications in buildingsen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsSubscriptionen_US
dc.reviewpeer reviewed-
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3763/aber.2009.0304en_US
dc.dept.handle123456789/141en
dc.relation.issue1en_US
dc.relation.volume3en_US
cut.common.academicyear2009-2010en_US
dc.identifier.spage83en_US
dc.identifier.epage119en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn1751-2549-
crisitem.journal.publisherTaylor & Francis-
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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